HR: 1330h
AN: H22D-0963    [PDF]
TI: Evaluation of Two Methods for Determining Surface Soil Moisture from Radar Imagery
AU: * Thoma, D
EM: dthoma@tucson.ars.ag.gov
AF: USDA-ARS Southwest Watershed Research Center, 2000 East Allen Road, Tucson, AZ 85719 United States
AU: Moran, M
EM: smoran@tucson.ars.ag.gov
AF: USDA-ARS Southwest Watershed Research Center, 2000 East Allen Road, Tucson, AZ 85719 United States
AU: Bryant, R
EM: rbryant@tucson.ars.ag.gov
AF: USDA-ARS Southwest Watershed Research Center, 2000 East Allen Road, Tucson, AZ 85719 United States
AU: Holifield, C
EM: cholifield@tucson.ars.ag.gov
AF: USDA-ARS Southwest Watershed Research Center, 2000 East Allen Road, Tucson, AZ 85719 United States
AU: Skirvin, S
EM: sskirvin@tucson.ars.ag.gov
AF: USDA-ARS Southwest Watershed Research Center, 2000 East Allen Road, Tucson, AZ 85719 United States
AU: Rahman, M
EM: mrahman@email.arizona.edu
AF: USDA-ARS Southwest Watershed Research Center, 2000 East Allen Road, Tucson, AZ 85719 United States
AU: Kershner, C
EM: Charles.M.Kershner@erdc.usace.army.mil
AF: U.S. Army Topographic Engineering Center, 7701 Telegraph Road, Alexandria, VA 22315 United States
AU: Watts, J
EM: Joseph.M.Watts@erdc.usace.army.mil
AF: U.S. Army Topographic Engineering Center, 7701 Telegraph Road, Alexandria, VA 22315 United States
AU: Slocum, K
EM: Kevin.Slocum@erdc.usace.army.mil
AF: U.S. Army Topographic Engineering Center, 7701 Telegraph Road, Alexandria, VA 22315 United States
AB: Distributed soil moisture data are useful for determining cross-country mobility, irrigation scheduling, pest management strategy, biomass production and potential for soil erosion and infiltration. Large area monitoring of surface soil moisture (to depths of 5 cm) is possible with radar remote sensing techniques, but accuracy must be assessed before it can be implemented operationally. Two methods for predicting surface soil moisture from radar satellite imagery were tested in sparsely vegetated, semi-arid Arizona rangelands. In the first approach, the Integral Equation Method (IEM) model was run in the forward direction to generate a Look-Up-Table (LUT) of radar backscatter for the expected range of surface roughness and moisture content in the study area. The LUT was used to derive surface soil moisture estimates from radar images acquired at the study site. In the second approach, a difference index was made from time series differences in radar backscatter signals from wet and dry soils. The difference index minimized variations in surface roughness and resulted in a direct relation between difference and surface soil moisture. For both approaches, results were validated against in situ measurements of surface soil moisture at 46 sites with dielectric probes at the time of satellite overpass. The modeling approach requires surface roughness inputs which may be difficult to obtain, whereas the difference technique requires only a dry surface reference backscatter for comparison with wetter surface backscatter to determine moisture content.
DE: 1866 Soil moisture
SC: Hydrology [H]
MN: 2003 Fall Meeting