{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T02:43:00Z","timestamp":1775097780336,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017700","name":"Henan Province Science and Technology Research Project","doi-asserted-by":"publisher","award":["232102111123"],"award-info":[{"award-number":["232102111123"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017700","name":"Henan Province Science and Technology Research Project","doi-asserted-by":"publisher","award":["42101362"],"award-info":[{"award-number":["42101362"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["232102111123"],"award-info":[{"award-number":["232102111123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101362"],"award-info":[{"award-number":["42101362"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soybeans are rich in high-quality protein and raw materials for producing hundreds of chemical products. Consequently, soybean cultivation has gained widespread prevalence across diverse geographic regions. Soybean breeding necessitates the development of early-, standard-, and late-maturing cultivars to accommodate cultivation at various latitudes, thereby optimizing the utilization of solar radiation. In the practical process of determining the maturity of soybean breeding materials within the breeding field, the ripeness is assessed based on three critical criteria: pod moisture content, leaf color, and the degree of leaf shedding. These parameters reflect the crown structure, physicochemical parameters, and reproductive organ changes in soybeans during the maturation process. Therefore, methods for analyzing soybean maturity at the breeding plot scale should match the standards of agricultural experts to the maximum possible extent. This study presents a hyperspectral remote sensing approach for monitoring soybean maturity. We collected five periods of unmanned aerial vehicle (UAV)-based soybean canopy hyperspectral digital orthophoto maps (DOMs) and ground-level measurements of leaf chlorophyll content (LCC), flavonoids (Flav), and the nitrogen balance index (NBI) from a breeding farm. This study explores the following aspects: (1) the correlations between soybean LCC, NBI, Flav, and maturity; (2) the estimation of soybean LCC, NBI, and Flav using Gaussian process regression (GPR), partial least squares regression (PLSR), and random forest (RF) regression techniques; and (3) the application of threshold-based methods in conjunction with normalized difference vegetation index (NDVI)+LCC and NDVI+NBI for soybean maturity monitoring. The results of this study indicate the following: (1) Soybean LCC, NBI, and Flav are associated with maturity. LCC increases during the beginning bloom period (P1) to the beginning seed period (P3) and sharply decreases during the beginning maturity period (P4) stage. Flav continues to increase from P1 to P4. NBI remains relatively consistent from P1 to P3 and then drops rapidly during the P4 stage. (2) The GPR, PLSR, and RF methodologies yield comparable accuracy in estimating soybean LCC (coefficient of determination (R2): 0.737\u20130.832, root mean square error (RMSE): 3.35\u20134.202 Dualex readings), Flav (R2: 0.321\u20130.461, RMSE: 0.13\u20130.145 Dualex readings), and NBI (R2: 0.758\u20130.797, RMSE: 2.922\u20133.229 Dualex readings). (3) The combination of the threshold method with NDVI &lt; 0.55 and NBI &lt; 8.2 achieves the highest classification accuracy (accuracy = 0.934). Further experiments should explore the relationships between crop NDVI, the Chlorophyll Index, LCC, Flav, and NBI and crop maturity for different crops and ecological areas.<\/jats:p>","DOI":"10.3390\/rs15194807","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T07:28:29Z","timestamp":1696318109000},"page":"4807","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Mapping Soybean Maturity and Biochemical Traits Using UAV-Based Hyperspectral Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Lizhi","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"},{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"}]},{"given":"Rui","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Changchun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"},{"name":"Institute of Quantitative Remote Sensing and Smart Agriculture, Henan Polytechnic University, Jiaozuo 454000, China"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Key Lab of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing 100083, China"}]},{"given":"Jingyu","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Bing","family":"Li","sequence":"additional","affiliation":[{"name":"Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475001, China"}]},{"given":"Hongbo","family":"Qiao","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Haikuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9766-5313","authenticated-orcid":false,"given":"Jibo","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,3]]},"reference":[{"key":"ref_1","first-page":"100265","article-title":"A Review on Plant-Based Proteins from Soybean: Health Benefits and Soy Product Development","volume":"7","author":"Qin","year":"2022","journal-title":"J. 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