<b>Tracking spatiotemporal dynamics of crop-specific areas through machine learning and statistics disaggregating</b>
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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 10km resolution based on crop statistics disaggregating, multi-source data integrating and machine learning, taking factors related with crop distribution in different regions and complex agricultural systems into accounts. Experiments were conducted in three study areas (Africa, China, and USA) respectively corresponding to three conditions of the information coverage of crop distribution (low, median, and high). 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. Our framework is a good attempt to integrate two strategies (top-down and bottom-up), creating more possibility for crop mapping to integrate statistic with remote sensing. 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 and maps of crop-specific areas covering 15 types from 2008-2022 in USA. Results show that our products has a relatively good consistency with independent reference map or statistics. Our products provide approximate estimates for spatiotemporal dynamics of crop-specific areas in multiple regions over several decades, which could be used as data basis for food security and environmental impact assessments.
绘制特定作物种植面积的时空动态分布，对于应对农业系统面临的各类挑战具有重要意义。但目前全球多数地区尚未构建出可对比的年度序列多期作物分布图集。本研究基于作物统计数据空间细化、多源数据融合与机器学习（Machine Learning）方法，结合不同区域作物分布相关影响因子与复杂农业系统特征，构建了一套可更新10km分辨率年度特定作物种植面积分布图的框架。本研究分别在非洲、中国、美国三个研究区域开展实验，这三个区域分别对应作物分布信息覆盖度低、中等、较高的三类场景。本框架收集了既往研究中常用的相关空间指标，并基于这些指标训练随机森林回归（Random Forest Regression）模型，以预测特定作物种植面积的时空动态。同时，基于概率层对年度作物统计数据进行空间细化，并通过多重约束条件实现数据的协调统一。本框架为整合自顶向下（Top-down）与自底向上（Bottom-up）两种研究策略做出了有益尝试，为作物分布图绘制中融合统计数据与遥感（Remote Sensing）数据提供了更多可能。最终生成的研究结果包括：非洲地区1961-2022年间涵盖42类作物的种植面积分布图集，中国地区1980-2022年间涵盖14类作物的种植面积分布图集，以及美国地区2008-2022年间涵盖15类作物的种植面积分布图集。结果表明，本研究生成的产品与独立参考地图或统计数据具有较好的一致性。本数据集可为多个区域数十年间特定作物种植面积的时空动态提供近似估算结果，可作为粮食安全与环境影响评估的基础数据支撑。
