the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Nov 2025
Spatio-temporal Monitoring of Agricultural Drought in China Based on Downscaled Soil Moisture Data
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 achieves significant improvements in both spatial resolution and accuracy, showing a 2.3–34.4 % reduction in ubRMSE and 1.2–52.7 % improvement in correlation coefficients compared 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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Status: open (until 28 Feb 2026)
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CC1: 'Comment on egusphere-2025-5122', Nima Zafarmomen, 25 Dec 2025
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AC1: 'Reply on CC1', Mengmeng Cao, 27 Dec 2025
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The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5122/egusphere-2025-5122-AC1-supplement.pdf
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AC1: 'Reply on CC1', Mengmeng Cao, 27 Dec 2025
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RC1: 'Comment on egusphere-2025-5122', Anonymous Referee #1, 03 Feb 2026
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This manuscript presents a comprehensive framework for developing a spatially and temporally continuous soil moisture dataset over China. The methodology is technically rigorous, well justified, and the resulting product is of clear relevance for drought monitoring and hydroclimatic research. The following comments are intended to clarify specific methodological aspects and improve the presentation and interpretability of the results.
Comments:
Outlier/QC description is too vague (Page 4, lines 157–161). The authors state that outliers were excluded via “rigorous quality control,” but they do not specify the method (e.g., SD-based screening, median absolute deviation, percentile/IQR filtering, temporal consistency checks, etc.). Please explicitly describe the QC procedure and provide a citation.
Baseline datasets should be introduced earlier and motivated. The two benchmark soil moisture products (Zhang et al., 2023; Meng et al., 2021) are used for comparison, but the Introduction would benefit from a short description of what these datasets are, and why they may be insufficient for agricultural drought monitoring in China (e.g., continuity limitations, regional bias, coarse spatial resolution, limited temporal coverage). This is especially important for Meng et al. (2021), which is China-specific, because the novelty here appears to be extended temporal coverage and finer target resolution (0.25° → 0.05°).
DEM resampling + slope derivation need methodological clarity (Page 4, lines 181–183). The paper states that SRTM 1 km DEM was resampled to 0.05°, and slope was derived using a 3×3 neighborhood gradient algorithm, but the resampling method is not provided. Bilinear interpolation is often preferable to nearest-neighbor for continuous topography to avoid blocky artifacts at 0.05°. Also, please clarify whether slope was computed after resampling to 0.05° (recommended to reduce artifacts) or computed at 1 km and then resampled.
Figure 2 readability/layout. Figure 2 is comprehensive but difficult to read at its current size and vertical arrangement. Consider using a layout that better leverages horizontal space (e.g., moving Steps 1 and 2 beside Step 3, or increasing font size and simplifying inset elements) to improve readability.
Figure 4 color convention + inset choice. Consider reversing the palette so that higher soil moisture is visually intuitive (commonly blue = wetter, red = drier). Also, the inset region shown does not strongly demonstrate “local-scale” improvements; choosing a more heterogeneous boundary/transition zone (e.g., shrubland–cropland or complex terrain/land cover) would better illustrate the benefit of the downscaled product.
I am not sure what is meant by this sentence on Page 12, lines 403–405, "As shown in the zoomed-in area of Fig. 4, critical subgrid features that were previously obscured in passive microwave observations are now resolved with a precision of 0.038 m³/m³.". Please clarify what precision means here.
Pearson's R inconsistency (Page 15, lines, 449–450). The manuscript writes that there is significant "negative correlation" but the numbers reported are positive (Pearson’s R=0.015 to 0.277, p<0.05).
Expand the discussion on generalizability. The manuscript argues that the China-specific hydroclimatic drivers mitigate biases relative to generic global products (Page 13, lines 431–433). It would strengthen the Discussion to briefly frame how this workflow could be transferred to other drought-prone regions (and what region-specific drivers would likely be needed), which could broaden impact/citability.
Their data availability statement lists input sources, but it’s not clear (from the statement excerpt) whether the new seamless 0.05° SM dataset and processing code are archived somewhere persistent.
Citation: https://doi.org/10.5194/egusphere-2025-5122-RC1
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This study presents a robust and scientifically significant contribution to the field of hydro-climatology and precision agriculture. The novelty of this work lies in its integrated methodological framework that successfully bridges the gap between coarse-resolution satellite observations and the high-resolution requirements of regional drought assessment.
Minor Comments for Improvement
While the manuscript is well-structured, the following minor points should be addressed to enhance clarity and depth:
1. The term "file-scale" appears to be a typo. It should likely be corrected to "field-scale" to accurately reflect the context of agricultural monitoring.
2. In the comparison between original and downscaled images, the "zoomed-in" areas are very helpful. However, it would be beneficial to explicitly state the specific geographic location (e.g., coordinates or province) of the zoomed-in sub-regions in the figure caption to provide better context.
3. You mention that TVDI and precipitation were dominant predictors. It would be valuable to include a small Feature Importance Plot (perhaps as a sub-panel in Figure 2 or 3) to quantitatively show the contribution of each environmental covariate.
4. In Section 5, you mention that the study is constrained by "static SSI thresholds." Briefly expanding on how dynamic thresholds (accounting for different crop phenological stages) might change the results in the future would strengthen the "Future Work" portion of the discussion.
5. Finally, while the study correctly identifies groundwater depletion and irrigation expansion as compounding drivers of drought in the YRB-HRB regions (Line 475), the discussion would be significantly strengthened by acknowledging the role of high-resolution vegetation data in modeling these interactions. I strongly recommend considering and discussing the implications of studies such as "Assimilation of sentinel‐based leaf area index for modeling surface‐ground water interactions in irrigation districts". Integrating such perspectives would provide a deeper theoretical link between satellite-derived vegetation indices (like the LAI used in your RF model) and the complex subterranean water dynamics that govern agricultural moisture availability in China’s intensive irrigation zones.