Description of Soil Moisture Retrieval Algorithm for ADEOS II AMSR
Thomas J. Jackson
USDA ARS Hydrology and Remote Sensing Lab

1. ALGORITHM DESCRIPTION
To a large degree, the research in microwave remote sensing of soil moisture has focused on the forward modelingproblem. This is the process of predicting the brightness temperature from soil properties using radiative transfer
theory. When measuring soil moisture we must be concerned with inversion of this model. This is moredifficult than forward modeling. There are five steps involved in extracting soil moisture using passive
microwave remote sensing. These are; normalizing brightness temperature to emissivity, removing the effects ofvegetation, accounting for the effects of soil surface roughness, relating the emissivity measurement to soil
dielectric properties, and finally relating the dielectric properties to soil moisture.
In our approach, soil moisture retrieval is based upon an algorithm developed by Jackson (1993). Brightness
temperature for a single AMSR channel (6.9 GHz H) is converted to emissivity using a surrogate for the physicaltemperature of the emitting layer. This emissivity is corrected for vegetation and surface roughness to obtain thesoil emissivity. The Fresnel equation is then used to determine the dielectric constant. Finally, a dielectric
mixing model is used to obtain the soil moisture. The theory describing this follows.
Fundamental basis for a smooth bare soil. The measurement provided is the brightness temperature, TB, that
includes contributions from the atmosphere, reflected sky radiation, and the land surface. Atmosphericcontributions are negligible at frequencies <6 GHz. Galactic and cosmic radiation contribute to sky radiationand have a known value that varies very little in the frequency range used for soil water content observations (Tsky~4 K). The brightness temperature of a surface is equal to its emissivity (e) multiplied by its physicaltemperature (T).
Based upon the above, the equation for TB is

TB = eT + [1 − e ]Tsky(1)

The second term of equation 1 will be on the order of 2 K and will be dropped for computational purposes. For
inversion equation 1 is rearranged as follows

e=

TB
T

(2)

If the physical temperature is estimated independently, emissivity can be determined. This can be done using
surrogates based on satellite surface temperature, air temperature observations, or forecast model predictions.
There are two important relationships that must be utilized to relate the sensor measurement to soil water content.
In the first it is necessary to link the sensor measurement to a basic property of the soil that changes with watercontent. By assuming that the target being observed is a plane surface with surface geometric variations andvolume discontinuities much less than the frequency, only refraction and absorption of the media need to beconsidered at low frequencies such as L band (at higher frequencies scattering must be included). This permits
the use of the Fresnel reflection equations (Ulaby et al., 1986). These equations predict the surface microwave
reflectivity as a function of dielectric constant (relative permittivity) of the target (εr) and the viewing angle (Θ)
based on the polarization of the sensor, horizontal (H) or vertic al (V). At these frequencies the reflectivity isequal to 1 minus the emissivity. The Fresnel equations can be simplified by including only the real part of thecomplex dielectric constant (the imaginary part of the complex dielectric constant is relatively small and often
ignored). This simplification makes it possible to invert the Fresnel equations to solve for εr’ given the measured
emissivity.

e H (Θ) = 1 −eV (Θ) = 1 −cos Θ − εr − sin 2 Θ2(3)

cos Θ + εr − sin 2 Θεr cos Θ − εr − sin 2 Θ2εr cos Θ + εr − sin 2 Θ(4)

For a bare soil surface, the target consists of an interface of air and soil plus a shallow contributing layer at the topof the soil column. Since the dielectric constant of air is a known value (~1), the reflectivity provides ameasurement of the dielectric constant of the soil. The Fresnel equations apply when the two media at theinterface each have uniform dielectric properties within the contributing depth. Although this is certainly valid
for air, however, for a soil surface this is not always a valid assumption. It should also be noted that the basic
formulations in equations 3 and 4 result in a larger dynamic range or sensitivity of emissivity to changes in the
dielectric constant for H polarization. It is possible to invert equation 3 to solve for the dielectric constant given
the measured emissivity.
The next critical relationship involves relating this derived dielectric constant to volumetric soil water content.
The dielectric constant of soil is a composite of the values of its components: air, soil and water. Although thedielectric constant is a complex number, for soil mixtures the real part is much more important and variable.
Values of the real part of the dielectric constant for air and soil particles are approximately 1 and 5, respectively.
For water the value of the dielectric constant varies with frequency and is about 80 at the lower frequencies
considered here (<6 GHz) (Ulaby et al., 1986).
The basic reason microwave remote sensing is capable of providing soil water content information is this large
dielectric difference between water and the other soil components. Since the dielectric constant is a volume
property, the volumetric fraction of each component must be considered. The computation of the mixture
dielectric constant (soil, air and water) has been the subject of several studies and there are different theories as tothe exact form of the mixing equation (Schmugge, 1980 and Dobson et al., 1985). A simple linear weighting
function is typically used.
The dielectric constant of water referred to above is that of free water in which the molecules are free to rotate andalign with an electrical field. It has been recognized for some time that not all the water in soil satisfies thiscondition. Schmugge (1980) suggested that some water in the soil had different properties. He proposed thatfor a given soil this could be estimated using soil texture in much the same way that pedo-transfer functions areused to estimate 15 bar and 1/3 bar water contents based on texture (Rawls et al., 1993). He proposed that theinitial water added to dry soil below a "transition" water content were held more tightly by the soil particles andhad the dielectric properties of frozen water (~3).
Vegetation and Surface Roughness. For natural conditions, varying degrees of vegetation will be encountered.
The presence of vegetation will have a major impact on the microwave measurement. Vegetation reduces thesensitivity of the retrieval algorithm to soil water content changes by attenuating the soil signal and by adding a
microwave emission of its own to the microwave measurement. The attenuation increases as frequency
increases. This is an important reason for using lower frequencies. As described in Jackson and Schmugge(1991), at lower frequencies it is possible to correct for vegetation using a vegetation water content-relatedparameter.
When there is vegetation, the observed emissivity is a composite of the soil and vegetation. To retrieve soil
water content it is necessary to isolate the soil surface emissivity (esurf). Following Jackson and Schmugge(1991), the equation describing this ise p = [1 − αp , f , v ][1 − γ p , f ,v ][1 + [1 − e surf
]γ p , f , v ] + e surfp
p γ p , f ,v(5)

Both the single scattering albedo (α) and the one-way transmissivity of the canopy (γ) are dependent upon thevegetation structure (v), polarization (p) and frequency (f).
The transmissivity is a function of the optical depth (τ) as described by the following equation

γ p , f ,v = exp[ −τ p , f , v sec Θ](6)At low frequencies the single scattering albedo can be assumed to be negligible, then substituting equation 6 into
equation 5 and rearranging yields

e surf= 1 + [ e p − 1] exp[ −2τ p , f , v sec Θ]p(7)

The vegetation optical depth is also dependent upon water content (W). In studies reported in Jackson et al.
(1982) and Jackson and Schmugge (1991), it was found that the following functional relationship between the
optical depth and vegetation water content could be appliedτ p , f ,v = b p , f ,vW(8)

There is a limited database of values of b available. The vegetation water content can be estimated using avariety of ancillary data sources. One approach is to establish a relationship between w and a satellite based
vegetation index such as the Normalized Difference Vegetation Index (NDVI) as described in Jackson et al.
(1999).
The emissivity that results from the vegetation correction is that of the soil surface. This includes the effects ofsurface roughness. These effects must be removed in order to determine the soil emissivity (esoil ) which is
required for the Fresnel equation inversion. One approach to removing this effect is a model described in
Choudhury et al. (1979) that yields the bare smooth soil emissivity

e soil= 1 − [1 − e surf
] exp[ −h p , f , g cos 2 Θ]
p
p

(9)

The parameter h is dependent upon the polarization, frequency and geometric properties (g) of the soil surface.
Typically, values are assigned based upon land use and tillage (Choudhury et al., 1979 and Jackson et al., 1999).

2. OPERATIONAL IMPLEMENTATIONN
For each AMSR pixel, the latitude and longitude (from the input file) are used to cross reference to the land coverand NDVI ancillary data files. The third set of ancillary data files representing soil texture and porosity, is not
mapped to any projection and the resolution is 0.083 degrees. The AMSR footprint is readily located in the soil
texture file and its latitude and longitude are retrieved from the input file.
Land cover is first used to screen the data for the selected number of categories that can be inverted for soil
moisture. This also removes water pixels. The next step is another screening to check for anomalous TB values.
Following this, an index developed by Ferraro et al. (1994) is used to screen out pixels with active rainfall. The
final screening is a check of the surface air temperature from the AMSR files versus TB.
Having passed all of the tests above, the footprint data is used to compute soil moisture. T B is divided by anadjusted surface air temperature to estimate emissivity.
Vegetation correction is performed using the pixel NDVI to compute the vegetation water content.
roughness effects are removed utilizing a single roughness parameter, fixed at 0.1 at this time.

Surface

The net result of these corrections is the soil emissivity. From this the dielectric constant of the soil is computed.
The value of the dielectric constant is then used with the dielectric mixing model and the soil texture and porosity
to compute the volumetric soil moisture.

3. ANCILLARY DATA SOURCES
Land Cover
The University of Maryland Geography Department produced a global land cover data base at a resolution of 8
km which is available at http://glcf.umiacs.umd.edu/. The codes for the land covers are as follows;
1
2
3
4
5
6
7
8
9
10
11
12
13

Evergreen Needleleaf Forests
Evergreen Broadleaf Forests
Deciduous Needleleaf Forests
Deciduous Broadleaf Forests
Mixed Forests
Woodlands
Wooded Grasslands/Shrubs
Closed Bushlands or Shrublands
Open Shrublands
Grasses
Croplands
Bare
Mosses and Lichens

AMSR data over areas covered by cover types 1 – 6 cannot be used for retrieving soil moisture due to the effects
of forests. Data over cover types 7 – 10 and 12 – 13 can be used for retrieval. Croplands are considered for soil
moisture retrieval if the NDVI is less than 0.5. Thus, the new land cover types are:
0
1
2
3
4
5

ocean and inland water – more than 20% of footprint covered by ocean or by inland water
good for retrieval – more than 60% of footprint covered by wooded grasslands/shrubs, closed
bushlands or shrublands, open shrublands, grasses, bare, mosses and lichensreasonable for retrieval – categories listed in 1. occupied more than 30% of footprint andcroplands less than 30%
conditional for retrieval – categories listed in 1. occupied less than 30% of footprint andcroplands over 30%
limited for retrieval – more than 60% of footprint covered by croplands
unable for retrieval – more than 30% of footprint covered by evergreen needleleaf, evergreen
broadleaf, deciduous needleleaf, deciduous broadleaf, and mixed forests and woodlands

NDVI
The technique used to incorporate vegetation effects requires NDVI information. These products are available
from various sources and can be acquired and updated to reflect current conditions. The GLI team may generate
data products that can be used for this purpose. However, since this is a research algorithm and arrangements toimport ancillary data sets such as NDVI are not in our control, we are providing an alternative that should be
adequate for most conditions in soil moisture retrieval.
We are providing a series of NDVI data sets that represent the historical averages for each 10 day period
throughout the year. The algorithm will retrieve the NDVI data set that is closest in time (day of the year) to theobservation date for the AMSR data. This NDVI data set then represents the average condition expected for thisdate. The quality of the estimate will depend upon how the current year deviates from the average. It might bepossible in the future to adjust these values for the current conditions by tracking climatological information andcomparing this to the year to year conditions in the records.
To develop the historical averages we used the Pathfinder AVHRR Land data sets. These are global, land surface
data derived from the Advanced Very High Resolution Radiometers (AVHRR) on the NOAA/TIROS operational
meteorological satellites (NOAA-7, -9, and –11) that have provided continuous daily and composite data set from
July 1981 through the present. The daily and composite products include 12 data layers, (NDVI, CLAVR flag,

QC flag, Scan Angle, Solar Zenith Angle, Relative Azimuth Angle, Ch 1 Reflectance, Ch 2 Reflectance, Ch 3
Brightness Temperatures, Ch 4 Brightness Temperatures, Ch 5 Brightness Temperatures, and Day of Year). The
composite is generated by comparing the NDVI values for each 8 km bin from 10 consecutive Daily Data Sets.
Because data at the edge of a scan may contain distortion and bi-directional effect biases, only data within 42
degrees of nadir are used in the composite. For each 8 km pixel, the day with the highest NDVI during a 10 day
period is chosen as the date for inclusion in the composite, and all 12 data layers are updated with data from that
date. This composite process is effective for removing most of the clouds and atmospheric contaminants, thus
providing as close to a cloud free field in each of the data layers as is possible (Holben, 1986). There are threecomposites per month. The first composite of each month is for days 1 to 10, the second composite is for days
11 to 20, and the third composite is for the remaining days.
The NOAA/NASA Pathfinder Land data team has completed their software development and data reproc essing.
Their data are distributed by the Goddard Distributed Active Archive Center (DAAC)(ftp://daac.gsfc.nasa.gov/data/avhrr/global_8km). Data are available from July 13, 1981 to the present. For thisanalysis, we only used data from 1982 to 1999. The NDVI composite is mapped in a global 8 km equal area gridusing the Goode Interrupted Homolosine projection. There are (36*18) data sets. The original AVHRR
Pathfinder NDVI 8 km 10-day composite data from 1982 to 1999 that were used in our processing are on
CDROMs (18 of them).
For each pixel in a 10-day composite data set which is not ocean, inland water or filler, all data points that fall in a
7x7 box centered at that pixel were averaged. In the averaging process, if there were any ocean, inland water orfiller pixels in the 7x7 box, these were not included in the average computation. After this process wascompleted for each individual NDVI data set, the values for each 10 day interval were averaged over the 18 year
record to produce the average annual time series 10-day composite.
Soil Texture
The soil texture and porosity data sets are a result of a study to estimate global soil water-holding capacities bylinking the Food and Agriculture Organization (FAO) soil map of the world with global pedon databases andcontinuous pedotransfer functions (PTF) (Reynolds et al., 2000). The FAO-UNESCO Soil Map of the World(SMW) at 1:5,000,000 is the most comprehensive soil map with global coverage. Great efforts have been madeto relate the FAO soil units to physical soil characteristics by statistically analyzing global pedon databases toestimate soil texture, bulk density and organic matter content. The data set images produced by Reynolds et al.
(2000) have a 5-min spatial resolution to preserve the spatial integrity of the SMW, which is equivalent to a 9 km
x 9 km cell size at equator. Soil properties were estimated at two depths, i.e., 0-30 cm and 30-100 cm. Only
the 0-30 cm depth is needed here. Three of these soil properties were placed in the ancillary directory; clay
content, sand content and porosity.
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Ferraro, R. R., Grody, N. C., and Marks, G. F. 1994. Effects of surface conditions on rain identification using the
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