Soil Moisture Inversion Via Semiempirical and Machine Learning Methods With Full-Polarization Radarsat-2 and Polarimetric Target Decomposition Data: A Comparative Study

In this article, surface soil moisture was retrieved from Radarsat-2 and polarimetric target decomposition data by using semiempirical models and machine learning methods. The semiempirical models and machine learning techniques employed were Oh (1992), Dubois (1995), Oh (2004) and Generalized Regression Neural Network (GRNN), Least Squares – Support Vector Machine (LS-SVM), Extreme Learning Machine (ELM), Kernel based Extreme Learning Machine (KELM), Adaptive Network based Fuzzy Inference System (ANFIS), respectively. In addition, Yamaguchi, van Zyl, Freeman-Durden, H/A/ $\alpha $ and Cloude polarimetric target decomposition methods were used in this study. For soil moisture inversion, firstly, preprocessing was applied to the Radarsat-2 image of two different dates with bare and moderately vegetated soil. Then, sigma nought coefficients and the polarimetric decomposition components were extracted as feature vector from preprocessed SAR image pixels corresponding to ground measured points. Lastly, sigma nought coefficients were used in semiempirical inversion models, and sigma nought coefficients and polarimetric decomposition components were used as input to machine learning methods. The best accuracy results for semiempirical models were 13.01 vol. % and 17.91 vol. % Root Mean Square Error (RMSE) for bare and moderately vegetated soil, respectively. The best accuracy for machine learning techniques were 4.04 vol. % and 2.72 vol. % RMSE for two dates, respectively. The results indicated that the machine learning techniques performed much better than the semiempirical models.