时空二维特征空间框架下土壤含水量和蒸散发的遥感估算方法研究

The triangle method based on the spatial relationship between remotely sensed land surface temperature and vegetation index has been widely used for the estimates of soil moisture and evapotranspiration. It allows the pixel distribution from the image to fix the boundary conditions for the model. Two methods, namely the spatial domain solution method and time domain solution method have been developed to calibrate the boundary conditions. However, only one dimension of the boundary conditions is considered in both of these two methods. This has not only weakened the theoretical basis of the triangle method but also limited its application. As a result, in most of the previous studies, only a few days under clear sky conditions were selected to demonstrate the applicability of the triangle method. .In the present study, a universal triangle method is proposed by the combination of these two solution methods. Specifically, the spatial domain solution method is transformed from a regional scale to a pixel scale. The boundary conditions of each pixel are composed of the theoretical dry edge determined by the surface energy balance principle and the wet edge determined by the air temperature. As a result, the retrieval of soil moisture and evapotranspiration is only related to the boundary conditions at pixel scale, regardless of the land surface temperature and vegetation index configuration over the spatial domain. The development of a universal triangle method with both space and time dimensions makes it possible to conduct a continuous monitoring of evapotranspiration and soil moisture. That is just the ability the traditional triangle method does not possess. Therefore, in view of its simplicity and relatively strong physical mechanism, the universal triangle method can be considered as an effective tool to investigate the spatio-temporal variations of key variables related to various environmental studies such as drought evaluation, agricultural management, hydro-meteorological predictions and ecological applications.

土壤含水量和蒸散发，是陆面水热过程的关键变量。准确获取两者的时空分布数据对于陆面水热过程的机理研究具有重要意义。传统的地表温度-植被指数特征空间法只考虑了特征空间干湿边界的一维特性，必须依靠足够多的像元样本来实现特征空间的构建，因此只能应用于大面积晴天条件下，无法实现对陆面水热过程的连续监测。针对上述问题，本项目首先基于地表能量平衡原理，将传统的空间维度特征空间法和时间维度特征空间法进行有机结合，在像元尺度构建具有时空二维属性的地表温度-植被指数特征空间；然后在此框架下，通过土壤湿度指数的提取进行土壤水分的遥感监测；最后，通过建立土壤湿度与蒸发比之间的定量关系，发展基于土壤湿度背景信息的遥感蒸散发模型。时空二维特征空间法将特征空间的概念由区域尺度转化为像元尺度，因此可以实现对陆面水热过程的连续监测。本项目可为大尺度陆面水热过程的遥感监测和机理研究提供理论基础与技术支撑。

土壤含水量与陆面蒸散发，是陆面水热过程的关键变量。准确获取两者的时空分布数据对于陆面水热过程的机理研究具有重要意义。在光学遥感基础上发展起来的地表温度-植被指数特征空间法，可以同时进行土壤含水量与陆面蒸散发的遥感估算，因此成为当前陆面水热过程遥感监测的主流方法之一。特征空间法成功应用的关键在于构建具有物理意义的干湿边界。传统的空间维度特征空间法和时间维度特征空间法都只考虑了干湿边界的一维特性，不但在物理机制方面存在缺失，而且只能应用于大面积晴天条件下，无法实现陆面水热过程的连续监测。本项目针对上述问题，（1）首先通过空气温度的引入，对传统特征空间法土壤湿度指数的提取方法进行了改进，基于地表能量平衡原理，在像元尺度构建了具有时空二维属性的地表温度-植被指数特征空间，实现了土壤含水量与陆面蒸散发的连续遥感估算；（2）然后基于土壤含水量与陆面蒸散发实测数据，对时空二维特征空间理论框架进行了统计分析，在日尺度和年尺度分别构建了实测数据驱动下的干湿边界优化方案，提出了时空二维特征空间法的简易化模型，避免了上述地表能量平衡方程复杂而繁琐的参数化过程，提高了特征空间法在陆面水热过程遥感监测中的效率；（3）最后对陆面蒸散发遥感估算结果进行了应用研究，系统评估了我国粮食生产的水资源安全态势，定量揭示了变化环境下非洲乍得湖流域径流变化的原因，为我国及“一带一路”沿线国家的水资源管理提供了技术支撑。本项目共发表SCI论文6篇，申请软件著作权1项。