A high-resolution (0.05°) global seamless continuity record (2002–2023) of near-surface soil freeze-thaw states via passive microwave and optical satellite data
Abstract. The global near-surface soil freeze-thaw (FT) states are crucial for understanding complex interactions with hydrological, ecological, and climatic processes. However, current remote sensing of FT states primarily relies on passive microwave remote sensing, which, despite its all-weather monitoring capabilities, suffers from low spatial resolution. This limitation restricts its application to hydroclimatological scales, precluding its use in finer-scale studies such as soil erosion and hydrometeorological applications. To address this issue, this study introduces a novel downscaling approach that integrates passive microwave and optical satellite data to generate a long-term (2002–2023), high-resolution (0.05°) dataset of global near-surface FT states, ensuring daily seamless continuity. The dataset was validated against in situ measurements, demonstrating that the high-resolution product maintains an overall accuracy of 83.78 %, consistent with the coarse-resolution microwave-based dataset, while offering enhanced spatial detail. Comprehensive global trend analyses provided new insights into the dynamics of FT cycles, revealing that the average annual number of frost days in regions north of 45° N is 187.8 ± 12.7 d, with 14.35 % of the area showing a decreasing trend in frozen persistence. Additionally, the average annual number of freeze onset dates is 240.3 ± 7.2, and 9.10 % of the area exhibits a trend of delayed freeze onset. The high-resolution record enables accurately monitoring FT states and providing detailed information for a refined understanding of hydrological and ecological effects globally. The global 0.05° near-surface soil FT state dataset (FT-HiDFA) is freely available at https://doi.org/10.11888/Cryos.tpdc.301551 (Zhao et al., 2024b).
Authors
- Yu Bai (ORCID: https://orcid.org/0000-0003-1707-3081)
- Xiaokang Kou (ORCID: https://orcid.org/0000-0003-1685-3166)
- Tianjie Zhao (ORCID: https://orcid.org/0000-0002-0914-599X)
- Jinbiao Zhu (ORCID: https://orcid.org/0000-0002-7664-7375)
- Yuei‐An Liou (ORCID: https://orcid.org/0000-0002-8100-5529)
- Lingmei Jiang (ORCID: https://orcid.org/0000-0002-9847-9034)
- Youhua Ran (ORCID: https://orcid.org/0000-0001-7774-4612)
- Ziqian Zhang (ORCID: https://orcid.org/0000-0003-3638-6294)
- Jiancheng Shi
- Pei Yu (ORCID: https://orcid.org/0000-0003-4041-4681)
- Jie Pan (ORCID: https://orcid.org/0000-0002-9974-651X)
- Jingyao Zheng
- Defeng Feng (ORCID: https://orcid.org/0009-0001-7510-3245)
Institutions
- Sun Yat-sen University (CN)
- Chinese Academy of Sciences (CN)
- Beijing Normal University (CN)
- Northwest Institute of Eco-Environment and Resources (CN)
- National Space Science Center (CN)
- Center For Remote Sensing (United States) (US)
- Aerospace Information Research Institute (CN)
- University of Chinese Academy of Sciences (CN)
- State Key Laboratory of Remote Sensing Science (CN)
- Shijiazhuang Tiedao University (CN)
Publication Details
- Journal
- Earth system science data
- Published
- 2025-11-20
- DOI
- https://doi.org/10.5194/essd-17-6273-2025
- Citations
- 1
- Primary Topic
- Soil Moisture and Remote Sensing
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- Japan Aerospace Exploration Agency
- National Key Research and Development Program of China