The remote sensing inversion and validation of land surface temperature based on ASTER data: A case study of the Heihe River basin

MA Junjun; WANG Chunlei; HUANG Xiaohong

doi:10.6046/zrzyyg.2021414

2023 Vol. 35, No. 1

Article Contents

Article navigation > Remote Sensing for Natural Resources > 2023 > 35(1): 198-204

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MA Junjun, WANG Chunlei, HUANG Xiaohong. 2023. The remote sensing inversion and validation of land surface temperature based on ASTER data: A case study of the Heihe River basin. Remote Sensing for Natural Resources, 35(1): 198-204. doi: 10.6046/zrzyyg.2021414

Citation:

MA Junjun, WANG Chunlei, HUANG Xiaohong. 2023. The remote sensing inversion and validation of land surface temperature based on ASTER data: A case study of the Heihe River basin. Remote Sensing for Natural Resources, 35(1): 198-204. doi: 10.6046/zrzyyg.2021414

The remote sensing inversion and validation of land surface temperature based on ASTER data: A case study of the Heihe River basin

1. School of Artificial Intelligence, North China University of Technology, Tangshan 063210, China；
2. Hebei Provincial Key Laboratory of Industrial Intelligent Perception, North China University of Technology, Tangshan 063210, China；
3. Consulting & Research Center of Ministry of Natural Resources, Beijing 100100, China

Received Date: 19 November 2021

Revised Date: 15 March 2023

Publish Date: 20 March 2023

Abstract

Abstract

Given the land surface types and atmospheric features of the Heihe River basin, this study calculated the surface emissivity of the study area using the ASTER Global Emissivity Database and the vegetation cover method (VCM) and estimated the atmospheric water vapor content using the improved multilayer feed-forward neural network (MFNN). Moreover, by establishing the coefficient lookup table of input parameter groups, this study developed an ASTER data-based split-window algorithm for the remote sensing inversion of land surface temperature. To validate the applicability and accuracy of the split-window algorithm, this study elevated the algorithm using the measured site data on the land surface temperature of the Heihe River basin in 2019 and MODIS instruments. Compared with the site data, the results of the split-window algorithm had root mean square errors of 1.81~3.01 K. In the cross-validation using the MODIS instruments, the split-window algorithm had relatively small errors and deviations, with root mean square errors of 1.11~1.75 K. Overall, the accuracy of the land surface temperature obtained from the inversion using the split-window algorithm can meet the needs of meteorological and climatological studies. Moreover, the development philosophy of the split-window algorithm can be used as a reference for similar thermal infrared sensors.
- ASTER data /
- land surface temperature /
- cross-validation

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References

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