A New Approach for Downscaling Soil Moisture by Merging Conditional Adversarial Networks and Physics-Based Passive Microwave Retrieval

Numerous satellite sources measure global soil moisture, but the spatial resolution of these products is usually coarse making it challenging to use them for various science applications. In this work, we develop a conditional generative adversarial network-based model to downscale satellite measured brightness temperature. Augmenting a physics-based Tau-omega model to the framework, high resolution soil moisture is retrieved. For training and validation, upscaled in-situ soil moisture measurements are utilized. Experimental results suggests that the proposed framework captures the soil moisture variations from in-situ sensors and helps in substantially improving the resolution of passive microwave satellite soil moisture maps.