*Corresponding author, e-mail ltoulios@nagref.gr; phone +302410671301; fax +302410671321

Potential of remote sensing techniques to improve the agriculture water footprint assessment and the virtual water trade accounting

Leonidas Toulios*a, Mireia Romaguerab, Eman Callejac, Gheorghe Stancalied, Argentina Nertand, Piotr Struzike, Anna Dalla Martaf, Ana Maria Tarquis Alfonsog, Zoltan Dunkelh, Jose

Rato Nunesi, Francesco Vuoloj

aHellenic Agricultural Organization DEMETER (NAGREF), Larissa, Greece bMireia Romaguera, University of Twente, The Netherlands

cIslands and Small States Institute, University of Malta, Malta dNational Meteorological Administration, Bucharest, Romania

eInstitute of Meteorology and Water Management - NRI, Poland fDIPSA-University of Florence, Italy

gTechnical University of Madrid, Spain hOMSZ-Hungarian Meteorological Service, Budapest, Hungary

iPolytechnic Inst. of Portalegre Agrarian Superior School, Elvas, Portugal jUniversity of Natural Resources and Life Sciences (BOKU), Vienna, Austria

ABSTRACT

Remote sensing (RS) has long been a useful tool in global and regional applications. The Water Footprint (WF) of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage. RS provides new tools for global WF assessment and represents an innovative approach to regional and global irrigation mapping, enabling the estimation of green and blue water use.

This paper presents an overview of the EU COST Action ES 1106 "Assessment of European agriculture water use and trade under climate change (EURO-AGRIWAT)", regarding the evaluation of the potential of remote sensing to improve the WF and Virtual Water Trade (VWT) assessment. The main objective is the analysis of the role of satellite data in the suitable models and indices concerned with the analysis of WF and VWT.

The main tasks include: an inventory of the existing and near future satellite data records for several European regions that could be used for the WF and VWT assessment; the study of satellite data resolution requirements, in time and space; the analysis of the assimilation of satellite data into models for the determination of green and blue water use; conclusions and recommendations concerning the possibility to integrate remote sensing into WF and VWT accounting.

The combination of RS data to assess the volume of irrigation applied, and the green and blue WF faces several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which will be studied.

Key words: Water footprint; virtual water trade; agriculture; irrigation; green water; blue water; climate change.

1. INTRODUCTION Water has been central to the evolution of human civilisation, providing a central role in ecosystem services through the supply of water for drinking, irrigation and other purposes, but also as a route for transport, a source of food and of recreation [1]. In view of all of these conflicting uses, the sustainable use of water has often been a controversial and difficult issue to tackle throughout the world [2]. Agriculture is often a main water user, particularly in areas with intensive agricultural practices. This gives rise to conflicts with other sectors,

*Corresponding author, e-mail ltoulios@nagref.gr; phone +302410671301; fax +302410671321

especially in regions that suffer from persistent water shortages. The pressure on water resources is exacerbated by an increasing world population changes [3] in standard of living (especially consumption of meat) and the consequent increasing need for food, feed and bio-energy. In Europe, 44 % of water abstraction is used for energy production, 24 % for agriculture, 21 % for public water supply and 11 % for industry. However, in southern Europe, agriculture accounts for 60 % of the total water abstracted. Climate change is expected to exacerbate this, as water becomes scarce, particularly in southern Europe [4,5,6,7]. Water scarcity is likely to impose limits not only on the area of irrigated land but also on the quantity of water. In this respect, the concept of water footprint (WF) has acquired a global recognition. The WF is defined as the quantity of water used to produce a good or a service [8]. In particular, the WF of an agricultural product is the volume of water used during the crop growing period [9] and it has three components: the green water, i.e. rain or soil moisture transpired by a crop, the blue water i.e. the amount of irrigation water transpired and the grey water, i.e. the volume of water required to dilute pollutants and to restore the quality standards of the water body. The WF concept gains importance when applied to trade between countries or regions, because imports and exports of agricultural products involve a virtual water transfer. Virtual water is referred as the content of water embodied into a product [10]. It is similar to the concept of WF but the former refers to the water volume embodied in the product alone, while the latter term refers to that volume, but also to which sort of water is being used and to when and where that water is being used. As virtual water varies depending on products and place of production, virtual water trade between the nations and/or regions can represent an instrument to achieve regional water efficiency. Thus, the evaluation of the real conditions in water sources and water demands and their regulation can contribute to the regional and global water security.

Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information over space and time. In particular, remote sensing techniques have been used in hydrological applications in order to estimate relevant variables such as evapotranspiration, precipitation, soil moisture, land cover type or vegetation indices at different temporal and spatial scales [11,12,13,14]. In this context, recent papers initiated the study of the potential of using these data obtained from remote sensing in the field of water management, for global water footprint studies and green and blue water assessment [15,16,17].

Several European projects were or are dealing with water use and management in agriculture, also in relation to climate change impacts (e.g. CLIMSAVE, CIRCE, COST734, WADI, ACCELERATES, ACQWA, PRECIRIEG, SIRRIMED, TELERIEG). The results of such projects were in some cases compared and evaluated with respect to their significance for operational water management at the stakeholder level, but a coordinated approach to provide practical recommendations is still missing. In this sense, a dedicated interdisciplinary and inter-institutional platform for scientific information exchange, consensus building and model improvement is required. In view of this, and in the frame of the European Cooperation in Science and Technology (COST) the Action ES 1106 “Assessment of EUROpean AGRIculture WATer use and trade under climate change (EURO-AGRIWAT)” is meant to offer an important transversal contribution to existing activities and an impulse for the development of future research directions. Moreover, EURO- AGRIWAT will extend the scientific focus towards the use of not yet deeply investigated tools (i.e. crop models - including nitrogen balance models, crop water balance or irrigation models, remote sensing, climatic projection and scenarios) for a more realistic and complete computation of the water amounts consumed for producing food, feed and energy crops in Europe.

The Objectives of MoU of this Action to be addressed regarding RS are the analysis of the role of satellite data in the suitable models and indices concerned with the Analysis of Water Footprint (WF) and the Virtual Water Trade (VWT) and the evaluation of the potential of remote sensing to improve the WF and VWT assessment.

The aim of this paper is to present the current situation regarding the possibilities of remote sensing techniques to improve the assessment of the water footprint and the virtual water trade accounting. How we can benefit of the use of remote sensing to assess water footprint and virtual water trade. The start for analyses in detail of the problem is based on the following scheme.

2. MATERIALS AND METHODS

For the above aim a specific working group was organized in the frame of COST ES 1106 to investigate and evaluate the limitations and consequently the possibilities of satellite remote sensing data to be applied in the assessment of these water issues.

Several tasks are pointed to the group in order to identify:

-Comparison between satellite sensor data at different spatial resolutions

*Corresponding author, e-mail ltoulios@nagref.gr; phone +302410671301; fax +302410671321

-Empirical relationships based on NDVI or other vegetation indices for the assessment of physical and/or biological variables useful in the specific models

-Field data and field trials and potential use of these data for remote sensing activities.

Further to that, a questionnaire (prepared in collaboration with the other working groups of the Action) will be disseminated to the Action Management Committee Members to fill in data regarding their country archives, aiming to the creation of an inventory of spectral ground truth data and data on biophysical and climate parameters related to water and biomass, based on satellite observations. This inventory could be also based on the recent results of the survey of agrometeorological practices and applications in Europe regarding climate change impacts [18,19]. In this inventory following criteria for products selection were specified, as: at least European coverage, availability of historical data (at least a few last years), current near real time products availability and possible continuity of service in future.

3. RESULTS AND DISCUSSION The Water Footprint (WF) of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. The Virtual water trade (VWT), first introduced by Allan [20] is defined to mean the water that otherwise would be needed to produce the net food imports that a country brings in from the outside. Virtual water is estimated using the required water in the importing country, and real water is estimated with the required water in the exporting country [21].

For the study of the potential of remote sensing techniques to improve the agriculture water footprint assessment and the VWT accounting the main issues concerned are the possibility of estimating physical and biological variables through remote sensing data and the availability of these data.

As remote sensing plays important role in monitoring of our entire planet in real time, the water cycle related processes are observed by hundreds of space missions, since the first meteorological satellite TIROS-1 launched in 1960.A schematic view of water cycle elements and related earth observations to be analyzed in this study are presented in Fig. 1.

Fig. 1: Schematic view of water cycle elements and related earth observations useful in Water Footprint determination

3.1.VARIABLES: For the identification of key variables that can be estimated using remotely sensed data (with Medium and High spatial resolution) and needed for WF and VWT assessment (evapotranspiration, precipitation, water storage, water stress, runoff, land use, etc) the following steps will be followed:

-Review the definition of water footprint and blue, green and grey water

-Investigate how variables needed in the water footprint calculation (blue, green and grey water) are obtained (based on non-remote sensing and on remote sensing approaches)

-Investigate how and if it is possible to integrate remote sensing approaches.

3.2. DATA: For the identification of sources of RS data used for estimating variables needed to be integrated in the water footprint and VWT calculation, based on satellites, data will be identified based on the following characteristics:

*Corresponding author, e-mail ltoulios@nagref.gr; phone +302410671301; fax +302410671321

-Temporal coverage (length of the time series) and temporal resolution (interval between observations)

-Spatial coverage (area covered: river basin, region, nation, continent) and spatial resolution (detail of the information we can observe in the area).

Two data types could be identified: the Landsat-like and Modis-like data. The combined use of the two implies the following research questions: How comparable are data in time and space? How much of the information we can get with Landsat-like data is still in MODIS-like data and with which accuracy?

3.3. FURTHER TASKS: Further to above, other research tasks that could be concerned are:

-The assessment of required spatial, spectral and temporal resolution of satellite data for the analysis of Water Footprint and the Virtual Water trade

-Suggestions and examples concerning the possibility to integrate remote sensing into WF and VWT accounting

-Assessment of the most appropriate set of vegetation indices and biophysical variables in the context of a cost-effective solution to monitor water stress, using satellite data.

-Comparison of NDVI distribution obtained from different remote sensors.

3.4. FIRST RESULTS: Untill now, preliminary study on the identification of possible satellite products/services useful and related to water footprint determination was done. Such satellite products (concerning water cycle) identified and selected for more detailed analysis, are:

- Precipitation intensity and amount - Soil moisture: surface and root zone - Snow cover characteristics: snow extent, snow water content - Evapotranspiration - Vegetation state and stress.

3.4.1. Precipitation (liquid and solid)

Due to its importance in water cycle, precipitation is the most widely observer parameter from satellites. It is also one of the most problematic parameters, regarding quality, being main source of green water. Available satellite products concern: rainfall intensity and accumulated rainfall amount (Fig. 2). Large variety of algorithms is used, starting from indirect IR based estimations available from geostationary satellites, through passive microwave measurements using: AMSR-E, SSM/I, AMSU to active microwave instruments which most popular is TRMM precipitation radar. Finally blended algorithms are used, merging frequent IR observations from geostationary orbit having better temporal and spatial resolution with microwave observations from polar orbit, less frequent but physically more direct to precipitation.

Snowfall (solid precipitation) measurements from space, is a critical issue for high latitude countries. Water accumulated in snow plays significant role in water supply during most important part of vegetation season. Satellite products for measurement of this variable are still not matured enough and generally based on microwave sensors with high frequency channels.

*Corresponding author, e-mail ltoulios@nagref.gr; phone +302410671301; fax +302410671321

Fig. 2: Example of rainfall accumulated in 24 hours over Europe (2.07.2012) – EUMETSAT H-SAF product

(http://hsaf.meteoam.it)

Examples of services providing satellite precipitation products are:

• CSU: - Climate Rainfall Data Center - Gridded Satellite Rainfall Maps

• EUMETSAT: - Multi-sensor Precipitation Estimate (MPE), experimental, - Operational products of the Satellite Application Facility on Support to Oprational

Hydrology and Water Management (HSAF) • GPCP: - GPCP Geostationary Satellite Precipitation Data Center (GSPDC)

- GPCP Global Precipitation Analysis • GSMaP: - JAXA Global Rainfall Watch

- Data Archive System • LaMMA: - Blended MW-IR over Italy and Central Mediterranean • NASA-GSFC: - Global Precipitation Analysis

- TRMM Data Organized by Data Product Groups - TRMM Online Visualization and Analysis System (TOVAS)

• NOAA-NCDC: - SSMI Global Gridded Hydrological Products • NOAA-NESDIS: - Microwave Surface and Precipitation Products System

- Microwave Integrated Retrieval System (MIRS) - Ensemble Tropical Rainfall Potential (eTRAP)

• NOAA-NWS: - Climate Prediction Center - Global Precipitation Monitoring • NRL Monterey: - Satellite Products • University of Birmingham: - Infrared and Passive Microwave Matched Rainfall Results

• University of California Irvine: PERSIANN CCS Global.

3.4.2. Soil Moisture

Soil moisture observations from space cover practically last 30 years. Different sensors were used for this purpose according to their availability in individual periods: visible, thermal IR, microwave passive and microwave active (Fig. 3). Also assimilation of satellite data into models for retrieval of soil moisture is interesting solution. The following products represent examples:

- H-SAF/TU Vienna surface soil moisture retrieved from METOP/ASCAT data H-07 (25 km resolution), - H-SAF/TU Vienna surface soil moisture retrieved from METOP/ASCAT data and downscaled to 1 km

resolution with use of other microwave sensors - H-08, - H-SAF/ECMWF soil moisture in root region in 4 layers (0-7 cm, 7-28 cm, 28-100 cm, 100-289 cm) with 25

km resolution.

*Corresponding author, e-mail ltoulios@nagref.gr; phone +302410671301; fax +302410671321

Fig. 3: Example of soil moisture in surface layer retrieved from ASCAT/METOP instrument (http://hsaf.meteoam.it)

3.4.3. Snow Cover characteristics - Snow Cover Extent and Snow Water Equivalent

For Snow Cover extent several products are available covering long period since 90-ties of XX century. Based mainly on visible sensors, the cloud obstruction is the main limiting factor. Required sun elevation is also critical for high latitudes. Separation of ice clouds and problems at cloud edges are still challenging. Examples of GlobSnow Snow extent, MODIS MYD10C1, US NSIC IMS Product (Fig. 4), H-SAF H-10 product, are presented by Struzik et al. [22].

For Snow water equivalent, microwave sensors are applied (AMSR-E, SSMI etc.) and additional ground measurements are frequently used. Their limitations concern the minimum and maximum snow depth and the snow status (dry/wet). Examples: Globsnow SWE, H-SAF snow water content H-13, Aqua AMSR-E Level 3 Snow Water Equivalent product.

Fig. 4: Example of snow cover extent in Europe based on NSIC IMS product

3.4.4. Evapotranspiration

Evapotranspiration plays important role if water cycle, especially in the areas with warm and dry conditions. The following products are examples of freely available satellite datasets:

- MOD16 Global Terrestrial Evapotranspiration Data Set - Land SAF ET product (European coverage) (Fig. 5).

Ice

Snow

Water

Land

*Corresponding author, e-mail ltoulios@nagref.gr; phone +302410671301; fax +302410671321

Fig. 5: Example of annual ET retrieved from EUMETSAT Land-SAF ET 30 min products for local studies (Prosna river

catchment in Poland) [23]

In the last years, due to the exceptional developments of satellite technology, a wide range of remote sensing-based evapotranspiration (ET) methods/models have been developed and evaluated [24]. The use of remote sensing data for ET estimation is mainly based on land surface temperature (LST) and reflectivity (using different spectral regions) due to satellite ability to spatially integrate over heterogeneous surfaces at a range of resolutions and to routinely generating areal products once long time-series data availability issues are overcome.

To estimate regional crop ET, three basic types of remote sensing approaches have been successfully applied [25]. Approaches differ in: (i) type and spatial extent of application (e.g. irrigation, dry-land agriculture); (ii) type of remote sensing data; and (iii) use of ancillary (micro-) meteorological and land cover data. The integration of remotely sensed data into methods/models of ET facilitates the estimation of water consumption across agricultural regions.

The first approach computes a surface energy balance (SEB) using the radiometric surface temperature for estimating the sensible heat flux (H), and obtaining ET as a residual of the energy balance [26]. Fig. 6 presents the LST derived from the Landsat ETM+ and the computed ET using a simple SEB ET over Dobrogea region of Romania, on 7th of June 2000. There are several factors which impact on the performance of single-source approaches, like the uncertainties about atmospheric and emissivity effects. LST impacts on all terms of the energy balance in particular on longwave radiation. The radiative surface temperatures provided by an infrared radiometer from a spaceborne platform are measured by satellite sensors such as LANDSAT TM/ETM+ [27,28], NOAA-AVHRR, MODIS [29] and ASTER.

Fig. 6: LST derived from the Landsat ETM+ (left) and the computed ET using a simple SEB ET over Dobrogea region of

Romania, on 7th of June 2000 (after Serban et al [28])

*Corresponding author, e-mail ltoulios@nagref.gr; phone +302410671301; fax +302410671321

The second approach uses vegetation indices (VI) derived from canopy reflectance data to estimate basal crop coefficient that can be used to convert reference ET to actual crop ET, and requires local meteorological and soil data to maintain a water balance in the root zone of the crop [30].

The third approach uses remotely sensed LST with Land Surface Models (LSMs) and Soil–Vegetation–Atmosphere (SVAT) models, developed to estimate heat and mass transfer at the land surface [31]. LSMs contain physical descriptions of the transfer in the soil–vegetation– atmosphere continuum, and with proper initial and boundary conditions provide continuous simulations when driven by weather and radiation data. The energy-based LSMs are of particular interest because these approaches allow for a strong link to remote sensing applications [32].

As an example of application, Romaguera et al. (2012a) showed the potential of comparing the evapotranspiration products obtained from two datasets, a remote sensing based model and a land surface model. They proposed a method for identifying irrigated areas and quantifying the blue evapotranspiration (ETb), or irrigation water evapotranspired from the field. The method compares actual ET (ETactual), or crop water use, values from the Global Land Data Assimilation System (GLDAS) and remote sensing based Land SAF evapotranspiration estimates obtained from Meteosat Second Generation (MSG) satellites. Since GLDAS simulations do not account for extra water supply due to irrigation, it is expected that they underestimate ETactual during the cropping season in irrigated areas. However, remote sensing techniques based on the energy balance are able to observe the total ETactual. As an example Fig. 7 shows the temporal evolution of daily ET obtained in a rainfed and in an irrigated pixel from the two datasets.

Fig. 7: Comparison of MSG-ET and GLDAS-ET in (a) an irrigated cropland in Spain, and (b) rainfed cropland in France

3.4.5. Vegetation state and stress

Vegetation state is well monitored from space by many Visible / Near Infrared sensors (AVHRR, MODIS, SPOT-Vegetation etc.) [33]. For global and regional purposes instruments onboard meteorological and environmental satellites deliver data for calculation of various vegetation indices (NDVI, EVI, SAVI etc.). Stress of vegetation related to drought can be monitored by indices using comparison to multiannual average (VCI, TCI, VHI) (Fig. 8).

*Corresponding author, e-mail ltoulios@nagref.gr; phone +302410671301; fax +302410671321

Fig. 8: Example of Vegetation Conditions Index (VCI) and Thermal Conditions Index (TCI) for 28th week of 2011, when the most dramatic drought since 100 years was observed in France and UK

The plants have a tolerance to water stress and keep the vegetation moisture content, which indicates the viability of plants, for some days. The new vegetation index named water stress trend (WST) [34] is based on the fact that variation of vegetation temperature depends on vegetation moisture content; the WST is defined by the temperature variation throughout the day, or during the daytime. The obtained distribution of WST to global satellite data (NOAA/AVHRR and Terra/MODIS), corresponds to "World Atlas of Desertification" of the United Nations Environment Programme report [35]. WST is able to detect a sign of damage from a drought, and is useful for early diagnosis of water stress and for detection of insufficiency of precipitation or damage of drought at an early stage.

The Normalized Difference Water Index (NDWI) is a satellite index derived from Near-Infrared (NIR) and Short Wave Infrared (SWIR) channels [36]. The NDWI value ranges from -1 to 1, with common range for green vegetation is -0.1 to 0.4. Examples of the NDWI maps over Romania, obtained from MOD09A1 products (8-day composite) for 2005 (rainy year) and 2007 (droughty year) are presented in the Fig. 9.

(a) (b)

Fig. 9: NDWI maps over Romania: (a) 2005, (b) 2007

The Normalized Difference Infrared Index (NDII) is a reflectance measurement that is sensitive to changes in water content of plant canopies. The NDII uses a normalized difference formulation instead of a simple ratio, and the index values increase with increasing water content [37]. The values of the NDII index range from -1 to 1, with common range for green vegetation 0.02 to 0.6. Fig. 10 presents the NDII maps over Romania, obtained from MOD09A1 products (8-day composite) for 2005 (rainy year) and 2007 (droughty year).

(a) (b)

Fig. 10: The NDII maps over Romania: (a) 2005, (b) 2007

4. CONCLUSION Remote sensing provides new tools for global WF assessment and represents an innovative approach to regional and global irrigation mapping, enabling the estimation of green and blue water use. Possible satellite products useful and related to water footprint determination have been identified. Such satellite products concerning water

*Corresponding author, e-mail ltoulios@nagref.gr; phone +302410671301; fax +302410671321

cycle and possibly used in WF and VWT assessment are the precipitation, the soil moisture, the snow cover and the vegetation evapotranspiration. The combination of RS data (proposed to assess the volume of irrigation applied and the green and blue WF obtained from the corresponding variable components) faces several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which will be further studied in detail.

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*Corresponding author, e-mail ltoulios@nagref.gr; phone +302410671301; fax +302410671321

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