Detecting Winter Cover Crops and Crop Residues in the Midwest US Using Machine Learning Classification of Thermal and Optical Imagery

Mallory Liebl Barnes,Mahsa Khodaee,Landon Yoder

doi:10.3390/rs13101998

Abstract

Cover crops are an increasingly popular practice to improve agroecosystem resilience to climate change, pests, and other stressors. Despite their importance for climate mitigation and soil health, there remains an urgent need for methods that link winter cover crops with regional-scale climate mitigation and adaptation potential. Remote sensing is ideally suited to provide these linkages, yet, 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 the Normalized Difference Vegetation Index (NDVI) for remotely sensing winter vegetation, such as winter cover crops, is challenging because vegetation reflectance signals are often confounded with reflectance of bare soil and crop residues. Here, we present new and established methods of detecting winter cover crops using remote sensing observations. We find that remote sensing methods that incorporate thermal data in addition to traditional reflectance metrics are best able to distinguish between winter farm management practices. We conclude by addressing the potential of existing and upcoming hyperspectral and thermal missions to further assess agroecosystem function in the context of global change.

Highlights

This study demonstrated the value of going beyond greenness for the detection of cover crop and crop residues, which have critical climate mitigation and broader ecological implications
As policymakers look towards additional mechanisms to encourage greater adoption of cover crops to promote carbon sequestration, such as carbon markets [57], the availability of reliable detection methods will be critical to advancing these opportunities as a monitoring tool
The availability of improved detection will enable better trend analysis that can benefit farmers by identifying how cover crops affect cash crop yields, soil moisture and nitrogen fertilizer retention, and soil organic matter. These opportunities are crucial because farmers in the US Midwest have been slow to adopt zero tillage practices, which leave crop residues on the land surface, and slower still to adopt cover crops [9,58]

Summary

Introduction

As one of the most intensively managed landscapes worldwide, agroecosystems provide a crucial opportunity to mitigate greenhouse gas emissions at a global scale [1]. One of the most promising climate mitigation and adaptation interventions in agroecosystems is the practice of cover cropping [2], where farmers plant a non-commercial crop during fallow periods when the soil is typically bare between cash crop harvest and the following season’s planting. Alongside zero tillage and mulching, represent a set of practices that are jointly referred to as conservation agriculture, which is practiced by farmers worldwide [3]. In temperate climates, such as the U.S Midwest

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