LAUSR

ABSTRACT In most remote sensing-based soil moisture (SM) retrieval methods, in-situ SM measurements are commonly used for validation purposes. Few studies have investigated whether such measurements can be used for calibration. In this paper, an observation-driven optimization method was proposed to estimate SM from remote sensing observations. Specifically, the optimization method was developed within the surface temperature-vegetation index (TVX) framework for the definition of objective function and constraints. In-situ SM measurements were used to optimize the theoretical boundaries of the TVX feature space. We demonstrated the applicability of the new method with Moderate Resolution Imaging Spectroradiometer (MODIS) products over the Southern Great Plains (SGP) of the United States of America. Results indicate that the accuracy produced using only one site for calibration has reached a level comparable with those produced by traditional methods. Moreover, the method has not only bypassed the complex parameterization of aerodynamic and surface resistance but also achieved continuous monitoring of SM. That is just the capacity that the traditional TVX method does not possess. Therefore, although our optimization method requires the ancillary of in-situ observations, its simplicity proves that it is a useful tool for a quick and continuous monitoring of SM over large heterogeneous areas.