Estimation of Carbon Sequestration Capacity of Cultivated Land Based on Improved CASA-CGC Model—A Case Study of Anhui Province

Quantifying carbon sequestration in cultivated land ecosystems is essential for achieving carbon neutrality and ensuring food security, yet current models often fail to capture the complex interactions between crop phenology and environmental factors at regional scales. This paper proposed an improved CASA-CGC model that couples crop phenological parameters with photosynthetic physiological processes, enabling precise carbon sink accounting at the growth cycle scale of cultivated land ecosystems. Results indicate that the carbon sequestration capacity of cultivated land in the province significantly increased from 2010 to 2022, with an average increase of 163.04 g C m−2, and the spatial pattern showed a centralized evolution characteristic. Model validation showed that the accuracy of the CASA-CGC model is significantly better than traditional methods. Compared with remote sensing inversion products and 93 ground measurement point data, the improved CASA-CGC model increased the R2 by 0.155 and reduced the RMSE by 4.19 compared with the tr-CASA model. The innovative introduction of the GeoDetector model reveals that the nonlinear interaction between natural and human factors dominates the carbon sequestration process (accounting for 60%), with the interaction effect between altitude and cropping system configuration being the strongest (q = 0.312), confirming that humans can significantly amplify the potential of natural carbon sinks by optimizing cropping systems.

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Publication Details

Journal
Agriculture
Published
2025-11-27
DOI
https://doi.org/10.3390/agriculture15232462
Citations
2
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
3.43
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article

Estimation of Carbon Sequestration Capacity of Cultivated Land Based on Improved CASA-CGC Model—A Case Study of Anhui Province

Kaifang Shi, Lina Zhang, Yingchun Wang, Bao Li et al.
2 citations
Agriculture
Remote Sensing in Agriculture
3.43
article

Estimation of Carbon Sequestration Capacity of Cultivated Land Based on Improved CASA-CGC Model—A Case Study of Anhui Province

Kaifang Shi, Lina Zhang, Yingchun Wang, Bao Li, Rui Zhang, Chun Dong
article en
2 citations

Abstract

Quantifying carbon sequestration in cultivated land ecosystems is essential for achieving carbon neutrality and ensuring food security, yet current models often fail to capture the complex interactions between crop phenology and environmental factors at regional scales. This paper proposed an improved CASA-CGC model that couples crop phenological parameters with photosynthetic physiological processes, enabling precise carbon sink accounting at the growth cycle scale of cultivated land ecosystems. Results indicate that the carbon sequestration capacity of cultivated land in the province significantly increased from 2010 to 2022, with an average increase of 163.04 g C m−2, and the spatial pattern showed a centralized evolution characteristic. Model validation showed that the accuracy of the CASA-CGC model is significantly better than traditional methods. Compared with remote sensing inversion products and 93 ground measurement point data, the improved CASA-CGC model increased the R2 by 0.155 and reduced the RMSE by 4.19 compared with the tr-CASA model. The innovative introduction of the GeoDetector model reveals that the nonlinear interaction between natural and human factors dominates the carbon sequestration process (accounting for 60%), with the interaction effect between altitude and cropping system configuration being the strongest (q = 0.312), confirming that humans can significantly amplify the potential of natural carbon sinks by optimizing cropping systems.

AgricultureVol. 15(23)
Chinese Academy of Sciences (CN), Northwest Institute of Eco-Environment and Resources (CN), Chinese Academy of Surveying and Mapping (CN), Nanjing Surveying and Mapping Research Institute (China) (CN), Anhui Normal University (CN)
Openalex Percentile: Top 7%
Remote Sensing in Agriculture
3.43
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