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.

在大多数基于遥感的土壤水分（SM）检索方法中，原位SM测量通常用于验证目的。很少有研究调查这种测量是否可用于校准。本文提出了一种基于观测驱动的优化方法，用于从遥感观测值中估计SM。具体来说，在表面温度-植被指数（TVX）框架内开发了优化方法，用于定义目标函数和约束条件。原位SM测量用于优化TVX功能空间的理论边界。我们证明了中分辨率成像光谱仪（MODIS）产品在美利坚合众国南部大平原（SGP）上的新方法的适用性。结果表明，仅使用一个位置进行校准所产生的准确性已达到与传统方法所产生的准确性相当的水平。此外，该方法不仅绕过了空气动力学和表面阻力的复杂参数设置，而且实现了对SM的连续监控。那只是传统TVX方法不具备的能力。因此，尽管我们的优化方法需要原位辅助 观察，它的简单性证明它是在大面积异质区域上快速连续监测SM的有用工具。