Spatio-temporal monitoring of agricultural drought in China based on downscaled soil moisture data
- 1College of Geographical Sciences, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou 450046, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China
- 2Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
- 3College of Global change and Earth System Science, Beijing Normal University, Beijing 100875, China
Abstract
Agricultural drought threatens China’s food and ecological security, and accurate spatio-temporal monitoring is key for disaster mitigation. Soil moisture is critical for drought assessment, but the accuracy of existing remote sensing-based SM products in China remains to be improved. This study develops a framework that synergistically integrates a spatiotemporally adaptive gap-filling algorithm with a machine learning-based downscaling approach, generating a seamless 0.05° monthly SM dataset for China from 2003 to 2023. The methodology harnesses the complementary strengths of random forest modeling and spatiotemporal reconstruction techniques to effectively fuse multi-source satellite observations, achieving dual improvements in SM data accuracy and spatial coverage. Using this dataset, the standardized soil moisture index was applied to characterize the spatio-temporal evolution of agricultural drought. Results demonstrate that (1) The downscaled SM dataset provides finer spatial resolution and improved accuracy. Across different land-cover types, it achieves a 2.3–43.4% reduction in ubRMSE and a 1.2–52.7% increase in correlation coefficients relative to benchmark datasets. (2) Drought characterization based on the downscaled SM dataset and SSI accurately identified the extent of agricultural drought, showing a significant spatiotemporal consistency with agricultural disaster area. (3) Agricultural drought intensified significantly across China during the study period, characterized by northward migration of drought center and spatially heterogeneous aridification patterns—decreasing severity from northwest to southeast while increasing from northeast to southwest. High-frequency drought zones were predominantly clustered in ecologically vulnerable regions, particularly the agro-pastoral ecotone of northern China. (4) Distinct intra-annual drought dynamics emerged, with a southwest-to-northeast expansion dominating from January to June, followed by bidirectional propagation from the Yellow River-Huaihe River Basin (YRB-HRB) to northwestern and southeastern regions from June to December. This study provides high-accuracy data support for agricultural drought monitoring and offers scientific insights for developing regional differentiated drought mitigation strategies, which are of great significance for ensuring national food security.
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BibTeX
@article{luo2026spatiotemporal,
title = {{Spatio-temporal monitoring of agricultural drought in China based on downscaled soil moisture data}},
author = {Xiaoyu Luo and Mengmeng Cao and Yaoping Cui and Xiangjin Meng and Yan Zhou and Yibo Yan},
journal = {Ecological Indicators},
year = {2026},
month = oct,
doi = {10.1016/j.ecolind.2026.115708},
url = {https://doi.org/10.1016/j.ecolind.2026.115708},
note = {CC BY},
}
RIS
TY - JOUR
TI - Spatio-temporal monitoring of agricultural drought in China based on downscaled soil moisture data
AU - Xiaoyu Luo
AU - Mengmeng Cao
AU - Yaoping Cui
AU - Xiangjin Meng
AU - Yan Zhou
AU - Yibo Yan
PY - 2026
DA - 2026/10/30
JO - Ecological Indicators
DO - 10.1016/j.ecolind.2026.115708
UR - https://doi.org/10.1016/j.ecolind.2026.115708
AB - Agricultural drought threatens China’s food and ecological security, and accurate spatio-temporal monitoring is key for disaster mitigation. Soil moisture is critical for drought assessment, but the accuracy of existing remote sensing-based SM products in China remains to be improved. This study develops a framework that synergistically integrates a spatiotemporally adaptive gap-filling algorithm with a machine learning-based downscaling approach, generating a seamless 0.05° monthly SM dataset for China from 2003 to 2023. The methodology harnesses the complementary strengths of random forest modeling and spatiotemporal reconstruction techniques to effectively fuse multi-source satellite observations, achieving dual improvements in SM data accuracy and spatial coverage. Using this dataset, the standardized soil moisture index was applied to characterize the spatio-temporal evolution of agricultural drought. Results demonstrate that (1) The downscaled SM dataset provides finer spatial resolution and improved accuracy. Across different land-cover types, it achieves a 2.3–43.4% reduction in ubRMSE and a 1.2–52.7% increase in correlation coefficients relative to benchmark datasets. (2) Drought characterization based on the downscaled SM dataset and SSI accurately identified the extent of agricultural drought, showing a significant spatiotemporal consistency with agricultural disaster area. (3) Agricultural drought intensified significantly across China during the study period, characterized by northward migration of drought center and spatially heterogeneous aridification patterns—decreasing severity from northwest to southeast while increasing from northeast to southwest. High-frequency drought zones were predominantly clustered in ecologically vulnerable regions, particularly the agro-pastoral ecotone of northern China. (4) Distinct intra-annual drought dynamics emerged, with a southwest-to-northeast expansion dominating from January to June, followed by bidirectional propagation from the Yellow River-Huaihe River Basin (YRB-HRB) to northwestern and southeastern regions from June to December. This study provides high-accuracy data support for agricultural drought monitoring and offers scientific insights for developing regional differentiated drought mitigation strategies, which are of great significance for ensuring national food security.
SN - 1470-160X
SN - 1872-7034
ER -