Mapping agroecosystem management practices from space and quantifying their impacts on crop productivity
Global food demand is increasing rapidly, making it crucial to sustain crop productivity under climate change. Agriculture in the United States, one of the most productive regions in the world, faces challenges to be economically, socially, and environmentally sustainable. Effective agricultural management is essential for sustaining crop production under variable climate conditions and informing sound policy decisions. However, information on field-level agricultural management practices, including cover cropping and planting dates, is very limited. Recent advances in satellite remote sensing provide a promising solution for detecting field-level, long-term, and large-scale agricultural management practices. Thus, this dissertation aimed to map field-level cover cropping practices and crop planting dates and quantify their impacts on crop yields for sustainable agriculture in the United States through satellite remote sensing. Chapter 2 analyzed the performance of satellite data with different spatial, temporal, and radiometric characteristics in field-level cover cropping detection. I found that the Harmonized Landsat-8 and Sentinel-2 (HLS, ~3-day and 30 m) outperformed MODIS (daily and 250 m) and MODIS-calibrated PlanetScope (near-daily and 3 m) in field-level cover cropping detection. The detection accuracies were affected by cover cropping field sizes, regional adoption rates, and cover crop species. Chapter 3 developed a dynamic feature-threshold framework utilizing multi-sourced satellite data, environmental variables, and machine learning to generate field-level cover cropping maps. I found that cover cropping adoption in most counties in the Midwestern United States was stagnant from 2000 to 2011 but had significantly increased from 2011 to 2021. The increase was highly correlated to the funding for conservation programs, highlighting the importance of incentive programs to promote sustainable agricultural practices. Chapters 4 and 5 established a phenology-based framework to generate field-level crop planting dates for summer (corn and soybeans) and winter crops (winter wheat) in the United States by taking advantage of satellite time series. The predicted planting dates could capture 77 % field-level variations for corn (mean absolute error, MAE R 4.6 days), 71 % for soybean (MAE = 5.4 days), and 77% for winter wheat (MAE = 6.3 days). The framework allowed us to derive field-level planting dates from satellite time series for major commodity crops in the United States. Chapter 6 investigated the recent trends in planting dates and their impacts on crop yields in the United States from 2001 to 2023. I found that: (i) the national median soybean planting dates have shifted earlier by 0.35 days/year (p < 0.05), whereas no significant change has been observed for corn. (ii) Soybean planting dates advanced significantly in 31.2% of counties, with only 0.3% showing significant delays. Corn planting dates advanced in 18.3% of counties, while 2.1% experienced significant delays. (iii) These county-level sowing date changes benefit average yields for corn and soybeans by ~1.4% and ~2.5%, respectively. Chapter 7 discussed the implications and outlook of this dissertation for sustainable agroecosystems. This study contributes to understanding dynamics in agricultural management practices from satellite remote sensing and providing observation-based evidence to inform agricultural management and government policies for sustainable crop productivity.
Cover cropping, Planting date, Remote Sensing, Agriculture
University of Illinois - Urbana-Champaign
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