Detecting The Moisture Content Of Forest Surface Soil Base On The Microwave Remote Sensing Technology

The moisture content of forest surface soil plays an important role in forest ecosystems. It is practically significant for forest ecosystem related research to use microwave remote sensing technology for rapid and accurate estimation of the moisture content of forest surface soil. With the aid of TDR-300 soil moisture content measuring instrument, the moisture content of forest surface soils of 120 sample plots at Tahe Forestry Bureau of Daxing’anling region in Heilongjiang province were collected. Taking moisture content of forest surface soils as the dependent variable and the polarization decomposition parameters of C band Quad-pol SAR data as independent variables, two types of quantitative estimation models (multilinear regression model and BP-neural network model) for predicting moisture content of forest surface soils were developed. The spatial distribution of moisture content of forest surface soil on the regional scale was then derived with model inversion. Results showed that the model precision was 86.0% and 89.4% with RMSE of 3.0% and 2.7% for the multilinear regression model and BP-neural network model, respectively. It indicates that BP-neural network model has a better performance than the multilinear regression model in terms of estimation of quantitative estimation of the moisture content of forest surface soil. The spatial distribution of forest surface soil moisture content in the study area was then obtained by using BP neural network model simulation with the Quad-pol SAR data.