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### 考虑土壤水分影响的比辐射率方法在地表温度反演中的应用

1. 1. 中国科学院地理科学与资源研究所陆地水循环及地表过程重点实验室,北京 100101
2. 中国科学院大学,北京 100049
3. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
• 收稿日期:2017-01-12 修回日期:2017-06-15 出版日期:2017-08-20 发布日期:2017-08-20
• 作者简介:

作者简介：董雪,女,山东德州人,硕士生,主要研究方向为定量遥感。E-mail：dongx.14s@igsnrr.ac.cn

• 基金资助:
基金项目：国家自然科学基金面上项目（41271380;41671354）

### Application of the emissivity method considering the effects of soil moisture for retrieving land surface temperature

Xue DONG1,2(), Jing TIAN1(), Hua WU3, Suhua LIU1,2

1. 1. Key Laboratory of Water Cycle and Related Land Surface Processes,Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
2. University of Chinese Academy of Sciences,Beijing 100049,China
3. State Key Laboratory of Resources and Environmental Information System,Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
• Received:2017-01-12 Revised:2017-06-15 Online:2017-08-20 Published:2017-08-20

Abstract:

The emissivity of natural surfaces is a major parameter determining land surface temperature（LST）. In addition,the surface cover type influences emissivity and soil moisture is closely related to emissivity. Here,methods for obtaining the emissivity of bare soil using the MODIS generalized split-window algorithm and Landsat mono-window algorithm were improved. According to the empirical logarithm linear formula between soil moisture and soil emissivity,based on remote sensing data and ground observation data from the Soil Moisture Experiment 2004 （SMEX04）- Arizona study area,we discuss if the accuracy of land surface temperature retrieval can be improved when surface emissivity acquisition methods consider effects of soil moisture. We found that the accuracy of both improved algorithms considering soil moisture effects were better than algorithms not considering soil moisture effects. The mean error of LST retrieved by the improved MODIS generalized split-window algorithm reduced 1.0~1.5K,and the root mean square error reduced by 0.4~0.8K. Moreover,the mean error of LST retrieved by the improved Landsat mono-window algorithm reduced by 0.7K,and the root mean square error reduced by 0.9K. As a whole,the accuracy of land surface temperature retrieval can be improved when surface emissivity acquisition methods consider the effects of soil moisture,especially areas where vegetation coverage is less. Sensitivity analysis results show that the influence of remote sensing soil moisture data with a 0.04cm3/cm3 error on LST retrieval algorithms considering soil moisture effects is not obvious.