{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T17:06:53Z","timestamp":1775927213142,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:00:00Z","timestamp":1653004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFB0504500"],"award-info":[{"award-number":["2018YFB0504500"]}]},{"name":"National Key R&amp;D Program of China","award":["41875038"],"award-info":[{"award-number":["41875038"]}]},{"name":"National Key R&amp;D Program of China","award":["42071348"],"award-info":[{"award-number":["42071348"]}]},{"name":"National Key R&amp;D Program of China","award":["42001291"],"award-info":[{"award-number":["42001291"]}]},{"name":"National Key R&amp;D Program of China","award":["2021BCA220"],"award-info":[{"award-number":["2021BCA220"]}]},{"name":"National Natural Science Foundation of China","award":["2018YFB0504500"],"award-info":[{"award-number":["2018YFB0504500"]}]},{"name":"National Natural Science Foundation of China","award":["41875038"],"award-info":[{"award-number":["41875038"]}]},{"name":"National Natural Science Foundation of China","award":["42071348"],"award-info":[{"award-number":["42071348"]}]},{"name":"National Natural Science Foundation of China","award":["42001291"],"award-info":[{"award-number":["42001291"]}]},{"name":"National Natural Science Foundation of China","award":["2021BCA220"],"award-info":[{"award-number":["2021BCA220"]}]},{"name":"Key R&amp;D projects in Hubei Province","award":["2018YFB0504500"],"award-info":[{"award-number":["2018YFB0504500"]}]},{"name":"Key R&amp;D projects in Hubei Province","award":["41875038"],"award-info":[{"award-number":["41875038"]}]},{"name":"Key R&amp;D projects in Hubei Province","award":["42071348"],"award-info":[{"award-number":["42071348"]}]},{"name":"Key R&amp;D projects in Hubei Province","award":["42001291"],"award-info":[{"award-number":["42001291"]}]},{"name":"Key R&amp;D projects in Hubei Province","award":["2021BCA220"],"award-info":[{"award-number":["2021BCA220"]}]},{"name":"LIESMARS Special Research Funding","award":["2018YFB0504500"],"award-info":[{"award-number":["2018YFB0504500"]}]},{"name":"LIESMARS Special Research Funding","award":["41875038"],"award-info":[{"award-number":["41875038"]}]},{"name":"LIESMARS Special Research Funding","award":["42071348"],"award-info":[{"award-number":["42071348"]}]},{"name":"LIESMARS Special Research Funding","award":["42001291"],"award-info":[{"award-number":["42001291"]}]},{"name":"LIESMARS Special Research Funding","award":["2021BCA220"],"award-info":[{"award-number":["2021BCA220"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The leaf area index (LAI), a key parameter used to characterize the structure and function of the vegetation canopy, is crucial to simulations of the carbon, nitrogen, and water cycles of Earth\u2019s system. In this paper, a neural network (NN) method coupled with vegetation canopy and atmospheric radiative transfer (RT) simulations is proposed to realize LAI retrieval without prior data support and complex atmospheric corrections. The look-up table (LUT) of the top-of-atmosphere (TOA) reflectance and associated input variables was simulated by 6S (6S simulation) based on the top-of-canopy (TOC) reflectance LUT simulated by PROSAIL. This was then used to train the NN to obtain the LAI inversion model. This method has been successfully applied to MODIS L1B data (MOD021KM), and the LAI retrieval of the vegetation canopy was realized. The estimated LAI was compared with the MODIS LAI (MOD15A2H) using mid-latitude summer data from 2000 to 2017 in the DIRECT 2.0 ground database. The experiments indicated that the LAI retrieved by the TOA reflectance (r = 0.7852, RMSE = 0.5191) was not much different from the LAI retrieved by the TOC reflectance (r = 0.8063, RMSE = 0.7669), and the accuracy was better than the MODIS LAI (r = 0.7607, RMSE = 0.8239), which proves the feasibility of this method.<\/jats:p>","DOI":"10.3390\/rs14102456","type":"journal-article","created":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T09:18:08Z","timestamp":1653124688000},"page":"2456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Retrieval of the Leaf Area Index from MODIS Top-of-Atmosphere Reflectance Data Using a Neural Network Supported by Simulation Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Weiyan","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Yingying","family":"Ma","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Xiaoliang","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Lin","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Chen","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0938-4202","authenticated-orcid":false,"given":"Shikuan","family":"Jin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"School of Electronic Information, Wuhan University, Wuhan 430079, China"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Shandong Provincial Engineering and Technical Center of Light Manipulations and Shandong Provincial Key Laboratory of Optics and Photonic Device, School of Physics and Electronics, Shandong Normal University, Jinan 250014, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5301","DOI":"10.1109\/TGRS.2016.2560522","article-title":"Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance","volume":"54","author":"Xiao","year":"2016","journal-title":"IEEE Trans. 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