Spatiotemporal Evolution of Agricultural Land Systems in the North China Plain Over Two Decades (2002–2022)

ABSTRACT Understanding agricultural land systems transformation is vital for food security, ecology, and rural development. This study analyzes spatiotemporal dynamics in the North China Plain (2002–2022), integrating environmental constraints, economic incentives, and policy interventions. By fusing MODIS (250 m) and Landsat (30 m) data via random forest, we generated high‐accuracy crop distribution maps for 4 years (2002, 2012, 2017, 2022), enhancing resolution and overcoming cloud limitations. The key transformations occurred across the main systems: winter wheat–summer maize (WW‐SM) proportion increased (34.4%–37.8%) but absolute area declined by 18.7% (46,233–37,603 km 2 ), shifting from water‐stressed Central Hebei to favorable regions in Northern Henan and Northwestern Shandong. Single maize expanded 10.8% (12,644–14,011 km 2 ), concentrated in groundwater‐depleted zones like Cangzhou, aligning with drought adaptation. Cotton cultivation plummeted (14.1%–3.3%) due to labor intensity and low profitability. Fruit/forest surged 164%, driven by policies and markets, whereas vegetables fragmented near urban areas. Transition analysis revealed 18% of WW‐SM areas converted to fruit/forest and vegetables during 2012–2017, coinciding with China's 2014 Groundwater Overdraft Control Regulation. Although adaptive strategies have mitigated water stress, persistent spatial mismatches in water allocation remain. Our integrated framework demonstrates robust capacity for agricultural landscape monitoring, providing actionable insights for the water‐energy‐food nexus.

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Publication Details

Journal
Land Degradation and Development
Published
2025-11-24
DOI
https://doi.org/10.1002/ldr.70316
Citations
1
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
1.72

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article

Spatiotemporal Evolution of Agricultural Land Systems in the North China Plain Over Two Decades (2002–2022)

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1 citations
Land Degradation and Development
Remote Sensing in Agriculture
1.72
article

Spatiotemporal Evolution of Agricultural Land Systems in the North China Plain Over Two Decades (2002–2022)

Yang Lu, Dengpan Xiao, Haonan Tan, Maoyuan Sun, Xiaomeng Yin, Chen Wang, Peipei Pan, Huizi Bai, Man Zhang
article en
1 citations

Abstract

ABSTRACT Understanding agricultural land systems transformation is vital for food security, ecology, and rural development. This study analyzes spatiotemporal dynamics in the North China Plain (2002–2022), integrating environmental constraints, economic incentives, and policy interventions. By fusing MODIS (250 m) and Landsat (30 m) data via random forest, we generated high‐accuracy crop distribution maps for 4 years (2002, 2012, 2017, 2022), enhancing resolution and overcoming cloud limitations. The key transformations occurred across the main systems: winter wheat–summer maize (WW‐SM) proportion increased (34.4%–37.8%) but absolute area declined by 18.7% (46,233–37,603 km 2 ), shifting from water‐stressed Central Hebei to favorable regions in Northern Henan and Northwestern Shandong. Single maize expanded 10.8% (12,644–14,011 km 2 ), concentrated in groundwater‐depleted zones like Cangzhou, aligning with drought adaptation. Cotton cultivation plummeted (14.1%–3.3%) due to labor intensity and low profitability. Fruit/forest surged 164%, driven by policies and markets, whereas vegetables fragmented near urban areas. Transition analysis revealed 18% of WW‐SM areas converted to fruit/forest and vegetables during 2012–2017, coinciding with China's 2014 Groundwater Overdraft Control Regulation. Although adaptive strategies have mitigated water stress, persistent spatial mismatches in water allocation remain. Our integrated framework demonstrates robust capacity for agricultural landscape monitoring, providing actionable insights for the water‐energy‐food nexus.

Land Degradation and Development
Hebei Academy of Sciences (CN), Hebei Science and Technology Department (CN), Hebei Normal University (CN)
National Natural Science Foundation of China, Hebei Normal University, Natural Science Foundation of Hebei Province
Openalex Percentile: Top 12%
Remote Sensing in Agriculture
1.72
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