the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The first 25-year, quarterly 10-m land change map of China's Loess Plateau reveals long-term and substantial soil erosion mitigation
Abstract. Unsustainable human activities have driven global ecological degradation. In China, decades of restoration policies have been implemented to reverse this trend in severely degraded regions with catastrophic soil erosion, transforming them into landscapes of ecological recovery. However, the evolution of soil erosion in these regions remains poorly quantified due to the absence of high-resolution, long-term, and high-frequency monitoring data. Here, to address this gap and provide a reliable spatiotemporal benchmark dataset, we conducted the first 10-m quarterly wall-to-wall land change mapping for China's flagship ecological restoration site: the Loess Plateau, based on the developed cross-temporal consistency-constraint deep learning framework. The dataset was generated using over 10 terabytes of Sentinel and Landsat imagery and documents land-cover dynamics across 100 quarterly time steps from 2000 to 2024, showing an overall accuracy of 81.44 % based on 40,000 annotated samples and 79.8 % for third-party validation sources. The resulting maps record pronounced land-cover dynamics, including forest expansion (+13,131 km2), cropland expansion (+28,095 km2), and bare land reduction (-65,029 km2) over the past decades. Furthermore, the produced dataset was combined with environmental factors to measure the 25-year quarter-level soil erosion, where comparison with government survey data shows strong consistency, with a mean absolute error of 4.50 %. The dataset further illustrates that long-term ecological interventions have substantially reduced erosion intensity in the region by 30 % over the past 25 years, from 13.34 to 9.35 t/(hm2·a). Based on this benchmark, the long-term, fine-grained soil erosion becomes possible to estimate. The data-driven analysis indicates that current erosion is most severe in the central and southwestern Loess Plateau, and scenario modeling based on multiple factors suggests that optimized vegetation distribution – including grassland expansion and cropland-to-forest conversion – could potentially reduce future erosion intensity to 6.42 t/(hm2·a). This dataset provides a comprehensive benchmark for erosion mitigation in the Loess Plateau and its underlying drivers, providing critical insights for sustainable land management, ecological restoration, and policy development both in China and across fragile ecosystems worldwide. The land-cover maps and soil erosion maps is available at
https://www.scidb.cn/en/s/ZJFB3u (Cheng et al., 2025).
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Status: final response (author comments only)
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RC1: 'Comment on essd-2025-807', Anonymous Referee #1, 30 Jan 2026
- AC1: 'Reply on RC1', Hongyan Zhang, 21 Mar 2026
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RC2: 'Comment on essd-2025-807', Anonymous Referee #2, 25 Feb 2026
General comments:
This manuscript synthesized Sentinal-2 and Landsat series high resolution data and applied an advanced framework, cross-temporal consistency-constraint learning, to map land cover (LC) time series 2000-2024 over China's Loess Plateau region. In this framework, encoder is applied on the time series to obtain consistency-constrained time series (CCTS), which enhanced the temporal coherence and semantic stability of land-cover representations, and first build segmentation model accounting for inter-temporal consistency in feature levels, then trained segmented features under the supervision of labeled datasets, and finally perturbed CCTS accounting for different noise in the original dataset to perform intra-temporal refinement over the classification and produce the final LC time series. After analysis of the land use change over the past two decades, authors applied the LC product to estimate the average annual soil loss per unit area and assess the water-induced soil erosion over the whole Loess Plateau area. Overall, this is solid work introducing a new fine-resolution dataset with comprehensive and in-depth application case study, which matches the scope of ESSD. There are several concerns from my side, which suggest further improvement before the manuscript can be accepted for publication.
Specific comments:
- As the major scope of ESSD, work shall focus on your data product first. The strategy using CCTS is claimed to bring more consistency, which is the biggest highlight in your data product, but lacks quantitative evaluation in the whole manuscript. I suggest authors quantitatively evaluate the improvement of spatial and temporal consistency in LC mapping with/without building CCTS.
- Grassland, cropland and barren land all have low PA, which will bring substantial uncertainty to your conclusions. Please consider the possible uncertainties that might change the conclusion of your analysis.
Technical corrections:
Please indicate in the title that the soil erosion discussed in this manuscript is specifically water-induced erosion.
Line 202: How exactly did you perturb the image to consider intra-temporal consistency?
Line 286: It seems like the meaningful spatial resolution is only 30m for 2000-2015, then your title claiming the data product to be a 10-m resolution is misleading?
Line 308: Why does alpha have two different values for warm and cold periods separately? Is it because of snow fall contributing almost no weathering or just the weathering rate reduced under low temperature?
Line 309: Is snowfall included or excluded?
Line 317: What's the reason for re-calibrating Chinese soil? It seems to be a linear bias correction formula that due to some systematic bias? I assume soil in China has no difference from other regions when represented by texture (sand, silt, clay, OC and bedrock)
Line 323: Cropland has a strong seasonal signal. But here in your equation you mentioned A is an annual value. Are you using the same NDVI and parameters for different seasons? Please clarify.
Line 391: Under fine resolution such as 10m, the regular 8 LC types might not be enough for mosaic landscape cases, e.g., the green space in built-up regions, such as parks, that can be classified as either forest or cropland. I would suggest authors to discuss this point.
Line 391: Are both validation LC products having the same LC type definition and follow the same criteria to map LC?
Line 399: "By contrast, GLC_FCS30D often misinterprets water bodies as wetland due to surrounding vegetation, and further misclassifies substantial grassland areas as cropland or bare land" Do you have reference to prove this conclusion? Is nearby vegetation the reason for misclassification?
Line 458: "In particular, 8.6% of the region was transformed from bare land to grassland". In table 5, Barren land PA is not high and mostly confounded by grassland. This also jeopardizes your further conclusion, for example, "the transformation from bare land to grassland produces the most erosion reduction" in line 527. Please discuss and provide uncertainty.
Citation: https://doi.org/10.5194/essd-2025-807-RC2 - AC2: 'Reply on RC2', Hongyan Zhang, 21 Mar 2026
Data sets
LP-QLC10: 25-year quarterly land change mapping in China’s Loess Plateau reveals long-term and substantial soil erosion mitigation Mofan Cheng et al. https://www.scidb.cn/s/ZJFB3u
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This manuscript presents a substantial advancement in both methodology and data availability for investigating soil erosion dynamics in fragile ecosystems, with a particular focus on the Loess Plateau. By integrating long-term time-series Landsat and Sentinel imagery with relevant environmental datasets, the authors develop a high-resolution land-cover and soil erosion dataset spanning 25 years, while further shortening the update frequency to a quarterly scale. Such temporal coverage and resolution represent a notable improvement over existing regional product. The reported mapping performance is robust for a large-scale application, with an overall accuracy of 81.44% for land-cover classification and a mean absolute error of 4.5% for soil erosion estimates. Based on an examination of the released dataset, this work is expected to provide a valuable data foundation for future studies on land-surface processes, ecological restoration, and environmental change across the Loess Plateau. Leveraging this self-produced dataset, the authors further analyze the spatial and temporal evolution of land cover and soil erosion, revealing a pronounced overall reduction in erosion intensity over the study period. The long-term and high-frequency observations enable novel insights into the seasonal heterogeneity of erosion driven by precipitation, the role of vegetation dynamics in erosion mitigation, and the influence of topographic factors. In addition, the attempt to assess potential erosion under an optimized vegetation configuration provides practical implications for soil conservation and land management strategies in the region.
The manuscript is clearly written and accompanied by high-quality data visualizations. Overall, this study makes a valuable contribution in terms of both data products and analytical perspectives, and I believe it is suitable for publication after revisions. The detailed comments are as follows: