Preprints
https://doi.org/10.5194/essd-2024-200
https://doi.org/10.5194/essd-2024-200
15 Jul 2024
 | 15 Jul 2024
Status: this discussion paper is a preprint. It has been under review for the journal Earth System Science Data (ESSD). The manuscript was not accepted for further review after discussion.

GSSM: A global seamless soil moisture dataset from 1981 to 2022 matching CCI to SMAP with a novel bias correction method

Yunjia Wang, Hao Sun, Zhenheng Xu, Jinhua Gao, Huanyu Xu, Tian Zhang, and Dan Wu

Abstract. Surface soil moisture is vital for Earth's environmental and energy cycles. However, it is still rare to have remote sensing soil moisture data with a long-term temporal extent, a global seamless spatial coverage, and a near-real-time update frequency. Here, we provided a global seamless soil moisture dataset from July 1981 to December 2022, matching CCI with SMAP through a novel soil moisture data bias correction method (fitting beta CDF matching, BCDF), and filling the gaps of corrected soil moisture through XGBoost Algorithms along with various soil moisture covariates. The new soil moisture dataset was abbreviated as GSSM and it has been validated with in situ observations, original CCI and SMAP data, and simulated gap areas. Results demonstrated that 1) the GSSM has similar accuracy with the SMAP and they are both more accurate than the original CCI data as compared with in situ observations at 399 global sites (averaged R=0.72, averaged ubRMSE<0.05); 2) the GSSM has the global spatial coverage, while filling the gaps of original CCI data through various soil moisture covariates (in artificial gaps verification, averaged R>0.86, averaged ubRMSE<0.04); 3) the GSSM has the same temporal variation characteristics with the original CCI dataset, while it can be combined with SMAP to obtain a long-term and near-real-time soil moisture dataset. Thus, GSSM provides long-term and seamless soil moisture data, paving the way for environmental disaster and water cycle process research.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Yunjia Wang, Hao Sun, Zhenheng Xu, Jinhua Gao, Huanyu Xu, Tian Zhang, and Dan Wu

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-200', Anonymous Referee #1, 15 Oct 2024
  • RC2: 'Comment on essd-2024-200', Anonymous Referee #2, 11 Nov 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-200', Anonymous Referee #1, 15 Oct 2024
  • RC2: 'Comment on essd-2024-200', Anonymous Referee #2, 11 Nov 2024
Yunjia Wang, Hao Sun, Zhenheng Xu, Jinhua Gao, Huanyu Xu, Tian Zhang, and Dan Wu

Data sets

GSSM: A global long term seamless soil moisture dataset (1981-2022) Hao Sun and Yunjia Wang https://data.tpdc.ac.cn/en/disallow/0f28a9b5-92eb-470a-80fe-472aa50a136f

Yunjia Wang, Hao Sun, Zhenheng Xu, Jinhua Gao, Huanyu Xu, Tian Zhang, and Dan Wu

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Short summary
We propose a novel matching method that can ensure the characteristics of the soil moisture time series and use a machine learning model to fill in the corrected data to solve the problem of low spatial coverage of soil moisture products. Finally, the dataset was obtained, namely long-term seamless CCI/SMAP monthly soil moisture products (GSSM). By obtaining this dataset, researchers can take into account the advantages of long time range, and high spatial coverage soil moisture products.
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