Remote Sensing in Hydrology and Water Management || Soil Moisture
Post on 09-Dec-2016
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-
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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)
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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 diel...