100-m-resolution surface soil moisture data during the thawing season on the Qinghai‒Tibet Plateau

The characteristics of the spatial distribution of surface soil moisture (SM) on the Qinghai‒Tibet Plateau (QTP) on a fine scale are unclear due to the lack of high-spatial-resolution SM datasets. To improve this situation, we first supplemented 659 SM datasets in areas on the QTP containing sparse monitoring stations from 2021-2022 and integrated published SM datasets. Based on Sentinel-1&2 and measured SM data, we developed an SM retrieval algorithm for the ascending and descending orbits. Then, 100-m-resolution SM spatial data were generated for the thawing season of 2017-2023 in the SAR signal-applicable area on the QTP. As validated by the measured data, the correlation coefficients of the retrieval results for the ascending and descending orbits were 0.72 and 0.69, respectively, and the bias reached 0.07 m³/m³ and an RMSE of 0.07 m³/m³ for both. These SM datasets exhibit notable promise for improving our understanding and analysis of the ecology and hydrology of different environments on the QTP.

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Publication Details

Journal
Scientific Data
Published
2025-03-24
DOI
https://doi.org/10.1038/s41597-025-04831-1
Citations
3
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
2.40

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article

100-m-resolution surface soil moisture data during the thawing season on the Qinghai‒Tibet Plateau

Yiwei Wang, Lingxiao Wang, Guangyue Liu, Zanpin Xing et al.
3 citations
Scientific Data
Soil Moisture and Remote Sensing
2.40
article

100-m-resolution surface soil moisture data during the thawing season on the Qinghai‒Tibet Plateau

Yiwei Wang, Lingxiao Wang, Guangyue Liu, Zanpin Xing, Luhui Yin, Defu Zou, Erji Du, Shibo Liu, Guojie Hu, Minxuan Xiao, Lin Zhao, Chong Wang, Zhibin Li
article en
3 citations

Abstract

The characteristics of the spatial distribution of surface soil moisture (SM) on the Qinghai‒Tibet Plateau (QTP) on a fine scale are unclear due to the lack of high-spatial-resolution SM datasets. To improve this situation, we first supplemented 659 SM datasets in areas on the QTP containing sparse monitoring stations from 2021-2022 and integrated published SM datasets. Based on Sentinel-1&2 and measured SM data, we developed an SM retrieval algorithm for the ascending and descending orbits. Then, 100-m-resolution SM spatial data were generated for the thawing season of 2017-2023 in the SAR signal-applicable area on the QTP. As validated by the measured data, the correlation coefficients of the retrieval results for the ascending and descending orbits were 0.72 and 0.69, respectively, and the bias reached 0.07 m³/m³ and an RMSE of 0.07 m³/m³ for both. These SM datasets exhibit notable promise for improving our understanding and analysis of the ecology and hydrology of different environments on the QTP.

Scientific DataVol. 12(1)
Chinese Academy of Sciences (CN), Nanjing University of Information Science and Technology (CN), Northwest Institute of Eco-Environment and Resources (CN), Lanzhou University (CN)
National Natural Science Foundation of China
Life in Land
Openalex Percentile: Top 13%
Soil Moisture and Remote Sensing
2.40
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