Optimization of land surface temperature inversion algorithm for Landsat 9 data

doi:10.13474/j.cnki.11-2246.2023.0204

Abstract

Abstract: As a key parameter of energy exchange between surface and atmosphere, land surface temperature (LST) is widely used in many fields. Based on the Landsat 9 TIRS data, this paper optimized and updated the parameters of single window algorithm model and split window algorithm model to realize surface temperature inversion, and combined the measured data of SURFRAD site and the land surface temperature products for accuracy verification analysis. The results show that the determination coefficients of the two algorithms are both greater than 0.96. The split window algorithm model has higher accuracy and the error (RMSE) is about 1.45 K, while the single channel algorithm model has lower accuracy and the error (RMSE) is about 1.61 K. Compared with the single channel model, the split window model is less sensitive to the parameters. In the range of high water vapor content, the results of the split window model are better than those of the single channel model. The error (RMSE) values of the land surface temperature inversion method proposed in this paper and the official land surface temperature products are both within 2.5 K, which can meet the application requirements of producing land surface temperature products with thermal infrared remote sensing data.

Key words: surface temperature, Landsat 9, single channel algorithm, split window algorithm, thermal infrared remote sensing

CLC Number:

P237

WEN Yafei, LIU Yu, WANG Guanghui, ZHANG Qiuzhao. Optimization of land surface temperature inversion algorithm for Landsat 9 data[J]. Bulletin of Surveying and Mapping, 2023, 0(7): 74-79.

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