{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T19:29:04Z","timestamp":1773170944219,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T00:00:00Z","timestamp":1571788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2017YFA0603001"],"award-info":[{"award-number":["2017YFA0603001"]}]},{"name":"the Key Research Program of the Chinese Academy of Sciences","award":["ZDRW-ZS-2019-1"],"award-info":[{"award-number":["ZDRW-ZS-2019-1"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41825002"],"award-info":[{"award-number":["41825002"]}],"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>The fraction of absorbed photosynthetically active radiation (FAPAR) is generally divided into the fraction of radiation absorbed by the photosynthetic components (    F A P A  R  g r e e n      ) and the fraction of radiation absorbed by the non-photosynthetic components (    F A P A  R  w o o d y      ) of the vegetation. However, most global FAPAR datasets do not take account of the woody components when considering the canopy radiation transfer. The objective of this study was to develop a generic algorithm for partitioning     F A P A  R  c a n o p y       into     F A P A  R  g r e e n       and     F A P A  R  w o o d y       based on a triple-source leaf-wood-soil layer (TriLay) approach. The LargE-Scale remote sensing data and image simulation framework (LESS) model was used to validate the TriLay approach. The results showed that the TriLay     F A P A  R  g r e e n       had higher retrieval accuracy, as well as a significantly lower bias (R2 = 0.937, Root Mean Square Error (RMSE) = 0.064, and bias = \u22126.02% for black-sky conditions; R2 = 0.997, RMSE = 0.025 and bias = \u22124.04% for white-sky conditions) compared to the traditional linear method (R2 = 0.979, RMSE = 0.114, and bias = \u221218.04% for black-sky conditions; R2 = 0.996, RMSE = 0.106 and bias = \u221216.93% for white-sky conditions). For FAPAR that did not take account of woody components (    F A P A  R  n o W A I      ), the corresponding results were R2 = 0.920, RMSE = 0.071, and bias = \u22127.14% for black-sky conditions, and R2 = 0.999, RMSE = 0.043, and bias = \u22126.41% for white-sky conditions. Finally, the dynamic     F A P A  R  g r e e n      ,     F A P A  R  w o o d y      ,     F A P A  R  c a n o p y       and     F A P A  R  n o W A I       products for a North America region were generated at a resolution of 500 m for every eight days in 2017. A comparison of the results for     F A P A  R  g r e e n       against those for     F A P A  R  n o W A I       and     F A P A  R  c a n o p y       showed that the discrepancy between     F A P A  R  g r e e n       and other FAPAR products for forest vegetation types could not be ignored. For deciduous needleleaf forest, in particular, the black-sky     F A P A  R  g r e e n       was found to contribute only about 23.86% and 35.75% of     F A P A  R  c a n o p y       at the beginning and end of the year (from January to March and October to December, JFM and OND), and 75.02% at the peak growth stage (from July to September, JAS); the black-sky     F A P A  R  n o W A I       was found to be overestimated by 38.30% and 28.46% during the early (JFM) and late (OND) part of the year, respectively. Therefore, the TriLay approach performed well in separating     F A P A  R  g r e e n       from     F A P A  R  c a n o p y      , which is of great importance for a better understanding of the energy exchange within the canopy.<\/jats:p>","DOI":"10.3390\/rs11212471","type":"journal-article","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T03:20:36Z","timestamp":1571973636000},"page":"2471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Retrieval of the Fraction of Radiation Absorbed by Photosynthetic Components (FAPARgreen) for Forest Using a Triple-Source Leaf-Wood-Soil Layer Approach"],"prefix":"10.3390","volume":"11","author":[{"given":"Siyuan","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Geomatics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7987-037X","authenticated-orcid":false,"given":"Liangyun","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7689-3031","authenticated-orcid":false,"given":"Xinjie","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xidong","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojin","family":"Qian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory of Environmental Remote Sensing and Digital City, School of Geography, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3923-6056","authenticated-orcid":false,"given":"Donghui","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory of Environmental Remote Sensing and Digital City, School of Geography, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"655","DOI":"10.2307\/2402584","article-title":"Vegetation and the atmosphere. 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