Cycle GAN Based Heterogeneous Spatial-Spectral Fusion for Soil Moisture Downscaling

Abstract

Soil moisture (SM) downscaling aims to solve the coarse resolution problem of passive microwave SM products. On the basis of SMAP SM products and related MODIS products, this study develops a deep residual cycle generative adversarial network (GAN) based heterogeneous spatial-spectral fusion method to downscale SMAP SM from 36km to 9km. On the one hand, the proposed method creatively regards the MODIS products that can reflect the SM state as the spectral features of SM in a broad sense and performs the heterogeneous spatial-spectral fusion between the low-resolution (LR) SM product and high-resolution (HR) MODIS products. On the other hand, considering the spatial correlation of SM, the proposed method utilizes a deep residual cycle generative adversarial network (GAN) to extract and fuse features of heterogeneous images through convolutions. Both qualitative and quantitative evaluation of experimental results shows that the proposed method can generate high accuracy SM products.