Data_Sheet_1_Mapping cover crop species in southeastern Michigan using Sentinel-2 satellite data and Google Earth Engine.docx

Cover crops are a critical agricultural practice that can improve soil quality, enhance crop yields, and reduce nitrogen and phosphorus losses from farms. Yet there is limited understanding of the extent to which cover crops have been adopted across large spatial and temporal scales. Remote sensing offers a low-cost way to monitor cover crop adoption at the field scale and at large spatio-temporal scales. To date, most studies using satellite data have mapped the presence of cover crops, but have not identified specific cover crop species, which is important because cover crops of different plant functional types (e.g., legumes, grasses) perform different ecosystem functions. Here we use Sentinel-2 satellite data and a random forest classifier to map the cover crop species cereal rye and red clover, which represent grass and legume functional types, in the River Raisin watershed in southeastern Michigan. Our maps of agricultural landcover across this region, including the two cover crop species, had moderate to high accuracies, with an overall accuracy of 83%. Red clover and cereal rye achieved F1 scores that ranged from 0.7 to 0.77, and user's and producer's accuracies that ranged from 63.3% to 86.2%. The most common misclassification of cover crops was fallow fields with remaining crop stubble, which often looked similar because these cover crop species are typically planted within existing crop stubble, or interseeded into a grain crop. We found that red-edge bands and images from the end of April and early July were the most important for classification accuracy. Our results demonstrate the potential to map individual cover crop species using Sentinel-2 imagery, which is critical for understanding the environmental outcomes of increasing crop diversity on farms.

覆盖作物（Cover crops）是一项至关重要的农业实践，可改善土壤质量、提升作物产量，并减少农田氮磷流失。然而当前学界对覆盖作物在大空间与时间尺度上的推广程度仍缺乏充分认知。遥感（Remote sensing）为在田块尺度乃至大时空尺度上监测覆盖作物的推广情况提供了低成本解决方案。截至目前，多数利用卫星数据开展的相关研究仅完成了覆盖作物存在性的空间制图，却未能识别具体的覆盖作物物种——这一环节至关重要，原因在于不同植物功能型（plant functional types）的覆盖作物（如豆科植物、草本植物）所执行的生态系统功能存在显著差异。本研究以密歇根州东南部的雷辛河流域为研究区，借助Sentinel-2卫星数据与随机森林分类器（random forest classifier），对代表草本功能型的冬黑麦（cereal rye）以及代表豆科功能型的红三叶（red clover）这两种覆盖作物物种开展空间制图。本研究针对该区域农业土地覆被（含上述两种覆盖作物）的制图结果具备中等到较高的精度，总体精度达83%。其中，红三叶与冬黑麦的F1值介于0.7至0.77之间，用户精度与生产者精度则处于63.3%至86.2%的区间内。覆盖作物最常见的误分类对象为残留作物残茬的休耕地，二者光谱特征往往高度相似——这是由于此类覆盖作物通常种植于现有作物残茬之上，或是套播于谷类作物之中。研究发现，红边波段（red-edge bands）与4月末至7月初的影像对提升分类精度的贡献最为显著。本研究结果证实，利用Sentinel-2影像绘制单一种类覆盖作物的空间分布图具备可行性，这对于理解农田作物多样性提升所带来的环境效益至关重要。