Soil Moisture estimation by ANN using Bistatic Scatterometer data

Abstract. The microwave response of bare soil surfaces is influenced by a variety of parameters such as surface roughness, vegetation density, soil texture and soil moisture. It makes the soil moisture estimation process more complex. In such condition, the estimation of the soil moisture using microwave data with fast and less complex computing technique is a significant area of research today. The artificial neural network (ANN) approach has been found more potential in retrieving soil moisture from microwave sensors as compared to traditional techniques. For this purpose, a back propagation artificial neural network (BPANN) based on Levenberg Marquardt (TRAINLM) algorithm was developed. The measurement of scattering coefficient was carried out over a range of incidence angle from 20° to 70° at 5° steps for both the HH- and VV- polarizations. The BPANN was trained and tested with the experimentally obtained data by using bistatic X-band scatterometer for different values of soil moistures (12%, 16%, 21% and 25%) at 30° incidence angle. The scattering coefficient and soil moisture data were interpolated into 20 data sets and these data sets were divided into training data sets (70%) and testing data sets (30%). The performance of the trained BPANN was evaluated by comparing the observed soil moisture and estimated soil moisture by developed BPANN using a linear regression analysis (least square fitting) and performance factor Adj_R². The values of Adj_R² were found 0.93 and 0.94 for HH- and VV- polarization at 30° incidence angle respectively. The estimation of soil moisture by BPANN with Levenberg Marquardt training algorithm was found better at both HH- and VV- polarizations.