Derivation of Areal Soil Physical Data from Satellite Measurements

Transcription

1 Hydrological Interactions Between Atmosphère, Soil and Vegetation (Proceedings of the Vienna Symposium, August 1991). IAHS Publ. no. 204,1991. Derivation of Areal Soil Physical Data from Satellite Measurements W.G.M. BASTIAANSSEN The Winand Staring Centre for Integrated Land, Soil and Water Research, PO Box 125, 6700 AC Wageningen, The Netherlands ABSTRACT Nowadays, the interrelation between hydrology and climate change is a debated issue. A better description of the heterogeinity of soil physical properties is recognized as being a crucial element in the improvement of simulating the hydrological cycle with Global Circulation Models (GCMs). A method based on Landsat-Thematic Mapper measurements of surface temperature, T 0, and surface albedo, r 0, has been developed to derive effective resistances for the transport of water and heat in a two-layer soil heat balance model. The method only holds when a fraction of the area under consideration is covered by non-evaporative land surfaces. For a regional scale equivalent with the scale of a GCM grid cell (100*100 km 2 ), the method showed for the Western Desert in Egypt at the moment of satellite overpass the following: an effective soil resistance to heat transfer of 0.41 W _1.m 2.K, to vapour transfer of 409 s.m" 1 and to liquid transfer of 29*10 6 s.m. The effective aerodynamic resistance to heat transfer was determined to be 46 s.m" 1. If radar techniques will be further developed to measure the depth of the evaporation front independently from the T 0 = f(r 0 ) relationship, estimated soil resistances can turn into effective diffusivity values. NOTATION D v vapour diffusivity m 2.s~ e actual vapour pressure mbar eff effective G 0 soil heat flux at the land surface W.rtT 2 H sensible heat flux W.m~ 2 h m matric pressure head m Kl global radiation W.m -2 k G 0 /Q*ratio k(h m ) unsaturated hydraulic conductivity cm.d~ L net terrestrial longwave radiation flux W.m Q* net radiation W.m -2 2 q^ liquid flux cm.d" q v vapour flux kg.rrf r ah aerodynamic resistance to heat transport s.m -1 r av aerodynamic resistance to vapour transport s.m -1 r-sh-, soil resistance to heat transfer W _1.m 2 -sh r sh above the evaporation front r sv rsoil sh below resistance the evaporation to vapour front transport s.m r n soil surface resistance albedo to liquid transport s.m (-) -1 T a air temperature K T 6 temperature at depth z e K T s soil temperature K T 0 surface temperature K z depth m z e depth of the evaporation front m z c depth between z e and water table m Y psychrometric constant mbar.fc ' apparent emissivity of the atmosphere 95

2 W. G. M. Bastiaanssen 96 M(h m ) e e e e ^sat XE X' PaCp Pi Pv T matric flux potential surface emissivity soil water content soil water content at depth z e soil water content at saturati latent heat flux apparent thermal conductivity air heat capacity water density vapour density Stefan Boltzmann constant 3-3 cm. cm cm 3. cm cm. cm W.m-2 W.m~- L.K~ J.m" 3.K~ kg.rrt 3 kg.m~ 3 W.m~ 2.K~ INTRODUCTION Meteorological measurements on a global scale indicate a significant increase of temperature during the last century. If this trend continues, a temperature rise between C per decade can be expected (Ministery of Housing, Physical Planning and Environment of The Netherlands, 1990). The earth's heat balance seems perturbed by the increased use of energy and chemical substances causing the greenhouse effect. More background information is necessary in order to make effective decisions to feasibly control the climate change. Background information has to be obtained by improving simulation models and current status of knowledge (Earth Observation and Global Change Decision Making, a National Partnership, 1990). Atmospheric Global Circulation Models (GCMs) are used twofold; (i) for day-to-day weather prediction, i.e. Numerical Weather Prediction Models (NWPMs), and (ii) for climate studies. The latter catégorie aims to simulate global scale atmospheric circulation in order to estimate the effect of hypothetical climate scenarios on the hydrological cycle (e.g. Adams et al., 1990). In sophisticated GCMs, prognostic equations for soil temperature and soil water content affecting the land surface processes are included. A proper estimation of the mean soil physical properties in each GCM grid cell (100*100 km 2 to 500*500 km ) is necessary to determine realistic soil water content and soil temperature profiles with GCMs. Knowledge on liquid, vapour and thermal diffusivity on the scale of a GCM grid cell is however poorly developed. This paper deals with a new methodology to retrieve soil physical properties from satellite data matching the scale of a GCM grid cell. The objective is to determine areal-effective resistances to the transport of water and heat in a vertical soil-air column containing two soil layers. Since resistance and conductivity are inversely proportional, quantification on the range of thermal and vapour diffusivity can be made. The location of the GCM cell is considered in a part of the Western Desert of Egypt where dry (rocks, dunes) and wet (natural depressions) areas with scarce vegetation are present. A quarter scene of a Landsat-Thematic Mapper of the study area was available to provide distributed values of surface temperature and surface albedo. The TM data were resampled to cover the resolution of a pixel (1*1 km) of the NOAA satellite. LAND SURFACE PROCESSES Because gradients on specific humidity, temperature and momentum are present just above the land surface, turbulent heat exchange between land and atmosphere occur. The conditions of the vadose zone however are crucial for the transport of water and heat into and away from the land surface. The water at the surface can not evaporate potentially when the soil underneath is dry. When the soil is dry, soil behaves more like an isolated medium than under wet conditions. Hence, the

3 97 Derivation ofareal soil physical data from satellite measurements soil-atmosphere system has to be considered as a continuous medium. Surface energy balance combination equations of the Penman-Monteith type describe those water and heat transfers in the soil-atmosphere continuum. Menenti (1984) accomplished the Penman-Monteith equation by defining an evaporation front in the soil. Vapour transport takes place between the evaporation front and the land surface. The subsequent equations for heat exchange between the surface-atmosphere and soil-surface are: (a) surface-atmosphere XE = (e(0) - e(z) ) Y r av (W.nT 2 ) (1) H = Pa C p r ah (T n -TJ (W.irT 2 ) (2) (b) soil-surface XE = (e(z e ) - e(0) ) Y r sv G = (T 0 -T e ) r sh (W.nT 2 ) (3) (W.rrT 2 ) (4) The energy required for heat exchange is released by the net radiation which is the difference of all ingoing and outgoing radiation fluxes emitted by the sun, atmosphere and land surface: Q* = (l-r 0 )Ki + e"t:t a 4 - ett 0 " (W.nT 2 ) (5) The soil physical properties affecting the land surface heat exchange processes are described with r sv (Equation 3) and r sh (Equation 4). If r sv and r sh can be regionally mapped by satellite remote sensing, two different aims are achieved: (a) the areal effective resistance is an expression for the lumped soil physical properties on the scale of a GCM grid cell; diffusion coefficients in soil can be mapped when the depth of the evaporation front is known (b) mean r sv and r sh values valid for a GCM grid cell can be applied to verify the predicted surface energy fluxes and the parameterization of soil water and heat flow in the GCM Item (b) has the restriction that the use of remote sensing data is only valid for short term predictions of atmospheric circulation, i.e. weather forecast by means of NWPMs. To address real time assimilation of satellite data in NWPMs, the Earth Observing System Data Information System, EOSDIS, play a dominant role (Earth Observing System NASA, 1990). EOSDIS will serve a broad distributed earth science community with EOS data. Actual information acquired through satellites is only temporarily valid and is useless to apply for climate change simulation purposes. The results described in this paper are only related to item (a).

4 W. G. M. Bastiaanssen 98 SOIL RESISTANCE Soil heat flow Heat conducted downwards or towards the land surface is usually calculated with the first term of Fourier law (G 0 = ^-'vt s ). Dividing the soil heat flow into two layers with the evaporation front being the interface, two different soil resistances to heat transport can u l be distinguished i.e. r sh and r sh (Fig. 1). For land surface processes, the resistance of the upper dry layer is most important since it contributes to the soil-atmosphere heat exchange and can be expressed by r u sh = z e /X/ (W -1.m 2.K). The heat flow penetrating the layer below the evaporation front can be calculated with r^1 = (z e - z(t s ))/A. W.m 2.K. When the land is evaporating according the atmospheric demand (z e =0), r u sh and r^1 become identical. Reference height Surface Evaporation front 6 e Je(T e) Groundwater level 6, FIG. 1 Schematization of a three-layer heatbalance model containing two soil layers in terms of resistances and driving forces to water and heat transport. Soil vapour flow As the soil dries, the evaporation front moves deeper. The vapour flow through the dry top layer is a summation of iso(thermal) Fickian vapour diffusion in combination with Knudsen diffusion and at times free convection of soil air (Menenti, 1984). To keep the parameterization simple, vapour flux, q v, can be written with a process-effective diffusion coefficient q v = D eff v vp v. When X,E=Xq v, r sv can be written as eff r sv = z e /D v (s.nt 1 ). Camillo and Gurney (1986) have proposed a simple empirical equation for diffusive transfer of water vapour from the location of vapour source to the land surface in the form of r sv (9. which means that r sv increases linear with decreasing soil water content and that z e 0 as long as e,.-e 0 )<o.i9

5 99 Derivation ofareal soil physical data from satellite measurements Soil liquid flow The iso-thermal water transport towards the site of evaporation in response to matric pressure head (h m ) gradients without gravitational forces can be described with the classical Darcy equation, q^ = k(h m )vh m. On the other hand, the matrix flux potential M(h m ) can be used. The matrix flux potential integrates the transport coefficient k (h ra ) and the driving force (vh m ). Then the liquid flux density becomes simply q x = vm(h m ). This formulation offers the possibility to lump the strong nonlinearity of h m (G) and k (h m ) relationships into one single value when h m at depth z e is fixed (Bastiaanssen and Metselaar, 1990). If the latent heat flux is identical to the Darcian flux (quasi-steady state), XE = Xp 1 M(h m ). The soil resistance for liquid transport then is r sl = (z c -z e ) (0 sat -9 e )/M (h m ) s.irt 1. LAND SURFACE PARAMETERS RETRIEVABLE FROM SATELLITE DATA Satellites are often referred to as promising devices, providing hydrologists with a new type of information (e.g. Schultz, 1989). This is however only partially true because many state variables such as superficial soil water content are not directly measurable by the sensor onboard of satellites. Only a correlation analysis between measured signals and ground truth data may bring solutions. Out of all the aforementioned hydro-meteorological variables, only surface temperature, T 0, can be directly measured. The surface temperature is a function of all land surface energy fluxes (see Equations 1 to 5) and may be conceived as a dynamic equilibrium variable of the land surface. The surface albedo, r 0, can be measured indirectly by means of spectral reflectance satellite data (Menenti et al, 1989a). Hence pairs of r 0, T 0 can be derived on a pixel by pixel basis to study hydrological phenomena. Fortunately, a relationship between r 0 and T 0 exists as well. Satellite observations and field measurements under completely different climatological circumstances and with different length scales have shown that albedo and surface temperature are mutually related (Gebhardt, 1986; Seguin et al., 1987; Rosema et al., 1988; Menenti et al., 1989b, Cure et al., 1989; Bastiaanssen, 1990). The slope of T 0 = f(r 0 ) has usually two different signs: a negative sign occurs at radiation controlled surface temperature, i.e. no evaporation. A positive sign occurs at evaporative controlled surface temperatures, i.e. wet areas (Menenti et al., 1989b). Assuming that T a does not vary with T 0 and K4. remains constant across the entire area, the slope between r 0 and T 0 can be analytically expressed according Equation 6. 5r 0 1 ÔL* ÔG 0 SB SIB 5T 0 KI 5T 0 5T 0 ST 0 5T 0 ANALYSIS OF LANDSAT THEMATIC MAPPER MEASUREMENTS OF THE WESTERN DESERT OF EGYPT Surface temperature and surface albedo A quarter scene of a Landsat-TM image (path 178/row 42) acquired on February 17 th 1987 has been analysed to study the effective soil resistance and related soil physical properties. The size of the analyzed study area was approximately 100*100 km 2. The northern part of the Western Desert of Egypt is the wet Qattara depression. The depth of the evaporation front in the depression is due to the presence of groundwater, locally shallow (10 cm). The albedo was found

6 W. G. M. Bastiaanssen 100 to have a range between r 0 = (Fig. 2). The range of corresponding observed temperatures is K. In order to study the attainability of meteorological satellites like NOAA (spatial resolution 1*1 km 2 ) to estimate soil physical properties, a resampling procedure of 40*40 TM-pixels (30*30 m 2 each) into one NOAA pixels has been carried out. The series of so obtained (T 0, r 0 ) pairs were fitted with multi-order polynomial functions. A third order polynomial function gave a better agreement (r=0.93) with the satellite data than the second (r=0.76) and first order (r=0.68) equations. The fittings equations are: 1 st order T n =+36.3r n (r cr 0 <0.27) T 2 nd n =-19.0r (r order l r 0-233r 2 0 >0.27) 0. 3 rd order T o = r n +421.lr 2 3 o r n (7) (8) (9) (10) = J>I,3- + -WMIIiS)jt 7i+. +1l H rtplj«ttjt pinna!;»!,... nm #$#4 * + + v/ ++ / + -I-. -/ / / Surface albedo ( - ) 1 SI order 2 nd order FIG. 2 Relationship between surface temperature and surf ace albedo for a GCM grid cell (100*100 km^) in the Western Desert of Egypt on February 17 th 1987 at h. local time. The 1 st to 3 r order polynomial fitting curves are indicated. 0.5 Because of a hydrological meaning, some points on the third order fitting curve should be highlighted (Fig. 2). The minimum and maximum values of albedo are respectively r 0 = 0.05 and r 0 = That physically means that pixels satisfying r 0 > 0.30 belong to the radiation controlled branch of the T 0 = f(r 0 ) relationship. From physiographical maps it can be found that latter group of pixels are all sand dunes and rocks, which confirms the previous findings of nonevaporative surfaces in that particular branche. Superficial soil water content was measured during extensive field campaigns carried out in the Western Desert. For r 0 = 0.30, a mean value for various soils of 8 = 0.04 cm 3.cm 3 was noticed. The bending point of the fitting curve (f"(r 0 )=0) is at r 0 = At this bend, T 0 is rising progressively which correspond to a drop of latent heat flux. Such a collapse of evaporation must be related either to z e or D eff v since the evaporation is controlled by r sv. It is very well possible that in soil with < r 0 < 0.30, iso-thermal vapour diffusion is eliminated and that thermal vapour diffusion is the dominant transport phenomena. Further it is peculiar that the first and third order curve start to deviate considerably

7 101 Derivation ofareal soil physical data from satellite measurements when r 0 < 0.12 i.e. when the soil is well-watered. This divergence is supposed to be the treshold value for potential evaporation and is characterized by a flat slope of the third order polynomial for pixels with r 0 < 0.12 (Fig. 2). Table 1 deals with the determined treshold values for the magnitude of land surface energy fluxes. TABLE 1 Important points on the To = fcrg) relationship with a meaning for the magnitude of land surface energy fluxes. D v e «XE 0.05<r 0 < potential 0.12<r 0 <0.173 shallow (iso)thermal actual-high 0.173<r 0 <0.30 shallow thermal actual-low 0.30<r 0 <0.40 deep low 0 Effective soil resistance to heat transfer After establishment of the r 0 treshold values, a simplified surface energy balance can be made. Namely the term 8A,E/8T 0 in Equation 6 can be neglected when r 0 > 0.30 since the variation of XE will be low (Table 1). Although H is dominant on G in the case of dry land surfaces r 0 >. 0,30, the impact of 8G 0 /5T 0 on 8r 0 /5T 0 has to be debated as well. Expressing 5G 0 /8T 0 as l/r sh, is not useful since r sh is exactly the soil resistance to be solved. Therefore 8G 0 /ST 0 has to be derived in an alternative manner. A good candidate is the G 0 /Q* ratio since that approach has been shown to be useful for regional surface energy balance studies (e.g. Kustas and Daughtry, 1990). Literature on G 0 /Q* indicate for bare soils a range of G 0 /Q* = Although many attempts have been stressed to relate the G 0 /Q* ratio to vegetation cover, quantitatively interpretations on the wide range of GQ/Q for bare soils are rather scarce. Bastiaanssen and Menenti (1989) related G 0 /Q* for bare soils empirically to both soil thermal diffusivity and (normalized) surface reflectance. The latter solution can be applied to determine 5G/8T 0 : SG 0 8G 0 8r 0 = (W.m^.K -1 ) (11) 8T 0 8r 0 8T 0 where 8G 0 8Q* 8k = k + Q* (W.m -2 ) (12) 8r 0 8r 0 8r 0 The term 8G 0 /8r 0 bring new solutions to determine l/r sh. To minimize the iteration procedure on thermal diffusivity -8G 0 /8r 0-8G 0 /8T 0 -r sh, a sensitivity analysis considering the effect of thermal diffusivity on 8G 0 /8r 0 has been performed first. The following values for thermal diffusivity were considered: 0.3, 0.6, 0.9 and 1.2*10~ 6 m 2.s -1. In the test, net radiation was calculated on the basis of Equation 5 with Ki = 547 W.m -2 and T a = 300 K (field measurements). The temperature data were calculated from r 0 using equation (10). The results are presented in Figure 3. From Figure 3 it is clear that 8G 0 /8r 0 was not much affected by thermal diffusivity so that thermal diffusivity is not actually needed

8 W. G. M. Bastiaanssen 102 beforehand. The generized slope of all pixels with r 0 <0.30 was 5G 0 /5r 0 = -82 (r=0.92) while the relation for dry land surface (r 0 > 0.30) is 8G 0 /6r 0 =+50 (r=0.96). The combination of a linearized slope of 5r 0 /ST 0 (Equation (7) and (8) with a linearized slope of 5G 0 /5r 0 yields a mean 6G 0 /6T 0 value as well. Since slopes with different signs were recognized, two mean 5G 0 /6T 0 values were found: when r 0 < 0.3 G 0 /5T 0 becomes -82*0.028 = while for r 0 > 0.30 SG 0 /5T 0 is 50* = ST 140 E G 0 =A r 82r 0 Thermaldiffusivity (m 2 s- 1 ) D m O x = 120- to CD 70 9) Surface albedo ( - ) ~~I 0.4 FIG. 3 Sensitivity analysis of the effect of thermal diffusivity on the slope of soil heat flux vs surface albedo for bare soils. A) slopes for different values of thermal diffusivity. B) mean slope after a thermal diffusivity dependent transformation. Since statistics of the entire image showed that pixels with r 0 < 0.30 cover 68% of the area, the areal effective r sh -value can be calculated from ôg 0 /ÔTr Hence r sh eff 0.41 W 1.m 2.K which corresponds to p a C_r sh e " 479 s.m -1. Unfortunately, the present state of remote sensing techniques does not allow to map z e as an independent distributed parameter. For the study area with alternating sand dunes, silty clay, limestone, shales and sandstone, it can theoretically be expected that X' vary between 0.2 and 4 W.m~ 1.K~ 1. Substituting latter values in r sh u = z e /X yields a range of z e = m. This are extreme values of the area. The effective value will probably in between z e = m, because z c is approximately 2.25 m as derived in the next sections. That means that X' = W.m -1.K -1. Effective air resistance to heat transfer Considering Equation 6 for the pixels with r 0 > 0.30 where ÔLE/ T 0 is

9 103 Derivation ofareal soil physical data from satellite measurements eliminated, it becomes possible to derive H/6T 0 from 8r 0 /8T 0, 5G 0 /8T 0 and 8Q* 1/5T 0. The resulting value is 8H/8T 0 = which coincides when p a C p = 1155 J.m _3.K -1 with r ah =46 s.irf 1. Since the pixels dealing with dry soils were scattered over the entire image, the value for r ah can be termed as effective, i.e. r eff ah. Effective soil resistance to vapour transfer Knowing all the terms of Equation 6, 8XE/8T 0 becomes the rest term. An additional difficulty in the derivation of r sv is that r av and r sv are interrelated as based on the electric analog (Equation (1) and (3)). From 6\E/8T 0 values and XE = 0.0 at K(r 0 = 0.3), the potential evaporation at r 0 = 0.10 can be calculated as A.E = 24 6 W.m -2. Considering that r av = r ah, the vapour pressure deficit can be approximated as being 6.5 mbar, i.e. a relative humidity of 81% at T a = 300 K. This humidity is a rather fair value for deserts in the early morning. TABLE 2 Partial derivation of all surface energy fluxes with respect to surface temperature when going gradually from wet (ro = 0.10) to dry land surfaces (r 0 = 0.40). r 0 T 0 8r 0/8T 0 8L*/5T 0 8G 0/8T 0 8H/8T 0 8>.E/ST 0 XE r sv (-) (K) (W.m~ 2.K _1 ) (W.m -2 ) (s.nt 1 ) 0,.10 0, ,,25 0,.30 0, ,.7 304, ,6 308.,2 307.,2 304., , , ,.028-0,.053-0, , , , , , ,.30-2,,30-2,.30-2,.30-2, , , ,.4 +25,.4 +25, With the entire range of evaporation known, the corresponding values of r sv can be determined (Table 2). Because r sv changes exponentially with T 0 it is impossible to derive a r sv eff directly (Fig. 4). The solution of a weighing procedure has therefore to be accepted. The procedure has been done on the basis of XE with the area covered by each XE class being the weighing factor. Since pixels with XE > 0 cover only 37% of the study area, the areal mean evaporation, becomes XE = 25 W.m -2 only. This coincides with an areal mean value of r sv = 409 s.nt 1 when r av = 46 s.m -1. Yet, p a C p r sh and r sv have the same order of magnitude. Taking into account the estimated range z e = m from the previous section on r sh, it may be concluded that D v eff = " 4 m 2.s _1. Effective soil resistance to water transfer in the liquid phase Under quasi-steady state conditions, Darcian flux must be identical to the latent heat flux. That includes that at XE = 25 W.m -2, r sl must be s.m -1. Since both gradient (8 sat -0 e )/z c and M(h m ) are unknown, soil hydraulic properties can not be interpreted from r sl. Taking therefore just an arbitrary value of M(h m ) = 20 cm 2.d -1 which is a reasonable value for sandy soils and the matric pressure head at the evaporation front being h m = -33,000 cm, the mean depth between the evaporation front and the water table can be determined as being 2.25 m.

10 W. G. M. Bastiaanssen 104 If contour lines of the water table are available, which is not the case, an effective M(h m ) value could be determined from r sl , Î Surface temperature (K) FIG. 4 Relationship between surface temperature and soil resistance to vapour transport for a GCM grid cell (100*100 km 2 ) located in the Western Desert of Egypt on February 17 tfl 1987 at h. local time. CONCLUSIONS In arid regions, the climate system brings on large surface temperature differences as a function of the surface albedo. A third order polynomial curve, fitting the T 0 =f(r 0 ) relationship of pixels covering the NOAA length scale, can be applied to evaluate the regional surface energy balance and effective soil resistances involved. Data on global radiation and air temperature has to be measured in the field. The presented method only holds when a fraction in the area of consideration is covered by non-evaporation land surfaces. The study area in the Western Desert of Egypt can be represented with a two-layer vertical soil column with the depth of the dry toplayer between m and the water table between m. The method showed at the moment of satellite overpass the following; an effective soil resistance to heat transfer of 0.41 W" 1.m 2.K, to vapour transfer of 409 s.itt 1 and to liquid transfer of s.rtt 1. The effective aerodynamic resistance to heat transfer was determined to be 4 6 s.rn -1. If radar techniques will be further developed to measure z e independently from the T 0 =f(r 0 ) relationship, effective resistances can turn into top soil diffusivities since resistance and conductance are inversely proportional. REFERENCES Adams, R.M., C. Rosenzweig and R.M. Peart (1990) Global Climate

11 105 Derivation ofareal soil physical data from satellite measurements Change and US Agriculture, Nature 345, Bastiaanssen, W.G.M. and M. Menenti C1989) Surface reflectance and surface temperature in relation with soil type and regional energy fluxes. In Bouwman (Ed.), Soils and the Greenhouse Effect. John Wiley & Sons, Chichester, p Bastiaanssen, W.G.M. (1990) Mapping vapour and heat transport coefficients in soil and air using Landsat observations in arid regions. Proc. IGARSS 1990 at Univ. of Maryland, USA, May Bastiaanssen, W.G.M. and K. Metselaar (1990) Correlation of remotely sensed land surface parameters and soil hydraulic properties; the parameterization of soil resistance. Proc. Int. Symp. on Remote Sensing and Water Resources, August 1990, ITC Enschede, The Netherlands, p Camillo, P.J. and R.J. Gurney (1986) A resistance parameter for bare soil evaporation models. Soil Sci. 141, Cure, W.W., R.B. Flagler and A.S. Heagle (1989) Correlations between canopy reflectance and leaf temperature in irrigated and droughted soybeans. Rem. Sens. Env. 29, Earth Observation and Global Change Decision Making, a National Partnership (1990) NASA-NOAA-ERIM Conference held on Oct at the National Press Club, Washington. Earth Observing System NASA (1990) Reference Handbook, Goddard Space Flight Center. Gebhardt, A. (1986) Zur grosmasstabigen differenzierung der bodenfeuchte mittels thermografie. Arch. Acker-Pflanzenbau Bodenkd., Berlin 30, 11, Kustas, W.P. and C.S.T. Daughtry (1990) Estimation of the soil heat flux/net radiation ratio from spectral data, Agr. and Forest Met. 49, Ministery of Housing, Physical Planning and Environment (19 90) Climate Change Policy in The Netherlands and Supporting Measures, Atmospheric Pollution and Climate Change. Menenti, M. (1984) Physical aspects and determination of evaporation in deserts applying remote sensing techniques. Ph.D. thesis, ICW report 10 (special issue), The Winand Staring Centre, Wageningen, The Netherlands. Menenti, M., W.G.M. Bastiaanssen and D. van Eick (1989a) Determination of hemispherical reflectance with Thematic Mapper measurements. Final Workshop NASA TM Science Program. Rem. Sens. Env. (special issue) 28, Menenti, M., W.G.M. Bastiaanssen, D. van Eick and M.H. Abd El Karim (198 9b) Linear relationships between surface reflectance and temperature and their application to map actual evaporation of groundwater. Adv. Space Res., Vol 9(1), Rosema, A., J.L. Fiselier and W.F. Rodenburg (1988) Meteosat thermal inertia mapping for studying wetland dynamics in the West- African Sahel. BCRS report 88-10a BCRS, Delft, The Netherlands. Schultz, G. (1989) Remote Sensing in hydrology. J. of Hydr. 100, Seguin, B., E. Assad, J.P. Freteaud, J. Imbernon, Y. Kerr and J.P. Lagouarde (1987) Suivi du bilan hydrique a l'aide de la télédétection par satellite. Application au Senegal. Report to EEC DG VIII EEC, Bruxelles.

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