Meteosat Second Generation opportunities for Land Surface Research and Applications

Meteosat Second Generation Opportunities

for Land Surface Research and Applications

This report has been prepared by

J. Cihlar (Canada Centre for Remote Sensing)A. Belward (Space Applications Institute, Joint Research Centre)Y. Govaerts (EUMETSAT)

With contributions from:

Alessandro Annoni (Space Applications Institute, Joint Research Centre)Gérard Dedieu (CESBIO, France)Simeon Fongang (University of Dakar, Senegal)Massimo Menenti (Winand Staring Centre, The Netherlands)Jose Pereira (Instituto Superior de Agronomia, Portugal)Bernard Pinty (Space Applications Institute, Joint Research Centre)Michael Rast (ESTEC/ESA)Alain Ratier (EUMETSAT)Clemens Simmer (University of Bonn, Germany)Michel Verstraete (Space Applications Institute, Joint Research Centre)Jürgen Vogt (Space Applications Institute, Joint Research Centre)José Moreno (University of Valencia, Spain)

EUMETSAT Scientific PublicationsISSN 1561-140XISBN 92-9110-031-5

EUM SP 01

Edited by EUMETSAT

Printed by Druckerei Drach, Germany

Copyright EUMETSAT 1999It is permitted to copy contents if clear reference to the source is given

Cover: Example of a 20 days composite albedo map computed on the basis of dailyMeteosat-5 data acquired from 1 to 20 June 1996 corresponding to a total of about 480 slots.The daily values of the albedo (Directional Hemispherical Reflectance estimated for afictitious Solar zenith angle of 30°) are retrieved using the algorithm proposed by Pinty et al.(1999). This image was produced at SAI/JRC by F. Roveda, using the same colour as inFigure 4.

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Table of contentsPREFACE........................................................................................................................................................................ 3

EXECUTIVE SUMMARY................................................................................................................................................ 4

1 INTRODUCTION.................................................................................................................................................. 6

1.1 BACKGROUND................................................................................................................................................... 61.2 STRUCTURE OF THE REPORT.............................................................................................................................. 7

2 MSG CAPABILITIES RELEVANT TO LAND APPLICATIONS.......................................................................... 9

3 APPLICATION THEMES AND KEY VARIABLES ............................................................................................ 13

3.1 OPERATIONAL NWP AND CLIMATE MODELLING ............................................................................................ 133.1.1 Description ........................................................................................................................................... 133.1.2 Relevant variables ................................................................................................................................ 133.1.3 Role of MSG.......................................................................................................................................... 14

3.2 NATURAL HAZARDS........................................................................................................................................ 143.2.1 Description ........................................................................................................................................... 143.2.2 Relevant variables ................................................................................................................................ 143.2.3 Role of MSG.......................................................................................................................................... 14

3.3 ECOSYSTEMS................................................................................................................................................... 153.3.1 Description ........................................................................................................................................... 153.3.2 Relevant variables ................................................................................................................................ 153.3.3 Role of MSG.......................................................................................................................................... 16

3.4 HYDROLOGY ................................................................................................................................................... 163.4.1 Description ........................................................................................................................................... 163.4.2 Relevant variables ................................................................................................................................ 163.4.3 Role of MSG.......................................................................................................................................... 17

3.5 SUMMARY ....................................................................................................................................................... 17

4 BIOPHYSICAL VARIABLES FROM SEVIRI DATA .......................................................................................... 21

4.1 LAND SURFACE TEMPERATURE AND EMISSIVITY............................................................................................. 214.1.1 Rationale............................................................................................................................................... 214.1.2 Status of retrieval algorithms relevant to MSG.................................................................................... 214.1.3 Original contribution of MSG .............................................................................................................. 224.1.4 R&D challenges.................................................................................................................................... 224.1.5 Synergy and scaling issues ................................................................................................................... 22

4.2 ALBEDO........................................................................................................................................................... 234.2.1 Rationale............................................................................................................................................... 234.2.2 Status of retrieval algorithms relevant to MSG.................................................................................... 234.2.3 Original contribution of MSG .............................................................................................................. 244.2.4 R&D challenges.................................................................................................................................... 244.2.5 Synergy and scaling issues ................................................................................................................... 25

4.3 AEROSOL......................................................................................................................................................... 254.3.1 Rationale............................................................................................................................................... 254.3.2 Status of retrieval algorithms relevant to MSG.................................................................................... 254.3.3 Original contribution of MSG .............................................................................................................. 264.3.4 R&D challenges.................................................................................................................................... 264.3.5 Synergy and scaling issues ................................................................................................................... 26

4.4 GLOBAL SOLAR AND PHOTOSYNTHETICALLY ACTIVE RADIATION .................................................................. 274.4.1 Rationale............................................................................................................................................... 274.4.2 Status of retrieval algorithm relevant to MSG ..................................................................................... 274.4.3 Original contribution of MSG .............................................................................................................. 274.4.4 R&D challenges.................................................................................................................................... 284.4.5 Synergy and scaling issues ................................................................................................................... 28

4.5 SOIL MOISTURE................................................................................................................................................ 284.5.1 Rationale…………………………………………………………………………………………………..……284.5.2 Status of retrieval algorithms relevant to MSG…………………………………………………………….29

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4.5.3 Original Contribution of MSG ............................................................................................................. 294.5.4 R&D Challenges................................................................................................................................... 294.5.5 Synergy and scaling issues ................................................................................................................... 30

4.6 FRACTION OF ABSORBED PHOTOSYNTHETICALLY ACTIVE RADIATION .......................................................... 304.6.1 Rationale............................................................................................................................................... 304.6.2 Status of retrieval algorithms relevant to MSG.................................................................................... 304.6.3 Original contribution of MSG .............................................................................................................. 314.6.4 R&D challenges.................................................................................................................................... 314.6.5 Synergy and scaling issues ................................................................................................................... 31

4.7 LEAF AREA INDEX............................................................................................................................................ 314.7.1 Rationale............................................................................................................................................... 314.7.2 Status of retrieval algorithms relevant to MSG.................................................................................... 314.7.3 Original contribution of MSG .............................................................................................................. 324.7.4 R&D challenges.................................................................................................................................... 324.7.5 Synergy and scaling issues ................................................................................................................... 32

4.8 EVAPORATION................................................................................................................................................. 324.8.1 Rationale............................................................................................................................................... 324.8.2 Status of retrieval algorithms relevant to MSG.................................................................................... 334.8.3 Original contribution of MSG .............................................................................................................. 344.8.4 R&D Challenges................................................................................................................................... 344.8.5 Synergy and scaling issues ................................................................................................................... 34

4.9 FIRE................................................................................................................................................................. 344.9.1 Rationale............................................................................................................................................... 344.9.2 Heat....................................................................................................................................................... 354.9.3 Smoke.................................................................................................................................................... 364.9.4 Burned area mapping with the char/scar signal .................................................................................. 374.9.5 Vegetation susceptibility to fire ............................................................................................................ 374.9.6 Possible applications and research, using MSG data for fire monitoring........................................... 37

5 EXAMPLES OF POTENTIAL APPLICATIONS AND RESEARCH USING MSG DATA................................. 39

5.1 NWP DATA ASSIMILATION .............................................................................................................................. 395.2 FIRE MONITORING............................................................................................................................................ 405.3 RESOURCE MANAGEMENT IN AFRICA ............................................................................................................. 415.4 MSG APPLICATIONS IN EUROPE...................................................................................................................... 415.5 DATA ASSIMILATION AND CARBON MODELS................................................................................................... 42

6. COMMON DATA PROCESSING REQUIREMENTS FOR LAND APPLICATIONS ....................................... 45

6.1 CALIBRATION .................................................................................................................................................. 456.2 CLOUD MASKING ............................................................................................................................................ 466.3 ATMOSPHERIC CORRECTIONS.......................................................................................................................... 46

7. CONCLUSIONS AND RECOMMENDATIONS................................................................................................. 47

8. REFERENCES..................................................................................................................................................... 49

9 ANNEXES............................................................................................................................................................ 57

9.1 MBWG TERMS OF REFERENCE....................................................................................................................... 579.1.1 Background........................................................................................................................................... 579.1.2 Objectives and proposed charter of the Working Group ..................................................................... 579.1.3 Membership and organisation.............................................................................................................. 589.1.4 Objective and scope of first workshop………………………………………………………………………58

9.2 MBWG MEMBERS.......................................................................................................................................... 609.3 MSG IMAGE CHARACTERISTICS...................................................................................................................... 61

9.3.1 Image data pre-processing…………………………………………………………………………………...619.3.2 Level 1.5 image data description.…………………………………………………………………………...619.3.3 MSG image data resolution…..……………………………………………………………………………...62

10 LIST OF ACRONYMS ......................................................................................................................................... 66

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Preface

In 2000, EUMETSAT will launch the first Meteosat Second Generation (MSG-1) satellite. This newgeneration geostationary satellite has been developed by the European Space Agency (ESA). A seriesof three or more satellites will provide operational observational and communication services from2001 until 2012. Coverage is centred on 0° Longitude above the equator. In comparison with thecurrent Meteosat satellites, the MSG system will feature enhanced observation and communicationcapabilities. In particular, its advanced Spinning Enhanced Visible and Infrared Imager Visible andInfrared Imager (SEVIRI) will provide data in 12 spectral channels instead of three, with a 15-minuteimaging frequency and a sub-satellite sampling distance of 3 km as opposed to 5 km on the presentMeteosat. Overall, the system will deliver about 20 times more data than its Meteosat predecessor.

The above enhancements have been driven by the increasingly stringent requirements of themeteorological user community in the areas of nowcasting, very short range forecasting andnumerical weather prediction. The combined improvements in spectral coverage, imaging frequencyand ground resolution were needed to better characterise clouds and the vertical structure of theatmosphere, to improve the sampling of dangerous weather patterns and to derive more accurateatmospheric motion vectors. Given these improvements, it is not surprising that the characteristics ofthe MSG system, as those of the NOAA/AVHRR imagers flown on meteorological polar satellites,will be valuable to non-meteorological user communities. In particular, the increased spectralcoverage and time-space sampling of MSG imagery are expected to open new avenues for the studyof land surface properties, their diurnal variation, and the associated land surface and land-atmosphere interaction processes.

Sharing this view, EUMETSAT and the Space Applications Institute (SAI) of the Joint ResearchCentre of the European Commission jointly decided to establish an MSG Biosphere Working Group(MBWG) which was prepared by leading scientists from Europe, North America and Africa tofurther analyse the relevance and potential of the MSG system, and to identify related land surfaceapplications opportunities and associated research requirements. Dr. Josef Cihlar from the CanadaCentre for Remote Sensing, Dr. Alan Belward from SAI/JRC, Ispra establishment, and Dr. YvesGovaerts from EUMETSAT coordinated the production of this report.

We believe that this report will make potential users more aware of the capabilities of a newEuropean space system, alone or in combination with other observing systems. It will also identifythe most attractive opportunities for its use, establish specific data processing requirements, andprovide guidelines for research deemed necessary to enable the full development of the variousbiospheric applications. Its findings should be considered in the context of the MSG ResearchAnnouncement of Opportunities jointly released in February 1999 by ESA and EUMETSAT.

Dr. T. Mohr Dr. R. WinterDirector of EUMETSAT Director of SAI

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Executive summary

A new generation of geostationary satellites is under construction in Europe. The Meteosat SecondGeneration (MSG) series consists of three satellites planned for operation between 2000 and 2012.Compared to the current Meteosat programme, MSG sensor technology will provide substantiallyimproved spectral and temporal coverage. Whilst these improvements were driven by the increasingobservation requirements of numerical weather forecasting, they will also result in much improveddata for other applications, especially over land.

Recognising this potential and the need to analyse the opportunities and challenges posed by theMSG mission, EUMETSAT and the Joint Research Centre of the European Commission jointlyestablished an MSG Biosphere Working Group (MBWG). To fulfil its mandate, the MBWG firstanalysed information requirements for weather and climate modelling, natural hazards, ecosystemsand hydrology. Based on these requirements, specific biophysical variables that can potentially beobserved with MSG were determined. For each variable, the issues associated with its accurateestimation were then analysed with respect to MSG capabilities, and the research and developmentneeds were identified. The required mission capabilities common to most applications were thencompared with the current mission implementation plans.

The MBWG concluded that MSG has the potential to make a major contribution to the landapplications in Europe and Africa. The region covered by MSG observations represents more than afifth of the Earth’s landmass, more than a fifth of its inhabitants, has some of the fastest growingpopulations, and contains some of the most economically, environmentally important and mostfragile ecosystems. Thus provision of timely environmental information for this land areaunquestionably has global importance. In addition to the capability of MSG to monitor Europe andAfrica, its unique attributes arise from the sun-target-sensor viewing geometry, the high frequencyrevisit capability and the choice of spectral channels that offer many new and unique opportunities.Furthermore, the programme is planned for at least 12 years, a period sufficiently long to encouragenew operational exploitation of the data for land applications.

The major new contributions to land observations from satellites centre on MSG capability to obtaindata very frequently, over a wide portion of the electromagnetic spectrum and with a consistent, yetinformation-rich, geometry. MSG also has an intrinsic advantage because the infrastructure to obtainand process the data in a timely fashion is already in place, as part of the European meteorologicalprogramme.

To realise the technical potential of MSG for operational land applications, several conditions mustbe met. Algorithms to obtain quantitative biophysical products from MSG data, accurately andreliably, must be developed as this is an essential foundation for the success of MSG in landapplications. The performance of such algorithms must be evaluated for applications important tousers through proof of concept demonstrations. An end-to-end operational system must be set up todeliver the information products to the intended users in a timely and reliable fashion.

The following recommendations are made by the MBWG to secure MSG success in landapplications and research:

1. EUMETSAT and other European agencies should support a sustained, focused R&D programmeemploying MSG data, together with other data types where appropriate, aimed at the

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development, testing and validation of accurate and robust algorithms capable of quantitativeestimation of land biophysical variables.

2. Critical R&D areas include the identification of land pixels contaminated by clouds and theextraction of aerosol information from MSG data, as a foundation for a successful extraction ofother biophysical variables.

3. Funding agencies that support satellite technology or applications R&D should examine thevarious aspects of MSG relevant to their areas of interest, and should collaborate in jointsponsorship of MSG research where appropriate.

4. EUMETSAT and other agencies promoting operational use of SEVIRI data over land shouldperiodically undertake a review of R&D progress to identify applications ready for proof-of-concept demonstration. Such reviews should be synchronised with the funding of researchprogrammes and with major environmental monitoring initiatives or opportunities in Europe orAfrica.

5. To enable successful, sustained use of MSG data for land applications and research,EUMETSAT should ensure that:• the best possible information on the calibration and radiometric degradation of the MSG solar

channels is available for the duration of the MSG programme;• Archived MSG level 1.5 data are available with the highest possible geometric accuracy;• Solar and viewing angles are available from the data archive;• Contemporaneous atmospheric information with MSG data is readily available to enable the

derivation of biophysical variable products, particularly vertical profiles of atmospheric watercontent, pressure and temperature;

• Cloud masks at pixel level and cloud physical properties including optical thickness (on apixel level if feasible) are available from the archive;

• MSG data are available in a user friendly way, along with appropriate tools such asconversion of the data into radiance values, extraction of geographic subareas, etc.;

6. Operational use of MSG data will necessitate a guaranteed, sustained generation of products andtheir timely delivery to users; appropriate institutional mechanisms must be found to this end.

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1 Introduction

1.1 Background

Through most of the history of satellite remote sensing, observations of the Earth’s surface werecarried out from polar orbiting platforms. Various reasons account for this preference, including theinherent advantages of covering all parts of the Earth and the higher ground resolution achievablefrom low earth orbits. In the range of satellite observing capabilities for land applications, thesuccessful use of meteorological satellites is a special case as sensors on these satellites were notoriginally intended for land remote sensing. An example is the Advanced Very High ResolutionRadiometer (AVHRR), originally designed for meteorological applications and flown on the NOAATIROS-N spacecraft series. Although their shortcomings for land surface studies have long beenrecognised, these instruments have been a major source of data for such studies almost from thelaunch of the first NOAA/AVHRR (Tucker, 1996). Whilst non-meteorological applications in manycases presented demands that differ from the NOAA/AVHRR mission requirements and at timesexceeded the original specifications, major results have nevertheless been achieved withNOAA/AVHRR data.

In the past, geostationary observation satellites have been used primarily by the meteorologicalcommunity for weather nowcasting1 and very short range forecasting2 because of the required highfrequency of coverage. However, there are numerous other applications where frequent revisits couldbe very useful if not essential. This need led to numerous exploratory land studies using Meteosatdata, both over Europe and Africa (Brisson et al., 1994; Dinku, 1996; Rosema and Fischer, 1990,Snijders, 1988). Results of these studies have provided evidence that properly designed sensors flownon geostationary platforms can be of great benefit for remote sensing of the Earth’s surface. InEurope, this experience and the success of using the NOAA/AVHRR data for land and ocean studies,resulted in more attention being given to surface applications as part of the follow-up to the Meteosatsystem, the Meteosat Second Generation (MSG) mission.

The MSG mission has been designed to continue providing data for weather forecasting, although itscapabilities have been considerably expanded in this regard compared to the Meteosat series. Thesenew capabilities also offer significant potential for land observations, partly because bettercharacterisation of atmospheric conditions depends to a great extent on our capacity to quantify thecharacteristics of the underlying surface. Satellite data describing clouds and land surfacecharacteristics are also increasingly useful in weather and climate prediction. This reflects thegrowing recognition of the important role that the land biosphere plays in determining weatherconditions and in the interactions with the atmosphere that result in seasonal to interannual climatevariations.

Most of the new MSG capabilities are embedded in the principal sensor, the Spinning EnhancedVisible and InfraRed Imager (SEVIRI). The spectral, radiometric and spatial characteristics ofSEVIRI should thus enable observations of land surface parameters and processes, in addition tothose of the atmosphere. Coupled with frequent imaging, these observing capabilities give access toinformation currently unavailable from polar orbiting satellites. However, their optimum utilisationcreates specific scientific challenges. Observations from geostationary platforms differ in several

1 Nowcasting refers to the continuous observation of atmospheric features, and the extrapolation of their development,over the following approximately three hours, for regional and local uses.2 Very short range forecasting goes a step further in the extrapolation of atmospheric features, by extending a typicaltime range from three to twelve hours, mainly on a regional scale but also on the synoptic scale, and based on objectivemodels.

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respects from those obtained from polar orbiting satellites. Because of the geostationary satellite-Earth geometry, the signal for each surface pixel is obtained at a fixed (and unique) azimuth andviewing angle, whilst the solar zenith angle varies throughout the day. This observing geometry isradically different from sun-synchronous polar orbiting satellites, for which the solar zenith angle isrelatively constant whilst the view and azimuth angles vary with each pixel. The second majordifference is in the revisit period. Whilst polar satellites provide one or two imaging opportunities perday, depending on the location on Earth and the instrument swath width, geostationary satellitesallow measurements at much shorter time intervals, 15 minutes in the case of MSG. A thirddifference relates to the fact that the nadir position for a geostationary satellite is fixed on the equator,thus its spatial resolution decreases monotonically towards the Earth's limb in all directions. As aconsequence, a single geostationary satellite cannot provide global imaging.

With the increasing importance of satellites to monitor the conditions and changes in the Earth’senvironment, the total number of missions is on the increase. Individual satellites are designed fordifferent objectives, and their capabilities are often complementary. Thus, the use of SEVIRI datashould not be considered in isolation from the rapidly evolving capabilities of other space-basedEarth observation systems. MSG will fly at the turn of the century with other polar orbitinginstruments specifically focussed on the Earth’s environment and land surface monitoring (e.g.VEGETATION, MODIS, MISR, MERIS). Because of their accurate calibration mechanisms, theseinstruments should be able to deliver reliable information on land surface properties, albeit with thelower revisit capabilities of polar orbiting systems.

In this overall context, it is important to focus on the unique MSG characteristics or those that arecomplementary to other sensors. The frequent image acquisition and the diversity of solar zenithangles deserve special attention, as they will give access to diurnal variations and to directionalsignatures of surface properties. It is also important to consider the potential synergy between MSGdata and other observations, with the understanding that synergy means a combined use of differentsources of information or a benefit to MSG from algorithmic research stimulated by other sensors.

With the above considerations in mind, EUMETSAT and the Joint Research Centre (JRC) of theEuropean Commission have jointly established an MSG Biosphere Working Group (MBWG) toanalyse the relevance and potential of the MSG system land capabilities, to identify relatedopportunities in the field of research and land applications, and to produce this report. The terms ofreference and the members of this Working Group, co-chaired by Dr. Cihlar, from the Canada Centrefor Remote Sensing (CCRS) and Dr. Belward from JRC, Ispra, are attached as Annex (9.1) and (9.2).

This report contains the main findings and recommendations of the Working Group, focusing on theMSG characteristics relevant for land applications. It aims to stimulate research and demonstrationactivities designed to exploit MSG data, within and outside the EUMETSAT Member States and theEuropean Union. The report is not intended to cover all land surface applications and research topics,as specific reports have been dedicated to that issue (e.g. GCOS, 1997).

1.2 Structure of the report

The main MSG mission performance and capabilities relevant to land applications are outlined inSection 2. Section 3 addresses major themes of applications and research that may drive theexploitation of SEVIRI data over land. For each theme, the associated relevant biophysical orgeophysical variables are emphasised. Potential methods based on state of the art algorithms toretrieve these various variables are discussed in Section 4. The original contribution of MSG is alsobriefly described, together with the research and development challenges required to best takeadvantage of SEVIRI data. Practical examples of significant importance are given in Section 5.

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Section 6 summarises the common requirements with respect to data calibration, cloud screening orquality control. General recommendations are given in Section 7.

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2 MSG capabilities relevant to land applications

The MSG system will have enhanced spectral, revisit and ground resolution capabilities with respectto the present Meteosat satellite. These capabilities will become available by mid-2001, after the six-month commissioning period of the launch of the first MSG satellite, planned for October 2000. Thissection summarises those capabilities that are more specifically relevant to land biosphere researchand applications.

Table 1: MSG SEVIRI spectral channels characteristics.

The 12 SEVIRI channels (see Table 1) are distributed throughout the short and long wave parts ofthe electromagnetic spectrum. This feature, in conjunction with the frequent repeat cycle, providethe basis for improved and new products to be used for applications such as Numerical WeatherPrediction (NWP) and climate monitoring. The image repeat cycle is 15 minutes with a samplingresolution at the sub-satellite point (SSP) of 3—3 km for all channels except the VIS highresolution band (HRV) which has a 1—1 km nadir resolution. The ground resolution and imagingfrequency have increased by a factor two with respect to Meteosat, and the number of channelshas been multiplied by four.

SEVIRI has a spectral capability similar to the NOAA/AVHRR instrument, i.e. the VIS 0.6, VIS 0.8,IR 3.9, IR 10.8 and IR 12.0 channels, but also IR 1.6 and IR 8.7 channels that can be used to monitorland surface properties. Most of the NOAA/AVHRR-based applications rely however on the

Bands Centre(µm)

Sub-satellitesampling

99%energyband (µm)

Dynamic range Noise

HRV (0.75) 1 km Similar toMeteosat

0-459 W/m2 µm(scaled at centrewavelength)

S/N > 4.3 for target of1% of max dynamicrange

VIS 0.6 0.635 3 km 0.56-0.71 0-533 W/m2 µm S/N > 10.1 for target of1% of max dynamicrange

VIS 0.8 0.81 3 km 0.74-0.88 0-357 W/m2 µm S/N > 7.28 for target of1% of max dynamicrange

IR 1.6 1.64 3 km 1.50-1.78 0-75 W/m2 µm S/N > 3 for target of1% of max dynamicrange

IR 3.9 3.92 3 km 3.48-4.36 0-335 K 0.35 K @ 300 K

IR 8.7 8.70 3 km 8.30-9.10 0-300 K 0.28 K @ 300 K

IR 10.8 10.8 3 km 9.80-11.80 0-335 K 0.25 K @ 300 K

IR 12.0 12.0 3 km 11.00-13.000-335 K 0.37 K @ 300 K

WV 6.2 6.25 3 km 5.35-7.15 0-300 K 0.75 K @ 250 K

WV 7.3 7.35 3 km 6.85-7.85 0-300 K 0.75 K @ 250 K

IR 9.7 9.66 3 km 9.38-9.94 0-310 K 1.50 K @ 255 K

IR 13.4 13.40 3 km 12.40-14.400-300 K 1.80 K @ 270 K

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statistical exploitation of spectral information and data compositing, essentially due to the under-sampling of the illumination and observation angular effects. Conversely, with a geostationaryorbiting satellite a pixel is always observed with the same viewing angle but different sun anglesthroughout the day. Consequently, the effects of surface anisotropy on the observed radiances arebetter sampled and the diurnal variations of surface properties better observed than with theNOAA/AVHRR sensor. Quantitative biospheric applications will therefore require dedicatedalgorithms that can take advantage of both the frequent observation capabilities and spectralcharacteristics of MSG. These issues are addressed in Section 4. In this section, MSG capabilities areonly briefly illustrated.

The SEVIRI capabilities to observe bidirectional reflectances during one day are illustrated in Figure1. In this example, the geometry of observation and illumination corresponding to a pixel located inSouthern Europe is given for three different illumination conditions: winter, spring and summer. Ascan be seen, more than 40 observations with different illumination conditions will be available duringdaytime in summer.

Figure 1: Polar plot of the SEVIRI illumination and viewing geometry for a pixel located in South Europe(Portugal). The radius and polar angle represent respectively the zenith and azimuth angles. The viewinggeometry with respect to the pixel normal is given by the star symbol. Sun zenith angles are given by the plussymbol for typical spring observations, — for summer ones and the diamond symbol for winter observations.

Figure 2 shows clear sky observations by the GOES-8 imager over desert, cropland and forest.The Top-Of-Atmosphere (TOA) brightness temperature curve illustrates the capability to monitordiurnal variations in the thermal infrared from a geostationary satellite. The images were taken on20 June 1997 between 0:15 UTC and 23:15 UTC (i.e. between 19 June, 18:15 and 20 June, 17:15local time at the sub-satellite point). It should be noted that SEVIRI will have an additional IRwindow channel at 8.7 µm which is not available on GOES-8.

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Figure 2: Diurnal variations of the observed TOA brightness temperatures in GOES-8 IR10.8 and IR12.0 bandsover desert, cropland and forest under clear sky conditions.

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Calibration, processing, products and services

The radiometric pre-processing of the images, from level 1.0, i.e. raw images, up to level 1.5, i.e.calibrated and geo-located images will be performed at EUMETSAT Central Facilities by the MSGIMage Processing Facility (IMPF). An on-board black body calibration system should ensure anabsolute calibration of the infrared channels of the order of 1K. The calibration of the solar channelswill rely on vicarious methods primarily based on the monitoring of bright desert targets. The relativeaccuracy of the derived calibration coefficients is expected to be in the range of 5 to 10%. SEVIRIimages will be geo-located with an absolute accuracy of one nominal pixel (i.e. < 3 km at the sub-satellite point) and a relative one (from image to image) less than 0.5 pixel RMS (i.e. 1.2 km at thesub-satellite point). Ground control points will be used to monitor the quality of the geo-locationprocess. Annex (9.3) contains detailed information on SEVIRI image characteristics.

The EUMETSAT MSG Meteorological Products Extraction Facility (MSG-MPEF) will generatereal-time meteorological products from SEVIRI level 1.5 image data (EUMETSAT 1998a,EUMETSAT 1998b), which will be available at the synoptic scale on a three-hourly basis. MSG-MPEF is a fully automated processing chain that relies extensively on quality control mechanismsfor all the different processing steps. In that context, the level 1.5 image format contains detailedancillary information on the radiometric and geo-location quality of the data. These products willbe used for nowcasting and numerical weather prediction. This facility will also provide feedbackto the black body calibration of the IR channels with a real-time vicarious calibration method. Inaddition, MSG-MPEF will derive a cloud mask over land surfaces at pixel resolution for everyimage. This mask will contain an indication of the confidence level of cloud detection for cloudypixels.

The synoptic meteorological products and associated quality control information will bedisseminated in near real-time via the Global Telecommunication System (GTS) of the WorldMeteorological Organization (WMO). Selected level 1.5 image data will be available in real-timevia the MSG/LRIT dissemination service to authorised users owning a Low Rate User Station(EUMETSAT 1998c). The full level 1.5 image data will be available in real-time via theMSG/HRIT dissemination service to authorised users owning a High Rate User Station(EUMETSAT 1998c). All level 1.5 and derived meteorological products will be archived in theEUMETSAT Unified-Meteorological Archive and Retrieval Facility (U-MARF) and available onrequest for off-line use.

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3 Application themes and key variables

Four major themes were considered particularly relevant to the MSG mission capabilities: weatherand climate modelling, natural hazards forecasting and monitoring, ecosystem observations andhydrology. For each theme, key applications were reviewed and pertinent variables that can beretrieved from space observations were identified.

3.1 Operational NWP and climate modelling

3.1.1 Description

The objective of Numerical Weather Prediction (NWP) is to predict how the atmosphere, and theassociated weather conditions, change with time. The outputs of short and medium range NWPsystems are used to guide routine weather forecasts for the general public and to support morespecialised forecasts for marine, aviation and agricultural activities. Geographical coverage of theNWP models ranges from regional areas, associated with mesoscale short range forecast models, tohemispheric or global coverage required for medium range forecasting of up to 10 days. Most of thevertical structure of the atmosphere from the surface to more than 60 kilometres in altitude isconsidered.

Climate variability and the global cycles of carbon and water couple the atmosphere, the ocean, thecryosphere and the terrestrial ecosystems, and consequently the Earth’s climate must be studied as acomplex “multi-component system” (Schimel et al., 1996). The complexity of the system makes theunderstanding of climate processes and variability a strong challenge, and it is necessary that manydifferent parameters be observed and studied simultaneously. Even weak trends in climate, ifcontinued over the long-term (the span of human lifetimes), can lead to immense environmentalimpacts and enormous expenditures to counter the adverse effects. The natural climate variation overmany time scales makes it difficult to detect the long-term effects of anthropological activities, so it isnecessary to monitor climate-related variables over periods of decades or even longer. Climatemodelling has undergone rapid development in recent years. Nevertheless, simplifiedparameterisations of physical processes continue to be used. These require updating to furtherimprove climate monitoring and prediction.

Whilst operational meteorology requirements are driven by spatial resolution (horizontal and vertical)and timeliness of the data, the requirements of climate monitoring and prediction are less demandingwith respect to spatial resolution and timeliness. However, they do require a much higher accuracyand long-term stability of data sets.

3.1.2 Relevant variables

The generic user requirements are based on the need for three-dimensional atmospheric fields asspecified by the WMO Commission for Basic Systems (WMO 1996). These requirements areupdated regularly according to the demands of the operational meteorology community and,particularly, for NWP. The parameters related to MSG and land surfaces are listed in Table 2.

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3.1.3 Role of MSG

The new dimension MSG can bring to NWP and climate modelling is the ability to providemeasurements over Europe and Africa every 15 minutes in a broad range of the electromagneticspectrum. This allows the diurnal cycle to be properly resolved and provides more opportunities forimaging the Earth’s surface through gaps in the cloud, thus yielding more complete surface products.In contrast, the polar orbiters of NOAA only sample the atmosphere/surface four times a day.

3.2 Natural Hazards

3.2.1 Description

The term “natural hazard” reflects the probability that a natural event occurs which brings risk tohuman life or to the resilience of the environment, but also damage to property or infrastructure. Theterms “hazard” and “risk” are often interchanged, although sometimes the economic consequences ofan event are included in the risk but not in the hazard. In all cases, natural hazards are linked to‘damage’, which becomes ‘disaster’ when it reaches certain proportions.

Natural hazards include not only those that are the result of natural events but also those resultingfrom human activity. Examples of the first category are avalanches, crop diseases, drought,earthquakes, fires, floods, hurricanes, insect infestation, landslide, volcanic activity, wildfire, tornado,tsunami and asteroids. Hazards resulting from human activity include acid rain, global warming,erosion, deforestation, desertification, fires, melting of ice caps, ozone depletion, reducedbiodiversity, animal extinction, etc.

Almost all hazards can be characterised by three phases: pre-crisis (before the event), crisis (duringthe event) and post-crisis (after the event). Risk management can thus be structured into three phasesas well, the first one dealing with risk assessment and long-term forecasting, the second related toshort term forecasting and disaster monitoring, and the third focused on damage assessment andrestoration planning. The knowledge of meteorological variables can help in determining theprobability of a hazard taking place and in providing the means to avoid, or at least limit, the damagethat the hazard might cause.


During a hazardous event, meteorological variables help monitoring by providing the means toestimate its duration, to predict likely developments and to obtain a first forecast of the likely damagethat it may cause. Meteorological information is also of paramount importance in the post-crisisphase. Table 2 shows some of the variables used in disaster management. All these variables need tobe provided in real-time but with different spatial and temporal resolutions, i.e., the frequency in theacquisition as well as the spatial resolution of each variable change according to the type of hazardunder study.

3.2.3 Role of MSG

The general application of space techniques in the field of natural hazards has been reviewed throughthe CEOS/IGOS (http://disaster.ceos.org) Disaster Management Support Project. Links between

15

time, spatial, and spectral resolution and natural hazards can be found in the NASA/Natural DisasterReference Database.

The major contribution of the MSG system to the management of natural hazards stems from itscapability to observe and characterise dangerous weather patterns, in support of weather nowcastingoffered by the operational meteorological services. Another key contribution is the MSG DataCollection Platform (DCP) capability, which allows real-time acquisition and relay of observationscollected by in situ networks dedicated to hazard monitoring.

3.3 Ecosystems

3.3.1 Description

This theme covers both natural and managed ecosystems. Questions and issues related to ecosystemsappear very diverse, but could be categorised into two broad categories.

The first group of questions relates to ecosystem functioning at seasonal and interannual time scales.The primary variable for socio-economic applications is vegetation productivity, whether of crops,pastures or forests, and its interannual fluctuation. Crop production estimates are useful informationfor policy makers, grain marketing agencies and others to anticipate grain supplies and potential foodshortages. Given the geographical coverage offered by MSG, the possibility of providing informationconcerning food early warning systems is especially relevant to Africa (FAO, 1996). In addition,ecosystem production is strongly connected to water availability. Estimates and forecasts of waterneeds are in turn related to irrigation planning and water resource management. Fluctuations inproductivity or water availability may be small or large, in the latter case possibly reaching hazardproportions.

Ecosystem functioning at daily to interannual scales, mainly characterised by fluxes and budget ofenergy, water and carbon, is also of strong interest to the scientific community because vegetationand soil properties regulate the exchange of energy and matter at the soil-vegetation-atmosphereinterface (Field and Avissar, 1998). Here the challenge is a better understanding of processes andfactors that drive these exchanges or affect them through feedback, and the building of predictivemodels. Such models can be used to predict the impact of climate change on ecosystem productivity(Walker and Steffen, 1996), to improve the representation of land surfaces in weather and climatemodels (Randall et al., 1996; Sellers et al., 1996a), and for other purposes.

A second class of questions is the long-term evolution of ecosystems, especially as a result of climatechange and human pressure (Walker and Steffen, 1996). In these cases, ecosystem composition andstructure, characterised by taxonomic data, biomass, tree density and carbon budget are the maincharacteristics of interest. One challenge is to detect the signal of a possible climate change throughmeasurable changes in ecosystem structure, boundaries or function. The questions of climate impact,ecosystem response and feedback to climate are also important here, although the time scales tend tobe longer than in the first group.


Ecosystem functioning depends on the interaction of the whole biotic community and its abioticenvironment. Various models have been developed to describe these processes. The models use anumber of variables or parameters. Whilst some of these cannot be estimated from satellite platforms(e.g. herbivore population), many other key ecosystem parameters can. Certain levels of plant

16

taxonomy can be inferred, elements of plant ecosystem structure can be defined, time course ofbiophysical properties such as Leaf Area Index (LAI) can be recorded, and water availability can beestimated through models employing data from satellite sensors. Table 2 shows the importantecosystem variables.

3.3.3 Role of MSG

Solar and thermal infrared radiance measurements provide useful information from which to deriveestimates of LAI and water availability. Whilst these parameters may be estimated using data fromcurrent sensors such as NOAA/AVHRR, it is expected that SEVIRI data will lead to significantimprovements. First, SEVIRI will provide brightness temperature with unprecedented temporalfrequency that should lead to a better assessment of water balance at regional scale. Second, the hightemporal frequency of measurements will facilitate the monitoring of vegetation growth andphenology through the estimation of variables such as LAI or productivity. Because of its spatialresolution, SEVIRI is particularly well suited for monitoring ecosystem functioning at regionalscales. When higher spatial resolution is needed, SEVIRI will complement other space sensors (e.g.VEGETATION, MERIS, MODIS) by providing information on variables regulating plant growth,such as the amount of solar radiation and precipitation.

3.4 Hydrology

3.4.1 Description

In general, hydrology is concerned with the cycle of water through its different phases (gas, liquid,frozen) and different spheres (soil, atmosphere, continental open water, cryosphere, ocean).Consequently, there is a large overlap with meteorology (weather) and climatology. Traditionally,hydrology is more concerned with the flow of water and its availability in soil and open waterbodies. The atmospheric part of the hydrological cycle is taken into account only in terms ofsources (liquid, solid precipitation) and sinks (evapotranspiration, runoff), without dealing withthe atmospheric limitations of the coupled budgets of water and energy. Hydrology is closelylinked to water management over the local to continental areas by assessing and predicting wateravailability, water quality, and the need and surplus in relation to agriculture, water consumption,waste water, waterways, rivers, floods and hydro-power management purposes.


The key hydrological variables accessible by satellites are either linked to the state of the surface(from the hydrological viewpoint) or to parameters governing the exchange of water between soiland atmosphere. Thus, various surface variables are important from the hydrological viewpoint(Table 2) :

1. Spectral reflectance provides information for flood monitoring (land/open water difference) andthe change of reflectance connected to drought due to the changes in vegetation.

2. Soil moisture controls the ability of the ground to absorb rain, thereby controlling runoff and tosustain vegetation. Soil moisture is thus important for the prediction of both floods and drought,and in general it is important for agricultural purposes.

3. Snow cover combined with snow water equivalent characterise an important reservoir to be takeninto account in flood prediction.

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4. Canopy structural properties such as LAI controls runoff directly by interception of precipitation.

5. Evapotranspiration determines the loss of soil moisture to the atmosphere, thus regulating runoffindirectly.

6. Canopy structure may be affected by vegetation stress and thus by drought, and it may influencerunoff through effect on the interception of precipitation.

7. Aerosols originating from the surface may give an indirect, qualitative indication of soil moisturedeficit or drought.

8. APAR and FPAR give information about the state of the surface especially its vegetation andupper soil moisture thus characterising droughts and desertification conditions and influences onrunoff.

3.4.3 Role of MSG

MSG may provide information for all the variables listed in Section 3.4.2. The frequent imagingand sounding capabilities may afford SEVIRI the opportunity to monitor soil moisture from thetemporal land surface temperature variations. Observations of surface anisotropy, sampledthrough particular variations of the geometry of solar illumination, may lead to an improvedclassification of surface type and description of the structure of vegetation cover, compared toinformation that could be obtained from unidirectional observations alone. It should beemphasised that in many of these applications, SEVIRI data will play a complementary role tothat of optical data from polar orbiting platforms.

3.5 Summary

The above four themes encompass a broad range of natural processes which have direct or indirectimpacts on socio-economic activities involving land. These environment-society connectionscross many spatial and temporal scales, from local to global and from minutes to decades.

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Figure 3: Spatial resolution versus spectral resolution in the solar spectral region (top) and revisit period in day(bottom) of various instruments. The spectral resolution is given by the number of bands. Spatial resolution isgiven in metres at the sub-satellite point and corresponds to the normal sensor operation mode. The revisitperiod is given for mid-latitudes; for the GOES imager, it corresponds to the continental USA. MSG is shown inred.

The range of processes and measurement requirements has a direct consequence for the satelliteobservations that need to be made, and therefore for the type of observation tools to be used. Thesatellite tools necessarily differ in their ability to measure individual environmental variables

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and/or the accuracy, resolution and timing with which the measurements are made. In this rangeof capabilities, MSG and similar geostationary satellites occupy the space of frequentrevisit/medium to coarse resolution/cross-spectral coverage. SEVIRI will cover a substantial partof the spatial-temporal domain, continental scale and variations from 15 minutes to 10 years.

MSG and SEVIRI, as its principal sensor of interest to biospheric land applications, are optimallysuited to the measurements of those environmental parameters that change rapidly with time, andthose where the signal change over time contains information about the parameter or the process ofinterest. From the above discussion (Section 3.1-3.4) it is evident that these relate principally toevents at the regional to continental scale. Atmospheric and weather conditions, including therelevant associated land surface processes, are the principal application area for these data over land.Because of surface-atmosphere coupling, surface observations relevant to weather prediction mayalso be important in other contexts. This includes most hydrological issues, due to the role of water inthe energy exchange with the atmosphere. Because of the dynamic role of vegetation in the mass andenergy exchange near the surface, biosphere is also to be considered in long-term weather forecasts.However, both hydrology and ecosystems have direct linkages to the economy and society, bothregionally and nationally. Thus, the above observations are important in their own right, in addition tothe NWP relevance. Various satellite observations and parameters are useful for the natural hazardstheme (Section 3.2). Appropriate input of satellite data in this area requires various observations andparameters but timeliness is of utmost importance.

Figure 3 illustrates the complementary role of various satellite measurements. For MSG, thisimplies that the data can be used in conjunction with that from other satellites, thus takingadvantage of the strengths and unique value of each data type. This is further explored in Section4.

Table 2 provides a summary of the main environmental applications of satellite data in the context ofthe four above themes (columns) and the key environmental variables for which information isneeded (rows). Emphasis has been placed on those variables for which MSG has the potential tomake an important contribution. It is evident that the MSG series is highly relevant to manyenvironmental issues with a direct and significant impact on the economy or society (columns inTable 2). Most of these applications have a large economic impact, and timely and accurateinformation would make effective response possible.

To realise the potential of SEVIRI, it is necessary to translate the raw satellite data into productswhich contain biophysical or geophysical information (rows in Table 2). Such products can then beused as input to models, or in other ways appropriate to each application or problem at hand. Thetransformation of raw data into products is a complex process requiring extensive research inmodelling, algorithm development, algorithm and product validation, and associated activities. Issuesspecific to individual variables are discussed in the next section.

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App

licat

ion

Now

cast

ing

Reg

iona

l NW

P

Glo

bal N

WP

Clim

ate

Fire

mon

itorin

g

Dro

ught

Des

ertif

icat

ion

Inse

ct m

igra

tion

Vol

cano

es

Flo

od m

onito

ring

GH

G e

xcha

nge

Pro

duct

ivity

Sea

sona

l/in

tera

nnua

ldy

nam

ics

Run

off

Albedo/reflectance 1 1 1 1 1 (rainpatterns)

1

LST and emissivity 1 1 1 1 1 1 1 1 1Soil moisture 1 1 1 1 1 1 1 1 1 1 1 1Snow cover 1 1 1 1 1 1 1Fractional cover 1 (1)LAI 1 1 1 1 1 1 1SW(down) 1 1 1 1LW(down) 1 1 1 (1)Lake temperatureEvaporation 1 1 1 1 1 1 1 1Fuel moisture 1 1

(emiss.ratios)

Canopy structure 1 1 1 1 1 1 1 1 1 1Fire duration 1 1 1 1Aerosols 1 1 1 1 1 1 1 1 1 1 1APAR 1 1 1 1 1 1FPAR 1 1 1 1 1 1Fuel loading (1)

Table 2: Applications and environmental variables relevant to MSG. The only variables rated are biophysical parameters which are required directly by an application, notas intermediate quantities (albedo/spectral reflectance and/or radiance/emission derived from MSG data will also be intermediate variables in many cases). 1= Variable isrequired for that application, and MSG is likely to make an important contribution. (1)= Variable is required for that application, and MSG is likely to make somecontribution.

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4 Biophysical variables from SEVIRI data

In principle, SEVIRI data are expected to be suitable for retrieving the following geophysicalvariables:

• Land surface temperature and emissivity;• Surface albedo;• Aerosol;• Global solar radiation;• Soil moisture;• Fraction of absorbed photosynthetically active radiation;• Leaf Area Index;• Evaporation;• Fire information.

This list is indicative rather than exhaustive. Additional variables from Table 2 could potentially beretrieved from SEVIRI data, but intrinsic advantages of the sensor characteristics are less evident. Itshould also be noted that the retrievals of the proposed variables are not independent. For example,atmospheric corrections are a pre-requisite to the derivation of surface properties. A properrepresentation of the coupled atmosphere-surface system should allow retrieval of basic surfaceproperties such as temperature or albedo. When these fundamental radiative transfer processes areproperly solved, sensor-independent biophysical variables such as the fraction of absorbedphotosynthetically active radiation, leaf area index or soil moisture may be derived.

4.1 Land surface temperature and emissivity

Names of variables: Land surface temperature [units: K]Emissivity [dimensionless]

4.1.1 Rationale

Land Surface Temperature (LST, measured in Kelvin [K]) is a key surface variable which links theprocesses of energy and water exchange between the surface and the atmosphere. Knowledge of thespatial distribution of LST and its temporal evolution over a wide range of time scales is therefore animportant issue for the accurate modelling of these processes (see also Section 4.5 and 4.8).Knowledge of the surface emissivity (dimensionless) is important both for an accurate LST retrievalfrom space and for the implementation of atmospheric correction methods in general. Similarly toLST, emissivity is a highly variable parameter over land surfaces, both spatially and temporally.

4.1.2 Status of retrieval algorithms relevant to MSG

Currently, LST can be retrieved from space with an accuracy of about 2-3 K from two or moreadjacent measurements made in the thermal infrared window (split-window approach) or throughmulti-angle measurements (Prata 1993, Vogt 1996). Limitations in the retrieval accuracy stem fromsensor performance and the difficulties in accounting for the coupled effect of the atmosphere and thesurface emissivity on the signal. Due to the spectral variability of emissivity, any number of spectralmeasurements will yield an underdetermined set of equations (n equations with n+1 unknowns due tothe n spectral emissivities and the atmospheric effect). For this ambiguity to be resolved, assumptionsmust be made or additional information must be available. The size of the sensor footprint and thephysical meaning of a surface temperature measured over a large surface pose problems to the

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interpretation and assimilation of such data in relevant biophysical models. The operational use ofspaceborne LST measurements has further been hampered by their restricted temporal resolution,whilst LST is a highly variable parameter both in space and in time.

4.1.3 Original contribution of MSG

SEVIRI will be the first instrument to deliver several measurements in the thermal infraredwindows with a very high temporal frequency for the European and African continents. It willthus introduce a new dimension to the retrieval and interpretation of such data. The availability offour measurements of emitted radiation in atmospheric windows (3.8 µm, 8.7 µm, 10.8 µm, 12.0µm) and of reflectance measurements in the visible and near-infrared spectral bands will be amajor asset. In conjunction with the high temporal resolution and a stable, high quality geometrythis will allow to further test and develop methods for the solution of the coupled atmosphere andemissivity problem. Possible approaches include the use of Temperature Independent SpectralIndices (TISI) (Becker and Li 1990, Li and Becker 1993) or the use of vegetation indices (Valorand Casselles, 1996). Contemporaneous atmospheric soundings with an adequate spatialresolution and accuracy may be another way to retrieve effective emissivities at the scale of thesurface radiation measurements.

4.1.4 R&D challenges

In order to allow the land community to take advantage of the SEVIRI data, the following researchand development issues seem to be of high priority:

• Determination of the required LST measurement accuracy for various proposed applications (thismay also be a function of scale) and comparison with the quality of alternative data sources.

• Careful evaluation of the performance of SEVIRI thermal sensors and their calibration in relationto land applications with stringent accuracy requirements.

• Evaluation of the view angle effects on the retrieval of brightness temperatures at the givenscale/spatial resolution.

• Evaluation of the use and further refinement of split-window algorithms such as those developedfor NOAA/AVHRR. This includes the possible use of four thermal measurements and theconsideration of the impacts of a reduced spatial resolution as well as a much higher temporalresolution. The validation of the LST retrievals is a scale-dependent problem and a majorchallenge.

• Further development of algorithms for the retrieval of surface emissivities at the givenInstantaneous Field Of View (IFOV) and/or the establishment of relevant databases for major landcover types. Since, however, the effective surface emissivity is a function of location, sensor viewangle and temporally variable surface composition within the IFOV, emissivity should preferablybe retrieved from the SEVIRI measurements themselves.

• Development, test and implementation of atmospheric correction algorithms usingcontemporaneous atmospheric soundings.

• Development of algorithms for the synergistic analysis of measurements made with a range ofsensors at differing spatial and temporal resolutions.

4.1.5 Synergy and scaling issues

Synergy with other instruments exists because of their spatial and temporal resolutions and in view ofthe information that can be extracted on LST and emissivity from these sensors. Examples arecontemporaneous measurements in the thermal infrared or in the visible and near-infrared (e.g.AVHRR, ATSR, MODIS, ASTER, VEGETATION, GLI, TM). Taking into account the highly

23

fragmented landscape pattern in Europe, the combined use of measurements at a range of spatial andtemporal scales will be a major asset for the analysis of the dynamics of the various ecosystems.

4.2 Albedo

Names of variables: Albedo [dimensionless]BRF [dimensionless]

4.2.1 Rationale

Spectral albedo at a horizontal physical boundary is defined as the ratio between the flux of radiationscattered in the upward hemisphere and the incoming (downward) flux of radiation, in a givenspectral band. Different definitions exist for specific concepts, depending on how the incomingradiation is accounted for, either spectrally or directionally. Albedo always involves integration overthe exiting solid angle; it may be relative to direct or diffuse radiation, or to narrow or broad spectralbands, for instance.

More specifically, the Bidirectional Reflectance Distribution Function (BRDF) is the ratio of thesurface leaving radiance I [in W m-2 sr-1] to the flux density Ei of incident collimated radiation [in Wm-2]. BRDF is thus expressed in sr-1. The Bidirectional Reflectance Factor (BRF) is the ratio ofBRDF of the surface over BRDF of a perfectly diffuse surface illuminated and observed underidentical conditions. BRF is dimensionless. With these notations, the albedo of a surface is equivalentto its directional hemispherical reflectance (Nicodemus et al., 1977).


The albedo of the Earth's surface is one of the most important variables for climate studies, as itcontrols the fraction of solar energy available to the surface system. In principle, this quantity shouldbe estimated by integrating the bidirectional spectral reflectance of the surface over all angles of theupward hemisphere. In practice, the accuracy achieved in the estimation of albedo thus depends onthe angular sampling used.

The anisotropy of natural surfaces is highly variable in space and time. Sufficient sampling of thebidirectional reflectance fields is thus crucial, and the accuracy with which it will be retrieved willaffect the quality of other products derived from SEVIRI data. So far, neither the surface BRF nor thealbedo values have been properly retrieved in most cases, in part due to the lack of data and also dueto the complexity of the scientific problems associated with the coupling of the surface and theaerosol-loaded atmosphere. Indeed, it is difficult to accurately characterise the surface withoutsimultaneously quantifying the interactions of the radiation field with the atmosphere because thesurface constitutes one of the boundary conditions of the radiation transfer problem that needs to besolved when analysing remote sensing data.

New approaches have been developed to exploit data that will be generated by advanced sensors suchas the MISR, MODIS, MERIS and POLDER instruments (e.g. Martonchik 1997; Martonchik et al.,1998a; Wanner et al., 1997; Leroy et al., 1997). Figure 4 shows an example of directionalhemispherical surface reflectance derived from Meteosat data (Pinty et al., 1999).

The most promising algorithms are those that treat the coupled surface-atmosphere problem jointly,so that they generate simultaneously an albedo and an aerosol product. The enhanced performance of

24

the new generation of instruments will offer a unique opportunity to evaluate the theoretical basis ofavailable models, and the latter will permit a fuller exploitation of the data.


The suitability of the spectral resolution of SEVIRI to jointly characterise the surface and theatmosphere needs to be investigated in detail. However, its capacity to acquire data at a relativelyhigh temporal frequency will open new opportunities, at least for those places and time periods forwhich the surface-atmosphere system does not change too fast. Indeed, within the limits of this ratherstrict assumption, data accumulation in time (for instance in the course of a day) will provide a rathergood sampling in the directional domain, since the illumination geometry will change continuouslyand predictably during such a period. This is a unique capability of the MSG platform.


A major challenge in this area is to assess the suitability of existing bidirectional surface reflectanceand atmospheric radiation transfer models to address the above issues in the context of SEVIRI. Theeffects of the spatial resolution and temporal sampling of the SEVIRI on the retrieval of theBRF/albedo products will have to be estimated. A further problem will be to adapt these advancedalgorithms to meet operational constraints of the processing systems. The latter requirement mayresult in the need for simplifying and adapting algorithms applicable in research mode to match theallotted computer resources, and to document the uncertainties associated with these algorithms andmodifications. Nevertheless, such potential limitations of the computer resources should notjeopardise the accuracy of the product.

Since albedo products will also be used by scientific communities involved with climatic issues forwhich the spatial and temporal scales of interest may be much larger than the typical sampling of theSEVIRI instruments, issues such as the spatial aggregation and historical reconstruction of BRF andalbedo will also need to be addressed.

Figure 4: Example of Directional Hemispherical Reflectance (DHR) at the surface derived from Meteosatdata for a sun zenith angle of 30°. The data results from a compositing of daily DHR between 21 June and30 June 1996. The left image shows the Nile river area. The right one shows a part of southern Africa.

25


Use of data from polar orbiting satellites will improve albedo estimation at high latitudes due to theirhigher revisit frequency. It should be noted that accurate estimation of surface albedo requiresdetailed knowledge of atmospheric composition. In this respect, significant progress can be expectedfrom the simultaneous use of multiple sensors to improve the knowledge of aerosols. Among therange of sensors that will be available during MSG operation, those with spectral bands in the blueregion, and/or those with contemporaneous multiple view angles of the same geographic areas willbe the most relevant.

4.3 Aerosol

4.3.1 Rationale

The variable “aerosol” encompasses different quantities such as aerosol concentration and type,phase function, vertical distribution, etc. The analysis of aerosols through remote sensing has a two-fold purpose:

a) The characterisation and quantification of the aerosol type, mass and distribution as acontribution to atmospheric and climate studies;

b) The correction of imaging and sounding data to improve the retrieval of the desired targetvariables.


Methods to estimate aerosol from Meteosat data over bright land surfaces have already beenestablished using satellite thermal infrared images (Figure 5). The daytime decrease of the emittedradiance in the presence of a dust layer can be exploited to infer the presence of particles greater thanapproximately one micrometre in the atmosphere (Legrand et al., 1989). In this case, the radiationemitted by the surface is attenuated by the dust layer; the emission of the latter, much colder than theground surface, does not compensate for this attenuation. This effect is highest at solar noon, whenthe temperature difference between the ground and the dust layer is at its peak.

26

Various algorithms and methods have become available for aerosol characterisation from polarorbiting satellites, the most commonly used being the Dense Dark Vegetation (DDV) approach(Kaufman and Sendra, 1988), which uses the radiance received in the Red/NIR range, i.e. the 0.635µm band in the case of SEVIRI. A well-suited site for the approach would be near the equator in theCongo basin and forested areas in Europe. Another method consists of using large water bodies incombination with measurements obtained in the 0.8 µm band. Suitable sites for MSG would be largewater bodies such as Lake Victoria. The representativeness of such information is, however, limitedto the neighbouring regions only. A third group of methods is based on the reduction of the contrastbetween adjacent heterogeneous land surfaces (Martonchik et al., 1998b). Finally, an inversion ofreflectances accounting for multiple scattering between the surface and the atmosphere should deliveruseful information on aerosol. Such an approach is described in Section 4.2.


The potential contribution of SEVIRI lies in the capability to provide daily aerosol information whichis needed for tracking air masses (successfully used already in the Meteosat Operational Programme,MOP) (e.g. Moulin 1997). Another important potential contribution is the characterisation of aerosolat the pixel level for the retrieval of surface parameters.


Refer to Section 4.2.4.


Using aerosol correction methods over land from sensors with a higher resolution than SEVIRI (e.g.MERIS, MODIS, MISR, VEGETATION) will help correct SEVIRI data products, improving itsquality for biospheric applications over limited areas and allowing to extend it spatially using thelarge scale SEVIRI measurements. In this context, complementary information in the blue part of thespectrum can be provided by other sensors such as MERIS or VEGETATION. These techniquesexploit the difference in the TOA reflectance between the blue and red parts of the spectrum; over

Figure 5: Infrared dust index, from green (low values), yellow, orange to red (high value). Sourcehttp://jaki.halo.hi.is/meduse/

27

vegetated surfaces, most of the observed difference is due to the aerosol loading in the atmosphere.Furthermore, major synergy is expected from the joint use of SEVIRI and SCIAMACHY on Envisat.SCIAMACHY will provide high resolution vertical and limb sounding measurements, to becombined with the high temporal resolution and areal coverage of SEVIRI. For validation activities,it will be important to consider the use of the operational AERONET ground station network, whichprovides input to radiation studies globally, based on its aerosol optical depth measurements from theground.

4.4 Global solar and photosynthetically active radiation

Names of variables : SW [units: Wm-2]PAR [units: Wm-2]

4.4.1 Rationale

Global solar radiation is the amount of solar energy incident at the earth surface during a given periodof time. It is integrated over the whole solar spectrum. In practice however, most of the solar energyis within 0.25 and 3.5 µm spectral interval. Global radiation is the sum of direct and diffuse radiation.PAR is the portion of the SW radiation in the spectral range of 0.4 to 0.7 µm. It is the radiation usedby plants for growth.

4.4.2 Status of retrieval algorithm relevant to MSG

Global solar radiation (SW) and PAR are a major driver of a number of land surface processes suchas evapotranspiration and photosynthesis. SW depends on time and location, and it is stronglymodulated by cloud cover. Estimates of SW and PAR with high spatial and temporal resolution areneeded by a number of users such as meteorologists, ecologists and agronomists. The design of solarenergy systems also requires SW statistics with separation of direct and diffuse radiation.

Several methods have been developed since the late 1970s in order to derive SW from satelliteradiance measurements (e.g. Zelenka et al., 1992). Physically based methods are now mature(Whitlock et al., 1990, Charlock and Alberta, 1996). Since the incoming solar energy at TOA is easyto compute, a simple difference between incoming and outgoing (measured by satellite) radiation atTOA gives the first order estimate of SW absorbed by the earth atmosphere system. Radiativetransfer calculations then provide an improved assessment of SW at ground level. Intercomparison ofSW methods performed under the auspices of the GEWEX-SRB project indicates that an accuracy ofabout 20 Wm-2 for monthly averages can be reached on a routine basis (Whitlock et al., 1995). ThePAR absorbed by the land surface can be estimated directly from the TOA measurements since theatmosphere absorbs PAR radiation only to a small extent (Li and Moreau, 1996; Moreau and Li,1996).


With current algorithms and data, the accuracy of SW retrievals decreases for broken cloud fields.Since cloud cover is highly variable, SW estimate also strongly depends on the frequency ofmeasurements. Due to its temporal and spatial resolution, SEVIRI will contribute to improve thesetwo issues. For PAR where the atmospheric ozone and water vapour have minimal effect, parameterfields could be produced for the entire MSG disc at frequent time intervals (subject to someinformation on aerosols.

28


Research and development is needed to maximise the benefit of SEVIRI capabilities for SWmapping. For example, radiative transfer of broken cloud fields is not easy to solve accurately,especially when an operational algorithm is sought. The anisotropy of cloud reflection, even forovercast sky, may induce large errors and should be accounted for. Snow cover may induceconfusion with cloud cover, and should be detected. Finally, the spectral richness of SEVIRI mayhelp to improve radiative transfer calculations.


SW, and especially direct irradiance, depends on aerosol optical depth (Section 4.3). External sourcesof information on this parameter may be needed. The meaning and usefulness of SW estimates over3×3 km areas is limited over mountainous regions. Some studies have been devoted to this issue andshould be considered (Stuhlmann et al., 1990).

4.5 Soil moisture

Name of variable: Soil Moisture (SM) [units: (MwMsoil-1) or (VwVsoil

-1)]

4.5.1 Rationale

Soil moisture (soil water content) is defined as the mass or volume of water by unit mass or volumeof dry soil. This definition implies that measurements of bulk dielectric properties, such as done inthe microwave region, are directly related to soil moisture. The observations provided by MSG have,therefore, no direct physical relation with soil water content. On the other hand, a measure of wateravailability would meet the requirements of research and applications in many areas. Wateravailability controls the partition of net radiation into sensible, latent and soil heat fluxes atheterogeneous land surfaces. Land surface temperature is a sensitive indicator of the relativemagnitude of these heat fluxes. It is somewhat easier to relate observations of surface temperature towater availability when dealing with truly homogeneous surfaces, i.e. bare soil and completecanopies (high LAI). For bare soil, the soil water content in the upper few centimetres is ofimportance, while for complete canopies it is rather soil water content at deeper depths in the rootzone. The response of partial canopies, i.e. a mixture of foliage and bare soil, to water availability ismore complex. The observed radiometric temperature is a composite of both the foliage and the soilsurface temperature. Physical factors controlling the thermal response of bare soils and foliage arealso quite different. Transport of soil water and the dependence of soil thermal properties on soilwater content is soil-specific. Plants control evaporation rate by opening and closing leaf stomata.The response of heterogeneous land surfaces to radiant and boundary layer forcing is thereforecomplex.

All of the above notwithstanding, significant research efforts have been dedicated to obtain a measureof water availability using observations in the optical region to model heat balance of heterogeneousland surfaces. In this context, indices of water availability are derived from estimates of actualevaporation and, as such, they are dealt with in Section. 4.8.

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4.5.2 State of the art on algorithms relevant to MSG

Methods to exploit the signal of SM on the basis of satellite-derived LST diurnal variations (refer toSection 4.1) from the GOES satellites have been proposed and studied for large areas by Wetzel et al.(1984) and Wetzel and Woodward (1987). The authors claim the possibility to separate up to fiveclasses of soil moisture by using the early morning LST rise (8:00-10:00 local solar time) as theprime source of SM information. Information about NDVI improved the estimate by accounting forvegetation cover and possibly type. An earlier study by Heilman and Moore (1980), based on aircraftdata, used the diurnal LST cycle for the estimation of SM in the top four centimetres of the soil. Themain weaknesses of the above methods are the necessity of mostly clear sky conditions at night andin the morning, and of conditions where horizontal temperature advection and the influence oftopography do not affect LST changes. Gillies and Carlson (1995) proposed a method to inputvegetation cover derived from NDVI directly into a Surface-Vegetation-Atmosphere Transfer(SVAT) model in addition to estimated atmospheric parameters. The soil moisture is adjusted in themodel to represent the LST estimated from NOAA/AVHRR at noon.

Early examples of algorithms to determine water availability through heat balance modeling werepresented by Deardorff (1978), Soer (1980) and Carlson et al. (1981). All these approaches relied onfitting temporal evolution of surface temperature calculated by means of a SVAT model to multi-temporal observations in the thermal infrared regions (typically a night-day pair). It was soon realized(e.g. Reiniger and Seguin, 1986) that this approach was severely limited by the amount and diversityof ancillary data required to determine model variables. The intuitive notion that many differentcombinations of model variables could give the same thermal response was confirmed later byanalyses such as the one of Beven and Fisher (1996). In this context MSG opens a new perspectivedue to high frequency of observations. A more detailed characterization of the temporal evolution ofsurface temperature may prove more effective in constraining SVAT simulations. This approach isdiscussed in more detail in Section 4.8.

4.5.3 Original Contribution of MSG

SEVIRI is well suited for SM approach based on the LST trend because of the high temporalresolution and the possibility of estimating several relevant vegetation characteristics for the samearea simultaneously. This strategy should be particularly effective over sparsely vegetated regions ofAfrica where the likelihood of clear-sky conditions is also relatively high.

4.5.4 R&D Challenges

It is fair to say that RS methods to retrieve indices of water availability through heat balancemodelling are not very robust and all applications presented in literature involve assumptions andconstraints specific of the environment studied. On the other hand, the heat balance is a generalphysical law (first law of thermodynamics) and holds under any circumstance. The challenges lie inthe simplified equations used to model heat transfer processes and the detail at which elements of thelandscape can be described (see e.g. Hall et al., 1992). Trade-offs between realism of the physicsembedded in the model and data requirements have been proposed and are likely to represent theobjective of research in this area. The relatively low spatial resolution of MSG brings about anadditional problem. The dependence of soil moisture on heat fluxes and observed radiances is non-linear. A mismatch of the spatial resolution of the observations relative to the actual spatial variabilityof land surfaces leads to a significant bias. The relatively low spatial resolution of MSG observationsis not optimal when dealing with highly heterogeneous land surfaces and assessment of the impact ofsuch errors on applications is a specific research priority.

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It has been shown by several studies (e.g. Smith and Cooper, 1996) that systematic errors of around10% are to be expected when the equations are applied at pixel level. Also, satellite-derived surfaceflux variability often overestimates the actual variability because the methods tend to overemphasisethe effect of local surface characteristics on the fluxes; this distortion will affect derived SM in asimilar way.


Synergistic effects for the SM retrieval discussed above should be investigated by using moredetailed information about surface and vegetation from other sensors, e.g. MERIS, MODIS andVEGETATION. This would lead to a better characterisation of the surface and its relation betweendiurnal temperature development and soil moisture. Such data should help in analysing subpixelinformation about the surface and potentially used as additional information in retrieval algorithmsbased on neural network approaches. The use of passive microwave sensors should also beinvestigated, to obtain a better closure on the retrieved soil moisture. Due to the indirect nature of theSM signal, the retrieved SM values will need careful validation. In addition, NWP SM outputs mayplay a useful role as a complementary method for SM estimation.

It should be noted that LST-based methods provide mostly near-surface SM information. For SMestimates in deeper layers, SVAT models are more appropriate because the soil profile dynamics canbe described explicitly.

4.6 Fraction of absorbed Photosynthetically Active Radiation

Name of variable: FPAR [dimensionless]

4.6.1 Rationale

The Fraction of absorbed Photosynthetically Active Radiation (FPAR) is the proportion of PAR(Section 4.4) actually absorbed by the vegetation canopy. FPAR is a non-dimensional quantity.


FPAR is required by models that aim to describe vegetation dynamics, mainly with respect toprimary productivity. Traditionally, FPAR has been estimated on the basis of vegetation indices. Thisapproach relies on the correlation between vegetation index values and local in situ measurements oron 1-D/3-D radiation transfer models (e.g. Govaerts et al., 1999; Gobron et al., 1999, Myneni et al.,1997; Cihlar et al., 1997). Such relations are usually established for limited regions and particularperiods of time. When based on models, these relations can be expected to be more generallyapplicable, often in conjunction with pre-determined land cover distributions.

Until recently, most vegetation indices have been computed with two spectral bands, the red andnear-infrared. With better sensors offering more spectral bands and observation directions, it will bepossible to develop advanced methods/tools, in particular advanced indices optimised to deriveFPAR. However, the vegetation index-based approach to estimate FPAR will progressively bereplaced by algorithms based on a physical understanding of radiation transfer theory. Dedicatedalgorithms have been designed to take advantage of the spectral and angular sampling offered byMODIS or MISR (e.g. Martonchik 1998; Justice et al., 1998). Some are quasi-operational whilstothers are more exploratory at this stage.

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The accuracy and reliability of the retrieval of FPAR depends strongly on the quality of theestimation of the BRF and albedo products (refer to Section 4.2). Hence, the original contribution ofSEVIRI with respect to FPAR is as for this latter variable. In addition, SEVIRI will make optimumuse of clear-sky data due to its frequent imaging.


The development of vegetation index-based FPAR estimation methods that are optimised for SEVIRIdata remains to be done, in particular to take full advantage of the angular and temporal sampling bySEVIRI. Whether and to what extent algorithms based on radiation transfer theory will be applicableto exploit SEVIRI data and to provide the expected products at the required accuracy will also needto be investigated.


The accurate estimation of FPAR relies heavily on the availability of surface parameters such as BRFand albedo.

4.7 Leaf area index

Name of variable: Leaf area index (LAI) [units: m2/m2]

4.7.1 Rationale

The Leaf Area Index (LAI) is one-half of the total (two-sided) area of leaf material per unit groundarea. It is a non-dimensional quantity (m2/m2) that is important for understanding and modellingradiative processes within the vegetation, interception of water and aerodynamic properties of thecanopy. LAI is therefore a very important quantity and as such is used in climate, water and radiationbalance, ecophysiological, agrometeorological, and other types of ecosystem models. LAI cannot bereadily determined in the field at a spatial scale corresponding to the resolution of SEVIRI, althoughsubstantial progress in developing field measurement methods has been made (e.g. Chen and Cihlar,1995; Chen, 1996).


As in the case of FPAR, existing LAI retrieval algorithms are based on vegetation indices (e.g. Chen,1996) or radiative transfer models (e.g. Myneni et al., 1990). The signal and the noise levelsassociated with LAI retrieval strongly depend on the value of the parameter itself. Between an LAIvalue of 0 and 1, the retrieval accuracy based on spectral satellite measurements is limited becausethe signal is low (conditioned mostly by the soil background). When the LAI value lies between 2and 4, the accuracy is better because the role of the vegetation in controlling the overall reflectance islarger. However, when LAI is greater than 5 or 6, the reflectance is again less sensitive to LAIbecause the canopy behaves as a radiatively semi-infinite medium (Knyazikhin et al., 1999). The

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saturation effect is less serious for coarse resolution satellite data because of the increased likelihoodof bare soil. The saturation effect also strongly depends on wavelength. In the near-infrared, remotesensing signals remain more sensitive to LAI than in the visible part of the spectrum (Gobron et al.,1997a).

Relations between LAI and vegetation indices are complex and diverse, because of intrinsic radiationtransfer issues, including the 3-D structure of canopies. In spite of these complications, useful LAIproducts can be obtained from satellite data with limited spectral resolution, provided that sufficientin situ data are obtained to develop cover type-specific relationships (e.g. Chen and Cihlar, 1996;Cihlar et al., 1997). Algorithmic foundation has also been developed to generate LAI productsthrough an inversion of radiative transfer models (e.g. Justice et al., 1998). These models are basedon radiation transfer theory, take advantage of the constraint imposed by the energy conservation law,and therefore require a good estimate of surface albedo (Gobron et al., 1997b).


Refer to Section 4.6.3.


The relevance and accuracy of existing algorithms will need to be evaluated in the context ofSEVIRI. Some of these approaches may need to be updated or modified to account for the particularspecifications and performance of this instrument. Similarly, substantial work is needed on themathematical and technological aspects of inversion procedures, particularly to address the issuesassociated with the manipulation of very large data sets. Field data collection and validation forvarious ecosystems and phenological conditions should be an essential component of this work.


Significant effort needs to be made to address scaling issues. Specifically, modern approachespermitting the description of the heterogeneity and scaling of spatially distributed fields (e.g. fractals,wavelets) should be investigated. Optical sensors with higher spatial resolution will play an importantrole in this work.

4.8 Evaporation

Name of variable: Evaporation [units: kg/m2/day]

4.8.1 Rationale

Evaporation, the phase transition of liquid water to vapour, has attracted a rather considerableamount of attention and research work over the last two centuries (see Brutsaert, 1982 for ahistoric overview and Menenti, 1999 for a recent remote sensing oriented review). This interest isdue to the role played by evaporation in many processes in the earth-atmosphere system and theresulting practical relevance of observing and understanding evaporation in the context ofhydrology, water management, meteorology, climatology, ecology and agriculture. The globalrole of evaporative processes is well established (Shukla and Mintz, 1982).

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Kustas and Norman (1996) referred to Wetherald and Manabe (1988) and Sato et al. (1989) whilementioning the impact of changes in available moisture released by evaporation on cloudformation, radiation budget and precipitation fields at global and continental scales. Many studieshave documented the impact of evaporation on mesoscale atmospheric processes (e.g. Wang etal., 1995; Beljaars and Viterbo, 1994; Segal and Arritt,1992).

Two fundamental concepts need to be distinguished when dealing with evaporation of land surfaces:potential evaporation E0 and actual evaporation Ea.

Potential evaporation is meant to indicate the largest water loss rate from a vegetated land surfacepossible under given weather and climate conditions, but not limited by water supply. It is anobviously difficult concept to define precisely. Thornthwaite (1944) gave the definition: “Potentialevapotranspiration is the loss of water from a moist soil tract completely covered with vegetationand large enough for oasis effects to be negligible” as quoted by Monteith (1994). The termevapotranspiration indicates the combined contribution of evaporation from soil and open waterwith transpiration through the leaves. Here the term evaporation will be used to refer to liquid tovapour phase transition independently of the source.

Water availability at land surfaces has a significant spatial and temporal variability so the actualwater loss rate varies accordingly and is less than (or at most equal to) the potential rate. This isactual evaporation. It has been appreciated long ago (Thornthwaite, 1944) that water availability,evaporation, partitioning of net radiation and temperature are strongly interrelated. The latter is ofparticular relevance in the context of remote sensing as discussed in some detail later on.

The rate of water loss through the land surface relates to three different processes which all havebeen studied (Menenti, 1993) in the attempt to develop a method to obtain spatial patterns of actualevaporation. These are:

A. heat transfer at the land-atmosphere interfaceB. water flow in a soil columnC. water transport in the atmosphere

Most of the methods relying on space or airborne instruments are based on simplified models ofheat transfer at the land-atmosphere interface (land surface, case A). The processes A, B and C arefurther constrained to situations for which a balance equation must be fulfilled. Actual evaporationis then estimated from the balance equation after determining the other terms.


A fundamental difficulty shared by many algorithms is the requirement for a representative nearsurface air temperature. At patch scale, such as in the earlier experimental studies this was not muchof an issue (Moran and Jackson , 1991). Large area applications, on the other hand, have beenhampered by this requirement. Near surface air temperature is strongly coupled with surfacetemperature. Air temperature at some higher elevation in the atmospheric boundary layer has alimited spatial variability because of mixing and, therefore, is a suitable reference temperature forheat balance studies of heterogeneous land surfaces. This concept was demonstrated by Brutsaert etal. (1992) and Menenti and Choudhury (1993). Recent work (e.g. Anderson et al., 1997) pointstowards extending RS algorithms to determine actual evaporation with parametrisations of PBLheating. Although such methods avoid the use of near surface air temperature, they need soundings ofthe PBL at various levels of accuracy, vertical resolution and temporal sampling.

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A major original MSG contribution will be the very high temporal resolution, which enables anexcellent monitoring of the reaction of LST to insolation and other changing meteorologicalconditions like clouds. These parameters drastically modify the evaporation, affecting both thesurface energy balance and the canopy resistance to it. In addition, the high resolution visible sensorenables to detect subscale cloud contamination, which is crucial in the interpretation of the infraredsignal in terms of LST and the surface energy balance in general, which is the basis of evaporationretrieval.

Satellite data have been used up to now in the context of evaporation mainly for LST and a roughcharacterisation of the surface or to estimate the absorbed solar radiation. SEVIRI, by means of itssounder channels, could be used to derive some information on the temperature and moisture of theatmospheric boundary layer and on the atmospheric stability (e.g. Rao and Fuelberg, 1997; Simmerand Fuhrhop, 1998), thus providing additional and very important input for the SVAT models.

4.8.4 R&D Challenges

The main challenge for retrieving evaporation from SEVIRI will be to find the appropriate SVATscheme and to derive the necessary parameters besides LST and absorbed solar radiation to drive themodel. It is expected that SVAT schemes currently used in atmospheric models are appropriate forthat purpose and should be investigated. The implementation of such models would also simplify andstrengthen a fruitful cooperation between the remote sensing community and the atmospheric modelcommunity. Especially, mesoscale models need better information about the changing surfacecharacteristics to improve forecasts. The expression of retrieved vegetation parameters in anappropriate way for SVAT models represents a major challenge, especially for the solar spectralregion. This problem is not however specifically related to SEVIRI but is also true for other sensors.


As for soil moisture (refer to Section 4.5.5), non-geostationary satellites can contribute mainly bysupplying more information about vegetation type and leaf area index in general, and especiallyfurnish information about sub-pixel scale heterogeneity. Current SVAT models are able to use thisinformation for the improvement of the model output.

4.9 Fire

4.9.1 Rationale

Fire is characterised by several parameters. MSG is an appropriate system for the production of dataand information dealing with various aspects of the remotely sensed fire signal, namely heat, smoke,and charring, and also to monitor the susceptibility of vegetation to fire at the landscape level.

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4.9.2 Heat

Several algorithms have been developed during the last 15-20 years to extract the fire thermal signalfrom remotely sensed imagery, using primarily the NOAA/AVHRR and also the GOES system. TheMatson-Dozier algorithm (Matson and Dozier, 1981) relies on data from the NOAA/AVHRRthermal channels to estimate sub-pixel active fire (i.e., combustion zone) size and temperature.Uncertainty about the emissivity of different surfaces, and sometimes difficulties in the estimation ofbackground surface temperature, create problems in the application of this algorithm (Langaas,1993). The other type of algorithm is based on the application of multiple thresholds to extract highheat sources, and to eliminate false detections. Earlier algorithms of this kind relied only on spatiallyfixed thresholds that could be empirically adjusted through time, for a given period of application(Grégoire et al., 1993; Kaufman et al., 1990). More recent versions include “contextual” criteria,implemented in the form of roving, variable size windows, which essentially perform a task oflocally-adaptive thermal edge detection (Prins and Menzel, 1992; Justice and Dowty, 1994; Flasseand Cecatto, 1996). A major problem with the implementation of these algorithms usingNOAA/AVHRR is the relatively low saturation temperature of the 3.75 µm channel, at about 321 K,since hot soil surfaces often reach these temperatures, and are a source of confusion (Grégoire et al.,1993). This same problem also affected the Matson-Dozier algorithm.

The specifications of SEVIRI appear very adequate to study the thermal signal of larger fires.Theoretical studies have shown that flame fronts covering a very minor proportion of the pixel areacan be detected (Matson and Dozier, 1981), and so the SEVIRI 3 km pixel size at the SSP should notbe considered much of a limitation, although detectability of small fires should be considered a topicfor further research. The higher saturation temperature for the SEVIRI thermal channels is alsoadvantageous for estimating sub-pixel fire characteristics, and to minimise false alarms from hotsoils. The increased spectral resolution of SEVIRI will facilitate the design of additional criteria toeliminate remaining problems with cloud edges, sunglint, etc. The high temporal frequency of MSGimagery is ideal to characterise the diurnal cycle of fire activity in Africa, thus overcoming temporalsampling problems that are unavoidable with instruments like the NOAA/AVHRR. Geographicalcoverage provided by MSG is ideal, considering that Africa is the most important continent from thestandpoint of global biomass burning (Lacaux et al., 1993). Coordination with the work beingdeveloped for South America fires using GOES (Prins et al., 1992) and similar programmes willallow for global geostationary coverage of fire activity.

Units for this variable are presence/absence of fire. Based on outcome of research into Matson-Dozier type algorithms, the units could evolve to combustion zone area (m2), and temperature (K).

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Figure 6 : Example of multi-spectral smoke detection with the GOES-8 ASADA algorithm.Source - http://cimss.ssec.wisc.edu/goes/burn/asada.html

4.9.3 Smoke

The massive 1998 fire events in Indonesia and northern Brazil, associated with the 1997/98 El NiñoSouthern Oscillation (ENSO), highlight the importance of remotely sensing the sources of smokegenerated by biomass burning, as well as its transport trajectories, and persistence over denselypopulated areas. Algorithms have been developed for the NOAA/AVHRR to detect smoke plumesassociated with active fires (Kaufman et al., 1990), and the Automated Smoke Aerosol DetectionAlgorithm (ASADA) is available for GOES-8 (Figure 6). The latter algorithm is based on single andmulti-band difference thresholds, contextual information, albedo calculations, illumination andviewing geometry data to distinguish smoke/haze from clouds, low-level moisture and sunglint (Prinset al., 1998).

Again, the SEVIRI instrument has the specifications required to generate a similar product.Specifically, the geographical coverage of the MSG disc, extending over the entire South Atlanticand reaching South America, may be suitable to track the westward motion of smoke produced byAfrican savannah fires, which is know to influence atmospheric chemistry in South America, atcertain times of the year (Lacaux et al., 1993).

Units for this variable are the presence/absence of smoke. Possibly, and in conjunction withadditional research, it may evolve to smoke albedo, in reflectance units.

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4.9.4 Burned area mapping with the char/scar signal

It is difficult to assess the extent of area burned, using only the thermal signal of active fires. This is avery short duration signal, and in the presence of persistent cloud cover, large areas will burn withouttheir thermal signal being detected. This is particularly true for fast spreading fires, such as thoseburning in savannah ecosystems. On the other hand, the loss of vegetation and darkening of thesurface cause by fire are much more persistent and, in general, can be detected for weeks to monthsafter the fire. The post-fire char/scar signal is therefore considered more adequate for accurateassessment of area burned (Pereira et al., 1997). Currently, there are no community-consensusalgorithms for burned area detection and mapping. However, those developed, or under developmentfor the NOAA/AVHRR and Along Track Scanning Radiometer (ATSR) are adequate forimplementation with SEVIRI. Some of these algorithms rely on static interpretation of compositedimagery, whilst others track changes in time series of imagery. Both types of algorithm usevegetation indices and thermal data, sometimes complemented by albedo, and mid-infraredreflectance (Eva and Lambin, 1998; Barbosa et al., 1997; Pereira, 1999). The geometriccharacteristics of SEVIRI should be adequate for accurate estimation of areas burned, at regional tocontinental scales, using either periodic composite images analysed in unitemporal mode, or changedetection procedures resulting from time-series analyses.

Algorithms for burned area mapping, suitable for regional/continental scale application (Barbosa etal., 1997) are still experimental, but the knowledge available in this research community ought to berelevant for methodological development for MSG.

Main research topics are: the feasibility of sub-pixel combustion zone size and temperaturedetermination; spectral unmixing of the post-burn signal to increase the accuracy of burned areaestimation (possibly in synergy with higher resolution instruments); compositing procedures forproducing cloud-free scenes that preserve the burn signal.

4.9.5 Vegetation susceptibility to fire

Vegetation susceptibility to fire is important both for Europe and Africa, but for different reasons. InEurope there are well-established and sophisticated fire detection and fire fighting infrastructures thatcan benefit from information on the susceptibility of vegetation to burn, at the landscape level.Essential ingredients of such systems are the monitoring of vegetation phenology and water stress,and land surface temperature. The high frequency of observation available with MSG makes itespecially adequate for integration of systematic ground level measurements (e.g. temperature)performed a few times a day, at fixed hours, with the satellite imagery. In Africa, knowledge of pre-fire vegetation phenology and water status is very important to assess the severity of fire effects,namely combustion efficiency, which is a critical element for estimating the magnitude ofatmospheric emissions.

4.9.6 Possible applications and research, using MSG data for fire monitoring

The following activity can be undertaken with MSG:

• Monitoring diurnal cycles of fire activity.• Regional to continental level burned area assessment and mapping.• Smoke coverage and transport mapping.

Results from these activities are useful and important for:

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• Regional ecological impact assessment.• Understanding land-use/land-cover dynamics.• Understanding fire-meteorology relationships.• Continental-global scale greenhouse gas emissions assessment.• Assessing the surface energy budget implications of temporarily lowered albedo over a few

million km2 of African savannahs.• Educating the general public about biomass burning issues.

Units for this variable are presence/absence of burn. Based on the outcome of spectral unmixing, theycould become the percentage of pixel area burned.

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5 Examples of potential applications and research using MSG data

5.1 NWP data assimilation

Accurate forecasts from an NWP system require, in addition to a good model representing theatmosphere’s dynamic and physical processes, an accurate description of its initial three-dimensionalstate. Small errors in the input specification of temperature, wind and humidity can rapidly grow todominate errors in the subsequent forecast. The best initial state is obtained by simultaneouslyassimilating into the model all available measurements from many different observing systems.Current state-of-the-art systems such as four dimensional variational assimilation (4Dvar; Rabier etal., 1997), can also include observations (e.g. TOA radiances) which are not simply related to themodel field variables (e.g. temperature and humidity) at asynoptic times.

The primary interest for NWP is to ultimately assimilate the SEVIRI surface sensing radiances overland surfaces. However, initially MSG products useful to validate the temporal behaviour of themodel surface fields will be required to facilitate the assimilation. For example, Figure 7 shows theimpact of including TOVS data over land on the forecast accuracy. To make such assimilationpossible, the NWP model surface fields over land must be able to provide an accurate first-guesssurface temperature and emissivity. Therefore, surface temperature and emissivity products fromSEVIRI will be necessary for evaluating model improvements of these parameters. The inclusion ofsurface (sensing) radiances will allow the lower tropospheric and surface model fields of temperature,humidity and wind to be modified during the 4DVar analysis so that they are more consistent withthe observed (sounding) radiances, taking into account their uncertainties relative to othermeasurements. Within 4DVar, the high temporal sampling of MSG will allow the radiances to havemore impact on the model dynamics than polar orbiter data (available only four times a day). SEVIRIdata use will build on the present experience with radiances from the water vapour channel onMeteosat; these data are being experimentally assimilated in NWP models to modify the uppertropospheric model humidity and wind fields.

Figure 7: 500 hPa geopotential root mean square of forecast error for 4DVar averaged over 32cases in May and December 1997 for the northern hemisphere. Forecasts from 3 sets of 4DVaranalyses are shown, red line: No TOVS radiances assimilated, blue line: TOVS channels used oversea, only stratospheric channels over land and green line: more TOVS channels used over land.

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Other parameters which are also of interest to validate and possibly modify the NWP model surfacefields are surface albedo, vegetation cover, aerosol optical depths and soil moisture. Therepresentation of these parameters in the model is at present crude or non-existent, but it is believedthat in the future their accurate representation will be beneficial to medium-range and seasonalforecasts. Hourly TOA radiative fluxes are also an important parameter which can be inferred fromSEVIRI data and will be valuable for checking the model radiation scheme which includes thebroadband albedo and emissivity representation over land surfaces.

5.2 Fire monitoring

African vegetation fires play a central role in tropical atmospheric chemistry, contributing 57% to alltropical biomass burning (49% from savannah fires and 8% from forest burns; Lacaux et al., 1993).Savannah fires are responsible for over 90% of the biomass burned in Africa, in contrast to SouthAmerica and Southeast Asia where forest fires are more important (Delmas et al., 1991). In globalterms, combustion of savannah vegetation in Africa accounts for almost one-third of the emissionsfrom biomass burning (Andreae, 1991). Aerosol and gaseous emissions from these vegetation fireshave not only regional, but also transcontinental and transoceanic atmospheric impacts. Ecologicaleffects of African biomass burning are especially important when long-term fire frequency patternsare altered, due to increased human pressure over the natural resource base. It is also true whenalmost irreversible land use/land cover changes take place, such as in tropical deforestation.

In Southern Europe, wildfires burn several hundred thousand hectares annually (CE, 1996), causingsubstantial economic losses and ecological damage. Massive investments have been made ininfrastructure for fire prevention, detection and fighting, but the problem remains severe. Therefore,MSG could be very useful in providing high temporal frequency monitoring of surface temperatureand vegetation greenness, which are important sources of information for contemporary fire dangerrating systems.

From the standpoint of improving the characterisation of these fire activity patterns, MSG holds thepotential for a very interesting and original contribution, based on its coverage of a wide geographicalarea, coupled with a very high frequency of observation. These features are ideal for providing athorough characterisation of the diurnal cycles of fire activity at the continental scale, which is animportant input to atmospheric circulation models. Integration of fire scar mapping with active firedetection, which appears feasible given the technical specifications of SEVIRI, will also improve thecompleteness and accuracy of fire activity data. In areas of more fragmented landscapes, where firescars are typically small, the spatial resolution of MSG may be insufficient for burn scar mapping.However, given the possibility of detection of sub-pixel hotspots demonstrated with other sensors,integration of the two will prove especially valuable.

As previously mentioned, it should be feasible to adapt active fire detection algorithms developed forthe NOAA/AVHRR and GOES sensors for use with the SEVIRI, without the need for extensiveadditional research. Automated burned area mapping is still at an earlier stage of development, butthe available research clearly indicates its feasibility (Kasischke et al., 1992, 1995; Cahoon et al.,1994; Eva and Lambin, 1998; Barbosa et al., 1997; Roy et al., 1997; Pereira, 1999) and the adequacyof SEVIRI for this task. Again, the high frequency of observation should be useful in time-seriesanalysis approaches, which require an image-to-image registration accuracy better than one pixel.These algorithms apply change detection concepts to burn mapping, searching for decreases in thevegetation signal in combination with decreasing albedo, rising surface temperature and increasingMedium-InfraRed (MIR) reflectance. Where a more static approach based on multitemporal imagecompositing is preferred, the high frequency of observation should guarantee a good likelihood ofobtaining cloud-free scenes, which tends to be problematic in the tropics. Further research will be

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needed here because although the inadequacy of the classical vegetation index maximum valuecompositing approach has been identified, and better alternatives suggested (Barbosa et al., 1999), acompositing procedure specific to deal with burned area analysis has not yet been developed.

The contribution of MSG towards improved fire danger rating systems will also be magnified byconcurrent research on variables related to the surface energy budget, surface temperature estimation,and the design of new and improved vegetation indices.

5.3 Resource management in Africa The geometric characteristics of SEVIRI and the MSG orbital position mean that the Africancontinent is imaged at high spatial resolution. The range 3 km to around 5 km compares favourablywith existing earth observation data sets used to study the continent in its entirety. Those involved inresource management issues in Africa who have previously used earth observation data sets need tobe made aware of the potential offered by MSG. Meteosat First Generation data have already found important uses outside the meteorologicalservices, as evidenced by the range of applications reported at the Third EUMETSAT User Forum inAfrica (EUMETSAT, 1998e). This means that an infrastructure is in place upon which MSG canbuild. Questions of ground station upgrades to deal with MSG and for staff training must of course beaddressed, but these fall outside the scope of this report. The enhanced capability of SEVIRI data offers exciting potential for African resource management.The parameters described in the previous sections support applications in areas as diverse asagriculture, food security, soil and water management, range management, forest resourceassessment, fire management, environmental protection and even animal and human health. It isaxiomatic that the SEVIRI data alone are not sufficient for the day-to-day management of such arange of issues. However, it is certain that information derived from SEVIRI can play an importantrole in all of these areas, especially given the operational nature of the MSG system. It must be emphasised that successful use of MSG-derived information for resource management inAfrica will largely depend on timely availability, perhaps even local access to the data. Many of theresource management issues referred to above, and expanded on in considerable detail in Section 4,are highly time-dependent. For example, MSG-derived estimates of primary production should findapplications in agricultural yield forecasting, range management, veterinary services and ongoingwork on desert locust management, but only if the information is made available in a timely fashion. Whilst SEVIRI data over Africa will find many applications in resource management, thecontribution they can make to scientific concerns should not be neglected. To name but two themes,new perspectives on Saharan dust transport or the black carbon aerosols and associated gas transportstudies from savannah fires will benefit from the availability of advanced products such as thoseoutlined in Section 4.

5.4 MSG applications in Europe

Over the last ten years Europe has developed the use of earth observation for operational cropyield forecasting and crop production estimation through the MARS (Monitoring of Agriculturewith Remote Sensing) project (European Commission, 1998). MARS relies on a combination ofagrometeorological models, soil databases, and regular satellite observations at both high and lowresolution. The spatial scales of these databases are such that data from SEVIRI, even at the limits

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of the earth disc, are suitable (e.g. the agrometeorological model operates on a 50 by 50 km grid).The geographic coverage of the routinely processed SEVIRI data cover will constrain the use ofthese data for such operational use to some extent as data outside a 60o area around the sub-satellite point are not processed. Nevertheless, SEVIRI data will be available for many important,agriculturally productive regions of Europe at low latitudes, and at high latitudes good coveragewill be provided by overlapping polar-orbiting satellites. The use of new products from MSGshould thus be evaluated as inputs to yield forecasting models for the regions covered. CombiningSEVIRI with data from polar orbiting satellites should be addressed to examine possible synergyin data use for areas benefiting from cover by both systems, and to look at ways of combiningresults obtained using SEVIRI alone with those obtained using only data from polar orbiters.

The geographical coverage offered by SEVIRI is less constraining for other applications in Europe.Forest fire management is one example. The areas outside the region covered by MSG do notgenerally look upon fire as a major risk, yet fire studies throughout the Mediterranean, where theproblem is immense, will clearly benefit. The benefits arise not only from the characteristics of thedata, but also from the operational context in which MSG will be managed. Because of the reliabilityand continuity of data delivery there will be opportunities to test new approaches to fire managementwithin Europe. Such tests can be extended beyond fire, to other hazards such as drought and flooding.The availability of an operational service (in terms of data supply) could provide a basis for assessinginstitutional roles in natural hazards mitigation and management for Europe.

Of course the use of MSG will not be restricted to Europe-wide applications and nations withgood coverage will need to examine a broad spectrum of biospheric applications as part of theirnational resource assessment and research programmes.

5.5 Data assimilation and carbon models

Emissions of CO2 from the combustion of fossil fuels and from changes in land use haveperturbed the global carbon cycle (Houghton, 1996; Meyer and Turner, 1994). This perturbation islikely to lead to significant climate changes and to further impact, positively or negatively,ecosystem functioning (Melillo et al., 1996). A key question is the behaviour of the global carboncycle in response to anthropogenic perturbations and with respect to the physical climate system,on time scales of up to several hundred years. There is also a need to assess climate change impactand biosphere response at regional scales.

Climate changes resulting from an enhanced greenhouse effect of CO2 will have strong impacts onthe terrestrial biosphere (Walker and Steffen, 1996). Terrestrial ecosystems capture atmosphericcarbon through photosynthesis and bind it in organic compounds. When plants or some of theircomponents (leaves, fine roots, etc.) die, organic matter falls upon the soil surface (surface litter) or isdispersed through the soil profile. This dead organic matter is decomposed by soil micro-organisms.In the context of the global carbon cycle, the fundamental quantity of interest is Net EcosystemProductivity (NEP), defined as the difference between net carbon uptake (Net Primary Productivity,NPP) and release by ecosystems. NEP directly influences the concentration of carbon dioxide in theatmosphere. Depending on the results of complex feedback between terrestrial biosphere and climate,ecosystems may slow down, or on the contrary, accelerate the rate of increase of CO2 in theatmosphere (e.g. Betts et al., 1997).

The assessment of water and carbon exchange at the soil-vegetation-atmosphere interface is thereforeof major interest for carbon cycle assessment as well as a variety of other purposes including weatherforecasting, climate modelling, water resource management and vegetation productivity (Melillo etal., 1996). Variables of interest are primarily latent and sensible heat flux, soil moisture in the surface

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layer and in the root zone, and carbon uptake/biomass production. These variables cannot bedetermined directly from satellite radiance measurements but they affect surface characteristics suchas fractional vegetation cover, LAI, FPAR, and LST. These characteristics have a major impact onremotely sensed radiances in various SEVIRI spectral bands. The challenge is therefore to establishthe transformations between satellite radiances and the variables of interest (see Section 4).

SEVIRI can contribute to global carbon cycle and climate change studies by providing continuousinformation useful to detect possible changes of ecosystem distribution and functioning (e.g. Myneniet al., 1997). Such monitoring of land biosphere can rely on the analysis of seasonal and interannualtrends of short and long wave measurements (Figure 8). In order to establish links with underlyingprocesses, the analysis should rely as far as possible on the monitoring of surface variables retrievedfrom SEVIRI data, such as LAI, instead of radiances. Second, SEVIRI data can be used as input toNPP models (e.g. Potter et al., 1993, Ruimy et al., 1996), which is one important term of the carbonbudget. In addition, SEVIRI will allow the monitoring of grassland and forest fires (Section 4.9).Since each pixel will always be viewed with the same angle, time series analysis will be easier. Thesimultaneous availability of short and long wave measurements, with high temporal samplingfrequency, will provide radiative constraints on vegetation process models and should improve themodelling results.

A number of vegetation models have been developed in order to assess crop yield, net primaryproduction or energy and mass exchanges with the atmosphere. In principle, all these models dealwith NPP, although their usefulness for studying the carbon cycle questions is not uniform. Althoughthe design and output variables of these models may vary, they schematically fall in three categories:

a) Statistical models where biomass production is related to some satellite-based index such asNDVI. The main limitation of this approach is its poor extendibility to different locations orenvironmental conditions (e.g. Tucker et al., 1985).

b) Parametric models reduce the large number of processes occurring in vegetation to a few statevariables. This is the case for the Monteith model (1997) where NPP is related to the amountof solar radiation absorbed by the canopy (APAR) through a conversion efficiency coefficient.NDVI and linear relationships are often used to estimate the fraction, FPAR, of solar radiationabsorbed by the canopy (e.g. Ruimy et al., 1994). More sophisticated methods, based on thecoupling of radiative transfer models and of Meteosat spectral and angular measurementscould be used to improve estimates of FPAR (see Section 4.6).

c) Process models attempt to describe in detail most of biological processes and include a waterbalance sub-model. They are driven by weather data, and generally describe the exchange ofmass and energy at the soil-vegetation-atmosphere interface in order to simulate vegetationgrowth and NPP (e.g. Kergoat, 1998, Lüdeke et al., 1994, Warnant et al., 1994, Woodward etal., 1995; Liu et al., 1997). These rather complex models are good candidates to developassimilation techniques (Kergoat et al., 1995). Since the current trend is to couple vegetationfunctioning and SVAT models, model outputs could include carbon fluxes and vegetationproductivity as well as latent heat flux and soil moisture (e.g. Cayrol et al. 1999).

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Figure 8: Annual variation of Net Primary Productivity over Africa, 1990 minus 1989, in g(C)m-2.Superimposed isolines correspond to the variation, in percent, of annual rainfall for the two years,relative to 1989. NPP was estimated with the TURC model (Ruimy et al., 1996) driven byNOAA/AVHRR data reprocessed at CESBIO. (From Maisongrande et al., in prep. )

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6. Common data processing requirements for land applications

6.1 Calibration

Accurate calibration of data is essential to the successful extraction of quantitative information fromsatellite data, because the extraction relies on numerical models that relate the satellite signal to theparameter of interest. Calibration errors will thus very probably be translated into significant errors inthe information obtained. In the case of land observations, the importance of calibration is furtherunderlined by the often small dynamic range of the radiometer signal from the land biosphere, andthe need to discern the part of the signal attributable to the parameter of interest. Since the calibrationrequirement is common to all quantitative applications of satellite data, its implementation is offundamental importance to the success of the satellite mission. Both absolute and relative (i.e.,stability) calibration are important. Absolute calibration allows measurements from one satellitesensor to be compared with another. Relative calibration will negate the impact of a bias incalibration on specific derived products. This can be achieved through product validation campaignsor other quality control measures.

SEVIRI carries on-board calibration for the thermal infrared bands, but no on-board calibration hasbeen provided for optical bands (i.e., HRV, VIS 0.6, VIS 0.8, NIR 1.6) primarily because of designand financial constraints. This configuration implies that the data processing stream must be designedto fully utilise the on-board calibration information, and that a parallel set of measurements andprocedures must be put in place to characterise the calibration behaviour of the optical channels. Thedevelopment of a method that can ensure an accurate calibration of these bands is an important pre-requisite for quantitative exploitation of SEVIRI observations in the solar spectral region. Substantialexperience exists from previous satellite missions, e.g. the NOAA/AVHRR and SPOT HRV, whichemployed vicarious calibration procedures. Elements of these procedures are stable reference targetsusually on the Earth’s surface, independent characterisation, and systematic satellite measurements ofthese targets and associated data processing and analysis. The targets must be carefully selected andtheir spectro-radiometric behaviour described through independent measurements. There are suchpotential targets within the disc sensed by SEVIRI, e.g. in the Sahara desert. These targets can becharacterised through a combination of ground measurements, aircraft measurements, ormeasurements by other satellite sensors. Preliminary model results suggest that absolute calibrationaccuracy of 5% is achievable in this manner. It is essential that the measurement protocol be relevantto the sensing characteristics (e.g. spectral bands, sensing geometry) of SEVIRI optical bands. Giventhe importance of vicarious calibration, the documentation of calibration targets such as bright desertor clouds within the SEVIRI observation disc whose size is compatible with the SEVIRI spatialresolution must receive a high priority (Govaerts et al. 1998). The SEVIRI image processing facilityhas already been designed to deliver calibrated observations referenced to a standard grid with theassociated quality indicators (see Annex 9.3). The possibility to use alternative calibration methodssuch as satellite inter-calibration, sunglint, moon calibration or Rayleigh scattering should also beexplored.

Regarding calibration, it is recognised that both accuracy (i.e. how well a measurement is knowncompared to an internationally agreed standard or scale, e.g. SI units) and precision (how well ameasurement can be repeated) are important. The level of calibration currently proposed forSEVIRI will not be sufficient to meet many land applications. However, individual applicationshave specific precision and accuracy requirements. It is therefore critical that the MSG mission beaccompanied by a rigorous vicarious calibration programme based on test sites common toinstruments with on-board solar channel calibration (e.g. VEGETATION and ATSR for cross-

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calibration), ideally with regular calibration underpinning flights. In addition, applicationsdevelopment work should include sensitivity studies to identify the improvements expected inalgorithm performance as a function of calibration level.

6.2 Cloud Masking

For land biosphere applications, the part of the signal originating in the atmosphere represents anundesirable perturbation. The most significant source is clouds, and cloud identification and maskingare therefore of crucial importance to obtaining high quality land surface products. Of special interestis partial contamination of the signal, due to small (sub-pixel) or translucent clouds. In these cases,the contamination may be more easily confused with the surface signal and thus erroneouslyinterpreted as a different level of the latter. Thus, a high quality cloud detection and maskingprocedure is a key to successful MSG applications to land biosphere. Such a procedure should makeeffective use of all information from the various SEVIRI spectral bands. Of special importance is thetemporal information as clouds are more likely to change between sensing periods than the Earth’ssurface. The cloud masking algorithms should be optimised through comparisons with masks derivedfrom other satellite data (especially those with higher spatial resolution) and should be subject toregular quality control procedures.

The MBWG therefore strongly recommends that:• a pixel-scale cloud mask be available as this would be of considerable benefit when promoting

the use of SEVIRI for land applications;• a cloud mask be archived for every 15 minutes interval at the pixel level;• it is essential that this mask separate cloud from snow, that it offers four classes:100% clear,

cloudy with 100% probability, 75% probability, 50% probability;• the following are considered as highly desirable variables to be stored at the pixel level: cloud

optical thickness, cloud top temperature, cloud top height, cloud type and cloud phase.• improving cloud masking be considered an important research theme in its own right.

It is anticipated that the EUMETSAT MSG-MPEF will produce a cloud mask product at thestandard pixel resolution for every image, including a quality indicator. Additional information oncloud physical properties at the pixel level such as cloud top height, cloud top temperature, cloudoptical thickness, cloud phase and type are relevant for applications involving net surfaceradiation budget. Research and development in these areas are therefore relevant in the generalframework of biospheric applications.

6.3 Atmospheric corrections

Corrections for atmospheric scattering and absorption effects over land surfaces should receivespecial attention. To this end, atmospheric information for times coinciding with SEVIRI dataacquisition must be readily available outside the meteorological community to enable thederivation of biophysical variable products. Data provided by ECMWF can be used for thatpurpose. Of particular importance are vertical profiles of atmospheric water content, pressure andtemperature.

Accounting for aerosol scattering effects is a challenging pre-processing step. Research in thisarea is highly recommended, based on SEVIRI alone or in combination with other sensors.

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7. Conclusions and recommendations

The basic conclusion emerging from the analysis described in Sections 3 to 5 is that the MSGprogramme will open a new area of satellite land applications. While to date geostationary satelliteshave been employed almost exclusively by the atmospheric community, this programme, andparticularly the SEVIRI sensor, will provide new ways of monitoring the Earth’s terrestrialenvironment.

The region covered by SEVIRI observations represents more than a fifth of the Earth’s landmass,accommodating more than a fifth of its inhabitants, which has some of the fastest growingpopulations and contains some of the most economically and environmentally important and fragileecosystems. Thus provision of timely environmental information for this land area unquestionablyhas global importance. Furthermore, the programme is planned to run for 12 years, a periodsufficiently long for users to commit to operational exploitation of the data for land applications.

From the technical perspective, the major new contributions centre on MSG capability to observe theland areas very frequently, over a wide portion of the electromagnetic spectrum, and with aconsistent, yet information-rich, geometry. MSG also has an intrinsic advantage because theinfrastructure to obtain and process the data in a timely fashion is already in place, as part of theEuropean meteorological programme. These have direct implications for corrections of the data tominimise unwanted atmospheric and other effects, for the extraction of a number of quantitativebiophysical variables, and for operational processing of the data.

From the scientific perspective there is a need for the land community to consider the use of SEVIRIfor the study of multiple research themes. SEVIRI data are well adapted for work that jointlycharacterises the surface and the atmosphere. Better atmospheric characterisation is of great interestin its own right but will also lead to better corrections of imaging data. Better image data in turnmeans improvements in the retrieval of target variables. The accumulation of data throughout the dayoffers reasonable sampling in the directional domain (and thus permits research in the area of albedoand BRF characterisation). It will also allow unprecedented diurnal study, such as analysis of activefire dynamics over whole continents. SEVIRI will be the first instrument to deliver severalmeasurements in the thermal infrared windows with a very high temporal frequency for the Europeanand African continents. It will therefore introduce a new dimension to the retrieval and interpretationof land surface temperature.

To realise the technical potential of MSG for operational land applications, several conditions mustbe met. Algorithms to obtain quantitative biophysical products from SEVIRI data, accurately andreliably, must be developed; this is an essential foundation for the success of MSG in landapplications. The performance of such algorithms must be evaluated for applications important tousers through proof of concept demonstrations. Importantly, an end-to-end operational system mustbe set up to deliver the information products to the intended users in a timely and reliable fashion.

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The following recommendations are made to secure MSG success in land applications and research:

1. EUMETSAT and other European organisations should stimulate and support a sustained, focusedR&D programme employing SEVIRI data, together with other data types where appropriate,aimed at the development, testing and validation of accurate and robust algorithms capable ofquantitative estimation of land biophysical variables.

2. Critical R&D areas include the identification of land pixels contaminated by clouds and theextraction of aerosol information from SEVIRI data, as a foundation for a successful extractionof other biophysical variables.

3. Funding agencies that support satellite technology or applications R&D should examine thevarious aspects of MSG relevant to their areas of interest, and should collaborate in jointsponsorship of MSG research where appropriate.

4. EUMETSAT and other agencies promoting operational use of SEVIRI data over land shouldperiodically undertake a review of R&D progress to identify applications ready for proof-of-concept demonstration. Such reviews should be synchronised with the funding of researchprogrammes and with major environmental monitoring initiatives or opportunities in Europe orAfrica.

5. To enable successful, sustained use of SEVIRI data for land applications and research,EUMETSAT should ensure that:• The best possible information on the calibration and radiometric degradation of the SEVIRI

solar channels is available for the duration of the MSG programme;• Archived SEVIRI level 1.5 data are available with the highest possible geometric accuracy;• Solar and viewing angles are available from the data archive;• Contemporaneous atmospheric information to SEVIRI data is readily available to enable the

derivation of biophysical variable products, particularly vertical profiles of atmospheric watercontent, pressure and temperature;

• Cloud masks at pixel level and cloud physical properties including optical thickness (on apixel level if feasible) are available from the archive;

• SEVIRI data are available in a user friendly way, along with appropriate tools such asconversion of the data into radiance values, extraction of geographic subareas, etc.;

6. Operational use of SEVIRI data will necessitate a guaranteed, sustained generation of productsand their timely delivery to users; appropriate institutional mechanisms must be found to this end.

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9 Annexes

9.1 MBWG Terms of reference

9.1.1 Background

The Meteosat Second Generation (MSG) imagery mission will provide about 20 times moreobservational data than the current Meteosat system. The major improvements over Meteosat are:

• the increase of the number of spectral channels in the visible and in the thermal infrared spectrumfrom 3 to 12,

• the increase of the ground resolution typically by a factor two (2.5 km at nadir for all full-discchannels and 1 km at nadir for the High Resolution VISible HRVIS channel),

• the doubled imaging frequency for all channels (15 minutes instead of 30 minutes).

Numerical data and products will be accessible in real-time and off-line, through the EUMETSATApplication Ground Segment and via High Rate User Stations (HRUS) and Low Rate User Stations(LRUS). The EUMETSAT Member States have established a baseline definition of the informationto be disseminated to LRUS. It should be noted that additional information, e.g. NOAA/AVHRRimagery, is planned to be made available to HRUS and/or LRUS Users.

In addition, with the EUMETSAT Polar System, NOAA/AVHRR imagery data and products fromthe Metop and NOAA satellites will become available from the EUMETSAT Application GroundSegment from 2003 onwards. This Ground Segment will include a multi-mission MeteorologicalArchive and Retrieval Facility (U-MARF) and a network of Satellite Application Facilities (SAF),which will offer thematic products and distributed user services. One of the SAF, the so-called LandSAF, is expected to be dedicated to land applications.

9.1.2 Objectives and proposed charter of the Working Group

This situation will create new opportunities for user communities, beyond the field of operationalmeteorology. Research and applications on biospheric processes and renewable resources (in Europeand Africa) are expected to benefit in particular from:

• the availability of vicarious calibrated imagery in several visible and short wave infrared windowchannels (i.e. 0.6, 0.8 and 1.6 µm), compatible with the monitoring of plant photosynthesisactivities,

• the availability of on-board calibrated measurements in several thermal infrared windowchannels (i.e. 3.9, 8.7, 11 and 12 µm), including split window channels,

• the access to some directional information, on a daily composite basis,

• the diurnal and subdiurnal sampling of thermal signatures,

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• the combined access to imagery and soundings from polar and geostationary systems.

In this context, it is necessary to prepare users to take advantage of the capabilities of the MeteosatSecond Generation system, and to stimulate research aimed at establishing the scientific basis for thedevelopment of applications. Specific MSG requirements of the key land applications, in terms ofcalibration, quality control and level 2 products are needed. These will be used to derive a SciencePlan identifying the steps necessary to meet such requirements.

In order to meet this challenge EUMETSAT and the Joint Research Centre have agreed to establish aWorking Group on “Potential of MSG for Continental Biospheric Applications and Research”,tasked to:

• analyse the capabilities and performances of the MSG imagery mission and its general relevanceto land applications and research on land surface processes,

• identify key land applications which could benefit from MSG capabilities, alone or combinedwith other existing data and products, and formalise the associated applications concepts (i.e. interms of objectives, inputs, processing, outputs, services and users, etc.), and characterise thebenefit expected from MSG data and products,

• determine a core of “prerequisite” requirements common to all or most of these applications, e.g.calibration, quality control and monitoring, and pre-processing,

• characterise for each application or research topic, specific requirements in terms of research,algorithm development, demonstration and validation,

• derive a structured and phased MSG Science Plan, identifying objectives to be met before thelaunch or after the launch of MSG-1,

• produce a structured report on “MSG opportunities for land surface applications and research”,aimed at stimulating coordinated research and demonstration activities, within and outside theEUMETSAT Member States and the European Union.

9.1.3 Membership and organisation

The working group will be composed of up to 15 recognised leading experts in remote sensing andland surface applications and research, designated by the JRC and EUMETSAT. EUMETSAT andthe JRC would each designate one co-chairman of the group. Two representatives of the Land SAFconsortium will be invited as members.

Three to four workshops will be organised in the 1997 to 1999 time frame to progress the work.Informal subgroups would be formed as appropriate, at the initiative of the co-chairmen, to addressspecific issues and to prepare appropriate inputs to meetings. The co-chairmen will also have theinitiative to solicit invited presentations by experts outside the group, in particular to review specificapplications, research projects or user requirements (e.g. in Africa, etc.). Intersession work would bearranged using e-mail.

9.1.4 Objectives and scope of first workshop

The first workshop will take place on 4-5 December 1997. Its primary objectives will be:

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• to introduce the MSG system and capabilities• to present the MSG SEVIRI imagery mission• to present relevant aspects of the MSG Ground Segment and User interfaces• to have a first overview of requirements, possible applications and research expected to benefit

from MSG• to agree a work plan for the Working Group.

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9.2 MBWG Members

Name and Affiliation

Alan Belward (Joint Research Centre/Space Applications Institute, Co-chairman)Josef Cihlar (Canada Centre for Remote Sensing, Co-chairman)Bernard Pinty (JRC/SAI)Jean Verdebout (JRC/SAI)Michel Verstraete (JRC/SAI)Rolf Dick (JRC/SAI)Jürgen Vogt (JRC/SAI)Alessandro Annoni (JRC/SAI)Gérard Dedieu (CESBIO, France)Massimo Menenti (Winand Staring Centre, the Netherlands)Gilbert Saint (CNES, France)Pavel Kabat (WSC, the Netherlands)Jose Moreno (University of Valencia, Spain)Jose Pereira (Instituto Superior de Agronomia, Portugal)Carlos da Camara (SAF Land, Portugal)Gaetano Zipoli (SAF Land, La.M.M.A., Italy)Michael Rast (ESTEC)Barry Wyatt (Institute of Terrestrial Ecology, UK)Simeon Fongang (University of Dakar, Senegal)Clemens Simmer (University of Bonn, Germany)Johannes Schmetz (EUMETSAT)Yves Govaerts (EUMETSAT)Stephen Tjemkes (EUMETSAT)

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9.3 MSG image characteristics

The latest news concerning the MSG mission characteristics can be found on EUMETSAT’s Webpage at: www.eumetsat.de.

9.3.1 Image data pre-processing

Level 1.5 images are generated by the MSG-IMPF. The main processing steps from 1.0 up to 1.5level data are described hereafter.

1. Radiometric processing: All channels are coded on 10 bits. The detector responses of each bandare linearised and equalised.

2. Calibration processing: The calibration coefficients are derived in real-time based on the blackbody information of each detector, accounting for the contribution of the front optics. Thiscontribution is modelled with the available telemetry, i.e., gains, offsets and temperatures. Theresults take into account all the radiometric transformations applied to the image data during theprocessing, i.e., equalisation and linearisation with optional feedback correction from the MPEFvicarious calibration if necessary.

3. Geometric rectification and quality assessment: All spectral channels are geo-located on acommon grid in the geostationary projection centred at 0 degree longitude. The image size is3712—3712 pixels except for the HRV band which has a size of 11136—5568 (NS—EW). Thegeometric quality is given in Table 3.

Geometric quality criterion RMSAbsolute accuracy < 3 km SSPRelative accuracy (image to image) < 1.2 km SSPRelative accuracy within an image (500 pixels) < 3 km SSPRelative accuracy within an image (16 pixels) < 0.75 km SSP

Table 3: Geometric quality accuracy of the level 1.5 image data at the Sub-Satellite Point.

The channel registration (line of sight alignment) to a common grid is expected to have anaccuracy of 600 m at SSP for the HRV and VNIR channels. The IR channels will be registeredwithin 750 m at SSP. The registration of the HRV, VNIR and IR channels is ensured via thegeometric accuracy. The quality control of the geo-location process will be based on theextraction of landmarks.

Note that the linearisation, equalisation and resampling processes should not affect by more than0.5 digital count RMS the image radiometric quality.

4. Level 1.5 data representation: The level 1.5 image are formatted, disseminated and archived inthe U-MARF.

9.3.2 Level 1.5 image data description

The level 1.5 image data is one of the main MSG products. This data level corresponds to theacquired image data corrected for all radiometric and geometric effects, geo-located using astandardised projection, with pixels constituting of calibrated and radiance-linearised information.

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This level 1.5 image data is hence the basic input data for the derivation of meteorologicalproducts or any further meteorological processing.

Comprehensive side information is provided together with the level 1.5 data, allowing fullinterpretation, validation and calibration of the image information (EUMETSAT 1998d). Theheader and trailer attached to the calibrated and geo-located image data contain detailed sideinformation on:• The conditions of level 1.0 image acquisition;• The satellite, Sun and celestial bodies positions at the time of image acquisition;• Detailed information on the radiometric and geometric transformation from level 1.0 to 1.5;• The radiometric image quality for both level 1.0 and 1.5;• The geometric image quality of level 1.5;• All the information necessary to the calibration.

The image data will actually be represented in numerical counts coded on 10 bits. All the informationthat is necessary to transform this count into radiance will be available in the file header and trailer.

9.3.3 MSG image resolution

Most biospheric applications will require information at the pixel resolution instead of the synopticscale. Due to the nature of the scanning mechanism and the curvature of the Earth, the sub-satellitepoint sampling distance increases as the satellite viewing angle increases. Figure 9 shows the East-West sampling distance over of the MSG disc for a satellite nominal position of 0° Longitude. Thisdistance remains in the range of 4 km over most of Europe and Africa. The corresponding South-North sampling distance is shown in Figure 10. In this case, the sampling distance remains almostunchanged along the equator. Figure 11 shows the pixel area normalised by the sub-satellite pointpixel area.

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Figure 9: MSG East-West sampling distance in km. The result is shown in an equidistant cylindrical projection.

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Figure 10: MSG North -South sampling distance in km. The result is shown in an equidistant cylindricalprojection.

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Figure 11: MSG normalised pixel area. The result is shown in an equidistant cylindrical projection.

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10 List of acronyms

4DVar four-Dimensional Variational assimilationAERONET AEROsol NETworkAPAR Absorbed Photosynthetically Active RadiationASADA Automated Smoke Aerosol Detection AlgorithmASTER Advanced Spaceborne Thermal Emission and Reflection RadiometerATSR Along Track Scanning RadiometerAVHRR Advanced Very High Resolution RadiometerBRDF Bidirectional Reflectance Distribution FunctionBRF Bidirectional Reflectance FactorCBS Commission for Basic SystemsCCRS Canada Centre for Remote SensingCEOS Committee on Earth Observation SatellitesCESBIO Centre d’Etudes Spatiales de la BiosphereDCP Data Collection PlatformDDV Dense Dark VegetationDHR Direct Hemispherical RefelctanceECMWF European Centre for Medium-Range Weather ForecastsENSO El Niño Southern OscillationESA European Space AgencyFPAR Fraction of absorbed Photosynthetically Active RadiationGCOS Global Climate Observing SystemGEWEX Global Energy and Water Cycle ExperimentGHG GreenHouse GasesGLI GLobal ImagerGOES Geostationary Operational Environmental SatelliteGTS Global Telecommunication SystemHRIT High Rate Information TransmissionHRUS High Rate User StationHRV High Resolution VisibleHRVIS High Resolution VISible channel of SEVIRIIFOV Instantaneous Field of ViewIGOS Integrated Global Observation StrategyIMPF IMage Processing FacilityIR InfraRedLAI Leaf Area IndexLRIT Low Rate Information TransmissionLRUS Low Rate User StationLST Land Surface TemperatureLW Long WaveMARS Monitoring of Agriculture with Remote SensingMBWG MSG Biosphere Working GroupMERIS MEdium Resolution Imaging SpectrometerMetop Meteosat Operational ProgrammeMIR Medium InfraRedMISR Multi-angle Imaging SpectroRadiometerMODIS MODerate resolution Imaging SpectroradiometerMOP Meteosat Operational ProgrammeMPEF Meteorological Products Extraction Facility

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MSG Meteosat Second GenerationNASA National Aeronautics and Space AdministrationNDVI Normalised Difference Vegetation IndexNPP Net Primary ProductivityNEP Net Ecosystem ProductivityNIR Near InfraRedNOAA National Oceanic and Atmospheric AdministrationNWP Numerical Weather PredictionPAR Photosynthetically Active RadiationPOLDER POLarization and Directionality of the Earth's ReflectancesR&D Research and DevelopmentRMS Root Mean SquareSAF Satellite Application FacilitySAI Space Applications InstituteSCIAMACHY SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHYSEVIRI Spinning Enhanced Visible and InfraRed ImagerSI Système InternationalSM Soil MoistureSSP Sub-Satellite PointSVAT Soil Vegetation Atmosphere TransferSRB Surface Radiation BudgetSW Short WaveTIROS Television Infrared Observation SatelliteTISI Temperature Independent Spectral IndicesTOVS TIROS Operational Vertical SounderTOA Top-Of-AtmosphereTM Thematic MapperU-MARF Unified Meteorological Archive and Retrieval FacilityUTC Universal Time CoordinatedVIS VISibleVNIR Visible and Near InfraRedWMO World Meteorological OrganizationWRCP World Climate Research ProgrammeWV Water Vapour

Meteosat Second Generation opportunities for Land Surface Research and Applications

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