Generation of High-Resolution Surface Soil Moisture over Mountain Areas by Spatially Downscaling Remote Sensing Products Based on Land Surface Temperature–Vegetation Index Feature Space

Junfei Cai; Wei Zhao; Tao Ding; Gaofei Yin

doi:10.34133/remotesensing.0437

journal article Jan 01, 2025

Generation of High-Resolution Surface Soil Moisture over Mountain Areas by Spatially Downscaling Remote Sensing Products Based on Land Surface Temperature–Vegetation Index Feature Space

Junfei Cai Wei Zhao

Tao Ding

Gaofei Yin

Journal of Remote Sensing Vol. 5 · American Association for the Advancement of Science (AAAS)

View at Publisher Save 10.34133/remotesensing.0437

Abstract

Spatial downscaling has been a key solution to get high-resolution surface soil moisture (SSM), which has attracted wide attention in remote sensing society. However, the impact from topographic reliefs, complexifying SSM spatial heterogeneity, has been rarely considered in previous downscaling studies. Here, we propose a novel approach for SSM downscaling based on the physical connection between the land surface temperature (LST)–vegetation index triangle feature space and SSM, where a self-adaptive calibration method was applied to determine the estimation coefficients via a sliding window with the topographic effect of LST alleviated in advance. The proposed method was evaluated at a typical mountain region in central USA from 2015 June 1 to September 30 via the 25-km original European Space Agency Climate Change Initiative SSM product and Moderate Resolution Imaging Spectroradiometer/Terra LST and normalized difference vegetation index products. Through the direct validation with the in situ soil moisture measurements from the Snow Telemetry network, the downscaled results show better performance than other previous methods, with the average value of the correlation coefficient, root-mean-square error, and unbiased root-mean-square error derived at the site level of 0.47, 0.103 m
3
/m
3
, and 0.056 m
3
/m
3
, respectively. Meanwhile, the good downscaling effect can be reflected by the downscaling performance evaluation index. Furthermore, an intercomparison with the Soil Moisture Active Passive-HydroBlocks SSM product also reveals the consistent spatial distribution and strong correlation of the downscaled results. Overall, these results confirm the potential application of the proposed method in generating seamless high-resolution SSM over mountain areas, which will contribute to related mountain studies.

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References

Details

Published: Jan 01, 2025
Vol/Issue: 5

Authors

J

Junfei Cai

Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China.; Chongqing Geomatics and Remote Sensing Center, Chongqing 401147, China.

W

Wei Zhao

Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China.

T

Tao Ding

Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China.; University of Chinese Academy of Sciences, Beijing 100049, China.

G

Gaofei Yin

Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China.

Funding

National Natural Science Foundation of China Award: 42071349 and 42222109

National Key Research and Development Program of China Award: 2020YFA0608702

Key Program of the Chinese Academy of Sciences for International Cooperation Award: 162GJHZ2023065MI

Science and Technology Research Program of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences Award: IMHE-CXTD-02

Cite This Article

Junfei Cai, Wei Zhao, Tao Ding, et al. (2025). Generation of High-Resolution Surface Soil Moisture over Mountain Areas by Spatially Downscaling Remote Sensing Products Based on Land Surface Temperature–Vegetation Index Feature Space. Journal of Remote Sensing, 5. https://doi.org/10.34133/remotesensing.0437

Generation of High-Resolution Surface Soil Moisture over Mountain Areas by Spatially Downscaling Remote Sensing Products Based on Land Surface Temperature–Vegetation Index Feature Space

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