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<p>Soil moisture (SM) has long been recognized as one of the essential climate variables. However, there are no satellite-derived global SM climatological records spanning over 30 years at fine spatiotemporal resolution. Based on the latest version of the 25 km CCI SM data and 1 km soil data derived from the SoilGrids, a spatially continuous CCI dataset was first obtained using the triple collocation analysis algorithm with the assistance of SM derived from the ERA5, GLEAM, and GLDAS. Subsequently, SM was derived at a fine resolution of 1 km using a downscaling approach considering the sub-grid variability of hydraulic parameters. Finally, the first satellite-derived SM climatological record (1980–2023) at fine spatiotemporal resolution with global seamless coverage was developed. A comprehensive validation against 372 global <i>in-situ</i> observations yielded an averaged unbiased root mean square error of 0.051 m<sup>3</sup>/m<sup>3</sup>. Specifically, a Bayesian statics-based credibility analysis of the validated accuracy was first conducted at four networks with dense observation stations. Results indicated an averaged credibility of 83.5% of the accuracy assessment at these networks.</p>

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SMCR: A first satellite-derived all-weather daily/1-km Soil Moisture Climatological Record (1980–2023)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月13日

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  • Shuangxue Zhai,
  • Pei Leng,
  • Chunfeng Ma,
  • Abba Aliyu Kasim,
  • Tian Ma,
  • Hongyuan Huo,
  • Si-Bo Duan,
  • Xiangyang Liu,
  • Zhao-Liang Li

摘要

Soil moisture (SM) has long been recognized as one of the essential climate variables. However, there are no satellite-derived global SM climatological records spanning over 30 years at fine spatiotemporal resolution. Based on the latest version of the 25 km CCI SM data and 1 km soil data derived from the SoilGrids, a spatially continuous CCI dataset was first obtained using the triple collocation analysis algorithm with the assistance of SM derived from the ERA5, GLEAM, and GLDAS. Subsequently, SM was derived at a fine resolution of 1 km using a downscaling approach considering the sub-grid variability of hydraulic parameters. Finally, the first satellite-derived SM climatological record (1980–2023) at fine spatiotemporal resolution with global seamless coverage was developed. A comprehensive validation against 372 global in-situ observations yielded an averaged unbiased root mean square error of 0.051 m3/m3. Specifically, a Bayesian statics-based credibility analysis of the validated accuracy was first conducted at four networks with dense observation stations. Results indicated an averaged credibility of 83.5% of the accuracy assessment at these networks.