LAUSR

Crops monitoring is a challengeable subject that radar images can help it. The applicability of Sentinel-1 SAR data with dual polarization provided a splendid opportunity to develop a method for estimating rice parameters. Heights of cereal and biomass are two significant characteristics of rice that can be estimated with assessing satellite data and field measurements by classical regression methods [multiple linear regression (MLR), relevance vector regression (RVR), and support vector regression (SVR)]. In this study, Sentinel-1 SAR data from April 2018 to September 2018 in Astaneh-ye Ashrafiyeh region in the north of Iran were used. To evaluate and analyze validation of regression methods, field measurements (gathered from 15 plots) were utilized. The efficiency of nonparametric methods (SVR and RVR) is much better than that of the parametric regression (MLR) for rice parameter estimations. Among nonparametric approaches, RVR method has better results than SVR, because of the highest correlation coefficient ( R 2 ) and lowest root mean square error (RMSE). R 2  = 0.92, RMSE = 162.1, and MAE = 971.9 and R 2  = 0.92, RMSE = 10.9, and MAE = 70.71 are the results of height and biomass, respectively.