PKULAST-An Extendable Model for Land Surface Temperature Retrieval From Thermal Infrared Remote Sensing Data

Abstract

Land surface temperature (LST) is one of important parameter in the earth energy and water budget. Recently, open-source modules or packages are emerging as all-in-one solutions to speed up the rapid development and critical application of LST from thermal infrared (TIR) remote sensing data. In this study, we developed a python package, dubbed Peking University LAnd Surface Temperature (PKULAST) model to address the retrieval of LST from TIR remote sensing data with different LST retrieval algorithms, such as Single Channel algorithm, Split Window algorithm, and the Temperature and Emissivity Separation algorithm. Extendable data structures in the model covers common conceptual models used in LST algorithms implementation, such as spectral response functions, surface spectral libraries (ASTER, ECOSTRESS etc.), atmospheric profiles (NCEP, ERA5, MERRA-2, etc.), making it a convenience to construct the processing chain of developing new LST retrieval algorithms and generating LST products. The ASTER imageries are tested to assess the performance of the PKULAST to derive accurate LST. The LST result is then evaluated using long-term ground-based longwave radiation observations collected at seven sites from 2000 to 2021 under cloud-free scenes, and the result shows that the PKULAST model can obtain LST with an uncertainty of 2.7 K in bias, and 4.4 K in RMSE. With the help of predefined extendable data structures and workflows for different sensors, atmospheric profile libraries and surface spectral libraries, new algorithms for retrieving LST can be developed easily. The PKULAST promises to be a tool that seamlessly interoperates with developing LST retrieval algorithms and generating LST scientific products.