{"id":{"repo_id":"maryland","oai_identifier":"oai:drum.lib.umd.edu:1903/35480"},"canonical_url":"https://search.dev.ndltd.org/etd/maryland/oai:drum.lib.umd.edu:1903/35480","repository":{"repo_id":"maryland","name":"University of Maryland","base_url":"https://api.drum.lib.umd.edu/server/oai/request"},"display":{"title":"ADVANCING NATIONAL-SCALE HIGH-RESOLUTION CROP MAPPING USING REMOTE SENSING","abstract":"The growing global population has intensified the need to increase agricultural production while minimizing environmental impacts for a sustainable future. Meeting these pressing challenges fundamentally depends on accurate, spatially explicit information on crop distribution. This dissertation advances national-scale crop type mapping in large countries by integrating remote sensing, sample-based field surveys, and machine learning to support both long-term and near-real-time agricultural monitoring. Three studies collectively contribute to methodological innovations in high-resolution crop mapping across diverse agricultural systems. First, I improved an existing crop mapping workflow that integrates field surveys with satellite-based classification, demonstrating its feasibility for generating the first openly available, national-scale 10-m maize and soybean maps for smallholder agriculture in China in 2019. Second, I further enhanced the workflow for industrial agriculture and produced annual 10-m maize and soybean maps across the Contiguous United States (CONUS) from 2019 to 2022. By comparing these maps to the widely used 30-m products, I quantified the advantages of higher-resolution crop mapping, showing that 10-m maps reduced 30-m mixed pixels by approximately 8% for maize and 9% for soybean across counties representing 99.9% of national cultivation. Finally, I evaluated the potential of progressive within-season crop mapping using Sentinel-2 time series and historical field data, and examined the earliest feasible date and phenological stage for accurate crop identification across the CONUS. Results show that, without current-year field labels, at least 50% of counties accounting for 79% of national cultivation could achieve 90% accurate identification of maize and soybean by July 29 and August 8, respectively. The identified spatial variability in the earliest reliable mapping timelines provides valuable guidance for region-specific map development to support timely crop monitoring and enhance national food security. Together, this dissertation contributes methodological advances for simultaneously obtaining unbiased crop area estimates and wall-to-wall crop maps in both smallholder and industrial agricultural contexts. It demonstrates the capability of remote sensing-based approaches to support spatially explicit, accurate, and timely crop monitoring from regional to national scales.","abstract_html":"The growing global population has intensified the need to increase agricultural production while minimizing environmental impacts for a sustainable future. Meeting these pressing challenges fundamentally depends on accurate, spatially explicit information on crop distribution. This dissertation advances national-scale crop type mapping in large countries by integrating remote sensing, sample-based field surveys, and machine learning to support both long-term and near-real-time agricultural monitoring. Three studies collectively contribute to methodological innovations in high-resolution crop mapping across diverse agricultural systems. First, I improved an existing crop mapping workflow that integrates field surveys with satellite-based classification, demonstrating its feasibility for generating the first openly available, national-scale 10-m maize and soybean maps for smallholder agriculture in China in 2019. Second, I further enhanced the workflow for industrial agriculture and produced annual 10-m maize and soybean maps across the Contiguous United States (CONUS) from 2019 to 2022. By comparing these maps to the widely used 30-m products, I quantified the advantages of higher-resolution crop mapping, showing that 10-m maps reduced 30-m mixed pixels by approximately 8% for maize and 9% for soybean across counties representing 99.9% of national cultivation. Finally, I evaluated the potential of progressive within-season crop mapping using Sentinel-2 time series and historical field data, and examined the earliest feasible date and phenological stage for accurate crop identification across the CONUS. Results show that, without current-year field labels, at least 50% of counties accounting for 79% of national cultivation could achieve 90% accurate identification of maize and soybean by July 29 and August 8, respectively. The identified spatial variability in the earliest reliable mapping timelines provides valuable guidance for region-specific map development to support timely crop monitoring and enhance national food security. Together, this dissertation contributes methodological advances for simultaneously obtaining unbiased crop area estimates and wall-to-wall crop maps in both smallholder and industrial agricultural contexts. It demonstrates the capability of remote sensing-based approaches to support spatially explicit, accurate, and timely crop monitoring from regional to national scales.","abstract_has_math":false,"creators":["Li, Haijun"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Geography","school":null,"contributors":[],"advisors":["Song, Xiaopeng"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-24T03:02:25Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.13016/fchf-2hgi"],"render_values":[{"text":"https://doi.org/10.13016/fchf-2hgi","href":"https://doi.org/10.13016/fchf-2hgi","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/1903/35480","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Song, Xiaopeng"]},{"key":"dc:contributor.department","label":"Department","values":["Geography"]},{"key":"dc:creator","label":"Author","values":["Li, Haijun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-07-01T05:47:13Z"]},{"key":"dc:date.issued","label":"Date","values":["2026"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.13016/fchf-2hgi"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1903/35480"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The growing global population has intensified the need to increase agricultural production while minimizing environmental impacts for a sustainable future. Meeting these pressing challenges fundamentally depends on accurate, spatially explicit information on crop distribution. This dissertation advances national-scale crop type mapping in large countries by integrating remote sensing, sample-based field surveys, and machine learning to support both long-term and near-real-time agricultural monitoring. Three studies collectively contribute to methodological innovations in high-resolution crop mapping across diverse agricultural systems. First, I improved an existing crop mapping workflow that integrates field surveys with satellite-based classification, demonstrating its feasibility for generating the first openly available, national-scale 10-m maize and soybean maps for smallholder agriculture in China in 2019. Second, I further enhanced the workflow for industrial agriculture and produced annual 10-m maize and soybean maps across the Contiguous United States (CONUS) from 2019 to 2022. By comparing these maps to the widely used 30-m products, I quantified the advantages of higher-resolution crop mapping, showing that 10-m maps reduced 30-m mixed pixels by approximately 8% for maize and 9% for soybean across counties representing 99.9% of national cultivation. Finally, I evaluated the potential of progressive within-season crop mapping using Sentinel-2 time series and historical field data, and examined the earliest feasible date and phenological stage for accurate crop identification across the CONUS. Results show that, without current-year field labels, at least 50% of counties accounting for 79% of national cultivation could achieve 90% accurate identification of maize and soybean by July 29 and August 8, respectively. The identified spatial variability in the earliest reliable mapping timelines provides valuable guidance for region-specific map development to support timely crop monitoring and enhance national food security. Together, this dissertation contributes methodological advances for simultaneously obtaining unbiased crop area estimates and wall-to-wall crop maps in both smallholder and industrial agricultural contexts. It demonstrates the capability of remote sensing-based approaches to support spatially explicit, accurate, and timely crop monitoring from regional to national scales."]},{"key":"dc:title","label":"Title","values":["ADVANCING NATIONAL-SCALE HIGH-RESOLUTION CROP MAPPING USING REMOTE SENSING"]}]}],"canonical_facts":{"dc:contributor.advisor":["Song, Xiaopeng"],"dc:contributor.department":["Geography"],"dc:creator":["Li, Haijun"],"dc:date.accessioned":["2026-07-01T05:47:13Z"],"dc:date.issued":["2026"],"dc:description.abstract":["The growing global population has intensified the need to increase agricultural production while minimizing environmental impacts for a sustainable future. Meeting these pressing challenges fundamentally depends on accurate, spatially explicit information on crop distribution. This dissertation advances national-scale crop type mapping in large countries by integrating remote sensing, sample-based field surveys, and machine learning to support both long-term and near-real-time agricultural monitoring. Three studies collectively contribute to methodological innovations in high-resolution crop mapping across diverse agricultural systems. First, I improved an existing crop mapping workflow that integrates field surveys with satellite-based classification, demonstrating its feasibility for generating the first openly available, national-scale 10-m maize and soybean maps for smallholder agriculture in China in 2019. Second, I further enhanced the workflow for industrial agriculture and produced annual 10-m maize and soybean maps across the Contiguous United States (CONUS) from 2019 to 2022. By comparing these maps to the widely used 30-m products, I quantified the advantages of higher-resolution crop mapping, showing that 10-m maps reduced 30-m mixed pixels by approximately 8% for maize and 9% for soybean across counties representing 99.9% of national cultivation. Finally, I evaluated the potential of progressive within-season crop mapping using Sentinel-2 time series and historical field data, and examined the earliest feasible date and phenological stage for accurate crop identification across the CONUS. Results show that, without current-year field labels, at least 50% of counties accounting for 79% of national cultivation could achieve 90% accurate identification of maize and soybean by July 29 and August 8, respectively. The identified spatial variability in the earliest reliable mapping timelines provides valuable guidance for region-specific map development to support timely crop monitoring and enhance national food security. Together, this dissertation contributes methodological advances for simultaneously obtaining unbiased crop area estimates and wall-to-wall crop maps in both smallholder and industrial agricultural contexts. It demonstrates the capability of remote sensing-based approaches to support spatially explicit, accurate, and timely crop monitoring from regional to national scales."],"dc:identifier":["https://doi.org/10.13016/fchf-2hgi"],"dc:identifier.uri":["http://hdl.handle.net/1903/35480"],"dc:language.iso":["en"],"dc:title":["ADVANCING NATIONAL-SCALE HIGH-RESOLUTION CROP MAPPING USING REMOTE SENSING"],"dc:type":["Dissertation"]},"updated_at":"2026-07-24T03:02:25Z"}