Abstract

The study proposed a methodology for the retrieval of precise Land Surface Temperature (LST) in Jharia Coalfield from night-time ASTER multispectral thermal infrared (TIR) data by split-window algorithm (SWA) using atmospheric transmittance and band-specific Land Surface Emissivity (LSE). For deriving night-time atmospheric transmittance, water vapor content was retrieved from night-time ASTER TIR data by modified split-window covariance and variance ratio approach. Improved LSE was retrieved by the proposed modified LSE model by integrating refined thermal emission-vegetation cover model, modified normalized difference water index and bandwidth-weighted red band reflectivity model. The retrieved SWA LST was compared with LST obtained by single-channel algorithm (SCA) across three coal fire test sites to demonstrate significant improvement in temperature contrast between coal fire and background pixels. Besides, SWA LST based coal fire thermal anomalies are significantly comparable (including substantially reduced false alarms) with in-situ observations than that of SCA LST.

摘要

该研究提出了一种利用大气透射率和特定波段地表发射率的分窗算法（SWA）从夜间 ASTER 多光谱热红外（TIR）数据中反演精确地表温度（LST）的方法。伦敦证交所）。为了获得夜间大气透射率，通过改进的裂窗协方差和方差比方法从夜间 ASTER TIR 数据中检索水汽含量。改进的LSE模型通过整合改进的热辐射-植被覆盖模型、改进的归一化水指数和带宽加权红波段反射率模型，得到改进的LSE模型。将检索到的 SWA LST 与通过三个煤火测试地点的单通道算法 (SCA) 获得的 LST 进行比较，以证明煤火和背景像素之间的温度对比度有显着改善。此外，与 SCA LST 相比，基于 SWA LST 的煤火热异常与现场观测具有显着可比性（包括显着减少的误报）。