A Bayesian Deep Image Prior Downscaling Approach for High-Resolution Soil Moisture Estimation

Abstract

Soil moisture (SM) estimation is a critical part of environmental and agricultural monitoring, with satellite-based microwave remote sensing being the main SM source. However, the limited spatial resolution of most current remote sensing SM products reduces their utility for many applications, such as evapotranspiration modeling and agriculture management. In this article, to address this issue, we propose a Bayesian deep image prior (BDIP) downscaling approach to estimate the high-resolution SM from satellite products. More specifically, the high-resolution SM estimation problem is formulated as a maximum a posteriori problem, and solved via a neural network comprising of a deep fully convolutional neural network (FCNN) for modeling the prior spatial correlation distribution of the underlying high-resolution SM variables, and a forward model characterizing the SM map degeneration process for modeling the data likelihood. As such, the proposed BDIP approach provides a statistical framework that integrates deep learning with forward modeling in a coherent manner for combining different sources of information, i.e., the knowledge in the forward model, the spatial correlation prior in FCNN architecture, and the remote sensing data and products. Experiments on the downscaling of SM active passive SM products using the moderate resolution imaging spectroradiometer products show that SM maps estimated using the proposed method provide greater spatial detail information than other downscaling methods, with the SM estimates very close to in situ measurements.

Keywords

• Bayesian
• convolutional neural network
• deep image prior
• MODIS
• SMAP
• soil moisture downscaling