Remapping Carbon Storage Change in Retired Farmlands on the Loess Plateau in China from 2000 to 2021 in High Spatiotemporal Resolution

Guo, Bingqian; Fang, Mingjie; Yang, Leilei; Guo, Tao; Ma, Chuang; Hu, Xiangyun; Guo, Zhaoxiang; Ma, Zemeng; Li, Qiang; Wang, Zhaoli; Liu, Weiguo

doi:https://doi.org/10.5194/essd-2025-222

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https://doi.org/10.5194/essd-2025-222

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03 Jun 2025

| 03 Jun 2025

Status: this preprint is currently under review for the journal ESSD.

Remapping Carbon Storage Change in Retired Farmlands on the Loess Plateau in China from 2000 to 2021 in High Spatiotemporal Resolution

Bingqian Guo, Mingjie Fang, Leilei Yang, Tao Guo, Chuang Ma, Xiangyun Hu, Zhaoxiang Guo, Zemeng Ma, Qiang Li, Zhaoli Wang, and Weiguo Liu

Abstract. The soil organic carbon pool is a crucial component of carbon storage in terrestrial ecosystems, playing a key role in regulating the carbon cycle and mitigating atmospheric CO₂ concentration increases. To combat soil degradation and enhance soil organic carbon sequestration on the Loess Plateau, the Grain-for-Green Program (GFGP) has been implemented. Accurately quantifying carbon capture and storage (CCS) resulting from farmland retirement is essential for informing land use management. In this study, the spatial and temporal distribution of retired farmlands on the Loess Plateau was analyzed using Landsat imagery from 1999 to 2021. To assess the effects of the length of farmland retirement, climate, soil properties, elevation, and other factors on CCS, climate-zone-specific linear regression models were developed based on field-sampled soil data. These models were then used to map the dataset of CCS across the retired farmlands. Results indicate that a total of 39,065 km² of farmland was retired over the past two decades, with 45.61 % converted to grasslands, 29.75 % to shrublands, and 24.64 % to forestlands. The length of farmland retirement showed a significant positive correlation with CCS, and distinct models were developed for different climatic zones to achieve high-resolution (30 m) CCS mapping. The total CCS from retired farmland on the Loess Plateau was estimated at 21.77 Tg in carbon equivalent according to the dataset, with grasslands contributing 81.10 %, followed by forestlands (11.16 %) and shrublands (7.74 %).

Received: 16 Apr 2025 – Discussion started: 03 Jun 2025

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Bingqian Guo, Mingjie Fang, Leilei Yang, Tao Guo, Chuang Ma, Xiangyun Hu, Zhaoxiang Guo, Zemeng Ma, Qiang Li, Zhaoli Wang, and Weiguo Liu

Status: final response (author comments only)

RC1:
'Comment on essd-2025-222', Anonymous Referee #1, 23 Aug 2025
This study builds paired field measurements of SOC for retired farmlands and adjacent croplands, develops SOC-change inversion models stratified by climate zones and ecosystem types, and maps post-retirement SOC stock changes (0–30 cm) on the Loess Plateau at 30 m resolution. The work aligns with carbon accounting needs under a “natural baseline + ecological engineering” context, and the data collection plus spatialization effort are of practical value. Meanwhile, the manuscript requires further strengthening and standardization in terminology/units, transparency of samples and modeling, and the design/validation of land-cover classification and uncertainty communication.

Major Comments
Standardize terminology to avoid ambiguity. (1) The manuscript defines post-retirement SOC stock change as carbon capture and storage (CCS), which is easily confused with engineered carbon capture and storage. Please use this term cautiously and, at first mention, clearly constrain it to the study-specific definition of ΔSOC; alternatively, consider a less ambiguous term such as carbon sequestration (or SOC stock change, ΔSOC). (2) The phrase "length of farmland retirement" for the time elapsed since retirement should be refined; consider using the more common "years since retirement" and apply it consistently.

Introduction—focus on the case for necessity. The introduction adequately motivates the importance of soil restoration and summarizes ecological restoration programs worldwide, but the discussion of uncertainty in estimating impacts of farmland retirement on SOC and the specific necessity of this study remains limited. Please sharpen the study aims by clarifying, relative to existing datasets/studies, where the present dataset and methods add value and which key problems the study is designed to resolve.

Section 2.2 (Identifying Retired Farmlands): classification/validation details. Retired farmland is mapped via SVM supervised classification, but classification and validation details are insufficient, which undermines the credibility of subsequent CCS (ΔSOC) estimation. Please add the classification scheme, sizes of training/validation sets, per-class accuracies, and the full confusion matrix.

Section 2.3 (Field Sampling and SOC Measurements): sampling and screening rules. The text states that initial sampling points were laid out at 5 km intervals and that unsuitable points were removed. Please clarify (1) the design of paired sites (how pairs were planned and located) and (2) the criteria for removing sites and the number removed.

Sample size and power by stratum. The design is “3 profiles per site × 3 depths per profile × 3 samples per depth,” yielding 135 paired profiles (cropland and adjacent retired land as a pair). Because modeling is stratified by multiple factors (ecosystem type × climate zone), please report the effective sample size (n) in each stratum, the train/validation split, and a brief power assessment to support model robustness.

Section 2.4 (Model Development and CCS Mapping). (1) Results indicate stratified mapping primarily via multiple linear regression (MLR). Please state explicitly which model(s) are used for final mapping and why they were chosen. (2) Specify the temporal scale of variables such as temperature and precipitation (multi-year means, growing-season metrics, or WorldClim bioclimatic variables). (3) List the final candidate predictors after correlation/collinearity screening and confirm that the VIF threshold is 10. (4) If terms like “optimal/best model” are used, provide the selection basis (multi-model/parameter comparisons and cross-validation metrics), or replace with “final selected model(s)” for rigor and transparency.

Section 3.1, Fig. 2—readability and area accounting. Increase font sizes and color contrast; consider trimming the number of years shown. In addition, please present side-by-side series (or a stacked chart) for annual retirement, reclamation, and final retained retirement areas to clarify that “cumulative annual retirement area > final retirement area” is mainly due to reclamation, avoiding misinterpretation as classification error.

Section 3.2—SOC change statistics without uncertainty. In addition to reporting means, please provide confidence intervals or standard errors and briefly describe the uncertainty estimation method.

Figure 4—units/terminology, outliers, n by stratum, and consistent zoning. (1) The caption reads “SOC stocks,” but the unit is g C kg⁻¹ (concentration). Please standardize terminology and units: if the focus is on stocks, convert using 0–30 cm thickness and bulk density to report kg C m⁻². (2) There are apparent outliers; please describe outlier detection and handling (data cleaning and/or robust estimation) to limit their influence on modeling and conclusions. (3) Report sample sizes (n) for each climate × ecosystem stratum to support assessment of model reliability. (4) Zoning standards are not fully consistent across sub-panels (forest: SH/SA; shrub: WT-SH/WT-SA/MT-SA; grassland: WT/MT). If a three-class climate grouping is adopted, consider unifying to WT-SH, WT-SA, and MT-SA.

Figure 5—consistent zoning for response curves. The climate-zone groupings are not fully aligned across sub-figures. Please indicate whether this is due to sample size or model-fit constraints, and make the zoning standard as consistent as possible across figures and text.

Line 226 wording (“seven regression equations were the best representative”). If no formal multi-model comparison was conducted, please avoid “optimal/best representative.” Use “final selected regression equations” and provide the selection basis (e.g., cross-validation metrics).

Table 1—transparency. Please add per-stratum sample sizes (n), variable definitions/units and preprocessing (e.g., standardization), and VIF diagnostics to facilitate verification and reproducibility.

Section 3.4—accounting conventions. (1) Clarify whether the mapping and totals represent potential increase or realized increase accounting for lag effects (i.e., years since retirement). (2) For pixels retired and later reclaimed, explain how they are handled in mapping and in area-integrated accounting. Please make the conventions explicit in Methods and keep them consistent in Results.

Consistency between Table 2 and modeling strata. Table 2 summarizes carbon increases by MT-SA/WT-SA/WT-SH, whereas earlier modeling strata differ across forest/shrub/grassland. Please align climate-zone definitions between modeling and reporting (or provide a clear crosswalk).

Uncertainty quantification. The discussion of uncertainty is qualitative. Please add pixel-level uncertainty layers/ranges and confidence intervals for zonal/regional aggregates, or provide the methods and results in the Supplement.

Figure standards and language editing. Aim for “read at a glance”: define acronyms at first use, standardize axes and units, and increase font sizes and color separability. Professional English editing is recommended to improve terminological consistency and grammar.
Citation: https://doi.org/10.5194/essd-2025-222-RC1
RC2: 'Comment on essd-2025-222', Anonymous Referee #2, 29 Aug 2025

The Loess Plateau experienced the most severe land degradation due to human disturbance and climate change, but now it has become the paragon of ecological restoration and soil and water conservation across the world through “Grain for Green Projects” that retire farmlands in this region. By comparing SOC differences between retired farmlands and adjacent agricultural lands, the study provides a more persuasive approach for carbon accounting in evaluating restoration benefits. To further strengthen the manuscript and enhance readability, I recommend a minor revision, and the specific comments are appended as:
Methodology
1. One of the most significant contributions of this study is its innovative methodology for accounting carbon benefits from ecological restoration. The authors should strengthen the review of commonly used approaches in the literature and explicitly highlight the method in this study as an objective.

2. Greater clarity is needed regarding the sampling process, particularly how and why certain sampling points were removed.

3. The study develops models for seven regional combinations. To support their validity, the sample sizes for each combination should be reported.

4. Numerous variables were introduced into the SOC change models. While the use of machine learning for variable selection is commendable, more detail is needed regarding the criteria and procedures used for selecting important variables.

5. The "Materials and Methods" section of the main text omits an introduction to the Random Forest algorithm, despite its results being presented in Figure 6b. This constitutes an oversight in the methodological description and requires revision. It is recommended that the authors add content introducing this algorithm and its application details to the "Methods" section.

Figures
1. Figure 1 (Study area, sampling sites and climatic zones) requires significant revision to enhance clarity and information integration. Currently, Figure 1 separates the presentation of sampling site distribution (subplot b) and climatic zonation (subplot c). As both depict the same region and are crucial for understanding the study's specific climatic zone modeling approach, displaying them separately reduces the efficiency of the figure's expression. Subplot (a), as a macroscopic location map, provides limited and redundant information. The authors should consider merging subplots (b) and (c) into a single integrated main map, using the climatic zonation as a base layer and clearly overlaying the field sampling points. Concurrently, the original subplot (a) could be reduced to an embedded small map for macroscopic positioning only (with a transparent border indicating the main map area and simplified internal elements), and a unified main legend should be created, with all captions placed below the figure.

2. The figure 2 is currently difficult to interpret. For improved readability, I recommend showing only a few representative years in the main text, while moving the remaining years to the supplementary material.
3. The core datasets in Figure 2 (Spatial distribution of retired farmlands) and Figure 3 (Area of ecosystem conversion types from retired farmlands) suffer from scientific ambiguity and require revision. The captions and legends of Figure 2 and Figure 3 fail to clearly and unambiguously define what each annual layer represents regarding "newly added ecosystem types converted from retired farmlands each year" and the "cumulative conversion status" in Figure 2(v). It is recommended that the authors define all layer data in the main text methodology, and revise the captions and legends of Figure 2 and Figure 3 to be concise, accurate, and completely self-explanatory (the caption should explicitly include "Annual New Retired Farmland Conversion" or "Cumulative Distribution," and the legend should clearly label items such as "Converted Forestland" instead of just "Forestland," which can be misleading as it implies all existing forestland).
4. Figure 4 (Comparison of soil organic carbon stocks) has critical omissions and ambiguities in the presentation and description of significance analysis, warranting revision. Figure 4 uses letter annotations to indicate potential significant differences, but the main text lacks a description of the statistical significance analysis methods used for these comparisons. In Figure 4a, the meaning of the letters and the precise scope of comparison are unclear; the X-axis label "1-7" in Figure 4b is difficult to understand, and the significance represented by its letter annotations is also unexplained. The authors should consider providing a detailed description of all significance testing and multiple comparison methods in the main text's methodology section. Concurrently, revise Figure 4's legend/caption to clearly explain the meaning of the letters and the scope of comparison, and correct Figure 4b's X-axis label to improve self-explanatory power.

5. Figure 5 (Relationship between length of farmland retirement and CCS) contains redundant axis labels and confusing Y-axis units, requiring revision. The current Y-axis label "CCS/g C·kg⁻¹" in Figure 5 may lead to technical confusion, as CCS quantification units are typically area-based (e.g., kg C/m²). Furthermore, the repetition of identical X-axis and Y-axis labels across the seven subplots creates visual redundancy. It is suggested that the authors revise the Y-axis unit to be consistent with the definition and calculation method of CCS. Additionally, to enhance the professionalism and conciseness of the figure, a shared axis label layout is recommended.

6. Figure 6 (Correlation matrix and variable importance) lacks self-explanatory power and information completeness, warranting revision. Figure 6a's correlation matrix has missing X-axis labels, and some variable names are displayed ambiguously; in Figure 6b's variable importance chart, all abbreviated variables (BD, BIO1-19, etc.) are not fully explained in the caption, affecting the figure's self-explanatory nature. The authors should consider completing the axis labels and variable display for Figure 6a, and append the full meanings of all abbreviated variables in the caption.
Results
1. On the Loess Plateau, pairwise comparisons of SOC are rarely conducted. Although field sampling is challenging and published references are limited, the uncertainty associated with the final SOC estimates should be explicitly presented to strengthen model evaluation.
Discussion
1. The observed dynamics of farmland retirement and reclamation from 2000 to 2021 are striking. The authors should provide some discussion on possible underlying drivers of these trends.

2. The discussion would benefit from practical recommendations on strategies to enhance SOC on the Loess Plateau, as well as reflections on potential ways to improve model accuracy beyond process-based approaches.

Citation: https://doi.org/10.5194/essd-2025-222-RC2

Bingqian Guo, Mingjie Fang, Leilei Yang, Tao Guo, Chuang Ma, Xiangyun Hu, Zhaoxiang Guo, Zemeng Ma, Qiang Li, Zhaoli Wang, and Weiguo Liu

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https://doi.org/10.5194/essd-2025-222-supplement

Data sets

The 30-meter resolution distribution of retired farmlands and their carbon sequestration on the Loess Plateau in China from 2000 to 2021 Leilei Yang https://doi.org/10.6084/m9.figshare.28785971

Bingqian Guo, Mingjie Fang, Leilei Yang, Tao Guo, Chuang Ma, Xiangyun Hu, Zhaoxiang Guo, Zemeng Ma, Qiang Li, Zhaoli Wang, and Weiguo Liu

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Using satellite images and soil samples, we found that 39,065 km² of farmland was retired across China’s Loess Plateau from 2000 to 2021, with nearly half converted to grasslands. Over two decades, these restored lands captured 21.77 million metric tons of carbon, Grasslands contributed over 80 % of this carbon storage, highlighting their critical role in climate mitigation.


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