{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T13:33:17Z","timestamp":1762522397450,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T00:00:00Z","timestamp":1577059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Key and Research Program","award":["2018GZDZX0034"],"award-info":[{"award-number":["2018GZDZX0034"]}]},{"name":"Sichuan Science and Technology Program","award":["2018GZDZX0014","2019YFG0202"],"award-info":[{"award-number":["2018GZDZX0014","2019YFG0202"]}]},{"name":"Key Laboratory of Equipment Pre-Research","award":["6142A010301"],"award-info":[{"award-number":["6142A010301"]}]},{"name":"Hebei Key and Research Program","award":["19255901D"],"award-info":[{"award-number":["19255901D"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>An accurate inversion of the fraction of absorbed photosynthetically active radiation (FPAR) based on remote sensing data is particularly important for understanding global climate change. At present, there are relatively few studies focusing on the inversion of FPAR using Chinese autonomous satellites. This work intends to investigate the inversion of the FPAR obtained from the FengYun-3C (FY-3C) data of domestic satellites by using the PROSAIL model and the look-up table (LUT) algorithm for different vegetation types from various places in China. After analyzing the applicability of existing models using FY-3C data and MOD09GA data, an inversion strategy for FY-3C data is implemented. This strategy is applied to areas with various types of vegetation, such as grasslands, croplands, shrubs, broadleaf forests, and needleleaf forests, and produces FPAR products, which are cross-validated against the FPAR products from the Moderate Resolution Imaging Spectro Radiometer (MODIS), Geoland Version 1 (GEOV1), and Global Land Surface Satellite (GLASS). Accordingly, the results show that the FPAR retrieved from the FY-3C data has good spatial and temporal consistency and correlation with the three FPAR products. However, this technique does not favor all types of vegetation equally; the FY-FPAR is relatively more suitable for the inversion of grasslands and croplands during the lush period than for others. Therefore, the inversion strategy provides the potential to generate large-area and long-term sequence FPAR products from FY-3C data.<\/jats:p>","DOI":"10.3390\/rs12010067","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T05:56:15Z","timestamp":1577166975000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Inversion of the Fraction of Absorbed Photosynthetically Active Radiation (FPAR) from FY-3C MERSI Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Weimin","family":"Hou","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Su","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbo","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107609","DOI":"10.1016\/j.agrformet.2019.06.008","article-title":"Assimilation of remote sensing into crop growth models: Current status and perspectives","volume":"276\u2013277","author":"Huang","year":"2019","journal-title":"Agric. 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