Calibration of Dobson model for improving soil moisture retrievals from AMSR satellite imagery

Abstract

Satellite-based retrieval of environmental variables has seen rapid advancements in recent years, although it remains challenged by various sources of uncertainty. In this study, we endeavor to enhance the accuracy of soil moisture estimation from AMSR imagery through parameter calibration. Our focus is on calibrating the Dobson dielectric mixing model, a component of the radiative transfer model that relies heavily on empirical relationships.

The calibration process is based on black-box optimization techniques for the retrieval problem. We have adapted the CORS optimization algorithm to address the specific characteristics of our task. We also considered different target functions for calibration.

To evaluate the efficacy of our framework, we conducted tests across a dataset comprising 118 ground stations in the United States. The outcomes reveal that optimizing parameter settings can provide limited improvement to the accuracy and, when configured accordingly, address specific issues such as bias correction. Calibration emerges as a potent tool for refining surface soil moisture retrievals, although its effectiveness tends to diminish in larger calibration areas.