{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:26:50Z","timestamp":1781195210974,"version":"3.54.1"},"reference-count":67,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T00:00:00Z","timestamp":1723161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFA0604302"],"award-info":[{"award-number":["2017YFA0604302"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022B1515130001"],"award-info":[{"award-number":["2022B1515130001"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42371483"],"award-info":[{"award-number":["42371483"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1811464"],"award-info":[{"award-number":["U1811464"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Ecological Informatics"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1016\/j.ecoinf.2024.102767","type":"journal-article","created":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T22:27:34Z","timestamp":1723328854000},"page":"102767","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["Better representation of vegetation phenology improves estimations of annual gross primary productivity"],"prefix":"10.1016","volume":"82","author":[{"given":"Hanliang","family":"Gui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinchuan","family":"Xin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuewen","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhua","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ecoinf.2024.102767_bb0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2022.109180","article-title":"Estimation of global GPP from GOME-2 and OCO-2 SIF by considering the dynamic variations of GPP-SIF relationship","volume":"326","author":"Bai","year":"2022","journal-title":"Agric. For. Meteorol."},{"issue":"1","key":"10.1016\/j.ecoinf.2024.102767_bb0010","doi-asserted-by":"crossref","first-page":"17973","DOI":"10.1038\/s41598-018-36065-0","article-title":"Reevaluating growing season length controls on net ecosystem production in evergreen conifer forests","volume":"8","author":"Barnard","year":"2018","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.ecoinf.2024.102767_bb0015","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1038\/s41597-022-01309-2","article-title":"A global 0.05 dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020","volume":"9","author":"Bi","year":"2022","journal-title":"Sci. Data"},{"issue":"11","key":"10.1016\/j.ecoinf.2024.102767_bb0020","doi-asserted-by":"crossref","first-page":"3675","DOI":"10.1111\/gcb.13326","article-title":"A new seasonal-deciduous spring phenology submodel in the community land model 4.5: impacts on carbon and water cycling under future climate scenarios","volume":"22","author":"Chen","year":"2016","journal-title":"Glob. Chang. Biol."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0025","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.scitotenv.2019.01.324","article-title":"Impact of physiological and phenological change on carbon uptake on the Tibetan Plateau revealed through GPP estimation based on spaceborne solar-induced fluorescence","volume":"663","author":"Chen","year":"2019","journal-title":"Sci. Total Environ."},{"issue":"4","key":"10.1016\/j.ecoinf.2024.102767_bb0030","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. Sci. Data"},{"issue":"11","key":"10.1016\/j.ecoinf.2024.102767_bb0035","doi-asserted-by":"crossref","first-page":"3457","DOI":"10.1111\/gcb.12625","article-title":"Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982\u20132011)","volume":"20","author":"Garonna","year":"2014","journal-title":"Glob. Chang. Biol."},{"issue":"4","key":"10.1016\/j.ecoinf.2024.102767_bb0040","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1111\/gcb.13168","article-title":"Variability and evolution of global land surface phenology over the past three decades (1982\u20132012)","volume":"22","author":"Garonna","year":"2016","journal-title":"Glob. Chang. Biol."},{"issue":"11","key":"10.1016\/j.ecoinf.2024.102767_bb0045","doi-asserted-by":"crossref","first-page":"1796","DOI":"10.3390\/rs12111796","article-title":"FluxSat: measuring the ocean\u2013atmosphere turbulent exchange of heat and moisture from space","volume":"12","author":"Gentemann","year":"2020","journal-title":"Remote Sens."},{"issue":"5","key":"10.1016\/j.ecoinf.2024.102767_bb0050","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1111\/gcb.14001","article-title":"Peak season plant activity shift towards spring is reflected by increasing carbon uptake by extratropical ecosystems","volume":"24","author":"Gonsamo","year":"2018","journal-title":"Glob. Chang. Biol."},{"issue":"1","key":"10.1016\/j.ecoinf.2024.102767_bb0055","doi-asserted-by":"crossref","first-page":"4765","DOI":"10.1038\/s41598-017-03818-2","article-title":"Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions","volume":"7","author":"Huntzinger","year":"2017","journal-title":"Sci. Rep."},{"issue":"5","key":"10.1016\/j.ecoinf.2024.102767_bb0060","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1029\/2018EF001087","article-title":"The sensitivity of satellite solar-induced chlorophyll fluorescence to meteorological drought","volume":"7","author":"Jiao","year":"2019","journal-title":"Earth's Future"},{"issue":"1","key":"10.1016\/j.ecoinf.2024.102767_bb0065","doi-asserted-by":"crossref","first-page":"3777","DOI":"10.1038\/s41467-021-24016-9","article-title":"Observed increasing water constraint on vegetation growth over the last three decades","volume":"12","author":"Jiao","year":"2021","journal-title":"Nat. Commun."},{"issue":"7638","key":"10.1016\/j.ecoinf.2024.102767_bb0070","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1038\/nature20780","article-title":"Compensatory water effects link yearly global land CO2 sink changes to temperature","volume":"541","author":"Jung","year":"2017","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.ecoinf.2024.102767_bb0075","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1038\/s41597-019-0076-8","article-title":"The FLUXCOM ensemble of global land-atmosphere energy fluxes","volume":"6","author":"Jung","year":"2019","journal-title":"Sci. Data"},{"issue":"6","key":"10.1016\/j.ecoinf.2024.102767_bb0080","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1111\/j.1365-2486.2012.02678.x","article-title":"Terrestrial biosphere model performance for inter-annual variability of land-atmosphere CO2 exchange","volume":"18","author":"Keenan","year":"2012","journal-title":"Glob. Chang. Biol."},{"issue":"7","key":"10.1016\/j.ecoinf.2024.102767_bb0085","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1038\/nclimate2253","article-title":"Net carbon uptake has increased through warming-induced changes in temperate forest phenology","volume":"4","author":"Keenan","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0090","series-title":"Rank Correlation Methods","author":"Kendall","year":"1975"},{"key":"10.1016\/j.ecoinf.2024.102767_bb0095","first-page":"1","article-title":"Understanding the representativeness of FLUXNET for upscaling carbon flux from eddy covariance measurements","volume":"2016","author":"Kumar","year":"2016","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0100","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.ecolmodel.2019.05.003","article-title":"PhenoPine: a simulation model to trace the phenological changes in Pinus roxhburghii in response to ambient temperature rise","volume":"404","author":"Kumar","year":"2019","journal-title":"Ecol. Model."},{"issue":"4","key":"10.1016\/j.ecoinf.2024.102767_bb0105","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.5194\/essd-10-2141-2018","article-title":"Global carbon budget 2018","volume":"10","author":"Le Qu\u00e9r\u00e9","year":"2018","journal-title":"Earth Syst. Sci. Data"},{"issue":"21","key":"10.1016\/j.ecoinf.2024.102767_bb0110","doi-asserted-by":"crossref","first-page":"2563","DOI":"10.3390\/rs11212563","article-title":"Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: a global, fine-resolution dataset of gross primary production derived from OCO-2","volume":"11","author":"Li","year":"2019","journal-title":"Remote Sens."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0115","article-title":"Interannual variations in GPP in forest ecosystems in Southwest China and regional differences in the climatic contributions","volume":"69","author":"Li","year":"2022","journal-title":"Eco. Inform."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0120","doi-asserted-by":"crossref","DOI":"10.1029\/2023GB007696","article-title":"Underestimated interannual variability of terrestrial vegetation production by terrestrial ecosystem models","author":"Lin","year":"2023","journal-title":"Glob. Biogeochem. Cycles"},{"key":"10.1016\/j.ecoinf.2024.102767_bb0125","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.02.026","article-title":"A comparison of resampling methods for remote sensing classification and accuracy assessment","volume":"208","author":"Lyons","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0130","doi-asserted-by":"crossref","first-page":"245","DOI":"10.2307\/1907187","article-title":"Nonparametric tests against trend","author":"Mann","year":"1945","journal-title":"Econometrica"},{"issue":"3","key":"10.1016\/j.ecoinf.2024.102767_bb0135","doi-asserted-by":"crossref","first-page":"747","DOI":"10.2307\/2401901","article-title":"Solar radiation and productivity in tropical ecosystems","volume":"9","author":"Monteith","year":"1972","journal-title":"J. Appl. Ecol."},{"issue":"6405","key":"10.1016\/j.ecoinf.2024.102767_bb0140","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1126\/science.aan5360","article-title":"Past and future global transformation of terrestrial ecosystems under climate change","volume":"361","author":"Nolan","year":"2018","journal-title":"Science"},{"issue":"8","key":"10.1016\/j.ecoinf.2024.102767_bb0145","doi-asserted-by":"crossref","DOI":"10.1088\/1748-9326\/11\/8\/084001","article-title":"Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data","volume":"11","author":"Park","year":"2016","journal-title":"Environ. Res. Lett."},{"issue":"1","key":"10.1016\/j.ecoinf.2024.102767_bb0150","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1038\/s41597-020-0534-3","article-title":"The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data","volume":"7","author":"Pastorello","year":"2020","journal-title":"Sci. Data"},{"issue":"3","key":"10.1016\/j.ecoinf.2024.102767_bb0155","doi-asserted-by":"crossref","DOI":"10.1029\/2006GB002888","article-title":"Growing season extension and its impact on terrestrial carbon cycle in the northern hemisphere over the past 2 decades","volume":"21","author":"Piao","year":"2007","journal-title":"Glob. Biogeochem. Cycles"},{"issue":"7","key":"10.1016\/j.ecoinf.2024.102767_bb0160","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1111\/gcb.12187","article-title":"Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends","volume":"19","author":"Piao","year":"2013","journal-title":"Glob. Chang. Biol."},{"issue":"6","key":"10.1016\/j.ecoinf.2024.102767_bb0165","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1111\/gcb.14619","article-title":"Plant phenology and global climate change: current progresses and challenges","volume":"25","author":"Piao","year":"2019","journal-title":"Glob. Chang. Biol."},{"issue":"1","key":"10.1016\/j.ecoinf.2024.102767_bb0170","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1111\/gcb.14884","article-title":"Interannual variation of terrestrial carbon cycle: issues and perspectives","volume":"26","author":"Piao","year":"2020","journal-title":"Glob. Chang. Biol."},{"issue":"7502","key":"10.1016\/j.ecoinf.2024.102767_bb0175","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1038\/nature13376","article-title":"Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle","volume":"509","author":"Poulter","year":"2014","journal-title":"Nature"},{"issue":"9","key":"10.1016\/j.ecoinf.2024.102767_bib331","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1111\/j.1365-2486.2005.001002.x","article-title":"On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm","volume":"11","author":"Reichstein","year":"2005","journal-title":"Glob. Chang. Biol."},{"issue":"1","key":"10.1016\/j.ecoinf.2024.102767_bb0180","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1111\/gcb.12023","article-title":"A plant\u2019s perspective of extremes: terrestrial plant responses to changing climatic variability","volume":"19","author":"Reyer","year":"2013","journal-title":"Glob. Chang. Biol."},{"issue":"1555","key":"10.1016\/j.ecoinf.2024.102767_bb0185","doi-asserted-by":"crossref","first-page":"3227","DOI":"10.1098\/rstb.2010.0102","article-title":"Influence of spring and autumn phenological transitions on forest ecosystem productivity","volume":"365","author":"Richardson","year":"2010","journal-title":"Philos. Trans. R. Soc. B"},{"issue":"3","key":"10.1016\/j.ecoinf.2024.102767_bb0190","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1175\/BAMS-85-3-381","article-title":"The global land data assimilation system","volume":"85","author":"Rodell","year":"2004","journal-title":"Bull. Am. Meteorol. Soc."},{"issue":"8","key":"10.1016\/j.ecoinf.2024.102767_bb0195","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.5194\/bg-15-2481-2018","article-title":"How does the terrestrial carbon exchange respond to inter-annual climatic variations? A quantification based on atmospheric CO2 data","volume":"15","author":"R\u00f6denbeck","year":"2018","journal-title":"Biogeosciences"},{"key":"10.1016\/j.ecoinf.2024.102767_bb0200","article-title":"Machine learning approach to predict terrestrial gross primary productivity using topographical and remote sensing data","volume":"70","author":"Sarkar","year":"2022","journal-title":"Eco. Inform."},{"issue":"7","key":"10.1016\/j.ecoinf.2024.102767_bb0205","doi-asserted-by":"crossref","DOI":"10.1029\/2021EF002634","article-title":"Increased global vegetation productivity despite rising atmospheric dryness over the last two decades","volume":"10","author":"Song","year":"2022","journal-title":"Earth's Future"},{"key":"10.1016\/j.ecoinf.2024.102767_bb0210","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s11120-013-9799-0","article-title":"Controls on seasonal patterns of maximum ecosystem carbon uptake and canopy-scale photosynthetic light response: contributions from both temperature and photoperiod","volume":"119","author":"Stoy","year":"2014","journal-title":"Photosynth. Res."},{"issue":"18","key":"10.1016\/j.ecoinf.2024.102767_bb0215","doi-asserted-by":"crossref","first-page":"9686","DOI":"10.1002\/2016GL069416","article-title":"High atmospheric demand for water can limit forest carbon uptake and transpiration as severely as dry soil","volume":"43","author":"Sulman","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0220","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.ecoinf.2018.06.006","article-title":"Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: climatic factors, CO2 concentration and land-cover change, 1982\u20132015","volume":"46","author":"Sun","year":"2018","journal-title":"Eco. Inform."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0225","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1016\/j.scitotenv.2019.03.025","article-title":"Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO2 trends","volume":"668","author":"Sun","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0230","article-title":"Estimating global maximum gross primary productivity of vegetation based on the combination of MODIS greenness and temperature data","volume":"63","author":"Tang","year":"2021","journal-title":"Eco. Inform."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0235","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.agrformet.2015.09.005","article-title":"Improving the performance of remote sensing models for capturing intra-and inter-annual variations in daily GPP: an analysis using global FLUXNET tower data","volume":"214","author":"Verma","year":"2015","journal-title":"Agric. For. Meteorol."},{"issue":"4","key":"10.1016\/j.ecoinf.2024.102767_bb0240","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1890\/15-1434","article-title":"Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize","volume":"26","author":"Wagle","year":"2016","journal-title":"Ecol. Appl."},{"issue":"13","key":"10.1016\/j.ecoinf.2024.102767_bb0245","doi-asserted-by":"crossref","first-page":"3018","DOI":"10.3390\/rs14133018","article-title":"Comparison of vegetation phenology derived from solar-induced chlorophyll fluorescence and enhanced vegetation index, and their relationship with climatic limitations","volume":"14","author":"Wang","year":"2022","journal-title":"Remote Sens."},{"issue":"10","key":"10.1016\/j.ecoinf.2024.102767_bb0250","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1111\/j.1365-2486.2009.01910.x","article-title":"Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982\u20132006","volume":"15","author":"White","year":"2009","journal-title":"Glob. Chang. Biol."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0255","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.agrformet.2012.05.002","article-title":"Interannual variability of net carbon exchange is related to the lag between the end-dates of net carbon uptake and photosynthesis: evidence from long records at two contrasting forest stands","volume":"164","author":"Wu","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0260","article-title":"Development of a global annual land surface phenology dataset for 1982\u20132018 from the AVHRR data by implementing multiple phenology retrieving methods","volume":"103","author":"Wu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"issue":"9","key":"10.1016\/j.ecoinf.2024.102767_bb0265","doi-asserted-by":"crossref","first-page":"2788","DOI":"10.1073\/pnas.1413090112","article-title":"Joint control of terrestrial gross primary productivity by plant phenology and physiology","volume":"112","author":"Xia","year":"2015","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0270","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2015.02.003","article-title":"Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics","volume":"161","author":"Xin","year":"2015","journal-title":"Remote Sens. Environ."},{"issue":"7","key":"10.1016\/j.ecoinf.2024.102767_bb0275","doi-asserted-by":"crossref","DOI":"10.1029\/2019MS001935","article-title":"A semiprognostic phenology model for simulating multidecadal dynamics of global vegetation leaf area index","volume":"12","author":"Xin","year":"2020","journal-title":"J. Adv. Model. Earth Syst."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0280","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2020.137948","article-title":"Combined MODIS land surface temperature and greenness data for modeling vegetation phenology, physiology, and gross primary production in terrestrial ecosystems","volume":"726","author":"Xu","year":"2020","journal-title":"Sci. Total Environ."},{"issue":"8","key":"10.1016\/j.ecoinf.2024.102767_bb0285","doi-asserted-by":"crossref","first-page":"eaax1396","DOI":"10.1126\/sciadv.aax1396","article-title":"Increased atmospheric vapor pressure deficit reduces global vegetation growth","volume":"5","author":"Yuan","year":"2019","journal-title":"Sci. Adv."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0290","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2019.111511","article-title":"A review of vegetation phenological metrics extraction using time-series, multispectral satellite data","volume":"237","author":"Zeng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0295","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.rse.2012.06.023","article-title":"Evaluating spatial and temporal patterns of MODIS GPP over the conterminous US against flux measurements and a process model","volume":"124","author":"Zhang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0300","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2022.109135","article-title":"Joint control of alpine meadow productivity by plant phenology and photosynthetic capacity","volume":"325","author":"Zhang","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0305","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2022.108819","article-title":"NIRv and SIF better estimate phenology than NDVI and EVI: effects of spring and autumn phenology on ecosystem production of planted forests","volume":"315","author":"Zhang","year":"2022","journal-title":"Agric. For. Meteorol."},{"issue":"4","key":"10.1016\/j.ecoinf.2024.102767_bb0310","doi-asserted-by":"crossref","first-page":"2725","DOI":"10.5194\/essd-12-2725-2020","article-title":"Improved estimate of global gross primary production for reproducing its long-term variation, 1982\u20132017","volume":"12","author":"Zheng","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"10.1016\/j.ecoinf.2024.102767_bb0315","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.agrformet.2016.06.010","article-title":"Explaining inter-annual variability of gross primary productivity from plant phenology and physiology","volume":"226","author":"Zhou","year":"2016","journal-title":"Agric. For. Meteorol."},{"issue":"8","key":"10.1016\/j.ecoinf.2024.102767_bb0320","doi-asserted-by":"crossref","DOI":"10.1029\/2021GB006957","article-title":"Large contributions of diffuse radiation to global gross primary productivity during 1981\u20132015","volume":"35","author":"Zhou","year":"2021","journal-title":"Glob. Biogeochem. Cycles"},{"key":"10.1016\/j.ecoinf.2024.102767_bb0325","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2023.109739","article-title":"A prognostic vegetation phenology model to predict seasonal maximum and time series of global leaf area index using climate variables","volume":"342","author":"Zhou","year":"2023","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.ecoinf.2024.102767_bb0330","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2022.159390","article-title":"Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning","volume":"857","author":"Zhu","year":"2023","journal-title":"Sci. Total Environ."}],"container-title":["Ecological Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S1574954124003091?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S1574954124003091?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T05:14:36Z","timestamp":1726377276000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S1574954124003091"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":67,"alternative-id":["S1574954124003091"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.ecoinf.2024.102767","relation":{},"ISSN":["1574-9541"],"issn-type":[{"value":"1574-9541","type":"print"}],"subject":[],"published":{"date-parts":[[2024,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Better representation of vegetation phenology improves estimations of annual gross primary productivity","name":"articletitle","label":"Article Title"},{"value":"Ecological Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.ecoinf.2024.102767","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"102767"}}