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<p>The critical point (CP) and permanent wilting point (PWP) are key soil hydraulic characteristics that control the land surface energy budget and water balance. There is a lack of available data for these parameters on the global scale. This study extracts CP and PWP through soil moisture drydown (SMD) and provides global yearly soil hydraulic properties from a long-term (2002–2023) remote-sensing soil moisture product (Neural Network-based Soil Moisture, NNsm). Validated against 1334 stations from the International Soil Moisture Network (ISMN), the results show that the global medians of CP and PWP based on the NNsm are robust over time, and outperform the Soil Moisture Active and Passive (SMAP) dataset in accuracy due to the advantage of daily temporal resolution. Furthermore, this dataset holds an advantage over existing products, as it is derived from a multi-year climatological mean state and solely from satellite-based soil moisture observation. The derived dataset is useful for those who wish to connect land-atmosphere characteristics with their interests, as well as calibrate land surface models.</p>

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A global dataset of remote sensing-based soil critical point and permanent wilting point

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月30日

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  • Yawei Xu,
  • Qing He,
  • Hui Lu,
  • Kun Yang,
  • Dara Entekhabi,
  • Daniel J. Short Gianotti

摘要

The critical point (CP) and permanent wilting point (PWP) are key soil hydraulic characteristics that control the land surface energy budget and water balance. There is a lack of available data for these parameters on the global scale. This study extracts CP and PWP through soil moisture drydown (SMD) and provides global yearly soil hydraulic properties from a long-term (2002–2023) remote-sensing soil moisture product (Neural Network-based Soil Moisture, NNsm). Validated against 1334 stations from the International Soil Moisture Network (ISMN), the results show that the global medians of CP and PWP based on the NNsm are robust over time, and outperform the Soil Moisture Active and Passive (SMAP) dataset in accuracy due to the advantage of daily temporal resolution. Furthermore, this dataset holds an advantage over existing products, as it is derived from a multi-year climatological mean state and solely from satellite-based soil moisture observation. The derived dataset is useful for those who wish to connect land-atmosphere characteristics with their interests, as well as calibrate land surface models.