{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129755"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129755","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Remote sensing of crop structure and field condition enabled through smart sensors and satellite data","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Li, Kaiyuan"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Natural Res & Env Sciences","degree_department":null,"school":null,"contributors":["Guan, Kaiyu","Bernacchi, Carl J.","Peng, Bin","Chen, Jingming","Chen, Min"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-01","date_published":"2025-05-01","updated_at":"2026-07-22T22:25:05Z","subjects":["Canopy structure monitoring","land surface temperature"],"languages":["en","eng"],"rights":["Copyright 2025 Kaiyuan Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129755","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Guan, Kaiyu","Bernacchi, Carl J.","Peng, Bin","Chen, Jingming","Chen, Min"]},{"key":"dc:creator","label":"Author","values":["Li, Kaiyuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-01","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Natural Res & Env Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Canopy structure monitoring","land surface temperature"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Kaiyuan Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129755"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Kaiyuan Li, accepted the attached license on 2025-04-30 at 09:09.","The student, Kaiyuan Li, submitted this Dissertation for approval on 2025-04-30 at 09:26.","This Dissertation was approved for publication on 2025-05-01 at 15:10.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22108 on 2025-10-19 at 19:54:47","Vegetation canopy structure is fundamental to understanding plant-environment interactions, particularly in relation to energy, water, and carbon fluxes. Monitoring canopy structure parameters are crucial for advancing our understanding of canopy function and improving the accuracy of ecological and agricultural models. However, the monitoring of crop canopy structure and field conditions still face challenges in terms of instrument and methodology. The primary focus of this research is to address one key science question: How can we efficiently characterize crop canopy structure and field condition to support improved monitoring of crop productivity? In this dissertation, we investigated three key canopy structure parameters, including leaf area index (LAI), leaf angle distribution (LAD), and clumping index (CI), as well as evaluated land surface temperature (LST) to better understand crop canopy and field conditions. Specifically, Chapter 2 provides a comprehensive evaluation of several widely used indirect optical instruments, aiming to enhance the understanding and guide the correct application and interpretation of these instruments for estimating average leaf angle. Additionally, two classical methods for estimating average leaf angle were tested, and modifications to one of the methods were proposed for enhanced accuracy. Building on Chapter 2, Chapter 3 further refines the methodologies used for LAD estimation. We developed a novel and accurate three-step approach for LAD estimation, which is superior in reducing the number of known variables during the inversion process, compared with traditional approaches. We also evaluated the impact of non-leaf plant elements on the indirect instruments and demonstrated that their impact is minimal during early and peak growth stages but becomes more significant during the senescent stage. Chapter 4 presents a novel approach to estimating the CI of row crops using a 30°-tilted digital camera, adapting three classical CI retrieval methods. The influencing factors of clumping index, including segment size, view zenith angle and seasonal trajectories, were thoroughly investigated. Chapter 5 evaluates several satellite LST products for their applicability in agricultural monitoring in the U.S. Corn Belt. This dissertation contributes to the efficient quantification of crop canopy structure using low-cost camera sensors, offering a scalable solution for acquiring extensive ground truth data. These advancements will support cross-scale sensing and improve the modeling and monitoring of canopy structure and crop productivity."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Remote sensing of crop structure and field condition enabled through smart sensors and satellite data"]}]}],"canonical_facts":{"dc:contributor":["Guan, Kaiyu","Bernacchi, Carl J.","Peng, Bin","Chen, Jingming","Chen, Min"],"dc:creator":["Li, Kaiyuan"],"dc:date":["2025-05-01","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Kaiyuan Li, accepted the attached license on 2025-04-30 at 09:09.","The student, Kaiyuan Li, submitted this Dissertation for approval on 2025-04-30 at 09:26.","This Dissertation was approved for publication on 2025-05-01 at 15:10.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22108 on 2025-10-19 at 19:54:47","Vegetation canopy structure is fundamental to understanding plant-environment interactions, particularly in relation to energy, water, and carbon fluxes. Monitoring canopy structure parameters are crucial for advancing our understanding of canopy function and improving the accuracy of ecological and agricultural models. However, the monitoring of crop canopy structure and field conditions still face challenges in terms of instrument and methodology. The primary focus of this research is to address one key science question: How can we efficiently characterize crop canopy structure and field condition to support improved monitoring of crop productivity? In this dissertation, we investigated three key canopy structure parameters, including leaf area index (LAI), leaf angle distribution (LAD), and clumping index (CI), as well as evaluated land surface temperature (LST) to better understand crop canopy and field conditions. Specifically, Chapter 2 provides a comprehensive evaluation of several widely used indirect optical instruments, aiming to enhance the understanding and guide the correct application and interpretation of these instruments for estimating average leaf angle. Additionally, two classical methods for estimating average leaf angle were tested, and modifications to one of the methods were proposed for enhanced accuracy. Building on Chapter 2, Chapter 3 further refines the methodologies used for LAD estimation. We developed a novel and accurate three-step approach for LAD estimation, which is superior in reducing the number of known variables during the inversion process, compared with traditional approaches. We also evaluated the impact of non-leaf plant elements on the indirect instruments and demonstrated that their impact is minimal during early and peak growth stages but becomes more significant during the senescent stage. Chapter 4 presents a novel approach to estimating the CI of row crops using a 30°-tilted digital camera, adapting three classical CI retrieval methods. The influencing factors of clumping index, including segment size, view zenith angle and seasonal trajectories, were thoroughly investigated. Chapter 5 evaluates several satellite LST products for their applicability in agricultural monitoring in the U.S. Corn Belt. This dissertation contributes to the efficient quantification of crop canopy structure using low-cost camera sensors, offering a scalable solution for acquiring extensive ground truth data. These advancements will support cross-scale sensing and improve the modeling and monitoring of canopy structure and crop productivity."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129755"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Kaiyuan Li"],"dc:subject":["Canopy structure monitoring","land surface temperature"],"dc:title":["Remote sensing of crop structure and field condition enabled through smart sensors and satellite data"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Natural Res & Env Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}