Surface Soil Moisture Estimation
by TRMM/PR and TMI
Shinta Seto

Taikan Oki and Katumi Musiake

Communications Research Laboratory,Tokyo, Japan

Institute of Industrial Science, University of Tokyo
Tokyo, Japan

Abstract—A surface soil moisture estimation algorithm bymicrowave radiometer is developed with radiative transfer model
for soil-vegetation-atmosphere layers (especially, vegetation layer
is focused).

I.

INTRODUCTION

The microwave radiometer has been recognized as a tool toestimate surface soil moisture especially when it observes
brightness temperature using lower frequency [1], [2]. At
present (April, 2003), six microwave radiometers including
TRMM (Tropical Rainfall Measuring Mission)/TMI (TRMM
Microwave Imager) are in space. In near future (2007,hopefully), eight microwave radiometers would contribute to
global observation with high temporal resolution (3hourly)
under GPM (Global Precipitation Measurement). The
development of soil moisture retrieval algorithm have somedifficulties because brightness temperature is significantly
affected by many physical parameters of land surface and theatmosphere other than soil moisture. Therefore, a radiativetransfer model is necessary for the development of algorithm
and it should contain physical processes as much as possible.
In this paper, we introduce a soil moisture estimation
algorithm and apply it into TRMM/TMI data with monthly
time step. In previous studies, vegetation layer was modeledwith two parameters such as optical thickness and single
scattering albedo. The problem is that these two parameters are
not physical ones, then they should be given a priori [1] orshould be estimated simultaneously with soil moisture [2].
Instead of those, vegetation is modeled with multiple physicalparameters in this study. In the following sections, the data ofTRMM/TMI, the radiative transfer equation, soil moisture
estimation algorithm, and the results will be introduced.
Because of the shortage of the space, the soil moisture
estimation by TRMM/PR (Precipitation Radar) is not explained.
Please refer to [3].
II.

rainfall is observed (judged by the product 2A12) are omitted
for each grid.
RADIATIVE TRANSFER MODELIII.

In this section, a radiative transfer model for soil,
vegetation, and the atmosphere used in this study is briefly
introduced. Microwave reflection and emission at land surface
is considered, but penetration through soil layer is not treated at
all because microwave is almost attenuated only at a few
centimeters below soil surface. Therefore, soil moisture andtemperature are defined at surface. The dielectric constant ofsoil is calculated by the equation of Dobson [4] with dry soil
density, bulk soil density, ȕ (a parameter dependent on soiltype), and soil moisture. Reflectivity at soil surface can beinduced by soil dielectric constant with Fresnel's Law, whileroughness effect on reflectivity is involved.
Actualreflectivity R is calculated by R=r exp(-h). Here, h denotes
roughness parameter and r means the reflectivity of ideal
smooth surface.
Vegetation layer is modeled based on the paper [5]. In thismodel, vegetation is composed of leaf, stem, and branch. Leaf
area index (LAI) is defined as a ratio of total surface area (oneside only) of leaves to the corresponding land surface area. In
addition to LAI, leaf thickness (as it is dry) and an index for thedistribution of leaf inclination $\delta$ are parameters
regarding to leaf. Stem is modeled as a cone vertical to the landsurface. As a parameter of stem, stem area index (SAI) isdefined as a ratio of total surface area of stems to thecorresponding land surface area. Branch is modeled as acylinder horizontal to the land surface. Branch and stem ratio(B/S) is defined as the ratio of projection area of total branches
on the land surface to the projection area of total stems on theland surface. In addition to that, vegetation water content is aparameter common to leaf, stem, and branch. An atmosphericradiative transfer model by Liu [6] is combined with the soil
and vegetation part mentioned above..

DATATRMM/TMI observes the brightness temperature in 10.65,
19.35, 37.0, 85.5GHz with both horizontal/vertical polarizationand in 21.3 GHz with horizontal polarization (frequency isdenoted as integer later on). Here, the instantaneous brightness
temperature data in 1998 (TRMM standard product 1B11,version5) were accumulated into monthly 0.25o grid. To avoid
the effect of precipitation, all the observations of the day when

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SENSITIVITY ANALYSIS

Sensitivity of brightness temperature to each land surface
parameter is examined in this section. A focused parameter ischanged from its minimum to its maximum value (shown incolumn 2 to 4 of Tab.I), then brightness temperature is
calculated with other parameters being the standard value. In
the most of the case, brightness temperature

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TABLE I.

PARAMETERS OF RADIATION TRANSFER MODEL AND SENSITIVITY OF BRIGHTNESS TEMPERATURE TO EACH PARAMETER

Common Parameter

MinMax

Std

10V

10H

19V

19H

37V

37H

PD10

FDH

Physical Temp.[K]Soil Moistureȕ
Dry Soil Density
Bulk Soil Density
Roughness
LAI[m2/m2]
Leaf Thickness
Leaf Inclination
SAI
B/S
Vegetation Water Content

280
0.0
1.3
1.0
2.0
0.0
0.0
0.1
-0.3
0.0
2.0
0.1

330
0.5
1.8
2.0
3.0
1.0
5.0
1.0
0.6
1.0
6.0
0.7

300
0.1
1.78
1.15
2.65
0.3
1.0
0.30.0
0.3
3.0
0.3

44.8
-2.5
0.2
0.0
-0.2
0.1
-53.7
14.8
-1.6
13.6
3.9
-18.9

42.5
-9.6
1.8
-0.4
-2.3
6.8
-27.7
20.1
-0.7
19.5
-2.8
-13.1

45.5
-1.7
0.1
0.0
-0.1
0.1
-39.1
13.1
-1.2
10.4
2.8
-18.9

43.5
-7.1
1.3
-0.3
-1.9
5.5
-14.1
18.1
-0.3
13.2
-3.7
-14.4

46.1
-0.9
0.1
0.0
-0.1
0.0
-30.2
9.0
-0.8
7.8
2.0
-20.6

44.4
-4.6
0.8
-0.3
-1.6
4.3
-4.8
12.80.1
7.9-4.4
-17.7

2.3
7.1
-1.6
0.4
2.1
-6.7
-26.0
-5.3
-0.9
-5.9
6.7
-5.7

1.9
5.0
-1.0
0.1
0.7
-2.5
22.9
-7.3
0.8
-11.6
-1.6-4.6

is a monotonous function of land surface parameter. The results
are shown from column 5 to 10. For example, column 5 (whichis labeled as 10V) shows the sensitivity, that is, the difference
between TB,10V (the brightness temperature observed with10GHz, vertical polarization) when the parameter is maximum
and that when the parameter is minimum. Column 11 indicatesthe sensitivity of PD(10). PD(10) is the Polarization Differenceof 10GHz; it is defined by PD(10)= TB,10V-TB,10H.Column 12shows the sensitivity of FD(H). FD(H) is the Frequency
Difference of horizontal polarization; it is defined by FD(H)=TB,37H-TB,10H.
Brightness temperature of each channel shows highsensitivity to the physical temperature of land surface, which
naturally varies well in space and time. This sensitivity can be
reduced by using PD or FD instead of brightness temperature
itself, while the sensitivity to soil moisture doesn't change somuch when we use PD or FD. Therefore, in order to estimate
soil moisture, PD and FD are suitable than brightness
temperature. In the rest of this section, the sensitivity of PD(10)and FD(H) is described. Their sensitivity to LAI is especiallystrong, and roughness, leaf thickness, SAI, B/S, and vegetation

Figure 1. Soil moisture estimates with different SAI and calculation by
VIC model

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water content have effects on PD(10) and FD(H) as well as soilmoisture. These parameters are called ``important parameters''.
They can not be neglected to estimate soil moisture.
Atmospheric profile has much effects on brightness
temperature observed with higher frequency. It is because
precipitation has large effects on microwave observation thatthe data during rainfall is excluded in this study.
V.

METHOD AND RESULTS

The resolution of soil moisture product as a goal of ourstudy is monthly and 0.25o. In the estimation process,parameters except for soil moisture are given in advance andsoil moisture is retrieved from an index such as PD(10).LAI,
which has high spatial and temporal variation by nature andaffects PD(10) well, is derived from monthly NDVI(Normalized Differential Vegetation Index) observed byNOAA/AVHRR(AdvancedVery High ResolutionRadiometer). Other important parameters are tuned for each
0.25 o grid to be fixed in time. The other parameters arenegligible, then fixed to the standard values shown in Tab.I.
Look-up tables were prepared in advance by forward
calculation of radiative transfer equation. If one can not findFigure 2. Soil moisture estimates with and without temporal change ofLAI

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Figure 3. Global map of soil moisture estimates for February in 1998

Figure 4. Same as Fig.3 for August in 1998

any solution with the look-up tables, virtual estimate is given asfollows. If observed (monthly average) PD(10) is smaller thanPD(10) corresponded with the soil moisture of 0%, estimate isset to be 0%, and if observed PD(10) is larger than PD(10)corresponded with the soil moisture of 50%, estimate becomes
50%.When one can find solution within 0% and 50%, it iscalled ``real estimate''.The U.S. standard atmospheric profile oftemperature, humidity, and pressure is used. Cloud andprecipitation is always set to be zero.

become unrealistic. Temporal change of LAI should beconsidered especially for highly variable vegetated area such as
the Sahel. In other cases, indices FD(H), PD(19), PD(37) areused instead of PD(10).FD(H) yields quite different seasonalpattern, and PD(19) and PD(37) give worse seasonal pattern
than PD(10), if we judge by the correlation coefficients with
VIC calculation. One of the reasons may be that the microwave
observation in higher frequency is much subject to theatmosphere.

The results are introduced mainly by the example of theSahel region (10-14 o N, 0-4 o E) in 1998. This region belongs
to semi-arid region, and annual plants grow caused by summer
rainfall. Figure1 shows the soil moisture estimates withdifferent SAI (one of important parameters).SAI affects not
only the absolute value but also the seasonal pattern of soil
moisture estimates.If SAI is set to be large, soil moisture
estimates show similar seasonal pattern with PD(10). That is
because LAI doesn't affect PD(10) well if SAI is large. 14 year
average of soil moisture calculated by land surface water andenergy balance model (VIC=Variable Infiltration Capacitymodel) [7] is put in the figure just as reference ofclimatological value. Supposing that the surface soil moisture
should show similar seasonal pattern with one calculated by
VIC (maximum in August),estimate with smaller SAI is judged
to be better. Small SAI doesn't contradict with geographicalcommon sense. Similar problem can occur with otherimportant parameters, and it is necessary to give suitable values
for important parameters to simulate valid seasonal pattern ofsoil moisture. Optimum value of SAI is determined for eachgrid so as to maximize the number of months when realestimate is gotten. The same way is applied for other importantparameters, and finally only one important parameter which
can increase the number of months with “real estimate” most istuned. Estimates with the tuning are well correlated with VIC
calculation (correlation coefficients R=0.94) and 94% of thetotal estimation get ``real estimates'' in this case, it is much
improved from the case without tuning (standard values areused, then the correlation coefficients is R=0.70 and 60% of thetotal estimation is ``real estimate'').

Figure3 and 4 show the tentative products of global soil
moisture map derived by the above method (PD(10) andmonthly LAI are used, tuning is done). Seasonal pattern isalmost simulated, but the absolute value is unrealistic in someregions, e.g. wet in the Sahara desert. In this paper, importantparameters are tuned and valid seasonal pattern is simulated
well. To make estimates more accurate as absolute value, theactual database of vegetation parameters should be prepared,
and the temporal change of vegetation water content should betaken in consideration.

Figure2 shows the comparison of estimates by different
methods. Parameter tuning is done in the same way for all thecases. If the LAI is fixed so that the minimum of soil moisture
estimates be 0% for each grid, seasonal pattern of estimates

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References
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[2]

[3]

[4]

[5]

[6]

[7]

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Land Surface Model 1980-1993.”, Journal of Climate, vol. 14, pp. 17901808, 2001.

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