The Lower Mississippi Alluvial Valley (LMAV) was home to about ten million hectare bottomland hardwood (BLH) forests in the Southern U.S. It experienced over 80 % area loss of the BLH forests in the past centuries and large-scale afforestation in recent decades. Due to the lack of a high-resolution cropland dataset, impacts of land use change (LUC) on the LMAV ecosystem services have not been fully understood. In this study, we developed a novel framework by integrating the machine learning algorithm, county-level agricultural census, and satellite-based cropland products to reconstruct the LMAV cropland distribution during 1850–2018 at a 30-m resolution. Results showed that the LMAV cropland area increased from 0.78 × 10⁴ km² in 1850 to 6.64 × 10⁴ km² in 1980 and then decreased to 6.16 × 10⁴ km² in 2018. Cropland expansion rate was the largest in the 1960s (749 km² yr⁻¹) but decreased rapidly thereafter, whereas cropland abandonment rate increased substantially in recent decades with the largest rate of 514 km² yr⁻¹ in the 2010s. Our dataset has three notable features: (1) the depiction of fine spatial details, (2) the integration of the county-level census, and (3) the inclusion of a machine-learning algorithm trained by satellite-based land cover product. Most importantly, our dataset well captured the continuous increasing trend in cropland area from 1930–1960, which was misrepresented by other cropland datasets reconstructed from the state-level census. Our dataset would be important to accurately evaluate the impacts of historical deforestation and recent afforestation efforts on regional ecosystem services, attribute the observed hydrological changes to anthropogenic and natural driving factors, and investigate how the socioeconomic factors control regional LUC pattern. Our framework and dataset are crucial to developing managerial and policy strategies for conserving natural resources and enhancing ecosystem services in the LMAV.

下密西西比河冲积谷（LMAV）是美国南部约一千万公顷底地硬木（BLH）森林的所在地，在过去的几个世纪中，BLH森林的面积损失超过80％，近几十年来大规模造林。由于缺乏高分辨率耕地数据集，因此尚未完全了解土地利用变化（LUC）对LMAV生态系统服务的影响。在这项研究中，我们通过集成机器学习算法，县级农业普查和基于卫星的农田产品，开发了一个新颖的框架，以30 m的分辨率重建了1850-2018年间的LMAV农田分布。结果表明，LMAV耕地面积从1850年的0.78×10 ⁴  km ²增加到6.64×10 ⁴  km1980年为²，然后在2018年降至6.16×10 ⁴  km ^2。农田扩张速率在1960年代最大（749 km ² yr ^-1），但此后迅速下降，而农田弃置率在最近几十年大幅增加，最大速率514 km ² yr ^-1在2010年代。我们的数据集具有三个显着特征：（1）精细的空间细节描述；（2）县级人口普查的整合；（3）包含由基于卫星的土地覆盖产品训练的机器学习算法。最重要的是，我们的数据集很好地反映了1930年至1960年耕地面积的持续增长趋势，而这一趋势被根据州级人口普查重建的其他耕地数据集所误解。我们的数据集对于准确评估历史森林砍伐和近期造林工作对区域生态系统服务的影响，将观测到的水文变化归因于人为和自然驱动因素以及调查社会经济因素如何控制区域LUC模式具有重要意义。