Abstract

The study focused on deriving the LST of the Raipur City of India and generating the relationships of LST with six selected remote sensing indices, like MNDWI, NDBaI, NDBI, NDVI, NDWI, and NMDI. The entire study was performed by using 210 cloud-free Landsat data of different months from 1988 to 2019. The LST retrieval mono-window algorithm was applied in the study. Based on Pearson's linear correlation coefficient (r), the study finds that LST builds a strong positive correlation (r = 0.65) with NDBI, a moderate positive correlation (r = 0.30) with NDBaI, a weak positive correlation with NDWI (r = 0.19), a strong negative relation with NMDI (r = −0.54), and a moderate negative correlation (r = −0.38) with MNDWI and NDVI. These relationships were consistent and stronger in earlier years. The LST-NDBI correlation is the most consistent (CV = 9.09), while the LST-NDBaI correlation is the most variable (CV = 60.21).

摘要

该研究的重点是推导印度赖布尔市的LST，并与6种选定的遥感指数（如MNDWI，NDBaI，NDBI，NDVI，NDWI和NMDI）建立LST的关系。整个研究是使用1988年至2019年不同月份的210个无云Landsat数据进行的。本研究采用LST检索单窗口算法。根据皮尔逊线性相关系数（r），研究发现LST 与NDBI建立强正相关（r = 0.65），与NDBaI建立适度正相关（r  = 0.30），与NDWI建立弱正相关（r  = 0.19） ），与NMDI的强负相关（r  = -0.54）和适度的负相关（r = −0.38）使用MNDWI和NDVI。在早些年，这些关系是一贯的并且牢固的。LST-NDBII相关性最一致（CV  = 9.09），而LST-NDBaI相关性最大（CV  = 60.21）。