Land Surface Temperature Estimation from Landsat-9 Thermal Infrared Data Using Ensemble Learning Method Considering the Physical Radiance Transfer Process

Xin Ye, Rongyuan Liu (), Jian Hui and Jian Zhu
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Xin Ye: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Rongyuan Liu: China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
Jian Hui: Hikvision Research Institute, Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou 310051, China
Jian Zhu: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Land, 2023, vol. 12, issue 7, 1-12

Abstract: Accurately estimating land surface temperature (LST) is a critical concern in thermal infrared (TIR) remote sensing. According to the thermal radiance transfer equation, the observed data in each channel are coupled with both emissivity and atmospheric parameters in addition to the LST. To solve this ill-posed problem, classical algorithms often require the input of external parameters such as land surface emissivity and atmospheric profiles, which are often difficult to obtain accurately and timely, and this may introduce additional errors and limit the applicability of the LST retrieval algorithms. To reduce the dependence on external parameters, this paper proposes a new algorithm to directly estimate the LST from the top-of-atmosphere brightness temperature in Landsat-9 two-channel TIR data (channels 10 and 11) without external parameters. The proposed algorithm takes full advantage of the adeptness of the ensemble learning method to solve nonlinear problems. It considers the physical radiance transfer process and adds the leaving-ground bright temperature and atmospheric water vapor index to the input feature set. The experimental results show that the new algorithm achieves accurate LST estimation results compared with the ground-measured LST and is consistent with the Landsat-9 LST product. In subsequent work, further studies will be undertaken on developing end-to-end deep learning models, mining more in-depth features between TIR channels, and reducing the effect of spatial heterogeneity on accuracy validation.

Keywords: land surface temperature; thermal infrared; remote sensing; ensemble learning; Landsat-9 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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