Mapping agroecosystem management practices from space and quantifying their impacts on crop productivity
type: CreativeWork
description: 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…
abstract: 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…
author: Zhou, Qu
language: en
keywords: Cover cropping, Planting date, Remote Sensing, Agriculture
date published: 2025-12
date modified: 2026-07-22T22:25:07Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois Urbana-Champaign

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2142/132736