{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:28:49Z","timestamp":1780918129851,"version":"3.54.1"},"reference-count":68,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T00:00:00Z","timestamp":1727481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32271832"],"award-info":[{"award-number":["32271832"]}],"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":["2023YFF1304004-06"],"award-info":[{"award-number":["2023YFF1304004-06"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of National Key Research and Development Plan","award":["32271832"],"award-info":[{"award-number":["32271832"]}]},{"name":"Key Project of National Key Research and Development Plan","award":["2023YFF1304004-06"],"award-info":[{"award-number":["2023YFF1304004-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Leaf Area Index (LAI) is a critical parameter that sheds light on the composition and function of forest ecosystems. Its efficient and rapid measurement is essential for simulating and estimating ecological activities such as vegetation productivity, water cycle, and carbon balance. In this study, we propose to combine high-resolution GF-6 2 m satellite images with the LESS three-dimensional RTM and employ different machine learning algorithms, including Random Forest, BP Neural Network, and XGBoost, to achieve LAI inversion for forest stands. By reconstructing real forest stand scenarios in the LESS model, we simulated reflectance data in blue, green, red, and near-infrared bands, as well as LAI data, and fused some real data as inputs to train the machine learning models. Subsequently, we used the remaining measured LAI data for validation and prediction to achieve LAI inversion. Among the three machine learning algorithms, Random Forest gave the highest performance, with an R2 of 0.6164 and an RMSE of 0.4109, while the BP Neural Network performed inefficiently (R2 = 0.4022, RMSE = 0.5407). Therefore, we ultimately employed the Random Forest algorithm to perform LAI inversion and generated LAI inversion spatial distribution maps, achieving an innovative, efficient, and reliable method for forest stand LAI inversion.<\/jats:p>","DOI":"10.3390\/rs16193627","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T05:45:27Z","timestamp":1727675127000},"page":"3627","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Research on Leaf Area Index Inversion Based on LESS 3D Radiative Transfer Model and Machine Learning Algorithms"],"prefix":"10.3390","volume":"16","author":[{"given":"Yunyang","family":"Jiang","sequence":"first","affiliation":[{"name":"Key Laboratory for Silviculture and Forest Ecosystem of State Forestry and Grassland Administration, Research Center for Urban Forestry, College of Forestry, Beijing Forestry University, 35 Tsinghua East Road, Haidian District, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Silviculture and Forest Ecosystem of State Forestry and Grassland Administration, Research Center for Urban Forestry, College of Forestry, Beijing Forestry University, 35 Tsinghua East Road, Haidian District, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaijiang","family":"He","sequence":"additional","affiliation":[{"name":"Faculty of Life Science, Jilin Provincial Academy of Forestry Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5845-510X","authenticated-orcid":false,"given":"Xinna","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Silviculture and Forest Ecosystem of State Forestry and Grassland Administration, Research Center for Urban Forestry, College of Forestry, Beijing Forestry University, 35 Tsinghua East Road, Haidian District, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory for Silviculture and Forest Ecosystem of State Forestry and Grassland Administration, Research Center for Urban Forestry, College of Forestry, Beijing Forestry University, 35 Tsinghua East Road, Haidian District, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengyang","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Silviculture and Forest Ecosystem of State Forestry and Grassland Administration, Research Center for Urban Forestry, College of Forestry, Beijing Forestry University, 35 Tsinghua East Road, Haidian District, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beidagou Forest Farm, Shunyi District, Beijing 102115, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4642-8435","authenticated-orcid":false,"given":"Raffaele","family":"Lafortezza","sequence":"additional","affiliation":[{"name":"Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Via Amendola 165\/A, 70126 Bari, Italy"},{"name":"Key Laboratory for Silviculture and Forest Ecosystem of State Forestry and Grassland Administration, Research Center for Urban Forestry, College of Forestry, Beijing Forestry University, 35 Tsinghua East Road, Haidian District, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1029\/2018RG000608","article-title":"An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications","volume":"57","author":"Fang","year":"2019","journal-title":"Rev. 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