{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:04:11Z","timestamp":1774944251051,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T00:00:00Z","timestamp":1691798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Key Laboratory of Desert and Oasis Ecology","award":["U22A20567"],"award-info":[{"award-number":["U22A20567"]}]},{"name":"State Key Laboratory of Desert and Oasis Ecology","award":["20224BAB213038"],"award-info":[{"award-number":["20224BAB213038"]}]},{"name":"Xinjiang Institute of Ecology and Geography","award":["U22A20567"],"award-info":[{"award-number":["U22A20567"]}]},{"name":"Xinjiang Institute of Ecology and Geography","award":["20224BAB213038"],"award-info":[{"award-number":["20224BAB213038"]}]},{"name":"Chinese Academy of Sciences","award":["U22A20567"],"award-info":[{"award-number":["U22A20567"]}]},{"name":"Chinese Academy of Sciences","award":["20224BAB213038"],"award-info":[{"award-number":["20224BAB213038"]}]},{"name":"Joint Funds of the National Natural Science Foundation of China","award":["U22A20567"],"award-info":[{"award-number":["U22A20567"]}]},{"name":"Joint Funds of the National Natural Science Foundation of China","award":["20224BAB213038"],"award-info":[{"award-number":["20224BAB213038"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["U22A20567"],"award-info":[{"award-number":["U22A20567"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["20224BAB213038"],"award-info":[{"award-number":["20224BAB213038"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The maximum light use efficiency (LUE) (\u03b50) is a key essential parameter of the LUE model, and its accurate estimation is crucial for quantifying gross primary production (GPP) and better understanding the global carbon budget. Currently, a comprehensive understanding of the potential of seasonal variations of \u03b50 in GPP estimation across different plant functional types (PFTs) is still lacking. In this study, we used a phenology-based strategy for the estimation of \u03b50 to find the optimal photosynthetic responses of the parameter in different phenological stages. The start and end of growing season (SOS and EOS) from time series vegetation indices and the camera-derived greenness index were extracted across seven PFT flux sites using the methods of the hybrid generalized additive model (HGAM) and double logistic function (DLF). Optimal extractions of SOS and EOS were evaluated, and the \u03b50 was estimated from flux site observations during the optimal phenological stages with the light response equation. Coupled with other obligatory parameters of the LUE model, phenology-based GPP (GPPphe-based) was estimated over 21 site-years and compared with vegetation photosynthesis model (VPM)-based GPP (GPPVPM) and eddy covariance-measured GPP (GPPEC). Generally, GPPphe-based basically tracked both the seasonal dynamics and inter-annual variation of GPPEC well, especially at forest, cropland, and wetland flux sites. The R2 between GPPphe-based and GPPEC was stable between 0.85 and 0.95 in forest ecosystems, between 0.75 and 0.85 in cropland ecosystems, and around 0.9 in wetland ecosystems. Furthermore, we found that GPPphe-based was significantly improved compared to GPPVPM in cropland, grassland, and wetland ecosystems, implying that phenology-based \u03b50 is more appropriate in the GPP estimation of herbaceous plants. In addition, we found that GPPphe-based was significantly improved over GPPVPM in cropland, grassland, and wetland ecosystems, and the R2 between GPPphe-based and GPPEC was improved by up to 0.11 in cropland ecosystems and 0.05 in wetland ecosystems compared to GPPVPM, and RMSE was reduced by up to 5.90 and 2.11 g C m\u22122 8 day\u22121, respectively, implying that phenology-based \u03b50 in herbaceous plants is more appropriate for GPP estimation. This work highlights the potential of phenology-based \u03b50 in understanding the seasonal variation of vegetation photosynthesis and production.<\/jats:p>","DOI":"10.3390\/rs15164002","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T10:40:31Z","timestamp":1692009631000},"page":"4002","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial Ecosystems"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9327-8878","authenticated-orcid":false,"given":"Yulong","family":"Lv","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5049-4633","authenticated-orcid":false,"given":"Hong","family":"Chi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"},{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"}]},{"given":"Peichen","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9589-7687","authenticated-orcid":false,"given":"Duan","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China"},{"name":"School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China"}]},{"given":"Jialiang","family":"Gan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China"}]},{"given":"Yifan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xinyi","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8410-0660","authenticated-orcid":false,"given":"Yifei","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"given":"Cun","family":"Chang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"}]},{"given":"Jun","family":"Wan","sequence":"additional","affiliation":[{"name":"Wuhan Regional Climate Center, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0685-4897","authenticated-orcid":false,"given":"Feng","family":"Ling","sequence":"additional","affiliation":[{"name":"Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2415","DOI":"10.1175\/1520-0477(2001)082<2415:FANTTS>2.3.CO;2","article-title":"FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux 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