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Monitoring of Urban Areas along the China\u2013Europe Railway based on Remote Sensing and Artificial Intelligence"],"award-info":[{"award-number":["Ecological Environment Monitoring of Urban Areas along the China\u2013Europe Railway based on Remote Sensing and Artificial Intelligence"]}]},{"name":"the 2020 China-CEEC Joint Education Project of Institutions of Higher Education Project","award":["42201381"],"award-info":[{"award-number":["42201381"]}]},{"name":"the 2020 China-CEEC Joint Education Project of Institutions of Higher Education Project","award":["OFSLRSS202209"],"award-info":[{"award-number":["OFSLRSS202209"]}]},{"name":"the 2020 China-CEEC Joint Education Project of Institutions of Higher Education Project","award":["X21019"],"award-info":[{"award-number":["X21019"]}]},{"name":"the 2020 China-CEEC Joint Education Project of Institutions of Higher Education Project","award":["2021YFE0117500"],"award-info":[{"award-number":["2021YFE0117500"]}]},{"name":"the 2020 China-CEEC Joint Education Project of Institutions of Higher Education Project","award":["Ecological Environment Monitoring of Urban Areas along the China\u2013Europe Railway based on Remote Sensing and Artificial Intelligence"],"award-info":[{"award-number":["Ecological Environment Monitoring of Urban Areas along the China\u2013Europe Railway based on Remote Sensing and Artificial Intelligence"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The value of leaf photosynthetic capacity (Vcmax) varies with time and space, but state-of-the-art terrestrial biosphere models rarely include such Vcmax variability, hindering the accuracy of carbon cycle estimations on a large scale. In particular, while the European terrestrial ecosystem is particularly sensitive to climate change, current estimates of gross primary production (GPP) in Europe are subject to significant uncertainties (2.5 to 8.7 Pg C yr\u22121). This study applied a process-based Farquhar GPP model (FGM) to improve GPP estimation by introducing a spatially and temporally explicit Vcmax derived from the satellite-based leaf chlorophyll content (LCC) on two scales: across multiple eddy covariance tower sites and on the regional scale. Across the 19 EuroFLUX sites selected for independent model validation based on 9 plant functional types (PFTs), relative to the biome-specific Vcmax, the inclusion of the LCC-derived Vcmax improved the model estimates of GPP, with the coefficient of determination (R2) increased by 23% and the root mean square error (RMSE) decreased by 25%. Vcmax values are typically parameterized with PFT-specific Vcmax calibrated from flux tower observations or empirical Vcmax based on the TRY database (which includes 723 data points derived from Vcmax field measurements). On the regional scale, compared with GPP, using the LCC-derived Vcmax, the conventional method of fixing Vcmax using the calibrated Vcmax or TRY-based Vcmax overestimated the annual GPP of Europe by 0.5 to 2.9 Pg C yr\u22121 or 5 to 31% and overestimated the interannually increasing GPP trend by 0.007 to 0.01 Pg C yr\u22122 or 14 to 20%, respectively. The spatial pattern and interannual change trend of the European GPP estimated by the improved FGM showed general consistency with the existing studies, while our estimates indicated that the European terrestrial ecosystem (including part of Russia) had higher carbon assimilation potential (9.4 Pg C yr\u22121). Our study highlighted the urgent need to develop spatially and temporally consistent Vcmax products with a high accuracy so as to reduce uncertainties in global carbon modeling and improve our understanding of how terrestrial ecosystems respond to climate change.<\/jats:p>","DOI":"10.3390\/rs15051172","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T01:39:47Z","timestamp":1677029987000},"page":"1172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Improved Estimation of the Gross Primary Production of Europe by Considering the Spatial and Temporal Changes in Photosynthetic Capacity from 2001 to 2016"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5425-7931","authenticated-orcid":false,"given":"Qiaoli","family":"Wu","sequence":"first","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute-Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China"},{"name":"Key Laboratory of Urban Spatial Information, Ministry of Natural Resources of the People\u2019s Republic of China, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"given":"Shaoyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100091, China"}]},{"given":"Yulong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute for a Secure and Sustainable Environment (ISSE), University of Tennessee, Knoxville, TN 37996, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4099-4906","authenticated-orcid":false,"given":"Conghe","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA"}]},{"given":"Weimin","family":"Ju","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2929-4255","authenticated-orcid":false,"given":"Li","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute-Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China"}]},{"given":"Jie","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"Key Laboratory of Urban Spatial Information, Ministry of Natural Resources of the People\u2019s Republic of China, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.5194\/essd-14-1917-2022","article-title":"Global Carbon Budget 2021","volume":"14","author":"Friedlingstein","year":"2022","journal-title":"Earth Syst. 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