# Presentation: 489847

## TRMM Precipitation Radar and Microwave Data Fusion for Soil Moisture Monitoring over Little River Watershed

**TRMM Precipitation Radar and Microwave Data Fusion for Soil Moisture Monitoring over Little River Watershed**

*Haroon Stephen, Sajjad Ahmad, and Thomas C. Piechota*

**Water Resources Laboratory, University of Nevada, Las Vegas, NV**

- Bindlish R., Jackson T. J., Wood E., Gao H., Starks P., Bosch D., and Lakshmi V. (2003), Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States, *Remote Sensing of Environment*, 85, 4, 507-515
- Bosch D. D., Sheridan M. J., and Marshall L. K. (2007), Precipitation, soil moisture, and climate database, Little River Experimental Watershed, Georgia, United States, *Water Resources Research*, 43, W09472, doi:10.1029/2006WR005834.
- Seto S., Oki T., and Musiake K. (2003), “Surface soil moisture estimation by TRMM/PR and TMI, *Proceedings of International Geoscience and Remote Sensing Symposium*, III, 1960-1962

**References**

- Normalized backscatter dependence on soil moisture is modeled with a linear equation given by

**Soil Moisture Modeling**

**Abstract**

- Although many techniques are used to derive soil moisture from radar backscattering coefficient (σ°) and radiometric temperature (Tb), very little attention has been given to develop soil moisture retrieval using σ°-Tb coupled models. The simultaneous data acquisition of TRMM Precipitation Radar (TRMM-PR) and Microwave Imager (TMI) provides an opportunity to study their interrelation in connection to the surface soil moisture.
- Little River Experimental Watershed (LREW), Georgia, has been extensively mapped for various meteorological and hydrological parameters. The small size and available soil moisture measurements make LREW appropriate to study the connections between σ° and Tb observations in relation to the surface soil moisture.
- We relate the *in-situ* soil moisture, rainfall, and temperature measurements to the σ° and Tb observations over LREW. The results reveal the potential of data fusion of σ° and Tb to estimate of surface soil moisture.

**Little River Experimental Watershed**

- Fig. 1: Little River Experimental Watershed landcover map showing the locations of gage stations [*ftp://www.tiftonars.org*].

- The Little River Experimental Watershed (LREW) is situated in the western headwaters area of the Suwannee River Basin centered at approximately N31.61° and W83.66°. This 334 km2 basin begins in Georgia and empties into the Gulf of Mexico. This watershed is heavily instrumented and provides data for hydrological, environmental, and agronomic studies. LREW consists of a network of rain gages that have been collecting precipitation data since 1960s. In 2001, instruments to measure soil moisture were also installed [*Bosch et al., 2007*]. LREW terrain is generally flat consisting of mainly agricultural and forest land cover. The watershed is approximately 50% woodland, 31% row crops, 10% pasture, and 2% water. In this research, we use the LREW precipitation, soil moisture, and temperature data during 2002 provided in the database at *ftp://www.tiftonars.org*. The land use and gage locations are shown in Fig. 1.

- where *A* is the σ° normalized to 7° and *B* is the slope of the fitted line. *A* has higher contribution from coherent scattering from the soil as a function of moisture content.

**Backscatter Incidence Angle Correction**

- Fig. 2: TRMM-PR backscatter as a function of incidence angle at three stations. The line is the model fit to the raw measurements (dots) for incidence angle between 3° and 15°.

- TRMM-PR observations are sensitive to the surface soil moisture [*Seto et al., 2003*]. TRMM-PR measures σ° at near-nadir (0°-17°) incidence angles. σ° is normalized to θ = 7° for this analysis. Moreover, σ° measurements at θ < 3° and θ > 15° are ignored [see Fig. 2]. The normalization is performed using a linear function

**Conclusions**

- Due to many factors effecting TRMM-PR and TMI measurements, data corrections are required to produce normalized measurement for comparison with soil moisture. This analysis reveals the dependence of normalized backscatter and normalized radiometric temperature on the soil moisture. The dependence varies with the season and reflects the seasonal changes in the land use. Backscatter and radiometric temperature measurements in combination can provide new insight into their dependence on soil moisture .

- Fig. 5: Plots of normalized backscatter mean deviation *vs*. soil moisture mean deviation at three gage stations (columns) during spring (top row) and fall (bottom row). The model fit is shown by a solid line.

- Fig. 6: Plots of normalized radiometric temperature *vs*. soil moisture at three gage stations (columns) during spring (top row) and fall (bottom row).

- μs and μA are the mean soil moisture and normalized backscatter, respectively. *D* is the slope of the line and is the backscatter soil moisture sensitivity for the surface under consideration. Figure 5 shows the plots of the mean deviations and the model fit at the three gage stations. The sensitivity factor *D* varies with surface type and time of year. Figure 6 shows the soil moisture variation with 10 GHz V-pol normalized radiometric temperature (Tb divided by the physical temperature). Comparison of Figs. 5 and 6 shows that backscatter and radiometric temperature have complementary characteristics for soil moisture retrieval.

- Figure 4 shows the time series of plots of various quantities during 2002 where the columns of plots correspond to three measuring stations. In general, backscatter variation follows the precipitation induced soil moisture (ms) variation. A rain event is followed by increased soil moisture and increase in vegetation cover. The increased soil moisture raises the backscattering coefficient whereas the followed vegetation growth continues to increase the backscattering coefficient after the rain event. In the ΔTb temporal variation, the decreased vegetation density is reflected by the minimum during the fall for all frequencies.

**Temporal Variation During 2002**

- Fig. 4: Soil moisture and precipitation measurements (top), normalized backscatter (middle), and polarization difference (bottom) time series during 2002 for three stations corresponding to the three columns.

- where *T**bm* is the mean radiometric temperature. *N**i* and *t**di* are the magnitude and the phase of the *i**th* harmonic. Figure 3 shows plots of Tb diurnal variations at three gage stations for vertical (V-pol) and horizontal (H-pol) polarizations. The polarization difference (ΔTbm) is sensitive to soil moisture and surface roughness. Increase in soil moisture increases ΔTb. On the contrary, increase in surface roughness due to increase in vegetation cover decreases ΔTbm.

**Radiometric Temperature Time-of-day Correction**

- The surface radiometric emission depends upon surface physical temperature, moisture content and land use [*Bindlish et al., (2003)*]. Due to 30° inclination of the TRMM orbit, the local time of day (td) of  Tb observation shifts every repeat cycle. The Tb measurements diurnal variation is corrected using a second order harmonic equation given by

- Fig. 3: TMI 10 GHz V-pol and H-pol radiometric temperature diurnal variation. The line is the second order harmonic fit to the raw measurements (dots).

- 3rd NASA/JAXA International TRMM Science Conference, Las Vegas, NV, February 4-8, 2008