A Neural Network Approach to Soil Electrical Conductivity Estimation on Earthen Levees Using Spaceborne X-band SAR Imagery

Abstract

Monitoring of soil moisture over earthen levees can help in revealing patterns of moisture variation which could indicate potential slope failures or other vulnerabilities. In this work a machine learning approach using a back propagation neural network (BPNN) and also a wavelet basis neural network (WBNN) is developed to estimate the soil electrical conductivity (EC) over an earthen levee system. Three scenarios based on the extracted features are investigated to estimate the conductivity. In scenario one, only radar backscatter coefficients are considered. In scenario 2, in addition to the backscatter features an average of backscatter in a sliding 3 × 3 window is used. In scenario 3, texture features are added, including statistical and wavelet features. The results show that using all backscatter and texture features (scenario 3) results in better correlation performance, with an 11 to 27 percent improvement compared to scenario 1 and a 1 to 17 percent improvement over scenario 2.