Studies of soil temperature on the basis of satellite data

A b s t r a c t. Thermal properties of soils can be investigatedfrom satellite level, using a number of satellite systems (a.o.LANDSAT ETM+, ASTER/TERRA). Studies conducted in thiswork were aimed at searching for relationships between brightnesstemperature of soil surface layer – derived from satellite images –and various properties of soil (type of soil, class of agriculturalsuitability, texture of top layer of soil, land use). As a result of theanalysis it was found that mainly the class of agricultural suitabilityand the type of soil have an important influence on brightnesstemperature of soil surface layer. Lower correlations with bright-ness temperature were observed in the case of texture of surfaceand under-surface layers of soil.

K e y w o r d s: remote sensing, brightness temperature,thermal image, satellite image

INTRODUCTION

The technique of thermal imaging allows to register andvisualize non-visible thermal radiation (own radiation ofobjects), which can inform on object temperature andemissivity. The possibility to register and examine thermalradiation in all-the-day cycle is undoubtedly an advantage ofthis spectral range. Unfortunately, most of the polar satellitesystems enable the collection of data only in the daytimeduring late morning hours, which significantly limits moni-toring of soils. Moreover, at this time of the day, thermaldifferentiation of terrain objects is relatively small.

Analysis of soil properties by satellite techniques is oneof the most difficult domains of remote sensing. Remotestudies of soils have a long history, though usually they wereconducted for arid or semi-arid areas (examination of impactof various factors on desertification processes). These areasare the most convenient objects for studying soil propertieswith the use of remote sensing, as they are related to largeopen spaces which are not covered with vegetation. Forareas with vegetation cover or for agricultural areas remote

sensing studies are much more difficult. In Poland researchworks on using thermal images for studying soils wereconducted already in 70’s of 20th century. They were mainlyrelated to application of aerial thermal images collected byTHP-1 scanner in the 3-5.5 �m range of long-wave infraredradiation. Because in this spectral range strong impact ofreflected sun radiation exists, the images were usuallyregistered at night-time. However, attempts at applyingthese images for assessment of soil moisture did not bringsatisfactory results (Bia³ousz, 1999; Bia³ousz and Pietrzak,1988). It was found that thermal images of terrain weremainly influenced by vegetation cover and by cultivationpractices.

Application of satellite data for studying soils in Polandis difficult due to two main factors. First, in Poland a majo-rity of soils is covered with natural vegetation or with agri-cultural crops, hence the registered radiation depends alsoon the type and state of vegetation cover. It implies an im-pact of many variables on the collected image. Due to thesereasons the examination of soils is carried out in early springor autumn. Secondly, high fragmentation of agriculturalland makes satellite studies of soil more difficult, as theresolution of images – particularly in the thermal band –does not allow for analysis of finer structures.

THEORETICAL FOUNDATIONS

Calculation of object temperature on the basis

of satellite data

Methods of thermal imaging of Earth surface are basedon the emission of long-wave infrared radiation by terrainobjects. The process of absorption of Sun radiation by theEarth surface and terrain objects is essential for thisphenomenon. During the day most of the Sun energy reaches

Int. Agrophysics, 2007, 21,

Studies of soil temperature on the basis of satellite data

K. Osiñska-Skotak

Faculty of Geodesy and Cartography, Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland

Received April 23, 2007; accepted August 9, 2007

© 2007 Institute of Agrophysics, Polish Academy of SciencesCorresponding author’s e-mail: [email protected]

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www.international-agrophysics.org

the Earth surface (it is partly absorbed, scattered andreflected by the atmosphere). That radiation which comes tothe Earth is partly reflected but partly absorbed and nextchanged to heat being next emitted in the form of long-waveradiation. This radiation can be registered by suitablesensors. Besides, a part of the energy is used for convection,evaporation, dew forming, etc. and has an influence onradiation balance of the Earth-atmosphere system.

A thermal image represents terrain in achromatic tones(digital number-DN) which are a function of objecttemperature (seen by instantaneous field of view of scanner)and of emissivity. Assuming that energy emitted by objectfully reaches the sensor, temperature of object can be simplydetermined, using inverse Planck law. Calculation of objecttemperature on the basis of satellite data (measured long-wave radiation) can be done through application of Plancklaw for blackbody:

M T cc

TBB ( , ) exp ,� � �

��

�

��

�

�

�

��

��

�

2 12 51

hh

k

where: MBB (�, T) – density of radiation energy of black-body at temperature, T; c – speed of light; � – wave-length;h – Planck’s constant (h = 6.6210-34 J s), k – Boltzmann’sconstant (k = 1.380710-23 J K-1).

Transforming Planck’s law to a formula:

T

L

radiometric

sat

�

��

��

�

K

K

2

1 1ln

,

and assuming K1, K2 and Lsat as: K 2 �h

k

c

�; K1

2

5�

2 h�

�

c;

Lsat = M (�, T) = Lsurf we obtain a relation in which tem-perature is the only unknown variable. K1 and K2 coef-ficients are constants for a given sensor (they depend ofsensor wavelength1), while Lsat radiation (radiation atsatellite level) is measured.

As a result of applying the above equation the tempe-rature value is obtained which does not take into account theattenuation of atmosphere layer between the object andsensor nor the emissivity of the object itself. That is radio-metric temperature (referred to in many publications asequivalent blackbody temperature), obtained as a result ofmeasurement of signal recorded by sensor. After atmos-pheric correction, such a temperature value can be determi-ned as could be obtained without atmosphere between theobject and sensor. This temperature, called brightness tem-perature, is related to radiating energy from an object andrepresents radiation flux balance of surface. It does not take

into account the emissivity of the object and is coupled witha definite field of view of the scanner. Considering objectemissivity, the temperature of active surface is obtainedwhich can be determined using Stefan-Boltzmann law forblackbody and for greybody:

T Trad surf� �1

4

� – denotes here mean emissivity coefficient for �� wave-length range registered by scanner.

The magnitude of the emissivity coefficient depends ona number of factors and its determination in the case of areaswith various cover types is very difficult. Change of emissi-vity causes that objects with identical contact (thermodyna-mic) temperature represent other emission than that determi-ned by the Planck formula for greybody and by the Stefan-Boltzmann formula. Incorrect emissivity coefficient cancause, in extreme cases, errors in calculation of real tempe-rature of over ten degrees. It has a significant contribution tothe accuracy of temperature determination. Hence, usuallyonly the brightness temperature of objects is determined.

Differentiation of thermal images due to time of

data acquisition – temporal changes of radiation

properties of soils

Thermal imaging can be done both at daytime and atnight time, because thermal scanners use a passive system ofdata collection which does not require an external source ofradiation. For recording an image, radiation emitted by theobject is only needed. Energy reflected from surface andclouds causes only disturbances which make that correctinterpretation of terrain imaged in thermal infrared band ismore difficult. So, images collected at night time are themost appropriate, because then long-wave radiation is lessdisturbed and more radiation can reach the sensor. However,both types of recording – during day and night – have theirdrawbacks and advantages; sometimes obtaining informa-tion on surface radiation at daytime is important.

During day the collected image is influenced by sunradiation reflected from clouds, diffusively scattered in theatmosphere, reflected from the ground and from otherobjects, as well as by so-called sky own radiation. It mainlyconcerns radiation recorded in shorter wavelengths. Duringnight the reflected radiation (from clouds, ground andsurrounding objects) is substantially eliminated, and radia-tion emitted by the object dominates, allowing to infer moreeffectively about its nature and properties.

Soil is a unique type of object, consisting both of solidfraction (mineral and organic particles), of liquid fraction(water) and of gas fraction (air and gases arising fromprocesses of organic matter decomposition). Temperature of

2 K. OSIÑSKA-SKOTAK

1For LANDSAT 4 TM6 K1, K2 constants are equal to: K1 = 67.162 m W cm-2 sr-1 �m-1 and K2 = 1284.3, while for LANDSAT 5 TM6:K1 = 60.776 m W cm-2 sr-1

�m-1 and K2 = 1260.56 K (Wukelic et al., 1989). In the case of thermal band of LANDSAT 7 constants are equalto: K1 = 666.09 W m-2 sr-1 µm-1, K2 = 1282.71 K.

soils is a very important factor which influences theiragricultural usefulness (Buckman and Brady, 1971). Itstimulates chemical and biological reactions in soils andinfluences plant development. Many processes begin in soilonly when a pre-defined temperature is reached (for instanceseed germination at temperature of 7 to 10�C). Temperatureof soils depends mainly on surface heat balance ie on theratio of absorbed heat energy to energy lost by soils.

Seasonal variation of temperatures of soils exists evenat big depths in soil profile. Analysing the diagram includedin the work by Buckman and Brady (1971), which showscurves representing mean monthly soil temperatures atvarious depths, it can be found that the highest seasonalvariation appears in top layers of soil.

That variation is higher than variation of mean 24 htemperatures of air. On the average, soil layer up to 15 cm iswarmer than the air over it. Deeper layers are warmer thanair in autumn and winter, while cooler in spring and summer,which results from delayed heat conduction. However, whatmost important is that changes in soil temperature (exceptfor the surface layer) proceed gradually, while changes in airtemperature can be rapid. In a short time air temperature canchange by over ten degrees, due to fast movement of otherair masses.

24 h and hourly changes of temperature of soils and airin temperate climate can have very different character. Theycan be characterized by similar variability, but differencesmay appear depending on local conditions. 24 h changes oftemperature of soil are highly dependent on season of theyear. In principle, the highest values and the highestvariation of soil temperature within 24 h appear close to itssurface. While moving more deeply in the soil profile,amplitude of daily variation becomes smaller, vanishing ata certain depth completely (Skawina et al., 1999). Duringspring and summer a maximum of soil temperature appearsat midday (12-13). For deeper layers of soil the maximummoves, according to depth increase, to afternoon hours(Bonn and O’Neil, 1993). In winter, the top layer of soilreveals smaller temperature variation and the peak tempe-rature is hardly determined.

Depending on soil type, 24 h changes of temperaturehave different amplitudes. Chamayou and Legros (1989)found that 24 h fluctuations of temperature for sandy soilsurface can reach 40�C, for silt soil – 33.6�C and for clay soil21.5�C. At 5 cm these fluctuations decrease significantly,reaching 19.4�C for sandy soil, 18.5�C for silt soil and13.7�C for clay soil, respectively. The deeper a soil layer thesmaller the 24 h changes of temperature. At 10 cmfluctuations of temperature for discussed soils reach: 12.3�Cfor sandy soil, 10.7�C for silt soil and 7.7�C for clay soil,while at 30 cm the corresponding values are: 1.6, 0.7 and0.6�C, respectively. As shown by results of long-termstudies, 24 h variation of soil temperature vanishes at depthsof tens of centimetres, whereas annual variation can even

reach over ten meters (Skawina et al., 1999). There is a greatimpact of weather conditions on thermal properties of soils.During completely cloudy days changes in soil temperatureare much smaller than during cloud-free weather.

Differences between air temperature and dry soil tem-perature can be very high. Temperature of soil can reach50-55�C. According to Chamayou and Legros (1989) maxi-mum temperatures for different soils are: 53.5�C for sandysoil, 46.7�C for silt soil and 35.9�C for clay soil. In the case ofwet soils the maximum values can decrease even by 20-30�C, depending on the degree of moisture.

Vegetation cover is the important element in formingthermal conditions of soils. Its presence has a great influence,limiting both vertical differentiation of soil temperature andmagnitude of time changes (Skawina et al., 1999). Tempe-rature measured just under the surface of soil not covered withvegetation can be over ten degrees higher than that with ve-getation cover. 24 h temperature variations for soil undervegetation are several times lower that for bare soil(Skawina et al., 1999). In the case of porous and dry soils thisvariation is higher than for compact and wet soils.

Typical profiles of minimum and maximum tempe-ratures for soil covered with green grass and for bare soil arepresented in a figure in the work by Bonn and O’Neill(1993). The impact of vegetation can be seen throughsmaller amplitudes of temperature. The effect of radiationheating and cooling of bare soil is expressed though higheramplitude of soil temperature than that of air temperature.Similar profiles of maximum temperature for bare soil andsoil covered with grass in May can be explained by the initialdevelopment of vegetation. In subsequent months the impactof vegetation is stronger. The highest difference betweenbare soil temperature and air temperature at a height of 2 mappears in summer months (August). On sunny days soiltemperature will be always higher than temperature of airover this soil.

Emissive and thermal properties of soils

Soils are characterized by relatively high variability ofemissivity coefficient, when compared to water surfaces.This variability is influenced by the physical-chemical soilproperties eg moisture, texture, grain size composition, poro-sity, organic matter content, colour, thermal properties eg

heat conduction capacity and inertia as well as conditions ofobservation (zenith angle of observation, sun incidence angle).

Typical emissivity coefficients for sand are within 0.90to 0.93. As follows from scientific publications, sandy soilsare characterized by emissivity coefficient in 8-14 �mwavelength range between 0.88 to 0.94, clay soils from 0.95to 0.96, loamy soils from 0.91 to 0.93, and silt soils ca. 0.94(Osiñska-Skotak, 2001). Soils with limestone origin haveemissivity coefficient of 0.95, while organic soils 0.997.

STUDIES OF SOIL TEMPERATURE ON THE BASIS OF SATELLITE DATA 3

Moisture is an important element having impact on heatemission by soils. The wetter the soil, the higher theemissivity (Salisbury and D’Aria, 1992b), which resultsfrom high emissivity of water itself. A similar relationshipexists for humus content – the richer in organic matter thesoil, the higher its emissivity coefficient (Salisbury andD’Aria, 1992b). This relationship is also illustrated in Fig. 1.The same soils, depending on wavelength, have differentemissivity coefficients. In general it can be concluded thatemissivity is lower for shorter wavelengths (3-5 �m) andhigher for longer wavelengths (8-14 �m).

Analysing changes of emissivity coefficient for small-grain sand in the 3-18 �m wavelength range (Mannstein,1994) it can be found that sand is characterized by higheremissivity in the 8-14 �m range than in the 3-5 �m range, butalso two minima can be observed in the far infrared band (ca.8-9 and 12-13 �m). Minima or maxima of emissivity can bealso noticed for other soils and rocks. For instance, mini-mum of radiation for silica is at ca. 9 �m, while for lime carbo-nate at around 7 �m. Such a distribution of extreme values ofemission in theory permits identification of various types ofrocks and soils due to temperature differences.

Variability of emissivity coefficient due to observationangle is small in the case of soils with slight roughness(Becker et al., 1986). It mainly results from the character ofsoil surface. In the case of newly ploughed soils ie with greatroughness, variability of emissivity coefficient due to obser-vation angle will be significant. Normann et al. (1999) repor-ted that soil emissivity changes slightly at observationangles of 0 to 10° (about 0.005). When observation angle in-creases from 0 to 30� emissivity decreases, but for largerangles (30-50�) an increase of emissivity is noticed. In the

50-60� range emissivity is constant and for observationangles larger than 60� it decreases again. In the case of satel-lite studies the problem of changes of emissivity due to obser-vation angle in negligible, as ETM+ and ASTER scannersare characterized by a small angle of view (less than 7.5°).

Similar analyses, but for temperature of soil surfaces,were done by Lagouarde from INRA and Kerr fromLERTS/CNES (1993). They examined, for two types of soil(smooth and newly ploughed), the relation betweenbrightness temperature and observation angle for differentSun positions. In the –30 to +30� range of observation the

temperature for smooth soil surface will vary by less than0.5°K comparing to nadir observation (independently fromthe Sun zenith angle). Newly ploughed soil will reveal a si-milar accuracy only for the –15 to +15° range. Maximumdifference of temperature for ploughed soil appears forobservation angle of ca. +60° and is equal to almost +4°K.

Depending on water content all thermal properties ofsoils change. This changeability appears with variousintensity. Soil moisture is one of the most important factorsinfluencing thermal capacity of soils, and hence theirpossibilities of heating and cooling. As soil moistureincreases, also thermal conductivity, thermal capacity andthermal inertia increase. In general, in the case of volumethermal capacity this relation is linear, while for thermalconductivity – exponential (Chamayou and Legros, 1989).Thermal capacity influences temperature of soil surface insuch a way that the higher thermal capacity the lowertemperature of soil surface, as heat is absorbed by deeperlayers of soils. Other thermal properties, like thermalconductivity, thermal diffusion and specific heat, also havean impact on soil temperature and its surface.


organic soil

sandsilt

soil-vegetation

gravel

1.00

0.99

0.98

0.97

0.96

0.95

0.94

0.93

0.92

0.91

0.901300 1200 1100 1000 900 800 700 h [1/cm]

7,7 8,3 9,1 10,0 11,1 12,5 14,3 λ [µm]

Em

issiv

ity

Fig. 1. Changes of emissivity of organic soil, sand, silt, gravel and soil-vegetation cover measured by MODIS scanner2 in thermal infraredrange (prepared on the basis of Snyder et al., 1997).

2MODIS - Moderate Resolution Imaging Spectrometer registers radiation in 0.60-14.54 �m range in 36 spectral bands.

The coefficient of thermal conductivity is higher forsand and sandy soils than for clays and peat. But the thermalinertia of sand and sandy soils is lower than that of clay andwet soils, which makes that for light soils faster heating, butalso faster release of heat appears. This feature is importantwhile interpreting thermal images collected early morningor after sunset. In theory, if imaging of terrain could bepossible eg two times within an hour, it would be also pos-sible to determine flux of heat received of lost by soils and toinfer its type. Increase of thermal conductivity in relation tosoil moisture is the highest in the case of sandy soils, lowerfor silts and clays, and minimal in the case of peat.

Thermal diffusion allows to evaluate how fast a soillayer is heated or cooled. It depends on the properties ofthermal conductivity and specific heat of soil. The higher thethermal diffusion coefficient the quicker the heat energy willbe transferred from soil surface to its deeper layers (Bac andRojek, 1979). It means that for the same energy flux a soilwith higher thermal diffusion coefficient will have lowertemperature than a soil with low diffusion coefficient.Generally, soils with coarse grain texture absorb heat andrelease it in the fastest manner. The ability to diffuse heatwhen water content in soil increases varies in accordance tothe type of soil. In general, first quite rapid increase ofthermal diffusion appears, and next its decrease is observed.Organic soils are an exception to this rule; for them thehigher the water content in soil the lower the coefficient ofthermal diffusion.

MATERIAL AND METHODSStudy area

Wyszków district, located at the border of two macro-regions – North and Middle Mazowian Lowlands (Kondracki,1974) – was selected as the study area. The selection of thisarea was due to availability of soil database for this districtwhich served for further analyses.

Wyszków district is located between two valleys: theLower Narew and Lower Bug rivers, and it is influenced bythese valleys. In its northern part the Wyszków district isplaced on a meadow covered, flooded terrace of meanderingNarew river. In turn, its southern part comprises a meadowcovered, flooded terrace of the Bug river with numerous oldriver beds, and a sandy dune terrace (mostly forested). Thecentral part of the study area is covered by moraine highlandand sandy lowland of the £om¿yñskie Miêdzyrzecze whichincludes pine forests of Puszcza Bia³a in the southern andeastern parts, while the northern part is agricultural.

The analysed area is flat, with very small elevationdifferences (up to 5 m), and is located at the elevation of 85to 115 m a.s.l. Only the edge between £om¿yñskie Miêdzy-

rzecze and the Valley of Lower Bug river is characterized byhigher elevation differences, reaching up to 30 m atescarpment. The following soil types exist within the studyarea (symbols according to soil-agricultural map); Podzols(A), Luvisols (Ax), Cambic Arenosols (Bw), Mollic Gley-sols (D, Dd, Dz), Fluvisols (F, Fb), Fluvic Histosols (E), Histo-sols and degraded Histosols (T), and Histo-humic Gleysols(M, Mw) and boggy soils (Mp). Their spatial distributionwithin the study area is presented in Fig. 2. Soils of low qua-lity dominate within the arable land. Only 5% of the arableland belongs to good and very good soils. Soils belonging tothe 6th and 7th classes of agricultural suitability3 dominatein this region; soils forming the 4th and 5th classes of agri-cultural suitability cover only 20% of the arable land.

Data sources

The research was carried out with the use of two satelliteimages collected the same year but in different seasons(spring and autumn). The first image was collected by theLANDSAT-7 ETM+ scanner on May 7, 2000, while thesecond was registered by the TERRA ASTER scanner onSeptember 28, 2000.

The LANDSAT-7 satellite, moving along a near-polarsun-synchronous orbit, collects data from an altitude of 705km. Data collection is done always at the same local time, at10 a.m. Thermal radiation is recorded by 8 detectors of theETM+ scanner (Enhanced Thematic Mapper Plus) in the10.4-12.5 �m wavelength range with spatial resolution of60×60 m. At present the ETM+ scanner delivers data in thethermal range with the best resolution, compared to othersatellite systems.

The TERRA satellite uses an orbit identical to that of theLANDSAT, but data are collected at 10.30 a.m. local solartime. The ASTER scanner (Advanced Spaceborne ThermalEmission and Reflection Radiometer) records thermal radia-tion in 5 narrower spectral bands, compared to the ETM+(band 10: 8.125-8.475, band 11: 8.475-8.825, band 12:8.925-9.275, band 13: 10.25-10.95, band: 10.95-11.65 �m)with spatial resolution of 90×90 m. Thermal radiation isrecorded by 10 detectors.

Radiometric temperature from both types of data can bedetermined with an accuracy of 0.3°K. Certainly, it does notmean that with such an accuracy the temperature of an objectcan be assessed. In the course of analysis of satellite data oneshould always keep in mind the size of instantaneous field ofview ie the dimensions of the smallest object which can beseen from satellite orbit. If this object is larger than theinstantaneous field of view of the scanner, radiometrictemperature will result from radiation emitted by the object.If it is smaller than the instantaneous field of view of the


3Classification according to soil agricultural maps classes: 1 and 2 – good and very good for wheat; 3 – suitable for wheat but seasonallydry; 4 – suitable for potatoes, rye, and wheat; 5 - suitable for potatoes and rye; 6 – suitable for rye, too dry for potatoes; 7 – limitedsuitability for rye (so dry); 8 – suitable for fodder crops (moist soils).

scanner, then the radiometric temperature will be a combi-nation of radiation emitted by the object and by surroundingobjects existing in the field of view.

For conducting analyses of relations between brightnesstemperature of soils and different soil properties, the soildata-base prepared by Pluto-Kossakowska (2003) andWyszyñska (2002) for the Wyszków district was used. Thedatabase was created on the basis of soil-agricultural maps ata scale of 1:25 000 and it was updated through interpretationof SPOT satellite data and field campaign. The databasecontains data concerning genetic type and subtype of soil,texture of surface layer, depth of change of texture, texture ofunder- surface layer, class of agricultural suitability and landuse type according to soil-agricultural map.

Methodology

Studies conducted in this work were aimed at identi-fication of relationships between brightness temperature ofsurface layer of soil – derived from satellite images – andvarious properties of soil. The most important was to find if(and to what extent) particular soil features have an impacton brightness temperature.

The studies were carried out in the following stages:1. Calculation of brightness temperature on the basis of

satellite images.2. Preparation of masks of soils not covered with

vegetation for both dates of image acquisition.3. Calculation of mean brightness temperature for each

soil unit (3695 polygons for arable land and meadows)existing in the soil database for both dates of imageacquisition.

4. Analysis of relations between brightness temperatureand various soil features for both dates of image acquisition.

5. Analysis of relations between brightness temperaturecoupled with aspect and various soil features for both datesof image acquisition.

6. Preparation of graphs illustrating the obtained rela-tions and analysis of the results.

In order to obtain brightness temperature of soils themethod of calculating brightness temperature on the basis ofsatellite images, prepared and verified in the doctoral thesis,was applied (Osiñska-Skotak, 2001). This method takes intoaccount the atmospheric attenuation of radiation recordedby the satellite scanner; it is adjusted to conditions at themoment of data collection through the use of groundmeasurements.

The presented studies were performed for soils notcovered with green vegetation, in order to minimize theimpact of other factors (degree of vegetation cover, heightand type of vegetation, biomass, etc.) on the results of analy-ses. To delineate bare soils the normalized difference vege-tation index (NDVI) was calculated on the basis of satelliteimages and a mask of soil not covered with vegetation wasprepared, assuming as a boundary value NDVI = 0.1.

In the next stage the mean brightness temperatures(based on satellite data) were calculated and standarddeviations for each soil unit (polygon from soil database)were determined. These data were a basis for analysingrelationships between brightness temperature of surfacelayer of soil and various soil features. The following soilfeatures were analysed:


A

Ax

B

Bw

D

Dd

Dz

E

F

Fb

M

Mp

Mw

N

T

Fores t

Soil types

Fig. 2. Soil database for agricultural land of the Wyszków district.

- type of soil,- class of agricultural suitability,- texture of top layer of soil.The same soil features were analysed in conjunction

with brightness temperature coupled with terrain aspect.In order to analyse the impact of insolation on changes

of thermal properties of soils, the digital terrain modelDTED Level 2 was used; it served for calculating the do-minant terrain aspect for each soil unit. The impact ofdominant aspect was analysed in 8 classes: N, NE, E, SE, S,SW, W, NW.

RESULTS

Temperature of surface layer of soil is influenced bycurrent meteorological situation. Hence, it is necessary togain knowledge about the meteorological conditions duringthe day of image acquisition in order to analyse properly thethermal properties of soil. Information about rainfall duringthe days preceding data collection is also useful.

May 7, 2000, was exceptionally warm and sunny, withslight wind (3-4 m s-1) from south and southeast. Air tempe-rature at 9.00 a.m. was 18°C; an hour later it reached 19.5�C.Air humidity at 9.30 was 45%. Before noon only smallclouds were observed. Temperature of soil in the 5 cm soil layerat Warszawa-Bielany meteorological station at 6.00 was16°C, and at 12.00 it already reached 27.2°C. Sun elevationon May 7, 2000, at the moment of data collection was 50°.

In turn, on September 28, 2000, sunny cloud-freeweather appeared, with slight wind (3-4 m s-1) from southand southeast. Air temperature at 9.00 was 13°C (at noon itreached 20�C). Air humidity was 77%. Preceding days were

also warm and sunny, without rain. On September 28, 2000,Sun elevation at the moment of ASTER/TERRA datacollection was only 34°.

Results of the analyses reveal that mean brightnesstemperature on May 7, 2000, for particular soil units (Fig. 3)was in the range of 16-34°C, while on September 28, 2000,in the range of 12-23°C. Mean temperature is discussed; itdoes not mean that more extreme values of brightnesstemperature do not appear for some soil units. Analysingstandard deviation of temperature for particular soil types itcan be observed that for Histosols, Fluvic Histosols, Fluvi-sols and boggy soils the highest standard deviations appear,while the lowest for cambic arenosols. This relationship isfully correct, as soils with a high amount of humus, depen-ding on location, can be characterized by greater moisturedue to their absorption properties. Cambic Arenosols (Bw)are characterized by low moisture, so their temperatureshould be less differentiated.

As a result of the conducted analyses various graphsillustrating mean brightness temperatures of soils wereproduced, depending on different soil features (type of soil,texture of surface layer, class of agricultural suitability),aspect of slope, and type of land use (arable land, meadows).

The relationship between brightness temperature of arableland/meadow soils and type of soil is also logical (Figs 4 and5). Wetter soils (Mollic Gleysols (D), Fluvisols (F, Fb),Fluvic Histosols (E)) with higher humus content have lowertemperatures than drier sandy soils (Podzols (A), Luvisols(Ax) and Cambic Arenosols (Bw)). Histo-humic Gleysols,degraded Histosols, Histosols and delluvial Mollic Gleysolshave similar brightness temperatures on both dates, for


oC

Fig. 3. Mean brightness temperature of soil units with and without vegetation cover, obtained on the basis of satellite images collected on:a – May 7, 2000, b – September 28, 2000.

a b

arable land as well as for meadows. Differentiation of meanbrightness temperature of soils according to the type of soil islower in autumn than in spring, but the relationship issimilar.

Histo-humic Gleysols have, on both dates, brightnesstemperature close to that of Luvisols and Cambic Arenosols.It is due to appearance of these soils on sands in the studyarea. They are characterized by low moisture and hence byhigh brightness temperature of surface layer.

Brightness temperature of meadow soils according tothe type of soil proves to be highly differentiated. The rangeof mean brightness temperature is almost 10°C in the case ofMay and ca. 2°C in the case of the autumn. Such a widerange of mean brightness temperature on May 7, 2000, ismost probably caused by diversified moisture of meadowsites, which is related to varied heat capacity of these soils.Soils with higher moisture (E, F), characterized by high heatcapacity, absorb heat slower, hence they have a lowerbrightness temperature at daytime. Histosols and degradedHistosols reveal – particularly in May – exceptionally highbrightness temperature, close to the temperature of Histo-

humic Gleysols. It results most probably from the fact thatmost peat-lands in the Wyszków district were reclaimed,causing that upper layers of peat started to rot. EutricFluvisols (Fb) on meadow areas, in May, are characterizedby exceptionally low temperature, which can result fromhigher level of water in the Bug river valley. Moreover, itshould be noticed that the smallest terrain element registeredby the LANDSAT ETM+ scanner is 60x60 m, so the tempe-rature is related to the mean value representing this area.

The texture of surface layer plays an important role informing thermal conditions of soils. Analysing the impact oftexture of surface layer of soil on temperature it can benoticed that for a majority of soil types the following relationis observed: the finer/more clayey a fraction appears in thelayer, the lower is its brightness temperature. For the sametype of soil, depending on texture of its surface layer, thedifference of brightness temperature can reach 3-4�C. In thecase of data from May 7, 2000 (Fig. 6a), the widest tempe-rature range exists for Podzols (A) and Cambic Arenosols(Bw). In turn, the smallest differentiation of temperature,resulting from texture of surface layer of soil, appears for


Fig. 5. Mean brightness temperature of meadow soils according to types of soil, obtained on the basis of satellite images collected on:a – May 7, 2000 and b – September 28, 2000.

ba

Brightn

ess

tem

pera

ture

(�C

)

Fig. 4. Mean brightness temperature of soils of arable land according to types of soil, obtained on the basis of satellite images collected on:a – May 7, 2000 and b – September 28, 2000.

a b

Brightn

ess

tem

pera

ture

(�C

)

Luvisols (Ax), Mollic Gleysols (D) and Histo-humicGleysols (M). In autumn (Fig. 6b), in spite of slight diffe-rences, the situation is similar.

The extremely high brightness temperatures of Histo-humic Gleysols can be explained by the fact that in the studyarea these soils appear on sands, so they are – in conse-quence - more dry and absorb more heat.

In Fig. 6b, for the autumn period, an exceptionally lowbrightness temperature is observed for Cambic Arenosolsand Fluvisols. While examining these two types of soilsmore thoroughly it was found that they are located close torivers and streams, and therefore are more wet due to oozing.

Very good correlation was found in the case of relationbetween brightness temperature of soils of arable land andclass of agricultural suitability (Fig. 7a, b). The lower the classof agricultural suitability (classes 2 to 7) the higher the tem-perature. On the average, temperature difference betweenthe worst and the best class of agricultural suitability reaches2-3°C. In the case of the autumn period the difference ismuch smaller; it results both from lower air temperature andfrom conditions of insolation – lower sun elevation. On bothdates soils of class 8 are more wet than soils of classes 2 and4, hence the lower brightness temperature. Opposite: soils ofclass 3 are more dry then soils of class 4, hence in this casea higher brightness temperature is observed.


Fig. 6. Mean brightness temperature of soils of arable land, according to texture of surface layer, obtained on the basis of satellite imagescollected on: a – May 7, 2000 and b – September 28, 2000 (pl – loose sand, ps – slightly clayey sand, psp - clayey loamy sand, pgl – clayeysand, pglp - clayey loamy sand, pgm – heavy clayey sand, pgmp – heavy clayey loamy sand, plz – loam, pli – clayey loam, gl – sandy clay,glp – loamy sandy clay, gs – clay, gsp – loamy clay, gc – heavy clay).

ba

Brightn

ess

tem

pera

ture

(�C

)

Fig. 7. Mean brightness temperature of soils of arable land, according to class of agricultural suitability, obtained on the basis of satelliteimages collected on: a – May 7, 2000 and b – September 28, 2000.

ba

Brightn

ess

tem

pera

ture

(�C

)

Analysis of impact of terrain aspect on brightnesstemperature revealed small differentiation of brightnesstemperature of soils. The highest temperatures appear atplaces where south, south-western and western aspectsdominate. As the study area is predominantly flat, in practiceeach part of the terrain is insolated from sunrise till sunset.Hence, small differentiation of brightness temperatureexists for different soil types according to aspect (1-2°C forMay and 0.5-1°C for September). However, local changes intemperature are observed close to forest areas. Those areas,when located to southeast of the forest ie in its shadow,reveal lower temperatures (a few degrees less, even to 10°C)than areas adjoining the forest from its north-western side.Differences in temperature in this case also depend on soilproperties and on degree of vegetation cover.

CONCLUSIONS

1. At present, thermal properties of Earth surface can beinvestigated from satellite level using a number of satellitesystems(a.o.NOAA/AVHRR,ENVISAT/MERIS,LANDSATETM +, MODIS, ASTER/TERRA). However, LANDSATETM+ images have the best ground resolution (60x60 m), sothey give the possibility to conduct studies with a higherlevel of spatial detail. In turn, ASTER data enable to collectthermal images in five narrow spectral bands with groundresolution of 90×90 m. They allow to conduct studies witha higher level of spectral detail ie to examine the thermalproperties of various objects.

2. As a result of the analysis concerning the impact ofsoil properties on brightness temperature of their surface –as observed from satellite level – it was found that mainlyclass of agricultural suitability and type of soil have theimportant influence on brightness temperature. Lower cor-relations with brightness temperature were observed in thecase of texture of surface and under-surface layers of soil.

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Studies of soil temperature on the basis of satellite data

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