Mapping Cover Crops in Southeastern Michigan with Sentinel-2 Remote Sensing data

cover crops;remote sensing;river raisin watershed;random forest;School for Environment and Sustainability

Wang, XueweiBlesh, Jennifer ;

University of Michigan

关键词: cover crops;    remote sensing;    river raisin watershed;    random forest;    School for Environment and Sustainability;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/148873/Wang_Xuewei_Thesis.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

The River Raisin watershed, which spans southeastern Michigan and northwestern Ohio, is ahotspot for negative environmental impacts caused by industrialized and conventional agriculturalpractices, particularly excess nitrogen leaching and phosphorus runoff polluting the Great Lakes.Planting cover crops is one way for farmers to reduce nutrient losses, and has been adopted bysome farmers in this area. To understand the extent of cover crop adoption in the region, in ourstudy we used optical remote sensing data from Sentinel-2 to determine the spatial distribution ofcover crops in the River Raisin watershed. The random forest classification algorithm achieved86.37 % overall accuracy, and for the cover crops in the region, 75.33% (producer’s accuracy -PA) and 80.55% (user’s accuracy -UA) for cereal rye, and 85.90% (PA) and 83.98% (UA) for redclover. In particular, the red edge wavelengths of Sentinel-2 were the most important bands forclassifying cover crops. Our study shows that we can use readily-available satellite data to mapcover crops with high accuracies in the US Midwest. This implication will better the assessmentprocess of the adoption and impacts of conservation practices on farms.