Assessment of spatial patterns in cover crop distribution in the Midwest US using imaging spectroscopy

Cover crops, which are planted during winter in between cash crops, improve ecosystem resilience to climate change, pathogens, and other stressors. Improved resiliency in agroecosystems from cover cropping could result in myriad benefits, including increased carbon sequestration, decreased consumptive water use, and greater capacity to maintain essential ecosystem function in response to increasing droughts, heat waves, and flooding. Cover cropping has not been analyzed extensively in remote sensing research. Methods used for remote sensing of crops from satellites traditionally leverage the difference between visible and near-infrared reflectance to isolate the signal of photosynthetically active vegetation. However, using traditional greenness indices like NDVI for remotely sensing winter vegetation (i.e. cover crops) is challenging because vegetation reflectance signals are often confounded with reflectance of bare soil and crop residues. Furthermore, assessment of aboveground biodiversity in agroecosystems requires the ability to distinguish between different species of cover crops in agricultural fields, which is not possible with traditional greenness-based remote sensing. Here, we present new and established methods of detecting cover crops using imaging spectroscopy validated with ground-truth data from agricultural surveys across the Midwest. We find that remote sensing methods that incorporate thermal data in addition to traditional reflectance metrics are best able to distinguish between winter land-use practices. We conclude by addressing the potential of existing and upcoming hyperspectral and solar induced fluorescence (SIF) missions to further assess agroecosystem function and biodiversity in the context of global change. Management approaches like cover crops are urgently needed to improve sustainability and resilience of agroecosystems and food resources.