Retrieval and validation of land surface temperature for atmospheres with air temperature inversion

被引：0
|
作者：
Zhan C. [1 ,2 ]
Tang B. [1 ,2 ]
Li Z. [1 ,2 ,3 ]
机构：
[1] State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing
[2] University of Chinese Academy of Sciences, Beijing
[3] Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing
来源：
Yaogan Xuebao/Journal of Remote Sensing | 2018年 / 22卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Air Temperature Inversion (ATI); ATI intensity; Generalized Split-Window algorithm; Land surface temperature error correction; MODIS;
D O I：
10.11834/jrs.20187043
中图分类号：
学科分类号：
摘要：
Land Surface Temperature (LST), which controls the basic interactions between the Earth's surface and the atmosphere, is significant in many aspects. By far, many algorithms have been proposed to retrieve LST from different satellite thermal infrared data. However, the influence of Air Temperature Inversion (ATI) on LST retrieval has not been considered in the development of existing algorithms. This study aims to analyze and reduce the influence of ATI on LST retrieval. Considering that the Generalized Split-Window (GSW) algorithm has been widely used, we choose this algorithm to retrieve LST in this study. Furthermore, considering that the LST retrieval error increases when ATI intensity increases, we add an error correction related to intensity to the GSW algorithm. To determine the relationship between the LST retrieval error and the ATI intensity, we manually change the normal profile in the Thermodynamic Initial Guess Retrieval (TIGR) database into the ATI profile with the intensity ranging from 1.0 K/100 m to 5.0 K/100 m and the step being 1.0 K/100 m because the intensities of the existing ATI profiles in the TIGR database are not large enough. The LST errors are calculated using the changed ATI profiles and the GSW coefficients derived from normal conditions. To improve the accuracy of the LST retrieval, we divide LST and Water Vapor Content (WVC) into different groups. After calculating the LST retrieval errors of all groups, we find that the LST retrieval error could be expressed as a quadratic function of ATI intensity. The coefficients that correspond to the correction of each group are derived by fitting the LST retrieval errors with various ATI intensities. Results show that the monomial coefficient and the constant of the quadratic function increase when the LST increases while the quadratic coefficient does not change significantly. In addition, the coefficients do not change regularly when the WVC increases. To test whether the proposed method could be used to reduce the influence of ATI on LST retrieval accuracy, we use both simulated data and in situ data. Simulation results show that the LST retrieval accuracy could be improved by 0.44 K when the ATI intensity is 1.7 K/100 m. In situ measurements at the Hailar site are also used to test this method. Results show that the proposed method could improve the LST retrieval accuracy by 0.47 K for the GSW algorithm in atmospheres with ATI. This study aims to add an error correction to the GSW algorithm to improve LST retrieval accuracy when the atmosphere shows ATI. Validation using both simulated data and in situ measurements indicates that the proposed method could effectively reduce the influence of ATI on LST retrieval. However, the application of the proposed method is restricted by the air temperature profile that it requires. A model by which the ATI could be determined from satellite data is expected to be developed in a future study. © 2018, Science Press. All right reserved.
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页码：28 / 37
页数：9
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