1 Department of Remote Sensing and Geographic Information Engineering, Faculty of Geosciences and Environmental
Engineering, Southwest Jiaotong University, Chengdu 611756, China; 2 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; 3 Institute of Farmland Water Conservancy and Soil-fertilizer, Xinjiang Academy of Agricultural and Reclamation Science,
Shihezi 832000, China; 4 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 5 College of Environment and Planning, Shangqiu Normal University, Shangqiu 476000, China
Land surface temperature retrieval for arid regions based on Landsat-8 TIRS data: a case study in Shihezi, Northwest China
1 Department of Remote Sensing and Geographic Information Engineering, Faculty of Geosciences and Environmental
Engineering, Southwest Jiaotong University, Chengdu 611756, China; 2 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; 3 Institute of Farmland Water Conservancy and Soil-fertilizer, Xinjiang Academy of Agricultural and Reclamation Science,
Shihezi 832000, China; 4 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 5 College of Environment and Planning, Shangqiu Normal University, Shangqiu 476000, China
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.
Abstract: 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.
This study was funded by the Project of Scientific and Technological Support to Xinjiang from Xinjiang Production and Construction Corps (2013AB017), the Doctorial Fund Project of Xinjiang Production and Construction Corps (2012BB001), the National Natural Science Foundation of China (31372354) and the International Science & Technology Cooperation Program of China (2013DFR30760).
通讯作者:
XiuChun YANG
E-mail: yangxiuchun@caas.cn
引用本文:
Lei YANG, YunGang CAO, XiaoHua ZHU, ShengHe ZENG, GuoJiang YANG, JiangYong HE, X. Land surface temperature retrieval for arid regions based on Landsat-8 TIRS data: a case study in Shihezi, Northwest China[J]. 干旱区科学, 2014, 6(6): 704-716.
Lei YANG, YunGang CAO, XiaoHua ZHU, ShengHe ZENG, GuoJiang YANG, JiangYong HE, XiuChun YANG. Land surface temperature retrieval for arid regions based on Landsat-8 TIRS data: a case study in Shihezi, Northwest China. Journal of Arid Land, 2014, 6(6): 704-716.
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2014, Vol. 6 
