Land surface temperature retrieval for arid regions based on Landsat-8 TIRS data: a case study in Shihezi, Northwest China

文献类型： 外文期刊

第一作者： Yang, Lei

作者： Yang, Lei;Cao, YunGang;Zhu, XiaoHua;Zeng, ShengHe;Yang, GuoJiang;He, JiangYong;Yang, XiuChun;Yang, XiuChun

作者机构：

关键词： thermal infrared remote sensing; land surface temperature retrieval; split-window algorithm; Landsat-8 TIRS

期刊名称：JOURNAL OF ARID LAND （ 影响因子：2.299； 五年影响因子：2.459 ）

ISSN： 1674-6767

年卷期： 2014 年 6 卷 6 期

页码：

收录情况： SCI

摘要： Scientific interest in geophysical information about land surface temperature (LST) is ever increasing, as such information provides a base for a large number of applications, including environmental and agricultural monitoring. Therefore, the research of LST retrieval has become a hot topic. Recent availability of Landsat-8 satellite imagery provides a new data source for LST retrieval. Hence, exploring an adaptive method with reliable accuracy seems to be essential. In this study, basing on features of Landsat-8 TIRS thermal infrared channels, we re-calculated parameters in the atmospheric transmittance empirical models of the existing split-window algorithm, and estimated the ground emissivity with the help of the land cover classification map of the study area. Furthermore, a split-window algorithm was rebuilt by virtual of the estimation model of the updated atmospheric transmittance and the ground emissivity, and then a remote sensing retrieval for the LST of Shihezi city in Xinjiang Uygur autonomous region of Northwest China was conducted on the basis of this modified algorithm. Finally, precision validation of the new model was implemented by using the MODIS LST products. The results showed that the LST retrieval from Landsat-8 TIRS data based on our algorithm has a higher credibility, and the retrieved LST is more consistent with the MODIS LST products. This indicated that the modified algorithm is suitable for retrieving LST with competitive accuracy. With higher resolutions, Landsat-8 TIRS data may provide more accurate observation for LST retrieval.

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