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Yu-Ze Zhang [1] and Xiao-Guang Jiang [1; 2; 3] and Hua Wu [1; 2; 4] and Ya-Zhen Jiang [1] and Zhao-Xia Liu [5] and Cheng Huang [1]

Academic Editor: Anthony R. Lupo

1, University of Chinese Academy of Sciences, Beijing 100049, China, ucas.ac.cn

2, State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China, cas.cn

3, Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China, cas.cn

4, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

5, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Wulumuqi, Xinjiang 830011, China, cas.cn

Received Mar 9, 2017; Revised Jun 7, 2017; Accepted Jun 21, 2017

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Land Surface Temperature and Emissivity (LST and LSE) are two key parameters in quantitative remote sensing and have been widely used in many fields such as meteorological and climate models, lithological mapping, and resources exploration [1, 2].

Over recent decades, great effort has been made to retrieve the LST and LSEs from multispectral thermal infrared (TIR) data, and some typical algorithms have been successfully used to determine the LST and LSEs from space measurements [3]. For example, the Split Window algorithm and Day/Night algorithm are used for the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Temperature and Emissivity Separation (TES) algorithm is used for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) [4–7]. According to the current multispectral land products, the general accuracy of the LST can be approximately 1 K [7–10]. However, in most algorithms, some empirical constraints on LSE are also needed to determine the LST, such as prior knowledge of the LSE in the Split Window (SW) algorithm and the assumption of a constant LSE during night and day in the Day/Night (D/N) algorithm [4, 5]. Unlike the LST product, the current LSE products are usually determined according to a classification map, which makes the accuracy of the LSE product highly dependent on prior knowledge [8, 10]....

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