Disaggregation of Remotely Sensed Land Surface Temperature: A Generalized Paradigm
Disaggregation of Remotely Sensed Land Surface Temperature: A Generalized Paradigm
复制标题
遥感地表温度的分解：广义范式
DOI：
10.1109/tgrs.2013.2294031
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发表时间：
2014-09-01
影响因子：
8.2
通讯作者：
Sun, Hao
中科院分区：
文献类型：
--
作者：
Chen, Yunhao;Zhan, Wenfeng;Sun, Hao
地表环境监测需要高时空分辨率的地表温度数据。LST解集（DLST）是一种有效的技术，通过合并两个子分支，包括热锐化（TSP）和温度解混（TUM），以获得高质量的LST。虽然DLST研究已经取得了很大的进展，但进一步的实践需要一个深入的理论范式来概括DLST，然后在进一步进行之前指导未来的研究。因此，我们提出了一个广义的范式DLST通过概念框架（C-框架）和理论框架（T-框架）。这是通过欧几里德范式从三个基本法律总结从以前的DLST方法：贝叶斯定理，托布勒的地理第一定律，和表面能量平衡。C-框架包含了对DLST的物理解释，而T-框架是通过从三个基本定律中提取一系列假设而创建的。两个具体的例子来说明这种推广的优点。在此基础上，我们进一步推导了该范式的线性实例，并对两种经典的DLST方法进行了分析。本研究最后讨论了这一范式对遥感领域密切相关主题的影响。这种范式开发的过程，以提高对DLST的理解，它可以用于指导未来的DLST方法的设计。
The environmental monitoring of earth surfaces requires land surface temperatures (LSTs) with high temporal and spatial resolutions. The disaggregation of LST (DLST) is an effective technique to obtain high-quality LSTs by incorporating two subbranches, including thermal sharpening (TSP) and temperature unmixing (TUM). Although great progress has been made on DLST, the further practice requires an in-depth theoretical paradigm designed to generalize DLST and then to guide future research before proceeding further. We thus proposed a generalized paradigm for DLST through a conceptual framework (C-Frame) and a theoretical framework (T-Frame). This was accomplished through a Euclidean paradigm starting from three basic laws summarized from previous DLST methods: the Bayesian theorem, Tobler's first law of geography, and surface energy balance. The C-Frame included a physical explanation of DLST, and the T-Frame was created by construing a series of assumptions from the three basic laws. Two concrete examples were provided to show the advantage of this generalization. We further derived the linear instance of this paradigm based on which two classical DLST methods were analyzed. This study finally discussed the implications of this paradigm to closely related topics in remote sensing. This paradigm develops processes to improve an understanding of DLST, and it could be used for guiding the design of future DLST methods.