Remote Sensing in Hydrology and Water Management || Soil Moisture

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  • 9 Soil Moisture

    Edwin T. Engman Head, Hydrological Sciences Branch, Laboratory for Hydrospheric Processes, NASA/Goddard Space Flight Center

    9.1 Introduction Soil moisture is an environmental descriptor that integrates much of the land sur-face hydrology and is the interface between the solid earth surface and the atmos-phere. By far the most important role for soil moisture is in controlling and regu-lating the interaction between the atmosphere and the land surface. The redistribu-tion of solar energy over the globe is central to studies in climate and weather. Water serves a fundamental role in this redistribution through the energy associ-ated with evapotranspiration, the transport of atmospheric water vapor, and pre-cipitation. Residence times for atmospheric water is on the order of a week and for soil moisture from a couple of days to months, emphasizing the active nature of the hydrologic cycle. Understanding the importance of the land-surface hydrology to climate has emerged as an important research area since the mid 1960s when re-searchers at the Geophysical Fluid Dynamics Laboratory placed a land hydrology component into their general circulation model (GCM) (see Manabe et aI., 1965; Manabe, 1969).

    The role of soil moisture is equally important at smaller scales. Recent studies with mesoscale atmospheric models have similarly demonstrated a sensitivity to spatial gradients of soil moisture. Chang and Wetzel (1991) have concluded that the spatial variations of vegetation and soil moisture affect the surface baroclinic structures through differential heating which in tum indicate the location and in-tensity of surface dynamic and thermodynamic discontinuities necessary to de-velop severe storms. An analysis of the 1988 drought in the mid-western United States by Atlas et aI. (1993) has shown that these conditions could be modeled accurately only when the soil moisture values were realistic. A reanalysis (Beljaars et aI., 1996) of the conditions leading to the 1993 floods in the United States illus-trated improved precipitation forecasts, both in quantity and location, when realis-tic soil moisture values were used in the model.

    Soils, their properties and their spatial distribution all reflect the historical hy-droclimate processes that formed the soils and control the current land surface response to meteorological inputs, i.e., precipitation and potential ET. Soil mois-ture patterns, both spatial and temporal, may be the key to understanding the spa-tial variability and scale problems that are paramount in scientific hydrology. Op-erational flood forecasting is based on limited measurements of rainfall and river

    G. A. Schultz et al. (eds.), Remote Sensing in Hydrology and Water Management Springer-Verlag Berlin Heidelberg 2000

  • 198 E.T. Engman

    stage and in some cases some type of soil moisture description usually in the form of an antecedent precipitation index. In many cases poor forecasts are attributed to lack of information about the initial conditions, i.e., soil moisture. For example, hydrologists at the NOAA Kansas City River Forecast Center believe that soil moisture has been their most troublesome parameter affecting forecasts (Wiesnet, 1976) and Georgakakos et al. (1996) have identified soil moisture as the most sensitive variable controlling runoff in the development of an operational flash flood prediction system.

    As important as this seems to our understanding of hydrology, soil moisture has not had widespread application in the modeling of these processes. The main rea-son for this is that it is a very difficult variable to measure, not at a point in time, but at a consistent and spatially comprehensive basis. The large spatial and tempo-ral variability that soil moisture exhibits in the natural environment is precisely the characteristic that makes it difficult to measure and use in Earth science applica-tions. For the most part our understanding of the role of soil moisture in hydrology has been developed from point studies where the emphasis has been on the vari-ability of soil moisture with depth. Much of our failure to translate this point un-derstanding to natural landscapes can be traced to a realization that soil moisture varies greatly in space but with no obvious means to measure the spatial variabil-ity. As a parallel consequence, most models have been designed around the avail-able point data and do not reflect he spatial variability that is known to exist.

    9.2 General Approach It has been shown that the soil moisture can be measured to some extent by a vari-ety of techniques using all parts of the electromagnetic spectrum. Successful meas-urement of soil moisture by remote sensing depends upon the type of reflected or emitted electromagnetic radiation. Table 9.1 summarizes the advantages and dis-advantages of each approach. However, it will be seen that only the microwave region of the spectrum can provide a quantitative approach to estimate soil mois-ture under a variety of topographic and vegetation cover conditions.

    Gamma radiation techniques. Airborne soil moisture measurement by gamma radiation is based on detecting the difference between the natural terrestrial gamma radiation flux for wet and dry soils. The presence of water in the upper soil layers increases the attenuation of the gamma radiation from below; thus the flux is less for wet soils then for dry soils. Quantitative estimates of soil moisture require calibration flight lines to determine the background soil moisture value, Mo, and the background gamma count rate, Co. The current soil moisture, M can be esti-mated according to

    M = C/Co(lOO+1.11Mo)-lOO 1.11


    where C is the measured gamma count rate. A more complete description can be found in Carroll (1981). Because the atmosphere also attenuates the gamma radia-

  • 9 Soil Moisture 199

    Table 9.1 Summary of remote sensing techniques for measuring soil moisture (after Engman, 1991)

    Wavelength region Pro~ert~ observed Advantages Disadvantages Gamma radiation Attenuation of natu- Existing airborne Limited spatial resolu-

    rally emitted radiation program. Averages tion. Limited to low over a line. elevation flights.

    Empirical calibration. Reflected solar Albedo. Index of Data available No unique relationship

    refraction between spectral reflectance and soil moisture. Surface only. Clouds.

    Thermal Infrared Surface temperature. High spatial resolu- Bare soil only. Clouds. Measured diurnal tion, wide swath. Surface topography range of surface or Relationship between and local meteorologic crop temperature. temperature and soil conditions can cause

    water pressure inde- noise. Surface layer pendent of soil type. only.

    Active micro-wave Backscatter coeffi- All weather, high Surface roughness, cient, dielectric con- resolution. vegetation, topogra-stant. phy, limited swath

    width. Radio fre-quency interference

    Passive micro-wave Brightness tempera- All weather, wide Limited spatial resolu-ture, soil temperature, swath, good sensitivity tion. Radio frequency emissivity, dielectric can compensate for interference dense constant. moderate ve~etation ve~etation

    tion flux from the soil, this approach is limited to low elevation aircraft flights, less than 300m above the land surface.

    Visible/near-infrared techniques. Reflected solar radiation is not a particularly useful approach to estimating soil moisture because it is very difficult to quantify the estimate. While it is true that wet soil will generally have a lower albedo than dry soil (Crist and Cicone, 1984), and this difference can theoretically be meas-ured, confusion factors such as organic matter, roughness, texture, angle of inci-dence, color, plant cover, and the fact that it is a transient phenomenon, all make this approach impractical (Jackson, et aI., 1978). Thermal infrared techniques. The thermal infrared portion of the spectrum offers a theoretically sound approach to measuring soil moisture. After meteorological inputs to the soil surface have been accounted for, surface temperature is primarily dependent upon the thermal inertia or the soil. The thermal inertia, in tum, is de-pendent upon both the thermal conductivity and heat capacity which increases with soil moisture according to the following relationship (Price, 1982) DTs = Ts(PM)- Ts(AM)= f(lID) (9.2)

  • 200 E.T. Engman

    where DTs is the diurnal temperature difference between the afternoon surface temperature Ts(PM) and the early morning surface temperature, Ts(AM), and Dis the diurnal thermal inertia given by

    D=wQck (9.3) where w corresponds to the day length, Qc is the volumetric heat capacity and k is the thermal conductivity. The diurnal thermal inertia D describes the ability for the soil to resist temperature change. For example, a dry sand has a relatively low value of D compared to a wet clay because the thermal conductivity of the sand is lower than that for the wet clay and the volumetric heat capacity for dry soil is lower than that for wet soils. Thus, by measuring the amplitude of the diurnal tem-perature change, one can develop a relationship between the temperature change and soil moisture. However, the relationship between the diurnal temperature and soil moisture depends upon soil type and is largely limited to bare soil conditions (van de Griend et aI., 1985). Microwave Techniques. Microwave techniques for measuring soil moisture in-clude both the passive and active microwave approaches, with each having distinct advantages. The theoretical basis for measuring soil moisture by microwave tech-niques is based on the large contrast between the dielectric properties of liquid water and of dry soil. The large dielectric constant for water is the result of the water molecule's alignment of the electric dipole in response to an applied elec-tromagnetic field. For example, at L-band frequency the dielectric constant of water is approximately 80 compared to that of dry soils which is on the order of 3-5. Thus, as the soil moisture increases, the dielectric constant can increase to a value of 20, or greater (Schmugge, 1983). Figure 9.1 illustrates the change in di-electric constant for soil at several microwave frequencies.

    For passive microwave remote sensing of soil moisture from a bare surface, a radiometer measures the intensity of emission from the soil surface. This emission is proportional to the product of the surface temperature and the surface emissivity which is commonly referred to as the microwave brightness temperature (TB) and can be expressed as follows (Schmugge, 1990): TB = t(H)lrTsky +(I-r)TsoiIJ+ Ta,m , (9.4)

    where t(H) is the atmospheric transmissivity for a radiometer at height H above the soil, r is the smooth surface reflectivity, T soil is the thermometric temperature of the soil, T atm is the average thermometric temperature of the atmosphere, and Tsky is the contribution from the reflected sky brightness. For typical remote sensing applications using longer microwave wavelengths (greater than 5 cm, which are better for soil moisture), the atmospheric transmission will approach 99%. The atmospheric, T atm, and sky, T sky, contributions are both on the order of 5K, each of which are small compared to the soil contribution. Thus neglecting these two terms, Eq. 9.4 can be simplified to

    TB = (1- r )Tsoil = eTsoil (9.5)

  • 9 Soil Moisture 201

    30 T 30.6l3~- SANl)

    55.9~/o SILT J3.5(.!.'o CLA. Y

    25 1.4 GHz

    ~ 6 w

    ,..: 20 ;z. 12 < f-V> ;z.

    8 15 18 u c;:: ti U-J --l :;.u 10 18GHz 25


    5 6 1.4

    0.1 0.2 0.3 0.4 0.5 VOLUMETRIC SOIL MOISTURE. Mv

    Fig. 9.1. The real and imaginary parts of the dielectric constant as a function of volumetric soil moisture for a loam soil measured at four frequencies (after Ulaby et al. 1986)

    where e = (I-r) is the emissivity and is dependent upon dielectric constant of the soil and the surface roughness. Thus over the nonnal rage of soil moisture, a de-crease in the emissivity from about 0.95 to 0.60 or lower can be expected. This translates to a change in brightness temperature on the order of 80 degrees K. Though the relationship between emissivity and brightness temperature is linear (see Eq. 9.5), the soil moisture has a non-linear dependence on reflectivity because the reflection coefficient R of the ground is related in a non-linear way to the di-electric constant of the soil (). For horizontal polarization, the reflection coeffi-cient is given by

    R=cosq-b cosq + b


    where b = ~ - sin 2 q and q is the angle of incidence. The expression for vertical polarization can be written in a similar way. The dielectric constant, , is a com-plex quantity and the empirical relationships between dielectric constant and soil moisture derived by Dobson et al. (1985) show that dielectric constant has a non-linear dependence on soil moisture. However, even though the brightness tem-perature -soil moisture relation has a strong theoretical basis, most algorithms are empirical in that they depend upon ground data for the relationship.

  • 202 E.T. Engman

    For the active microwave approach over a bare soil, the measured radar back-scatter, ss, can be related directly to soil moisture by

    (9.7) where R is a surface roughness term, a is a soil moisture sensitivity term, and Mv is the volumetric soil moisture. Although R and a are known to vary with wave-length, polarization, and incidence angle, there is no satisfactory theoretical model suitable for estimating these terms independently. Thus, as is the case for the pas-sive microwave approach, the relationship between measured backscatter and soil moisture requires an empirical relationship with ground data, even for bare soils.

    An additional approach for using soil moisture data derived with microwave ap-proaches is through change detection. This approach can be used for both passive or active microwave data. The change detection method minimizes the impact of target variables such as soil texture, roughness, and vegetation because these tend to change slowly, if at all, with time.

    9.3 Sensor-Target Interactions As discussed above, microwave techniques for measuring soil moisture have a strong theoretical basis. In addition, they are not limited to cloud-free and bare-soil conditions because the microwave approach can sense through cloud cover and, in many cases, through a vegetation canopy. Each of the two basic approaches, pas-sive and active, offer different but distinct advantages. The differences being in their instrument characteristics and their interaction with the characteristics of the target.

    There are a number of target and target-sensor characteristics that affect the measurement of soil moisture. These include the effects of soil texture, the depth of measurement, surface roughness, vegetation effects, and instrument parameters such as incidence angle and frequency. Each of these are discussed in more detail below.

    Soil Texture. Soil texture affects the microwave sensing of soil moisture in the way that the dielectric constant changes with the relative amounts of sand, silt, and clay in the soil. Figure 9.2 shows this effect with laboratory data and an empirical model developed by Wang and Schmugge (1980). However, it can be seen that this effect is relatively small and given the overall accuracy of the methods and uncertainty in other factors, texture effects can be neglected for practical purposes.

    Measurement Depth. The same principles control the depth of soil that is being measured by the microwave technique, whether it is passive, as discussed above, or active. In a series of careful field experiments with a C-band, HH polarization radar, Bruckler et al. (1988) showed experimental results of penetration depth compared with soil moisture that followed very closely to the theoretical curve for a uniform profile.

    The relationship between emissivity and soil moisture depends upon the dielec-tric contrast across the air-soil interface. Consequently, this results in some uncer-tainty as to exactly how thick the soil layer is for determining the dielectric con-

  • 9 Soil Moisture 203








    0 .1 0 .2 0.3 0.4 0 .5 0.6 VOL METRIC WATER CONTENT (cm3/cm3)

    Fig. 9.2. A comparison of laboratory measurements of the real and imaginary parts of the di-electric constant and model predictions (smooth curves) for three soils as functions of volumetric soil moisture at 1.4 GHz (after Wang and Schmugge, 1980). Wt is the transition moisture con-tent where the dielectric constant approaches that for a liquid

    stant. According to Wilheit (1975), the layer of soil would be on the order of a tenth of a wavelength or less. Mo et al. (1980) detennined that the radiometric sampling depth is between 0.06 and 0.1 times the wavelength. In an experiment comparing dry-down measurements of soil layers at three frequencies, Newton et al. (1982) found that for L-band (21 cm wavelength) the sampling depth was about two-tenths of the wavelength. The fact of the matter is that measurement depth is not a constant but related to the total amount of water in the soil layer, and thus to the moisture content, and to the operational frequency of the sensor. A reasonably good idea about the measurement depth can be obtained from penetration depth, dp, inside the soil. This is given by

    dp = k IIm.J] (9.8) where k = 2p/l is free space propagation constant, e is dielectric constant of soil, 1m denote the imaginary part and the vertical bars refer to the absolute value. The

  • 204 E.T. Engman

    plot of penetration depth versus soil moisture for various frequencies is shown in Fig. 9.3 (Ulaby et aI., 1982). Surface Roughness. Microwave emission from the soil is related to the reflectiv-ity of the surface, which if smooth can be calculated by the Fresnel equations. Smoothness in microwave terms is a relative term, being dependent upon the wavelength. That is, a surface that is smooth for one wavelength, say 21 cm L-band (1.4 GHz), may not be smooth for 6 cm C-band (4.9 GHz), and 2.8 cm K-band (10.7 GHz). The effect of a rough surface is to increase the surface emissivity and thus to decrease the sensitivity to soil moisture (Newton and Rouse, 1980).

    Choudhury et al. (1979) have shown that surface roughness can affect the soil reflectivity r' in the following way:

    r'= r exp(- h cos2 q) (9.9)

    where r is the smooth surface reflectivity, h is a roughness parameter (= 4 cr2 k2) proportional to the root mean square height variations of the soil surface, cr, and q is the incidence angle. Here the roughness parameter, cr, appears as squared in the exponential. Therefore, the value of reflectivity will decrease rapidly with the slight increase of cr. This is what makes reflectivity very sensitive to s compared to soil moisture. It should be mentioned here that the roughness-reflectivity depend-



    ]0-3 ..... __ --''--__ ...... ___ ....... __ --' o 0.1 0.2 0.3 0.4


    Fig. 9.3. Penetration depth as a function of volumetric soil moisture and frequency (after Ulaby et al. 1986)

  • 9 Soil Moisture 205

    ence issue is under a great deal of investigation, because relation 9.6 is violated under certain conditions of roughnesses and incidence angles. Wang et al. (1980) assumed the random roughness was independent of incidence angle and simplified Eq. 9.6 to

    R = Ro exp(- h) (9.10) More recent work by Promes et al. (1988) has shown that, for L-band, assuming

    smooth field emissivity for most dry land agricultural conditions in which the row height to row spacing is less than 2 will result in an error ofless than 3%.

    Theis et al. (1986) have demonstrated the possibility of using a multisensor ap-proach for improving the estimates of soil moisture under field conditions. In this case, the effects of surface roughness were accounted for with scatterometer meas-urements. These were then used in a soil moisture equation which included terms related to the emissivity measured by the radiometer and to the scatterometer roughness term. Inclusion of the roughness term improved the r'2 values from 0.22 to 0.65 for C-band and from 0.69 to 0.95 for L-band.

    Although roughness may not be a serious limitation for passive sensors, at least for most natural surfaces, it is a major factor for radar. In many cases the effects of roughness may be equal or greater than the effects of soil moisture on the back-scatter. Thus the soil moisture problem becomes one of determining the roughness effect independently so that a model can be inverted to yield a measure of soil moisture.

    The role of surface roughness in soil moisture estimation for the active case needs to be understood through surface scattering processes. The theoretical work on surface scattering can be divided into three categories: The small perturbation model (SPM), the physical optics model (POM) and geometrical optics model (GOM). In a broad sense, the geometrical optics model is best suited for a very rough surface, the physical optics model is suitable for surfaces with intermediate scales of roughness, and the small perturbation model is suitable for surfaces with short correlation lengths. Figure 9.4 describes the region of validity for the three models in terms of KI and Kcr, where K is the wave number, I is the correlation length, and cr is the standard deviation of the surface roughness heights. The mathematical expressions to calculate surface backscatter using these models and their regions of validity in terms of RMS height, correlation length and wavelength can be found in Ulaby et al. (1982). An examination of these surface backscatter-ing expressions employing different scattering models shows that even though the backscatter increases due to the increase of surface roughness, the soil moisture sensitivity to backscatter diminishes due to sharp rate of decrease in the value of reflectivity. As a result of two competing effects, the roughness effects overshadow the soil moisture effects.

    Unfortunately, even if roughness data are available, Oh et al. (1992) have shown that the typical values of Kcr and KI found in the natural environment result fall in the area outside of the various models regions of validity (Fig. 9.4). Consequently, most people have had little success using these models.

    However, based on the scattering behavior in limiting cases and experimental data, Oh et al. (1992) have developed an empirical model in terms of the rms

  • 206 E.T. Engman




    Kcr 4


    10 15 20 KI

    Fig. 9.4. Illustration of the regions of validity for the Small Perturbation Model (SPM), the Physical Optics Model (POM) and the Geometric Optics Model (GOM) as functions of the roughness and correlation lengths normalized by the wave number, K (after Oh et al. 1992)

    roughness height, the wave number and the relative dielectric constant. By using this model with multipolarized radar data the soil moisture content and the surface roughness can be determined. The key to this approach is the copolarization ratios (hh/vv) and cross-polarization ratios (hv/vv) are given explicitly in the terms of the roughness and the soil dielectric constant. Results from this model look very good and if further testing proves as valid, this approach will be a major step forward in determining soil moisture from radar backscatter. Furthermore, this model appears to work well in the roughness domains that the more classical methods have failed in the past.

    Vegetation Cover. The effect of vegetation is to attenuate the microwave emis-sion from the soil; it also adds to the total radiative flux with its own emission. The degree to which vegetation affects the determination of soil moisture depends upon the mass of vegetation and the wavelength. Barton (1978) used an aircraft mounted 2.8 cm radiometer to measure soil moisture over bare soils and uniform grass cover. Although he demonstrated a strong relationship between brightness tem-perature and moisture for the bare fields, no relationship for the grass sites could be perceived.

    In studies over bare soil and sorghum, Newton and Rouse (1980) found no sen-sitivity to soil moisture with the 2.8 cm measurements over the sorghum, but with

  • 9 Soil Moisture 207

    the 21 cm data the radiometer was sensitive to soil moisture even under the tallest sorghum.

    Basharinovand Shutko (1975) and Kirdiashev et al. (1979) studied a variety of crops in the USSR with wavelengths varying from 3 cm to 30 cm. For wavelengths greater than 10 cm, their results indicate that one can expect a decrease in sensi-tivity of about 10-20% for small grains over what would be expected for bare soil. With broad leaf crops such as com, the sensitivity could decrease by as much as 80% for wavelengths shorter than 10 cm, and 40% for a 30 cm wavelength. Thus, from these studies the wavelength effect can be seen, that is, a vegetation canopy is more transparent for longer wavelengths than for shorter wavelengths.

    Jackson et al. (1982) developed a parametric approach based on a theoretical model proposed by Basharinov and Shutko (1975). This model treats the vegeta-tion as an absorbing layer that can be quantified in terms of the water content of the vegetation by the following relationship:

    Mv = 78.9-78.4[1+(e-1)exp(0.22W)] (9.11 ) where Mv is the volumetric soil moisture (0-2.5 cm), e is the measured emissivity, and W is the water content of the vegetation (kg/m2). Figure 9.5 illustrates the effect of vegetation on soil moisture. Jackson and Schmugge (1991) have analyzed a large amount of published data to verify previous findings and they have defined a vegetation parameter that is based on the optical depth of the canopy. This pa-rameter appears to be inversely related to the wavelength and can represent four types of vegetation classes (leaf dominated, stem dominated, grasses, and trees and shrubs). However, at longer wavelengths, a single value of the parameter might be

    ~ e..... e '" or.

    M 40 d> (oJ a: ;:;l ....

    ;a 30 0

    ~ ~ '-"

    "" u c:2 ....

    (oJ 2: ;:;l

    ~ ;;


    Fig. 9.5. The relationships between normalized brightness temperature and volumetric soil moisture for different types of vegetation, L-band, H polarization (after Jackson et al. 1982)

  • 208 E.T. Engman

    used for any cover type. Furthermore they speculate on how this parameter could be estimated using visible and near infrared satellite data in an operational sense. These studies point out the possibility of a total satellite remote sensing approach for soil moisture without any ground sampling.

    Dead vegetation can also have an attenuating effect on the microwave emissions from the soil as was demonstrated by Schmugge et al. (1988). Aircraft experiments with an L-band pushbroom microwave radiometer over the Konza prairie grass-lands showed that, for areas that had not been burned, a buildup of a thatch layer serves as a highly emissive layer above the soil, thus masking the emission of the soil itself. Where there was an absence of this thatch layer because of burning or grazing, the microwave sensitivity to soil moisture was as expected for bare soil.

    As with the roughness case, the effect of vegetation on the active microwave sensing of soil moisture is greatly dependent upon the instrument incidence angle, frequency, and polarization. These effects are illustrated in Fig. 9.6 for a corn canopy. In the top half of Fig. 9.6, it can be seen that the attenuation for the hori-zontal polarization is relatively weak, but the vertically polarized data are attenu-ated to a much greater degree because of their relationship to the canopy structure,

    6 (a) I T HH LBAND 5 -


    ~ 2 -:r: Ii: 1 w a 20 30 .40 50 60 70 80 90 z ANGLE OF INCIDENCE (deg) 0 ;:::

  • 9 Soil Moisture 209

    which consists primarily of vertical stalks. The effect of frequency on penetration depth can be seen in the lower part of Fig. 9.6. It is readily apparent that the pene-tration depth increases with a decrease in frequency or an increase in wavelength.

    With radar the effect of the vegetation canopy adds more complexity to the problem. Now to determine soil moisture, one must determine the soil roughness effects and the effects of the vegetation canopy, which is not a trivial exercise.

    All kinds of vegetation contain water along with some plant structure. Both of these vegetation parameters are responsive to microwaves. Therefore, the radar response from vegetated areas will have the integrated effect of vegetation and soil. Most of the microwave models have been constructed by replacing the vege-tated regions with a random medium whose statistical characteristics are related to physical quantities of the medium.

    9.4 Hydrologic Examples Within the past few years there have been a number of very successful demonstra-tions of measuring soil moisture with both passive and active microwave systems. The successes with the passive systems have been limited to aircraft campaigns but the SAR results have been obtained from aircraft, the Space Shuttle and satellites.

    The examples of successful use of passive microwave radiometers for measuring soil moisture have been associated with large scale field campaigns designed to examine the role of soil moisture in hydrology and land-atmosphere interactions. These field campaigns have been conducted in a fairly typical way as far as the remote sensing was concerned. During the experiment period, extensive ground data and hydrologic data were collected. Soil moisture measurements consisted of neutron meter, TDR, and a large number of gravimetric samples. In addition, a number of more traditional hydrologic measurements were made such as rainfall, streamflow, meteorological variables and ground based energy and flux stations. Two AMES Research Center based NASA aircraft have typically participated in the experiments, the DC-8 and C-130. The DC-8 carried the three frequency pola-rimetric Synthetic Aperture Radar (AIRSAR) and the C-130 carried the NS001 thematic mapper simulator, the Thermal Imaging Mapper, the Electronically Steered Thinned Array Radiometer (EST AR) or its predecessor the Push Broom Microwave Radiometer (PBMR)and a USDA laser profiler. Some of the more successful campaigns are briefly described below:

    The First ISLSCP Field Experiment (FIFE) was carried out over a 15 x 15 km grassland site near Manhattan, Kansas. This area became the focus for an extended monitoring program of satellite, meteorological, biophysical, and hydrological data acquisition from early 1987 through October 1989. During FIFE the airborne L-band, four-beam Push Broom Microwave Radiometer (PBMR) was used to map the spatial distribution of soil moisture over a small (37.7 ha) drainage basin. By using low elevation, overlapping flight lines during a drying period, the spatial patterns of soil moisture were mapped as shown in Fig. 9.7 (Wang et aI., 1989). These patterns resembled those developed from a simple draining slab model and

  • 210 E.T. Engman

    May 28.1987

    , I , , , ,


    June 4, 1987

    \ sou. "\10l101lIH". : tl)YIOH L'

    June 4. 1987 Sl'r\U tAPPW:OX,1


    Fig. 9.7. The temporal and spatial patterns of soil moisture in a small drainage basin illustrating the drying pattern (after Wang et aI., 1989) and the area of drainage (cross hatched area) neces-sary to sustain base flow which over lays the high soil moisture region (after Engman et aI., 1989)

    illustrated the capability to identify the active regions of a basin (Engman et aI., 1989).

    Monsoon 90 and 91 were campaigns to investigate the effects of changmoisture on the surface radiation balance, the hydrologic cycle, and the feedbacks to the atmosphere. The experiments were conducted in a hydrologically instrumented semiarid rangeland watershed, the Walnut Gulch Experimental Watershed oper-ated by the USDA-Agricultural Research Service, which is located in the south-western United States about 120 km southeast of Tucson, Arizona. One of the really first examples of using passive microwave measurements in a hydrologic context was reported by Jackson, et al. (1993). Using both the PBMR and the EST AR during Monsoon 90 and 91, they showed how soil moisture patterns were related to the extent and amount of rainfall resulting from small, isolated convec-tive storms. Goodrich et al. (1994) compared the measured rainfall, modeled soil moisture and the microwave mapped soil moisture. In studies of prestorm rainfall, they concluded that both the measured passive microwave soil moisture and the

  • 9 Soil Moisture 211

    model produced estimates (from measured rainfall) produce adequate definition of the antecedant rainfall or soil moisture condition.

    A Multisensor Aircraft Campaign (MAC) was conducted in July of 1990 with a focus on microwave sensors, soil moisture and humid region hydrology. The ex-periment was conducted at the u.s. Department of Agriculture, Agricultural Re-search Service's Northeast Watershed Research Center experimental watershed near Klingerstown P A. The passive microwave measurements showed a good correlation with the ground data and may yield a reliable technique for calibrating hydrologic models (Wood et aI., 1993) and improve the water budget calculations of a basin (Lin et aI., 1994).

    Multisensor Aircraft Campaigns for hydrology (W ASHIT A 92 and 94) were conducted in June of 1992 and April and October of 1994 over the Little Washita Watershed, a u.s. Department of Agriculture (USDA) research facility near Chickasha, Oklahoma. The Little Washita Watershed is a 610 sq. km drainage basin situated in the southern part of the Great Plains in southwest Oklahoma. This MAC focused on both passive microwave and Synthetic Aperture Radar (SAR). The Passive microwave instrument was theL-band Electronically Steered Thinned Array Radiometer (EST AR) while the SARs were the JPL AIRSAR (polarimetric C, Land P band) and the Shuttle Imaging Radar (SIR-C/X-SAR) which had X, C and L bands. An even more dramatic example of hydrologically significant soil moisture was derived with the ESTAR during the WASHITA 92 (Jackson et aI., 1995). From a hydrologic perspective we were able to follow a drying period from very wet to dry over a period of ten days. It had rained for 26 consecutive days in Oklahoma when we arrived and initial conditions were very wet. The drying pat-tern as well as the spatial variability reflected by different soil properties is shown in Colour Plate 9-.A. Colour Plate 9.A illustrates the spatial and temporal changes in soil moisture measured with EST AR during 1992 in which we were able to follow a dry down from an extremely wet condition. Colour Plate 9.B illustrates the changes in soil moisture observed in1994 with the shuttle imaging radar.

    The patterns illustrated in Colour Plate 9.A and their similarity to the soil texture map has inspired research to investigate if certain physical and hydrologic proper-ties might be determined from a temporal series of the rates of change in soil moisture. The 1992 EST AR data have been analyzed to produce estimates of soil texture (represented by the percent sand to clay ratio, and the saturated hydraulic conductivity (Mattikalli et aI., 1998). Examples of these results are shown in Col-our Plate 9.C.

    Verhoest et aI., (1998) used a principal components analysis to separate the ef-fects of vegetation, topography and soil moisture from each other using eight ERS-1&2 scenes over the Zwalm catchment in Belgium. The resulting product (Colour Plate 9.D) is an excellent rendition of what one would expect from a partial con-tributing area process. The dark blue areas represent the storm runoff contributing areas and the orange and yellow represent regions that could be considered to be recharge areas and not areas that produce storm runoff.

  • 212 E.T. Engman

    9.5 Future Microwave Remote Sensing of Soil Moisture The previous sections have discussed the basis of microwave remote sensing for soil moisture and presented a discussion of various target-sensor interactions. As promising as microwave remote sensing for soil moisture appears to be, the future for using microwave data for operational use is somewhat uncertain. For the next few years, researchers will be limited by the lack of suitable data. Currently the ERS-l&2 SAR from the European Space Agency, the JERS-l SAR from the Japa-nese and the Canadian RADARSAT are the only operational active microwave satellite sensors with frequencies suitable for soil moisture. Although these instru-ments should be valuable data sources for extending our knowledge of SAR for measuring soil moisture, to date very little data have been available for this pur-pose. Fortunately there have been some intensive and science-driven aircraft ex-periments conducted in the last several years and these data are beginning to be-come available to the scientific community. These should be invaluable for pro-viding sample data for developing and testing algorithms as well as answering some of the target-sensor questions.

    Looking ahead to when there may be more microwave sensors on orbiting plat-forms, one confronts the basic differences between passive and active instruments and the intended use of the data. Comparing the instruments simplistically, the active sensors have the capability to provide high spatial resolution data (on the order of tens of meters) but their sensitivity to soil moisture may be confused more by roughness, topographic features, and vegetation than the passive systems. On the other had, the passive systems, although less sensitive to target features, can provide spatial resolutions only on the order of tens of kilometers. One then must consider how the data will be used. Meteorological and climate models currently use computational cells on the order of 10-100 km which may be well within the capacity of future passive systems. However, if one is interested in more detailed hydrologic process studies and partial area hydrology, the passive data would appear to be of little use. It is in this context that the active systems appear prom-ising. For example, existing and planned SAR'S can provide at least 20-30 m reso-lution over a swath width of 100 km. RADARS AT has the capability of a scanning mode (SCANSAR) to cover a much wider swath (300-500 km) at a reduced reso-lution (250 m) as well as a high precision mode with a spatial resolution ap-proaching 8 m. There are a number of soil moisture missions on the drawing boards or in the proposal stage that would provide a major boost for the applica-tions of soil moisture in hydrology. These include two proposals based on passive microwave aperture synthesis, the NASA HYDROST AR and the CNES/ESA SMOS and the NASA-JPL LIGHTSAR with a polarametric L-band sensor. More certain is the AMSR passive microwave (C-band) instrument that will fly on the Japanese ADEOS II and the EOS-PM and the CMIS instrument that will be on future US weather satellites.

    Research Needed. There are going to be more and more orbiting microwave sen-sors suitable for measuring soil moisture in both the near and distant future. How-ever, before the hydrologic community makes good use of t.h.ese data there are a number of research questions that must be addressed. Perhaps the foremost re-

  • 9 Soil Moisture 213

    search need is to have the hydrology community try using the available data with hydrologic models. This will undoubtedly lead to an improved understanding of the value of these data band and the modification or development of new models to effectively use these data.

    There is a need to develop and improve algorithms to extract volumetric soil moisture directly from the microwave measurement (backscatter coefficient or brightness temperature). To do this, the other target characteristics of vegetation and surface roughness will have to be parameterized. As discussed previously, there is great progress being made in these areas but much more needs to be done. Connected directly to this need is a need to better understand the effects of surface roughness on the measured microwave response with respect to incidence angle, azimuth angle, wavelength, and polarization. New methods to measure surface roughness need to be explored. The dual frequency or Dk radar technique has been quite effective to measure sea-surface roughness (Schuler et aI., 1991). Imple-mentation of this technique to soil surfaces can provide accurate measure of roughness over a very large scale. Also, there is a need to understand the effect of the vegetation canopy on the microwave response. Progress has been made in this area with both the modeling of radar (Ulaby et al. 1990) and a dual frequency, dual polarization approach for passive microwave (Njoku, 1999). Vegetation variables include the geometry for the individual plant as well as the canopy as a whole, the water content (and perhaps the biochemical makeup) of the plant, and its stage of growth. Microwave variables would include the incidence angle, the azimuth an-gle, wavelength, and polarization. Some difficult problems such as soil moisture estimation from rocky soil, effects of discontinuous canopy or vegetation clumps on soil moisture estimation, etc., also need to be addressed.

    There is a need to investigate the use of change detection algorithms or statisti-cal techniques like the principal components analysis (Verhoest et al. 1998) for determining the relative soil moisture of an area and whether or not this informa-tion can be useful for hydrologists. Change detection should minimize the influ-ence of target variables such as roughness and vegetation, at least over short time intervals. With change detection it is assumed that the only target change occurring is the soil moisture. Thus, any measured changes in brightness temperature or backscatter can be related directly to changes in soil moisture. Fortunately, both the brightness temperature and backscatter relationships with soil moisture are approximately linear. There is also a reasonable basis for expecting change detec-tion methods to provide adequate data for agricultural and hydrologic applications if the data are collected from a long term orbiting platform. Long term (multi sea-son or year) data will establish the upper (wet) and lower (dry) limits for the change algorithm.

    There is a need to develop software procedures for correcting the effects of ter-rain on the microwave response. Active microwave (SAR) is especially sensitive to this. This includes foreshortening, layover, and local incidence angle effects. Also, a potential issue is the relative accuracy of the DEM data with respect to the spa-tial resolution of the microwave data and the potential effect of subpixel variability on the measured signal.

    There is also a need to investigate the potential for polarimetric SAR and its po-tential for abstracting target information such as the surface roughness and vegeta-

  • 214 E.T. Engman

    tion characteristics. Studies of this technique need to be carried out with carefully conceived ground data collection programs.

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