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<p>Mapping spatiotemporal dynamics of crop-specific areas is of great significance in addressing challenges faced by agricultural systems. But comparable multi-phase crop maps in year series have not yet been developed in most regions of the global. In this study, we developed a framework for updating annual crop-specific area maps at 10 km resolution based on crop statistics disaggregating, multi-source data integrating and machine learning. In our framework, we collected related spatial indicator used in previous studies and trained random forest regression models to predict spatiotemporal dynamics of crop-specific areas based on them. Annual crop statistics were further disaggregated based on probabilistic layer and harmonized based on multiple constraints. Finally, our results include maps of crop-specific areas covering 42 types from 1961–2022 in Africa, maps of crop-specific areas covering 14 types from 1980–2022 in China. Results show that our products have a reasonable level of consistency with independent reference map or statistics. Our products could be used as data basis for food security and environmental impact assessments.</p>

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Crop Statistic to Annual Map: Tracking spatiotemporal dynamics of crop-specific areas through machine learning and statistics disaggregating

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月16日

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  • Xiyu Li,
  • Le Yu,
  • Zhenrong Du,
  • Xiaoxuan Liu

摘要

Mapping spatiotemporal dynamics of crop-specific areas is of great significance in addressing challenges faced by agricultural systems. But comparable multi-phase crop maps in year series have not yet been developed in most regions of the global. In this study, we developed a framework for updating annual crop-specific area maps at 10 km resolution based on crop statistics disaggregating, multi-source data integrating and machine learning. In our framework, we collected related spatial indicator used in previous studies and trained random forest regression models to predict spatiotemporal dynamics of crop-specific areas based on them. Annual crop statistics were further disaggregated based on probabilistic layer and harmonized based on multiple constraints. Finally, our results include maps of crop-specific areas covering 42 types from 1961–2022 in Africa, maps of crop-specific areas covering 14 types from 1980–2022 in China. Results show that our products have a reasonable level of consistency with independent reference map or statistics. Our products could be used as data basis for food security and environmental impact assessments.