Downscaling GNSS-R Based Vegetation Water Content Product Using Random Forest Model

Vegetation water content (VWC) is recognized as an important parameter in vegetation growth study. Recently, the ground-based GNSS-R method is emerging in monitoring VWC owing to its high accuracy. However, the small footprint and sparse distribution hinder its application. Therefore, we propose a method to improve the spatial resolution of GNSS-R VWC products by downscaling with other products highly correlated with VWC, using random forest (RF). Satisfactory downscaling results with cross-validation R values of 0.83 and RMSE of 0.025 were obtained. VWC images with a 500-m spatial resolution were then acquired, which is consistent with the distribution of NDVI and GPP, further indicating the accuracy of the downscaling results.