{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T23:35:07Z","timestamp":1778024107034,"version":"3.51.4"},"reference-count":103,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42271104"],"award-info":[{"award-number":["42271104"]}],"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":["GXWD20201231165807007-20200814213435001"],"award-info":[{"award-number":["GXWD20201231165807007-20200814213435001"]}],"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":["JCYJ20220531093201004"],"award-info":[{"award-number":["JCYJ20220531093201004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["42271104"],"award-info":[{"award-number":["42271104"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["GXWD20201231165807007-20200814213435001"],"award-info":[{"award-number":["GXWD20201231165807007-20200814213435001"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["JCYJ20220531093201004"],"award-info":[{"award-number":["JCYJ20220531093201004"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Program","award":["42271104"],"award-info":[{"award-number":["42271104"]}]},{"name":"Shenzhen Science and Technology Program","award":["GXWD20201231165807007-20200814213435001"],"award-info":[{"award-number":["GXWD20201231165807007-20200814213435001"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20220531093201004"],"award-info":[{"award-number":["JCYJ20220531093201004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As the largest source of uncertainty in carbon cycle studies, accurate quantification of gross primary productivity (GPP) is critical for the global carbon budget in the context of global climate change. Numerous vegetation indices (VIs) based on satellite data have participated in the construction of GPP models. However, the relative performance of various VIs in predicting GPP and what additional factors should be combined with them to reveal the photosynthetic capacity of vegetation mechanistically better are still poorly understood. We constructed two types of models (universal and plant functional type [PFT]-specific) for solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation (NIRv), and Leaf Area Index (LAI) based on two widely used machine learning algorithms, i.e., the random forest (RF) and back propagation neural network (BPNN) algorithms. A total of thirty plant traits and environmental factors with legacy effects are considered in the model. We then systematically investigated the ancillary variables that best match each vegetation index in estimating global GPP. Four types of models (universal and PFT-specific, RF and BPNN) consistently show that SIF performs best when modeled using a single vegetation index (R2 = 0.67, RMSE = 2.24 g C\u00b7m\u22122\u00b7d\u22121); however, NIRv combined with CO2, plant traits, and climatic factors can achieve the highest prediction accuracy (R2 = 0.87, RMSE = 1.40 g C\u00b7m\u22122\u00b7d\u22121). Plant traits effectively enhance all prediction models\u2019 accuracy, and climatic variables are essential factors in improving the accuracy of NIRv- or LAI-based GPP models, but not the accuracy of SIF-based models. Our findings provide valuable information for the configuration of the data-driven models to improve the accuracy of predicting GPP and provide insights into the physiological and ecological mechanisms underpinning GPP prediction.<\/jats:p>","DOI":"10.3390\/rs14246316","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T02:54:21Z","timestamp":1670986461000},"page":"6316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Exploring the Best-Matching Plant Traits and Environmental Factors for Vegetation Indices in Estimates of Global Gross Primary Productivity"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4306-1132","authenticated-orcid":false,"given":"Weiqing","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China"},{"name":"Key Laboratory of Earth Surface System and Human-Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5235-5194","authenticated-orcid":false,"given":"Zaichun","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China"},{"name":"Key Laboratory of Earth Surface System and Human-Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3269","DOI":"10.5194\/essd-12-3269-2020","article-title":"Global Carbon Budget 2020","volume":"12","author":"Friedlingstein","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.agrformet.2013.01.003","article-title":"Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity","volume":"173","author":"He","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1126\/science.1184984","article-title":"Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate","volume":"329","author":"Beer","year":"2010","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1111\/j.1365-2486.2009.01908.x","article-title":"Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP)","volume":"16","author":"Damm","year":"2010","journal-title":"Glob. Chang. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.envexpbot.2013.10.009","article-title":"Photosynthetic light use efficiency from satellite sensors: From global to Mediterranean vegetation","volume":"103","author":"Garbulsky","year":"2014","journal-title":"Environ. Exp. Bot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5263","DOI":"10.1029\/93JD03221","article-title":"Methodology for the estimation of terrestrial net primary production from remotely sensed data","volume":"99","author":"Ruimy","year":"1994","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1002\/2015RG000483","article-title":"Spatiotemporal patterns of terrestrial gross primary production: A review","volume":"53","author":"Anav","year":"2015","journal-title":"Rev. Geophys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"653","DOI":"10.5194\/bg-12-653-2015","article-title":"Recent trends and drivers of regional sources and sinks of carbon dioxide","volume":"12","author":"Sitch","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, J.M., Mo, G., Pisek, J., Liu, J., Deng, F., Ishizawa, M., and Chan, D. (2012). Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Glob. Biogeochem. Cycles, 26.","DOI":"10.1029\/2010GB003996"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1016\/j.rse.2008.11.013","article-title":"Comparison of three models for predicting gross primary production across and within forested ecoregions in the contiguous United States","volume":"113","author":"Coops","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"170191","DOI":"10.1038\/sdata.2017.191","article-title":"TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015","volume":"5","author":"Abatzoglou","year":"2018","journal-title":"Sci. Data"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1038\/s41561-019-0318-6","article-title":"Drought impacts on terrestrial primary production underestimated by satellite monitoring","volume":"12","author":"Stocker","year":"2019","journal-title":"Nat. Geosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2003.11.008","article-title":"Satellite-based modeling of gross primary production in an evergreen needleleaf forest","volume":"89","author":"Xiao","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.agrformet.2006.12.001","article-title":"Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes","volume":"143","author":"Yuan","year":"2007","journal-title":"Agric. For. Meteorol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1002\/2013JG002449","article-title":"Large-scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands","volume":"119","author":"He","year":"2014","journal-title":"J. Geophys. Res. Biog."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.agrformet.2015.03.016","article-title":"Uncertainty in simulating gross primary production of cropland ecosystem from satellite-based models","volume":"207","author":"Yuan","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1016\/j.rse.2010.12.001","article-title":"Remote estimation of gross primary production in maize and support for a new paradigm based on total crop chlorophyll content","volume":"115","author":"Peng","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3424","DOI":"10.1016\/j.rse.2011.08.006","article-title":"Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration","volume":"115","author":"Wu","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_19","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"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4291","DOI":"10.5194\/bg-13-4291-2016","article-title":"Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms","volume":"13","author":"Tramontana","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"G00J07","DOI":"10.1029\/2010JG001566","article-title":"Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations","volume":"116","author":"Jung","year":"2011","journal-title":"J. Geophys. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1029\/2018GB006135","article-title":"Underestimation of Global Photosynthesis in Earth System Models Due to Representation of Vegetation Structure","volume":"33","author":"Braghiere","year":"2019","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105005","DOI":"10.1088\/1748-9326\/aa8978","article-title":"Regional contribution to variability and trends of global gross primary productivity","volume":"12","author":"Chen","year":"2017","journal-title":"Environ. Res. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1038\/s41559-018-0714-0","article-title":"Enhanced peak growth of global vegetation and its key mechanisms","volume":"2","author":"Huang","year":"2018","journal-title":"Nat. Ecol. Evol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TGRS.2013.2237780","article-title":"Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance","volume":"52","author":"Xiao","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"927","DOI":"10.3390\/rs5020927","article-title":"Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011","volume":"5","author":"Zhu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7410921","DOI":"10.34133\/2021\/7410921","article-title":"A Bibliometric Visualization Review of the MODIS LAI\/FPAR Products from 1995 to 2020","volume":"2021","author":"Yan","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1890\/08-2022.1","article-title":"Near-surface remote sensing of spatial and temporal variation in canopy phenology","volume":"19","author":"Richardson","year":"2009","journal-title":"Ecol. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.02.057","article-title":"Vegetation baseline phenology from kilometric global LAI satellite products","volume":"178","author":"Verger","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e1602244","DOI":"10.1126\/sciadv.1602244","article-title":"Canopy near-infrared reflectance and terrestrial photosynthesis","volume":"3","author":"Badgley","year":"2017","journal-title":"Sci. Adv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"eabc7447","DOI":"10.1126\/sciadv.abc7447","article-title":"A unified vegetation index for quantifying the terrestrial biosphere","volume":"7","author":"Walther","year":"2021","journal-title":"Sci. Adv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pierrat, Z., Magney, T., Parazoo, N.C., Grossmann, K., Bowling, D.R., Seibt, U., Johnson, B., Helgason, W., Barr, A., and Bortnik, J. (2022). Diurnal and Seasonal Dynamics of Solar-Induced Chlorophyll Fluorescence, Vegetation Indices, and Gross Primary Productivity in the Boreal Forest. J. Geophys. Res. Biog., 127.","DOI":"10.1029\/2021JG006588"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.rse.2015.07.015","article-title":"Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data","volume":"168","author":"Tramontana","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"eaam5747","DOI":"10.1126\/science.aam5747","article-title":"OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence","volume":"358","author":"Sun","year":"2017","journal-title":"Science"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1111\/gcb.15373","article-title":"Moisture availability mediates the relationship between terrestrial gross primary production and solar-induced chlorophyll fluorescence: Insights from global-scale variations","volume":"27","author":"Chen","year":"2021","journal-title":"Glob. Chang. Biol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5186","DOI":"10.1111\/gcb.15775","article-title":"Seasonal changes in GPP\/SIF ratios and their climatic determinants across the Northern Hemisphere","volume":"27","author":"Chen","year":"2021","journal-title":"Glob. Chang. Biol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9795837","DOI":"10.34133\/2021\/9795837","article-title":"Sensitivity of Estimated Total Canopy SIF Emission to Remotely Sensed LAI and BRDF Products","volume":"2021","author":"Zhang","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1038\/nature16489","article-title":"The global spectrum of plant form and function","volume":"529","author":"Diaz","year":"2016","journal-title":"Nature"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1038\/s41586-021-03939-9","article-title":"The three major axes of terrestrial ecosystem function","volume":"598","author":"Migliavacca","year":"2021","journal-title":"Nature"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"13730","DOI":"10.1073\/pnas.94.25.13730","article-title":"From tropics to tundra: Global convergence in plant functioning","volume":"94","author":"Reich","year":"1997","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1038\/nature02403","article-title":"The worldwide leaf economics spectrum","volume":"428","author":"Wright","year":"2004","journal-title":"Nature"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"108277","DOI":"10.1016\/j.agrformet.2020.108277","article-title":"Assessing variability of optimum air temperature for photosynthesis across site-years, sites and biomes and their effects on photosynthesis estimation","volume":"298\u2013299","author":"Chang","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"108527","DOI":"10.1016\/j.agrformet.2021.108527","article-title":"Divergent impacts of atmospheric water demand on gross primary productivity in three typical ecosystems in China","volume":"307","author":"Chen","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e01724","DOI":"10.1002\/ecs2.1724","article-title":"A global study of GPP focusing on light-use efficiency in a random forest regression model","volume":"8","author":"Wei","year":"2017","journal-title":"Ecosphere"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, X., and Xiao, J. (2019). A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data. Remote Sens., 11.","DOI":"10.3390\/rs11050517"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1016\/j.rse.2011.01.001","article-title":"Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling","volume":"115","author":"Yuan","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_49","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"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1038\/nature16476","article-title":"Plant functional traits have globally consistent effects on competition","volume":"529","author":"Kunstler","year":"2016","journal-title":"Nature"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2870","DOI":"10.1038\/s41598-018-21172-9","article-title":"Future global productivity will be affected by plant trait response to climate","volume":"8","author":"Madani","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.scitotenv.2017.11.171","article-title":"The relationships between leaf economics and hydraulic traits of woody plants depend on water availability","volume":"621","author":"Yin","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1111\/gcb.14904","article-title":"TRY plant trait database\u2013enhanced coverage and open access","volume":"26","author":"Kattge","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"E10937","DOI":"10.1073\/pnas.1708984114","article-title":"Mapping local and global variability in plant trait distributions","volume":"114","author":"Butler","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Simard, M., Pinto, N., Fisher, J.B., and Baccini, A. (2011). Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res., 116.","DOI":"10.1029\/2011JG001708"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1038\/s41586-018-0555-7","article-title":"Widespread seasonal compensation effects of spring warming on northern plant productivity","volume":"562","author":"Buermann","year":"2018","journal-title":"Nature"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3743","DOI":"10.1111\/gcb.12610","article-title":"Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes","volume":"20","author":"Fu","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1038\/nature15402","article-title":"Declining global warming effects on the phenology of spring leaf unfolding","volume":"526","author":"Fu","year":"2015","journal-title":"Nature"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3520","DOI":"10.1111\/gcb.12945","article-title":"Time-lag effects of global vegetation responses to climate change","volume":"21","author":"Wu","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/014311600210191","article-title":"Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data","volume":"21","author":"Loveland","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","first-page":"16","article-title":"Atmospheric CO2 records from sites in the SIO air sampling network","volume":"93","author":"Keeling","year":"1994","journal-title":"Trends"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Li, X., and Xiao, J.F. (2019). Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2. Remote Sens., 11.","DOI":"10.3390\/rs11212563"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1016\/j.ecolmodel.2011.02.007","article-title":"Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy","volume":"222","author":"Vincenzi","year":"2011","journal-title":"Ecol. Model."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.geoderma.2018.11.044","article-title":"Estimation of soil temperature from meteorological data using different machine learning models","volume":"338","author":"Feng","year":"2019","journal-title":"Geoderma"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/01431160903464146","article-title":"Modelling the vegetation\u2013climate relationship in a boreal mixedwood forest of Alberta using normalized difference and enhanced vegetation indices","volume":"32","author":"Jahan","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3390\/rs2030673","article-title":"Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques","volume":"2","author":"Panda","year":"2010","journal-title":"Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"e2020GL091098","DOI":"10.1029\/2020GL091098","article-title":"On the Covariation of Chlorophyll Fluorescence and Photosynthesis Across Scales","volume":"47","author":"Magney","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"e2020JG006136","DOI":"10.1029\/2020JG006136","article-title":"Global-Scale Consistency of Spaceborne Vegetation Indices, Chlorophyll Fluorescence, and Photosynthesis","volume":"126","author":"Doughty","year":"2021","journal-title":"J. Geophys. Res. Biog."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2979","DOI":"10.1111\/gcb.13200","article-title":"Satellite chlorophyll fluorescence measurements reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests","volume":"22","author":"Walther","year":"2016","journal-title":"Glob. Chang. Biol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"108819","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."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"5294","DOI":"10.1029\/2019GL082716","article-title":"Phenology Dynamics of Dryland Ecosystems Along the North Australian Tropical Transect Revealed by Satellite Solar-Induced Chlorophyll Fluorescence","volume":"46","author":"Wang","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"3990","DOI":"10.1111\/gcb.14297","article-title":"Solar-induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO-2 and flux tower observations","volume":"24","author":"Li","year":"2018","journal-title":"Glob. Chang. Biol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"9845432","DOI":"10.34133\/2022\/9845432","article-title":"Prospects for Solar-Induced Chlorophyll Fluorescence Remote Sensing from the SIFIS Payload Onboard the TECIS-1 Satellite","volume":"2022","author":"Du","year":"2022","journal-title":"J. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Hinojo-Hinojo, C., and Goulden, M.L. (2020). Plant Traits Help Explain the Tight Relationship between Vegetation Indices and Gross Primary Production. Remote Sens., 12.","DOI":"10.3390\/rs12091405"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1175\/1520-0442(2004)017<2281:ATMFCT>2.0.CO;2","article-title":"A Two-Big-Leaf Model for Canopy Temperature, Photosynthesis, and Stomatal Conductance","volume":"17","author":"Dai","year":"2004","journal-title":"J. Clim."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1038\/nclimate3105","article-title":"Satellite based estimates underestimate the effect of CO2 fertilization on net primary productivity","volume":"6","author":"Keenan","year":"2016","journal-title":"Nat. Clim. Chang."},{"key":"ref_79","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"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"13428","DOI":"10.1038\/ncomms13428","article-title":"Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake","volume":"7","author":"Keenan","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2019.01.016","article-title":"What is global photosynthesis? History, uncertainties and opportunities","volume":"223","author":"Ryu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1038\/nclimate3004","article-title":"Greening of the Earth and its drivers","volume":"6","author":"Zhu","year":"2016","journal-title":"Nat. Clim. Chang."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1093\/jxb\/ern053","article-title":"Rubisco, Rubisco activase, and global climate change","volume":"59","author":"Sage","year":"2008","journal-title":"J. Exp. Bot."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.envexpbot.2007.10.016","article-title":"Climatic warming changes plant photosynthesis and its temperature dependence in a temperate steppe of northern China","volume":"63","author":"Niu","year":"2008","journal-title":"Environ. Exp. Bot."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.agrformet.2014.04.001","article-title":"Response of vegetation growth and productivity to spring climate indicators in the conterminous United States derived from satellite remote sensing data fusion","volume":"194","author":"Kim","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"e2020AV000228","DOI":"10.1029\/2020AV000228","article-title":"Observational Constraints on the Response of High-Latitude Northern Forests to Warming","volume":"1","author":"Liu","year":"2020","journal-title":"AGU Adv."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"5018","DOI":"10.1038\/ncomms6018","article-title":"Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity","volume":"5","author":"Piao","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1038\/nclimate1836","article-title":"Temperature and vegetation seasonality diminishment over northern lands","volume":"3","author":"Xu","year":"2013","journal-title":"Nat. Clim. Chang."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5194\/bg-13-45-2016","article-title":"Environmental controls on the increasing GPP of terrestrial vegetation across northern Eurasia","volume":"13","author":"Dass","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s004840000066","article-title":"The importance of phenology for the evaluation of impact of climate change on growth of boreal, temperate and Mediterranean forests ecosystems: An overview","volume":"44","author":"Kramer","year":"2000","journal-title":"Int. J. Biometeorol."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1111\/j.1365-2486.2006.01123.x","article-title":"Variations in satellite-derived phenology in China\u2019s temperate vegetation","volume":"12","author":"Piao","year":"2006","journal-title":"Glob. Chang. Biol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s40663-021-00309-9","article-title":"Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China","volume":"8","author":"Ren","year":"2021","journal-title":"For. Ecosyst."},{"key":"ref_93","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\u2013227","author":"Zhou","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"024027","DOI":"10.1088\/1748-9326\/8\/2\/024027","article-title":"Earlier springs decrease peak summer productivity in North American boreal forests","volume":"8","author":"Buermann","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1111\/gcb.13081","article-title":"Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China","volume":"22","author":"Liu","year":"2016","journal-title":"Glob. Chang. Biol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"034009","DOI":"10.1088\/1748-9326\/ab65cc","article-title":"Radiance-based NIRv as a proxy for GPP of corn and soybean","volume":"15","author":"Wu","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"111209","DOI":"10.1016\/j.rse.2019.05.028","article-title":"A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence","volume":"232","author":"Zeng","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Ma, Y., Liu, L., Chen, R., Du, S., and Liu, X. (2020). Generation of a Global Spatially Continuous TanSat Solar-Induced Chlorophyll Fluorescence Product by Considering the Impact of the Solar Radiation Intensity. Remote Sens., 12.","DOI":"10.3390\/rs12132167"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"5779","DOI":"10.5194\/bg-15-5779-2018","article-title":"A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks","volume":"15","author":"Zhang","year":"2018","journal-title":"Biogeosciences"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"3731","DOI":"10.1111\/gcb.14729","article-title":"Terrestrial gross primary production: Using NIRVto scale from site to globe","volume":"25","author":"Badgley","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"L19405","DOI":"10.1029\/2012GL053461","article-title":"Correlations among leaf traits provide a significant constraint on the estimate of global gross primary production","volume":"39","author":"Wang","year":"2012","journal-title":"Geophys. Res. Lett."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"7352","DOI":"10.1002\/ece3.2479","article-title":"Potential and limitations of inferring ecosystem photosynthetic capacity from leaf functional traits","volume":"6","author":"Musavi","year":"2016","journal-title":"Ecol. Evol."},{"key":"ref_103","first-page":"2128","article-title":"Key canopy traits drive forest productivity","volume":"279","author":"Reich","year":"2012","journal-title":"Proc. Biol. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6316\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:40:41Z","timestamp":1760146841000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"references-count":103,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246316"],"URL":"https:\/\/doi.org\/10.3390\/rs14246316","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,13]]}}}