Estimation of soil moisture using Radarsat repeat-passes

The method for developing a soil moisture inversion algorithm can be approached in two ways: the multiple-incident angle approach and the change detection method. The paper discusses how these two methods can be used to predict surface soil moisture. In the multiple incident angle approach surface roughness can be mapped, if multiple incident angle viewing is possible, and if the surface roughness is assumed constant during data acquisitions. A backpropagation neural network is trained with the data set generated by the IEM (Integral Equation Method) model. The training data set includes possible combinations of backscatter as a result of variation in soil moisture within the period of data acquisitions, and the inputs to the network are backscatter acquired at different incident angles. The outputs are correlation length and r.m.s. height. Once the roughness is mapped, soil moisture can be determined. The next approach is the application of the change detection concept. In this approach, the change in soil moisture over two different periods is determined from Radarsat data using a simplified algorithm. The methods would be applied to Radarsat data acquired over Ames, Iowa during the period June-July 2002.