ABSTRACT Land surface temperature (LST) depends primarily on the land surface material and climatic conditions. The present study focuses on deriving the LST of Raipur City and generating the relationship between LST and some land surface indices, like NDVI, NDWI, NDBI, NMDI, and NDBaI for better land-use planning and environmental management inside the city. These land surface indices respond in different ways with the changes of LST in an urban landscape. There are only a few numbers of research works available on the relationship of LST and land surface indices in a tropical city for pre-monsoon season. The present study has been performed on a total of fifteen multi-date Landsat data sets of the pre-monsoon season from 2002, 2006, 2010, 2014, and 2018. The mono-window algorithm has been applied in retrieving the LST. Results show that LST builds a positive relation with NDBI, NDBaI, and NDWI and a negative relation with NDVI and NMDI. These relationships are stronger in the area below mean LST (low LST zones) and weaker in the area above mean LST (high LST zones). It indicates that the values of LST are largely influenced by the different land surfaces, like vegetation, water, soil, and built-up area.

中文翻译：

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以印度赖布尔市地表温度与地表指数关系为例

摘要 地表温度 (LST) 主要取决于地表材料和气候条件。本研究的重点是推导赖布尔市的 LST，并生成 LST 与一些地表指数（如 NDVI、NDWI、NDBI、NMDI 和 NDBaI）之间的关系，以更好地进行城市内部的土地利用规划和环境管理。这些地表指数随着城市景观中LST的变化以不同的方式响应。关于季风季前热带城市LST与地表指数关系的研究工作很少。本研究对 2002、2006、2010、2014 和 2018 年季风季节的 15 个多日期 Landsat 数据集进行了研究。单窗口算法已应用于检索 LST。结果表明，LST 与 NDBI、NDBaI 和 NDWI 呈正相关，与 NDVI 和 NMDI 呈负相关。这些关系在低于平均 LST 的区域（低 LST 区域）中更强，而在高于平均 LST 的区域（高 LST 区域）中较弱。这表明 LST 的值在很大程度上受不同地表的影响，如植被、水、土壤和建成区。