Artificial neural network for the estimation of soil moisture and surface roughness

Estimation of the soil moisture and soil roughness by using microwave data with less complex and fast method is a significant area of research today. For this purpose an Artificial Neural Network (ANN) based algorithm is used and tested in present study. The ANN model is calibrated and tested with the experimentally obtained data by using X-band scatterometer for different field roughness 3.78, 1.83 and 1.63 cm and at fixed value of soil moisture 22.8%. The measurement of scattering coefficient was carried out over a range of incidence angle from 20° to 70° by 5° steps for both the HH (horizontal transmitter and horizontal receiver) and VV (vertical transmitter and vertical receiver) polarization. Two training algorithm of Feed Forward Backpropagation neural network namely Levenberg-Marquardt (TRAINLM) and Gradient-Descent (TRAINGD) were used for analysis. The performance of the ANN models with different algorithm is evaluated by comparing the direct measured value of soil roughness and soil moisture with the soil roughness and soil moisture estimated by the ANN. Our work suggests that ANN model with training algorithm (TRAINLM) is more suitable for the soil moisture and surface roughness prediction in comparison to (TRAINGD) and ANN modeling may be the promising alternative for the soil moisture and surface roughness estimation. The main advantage of the ANN approach for the surface roughness and soil moisture estimation is its potential for world wide reporting.

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