{"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":1774944251031,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,4]],"date-time":"2018-05-04T00:00:00Z","timestamp":1525392000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>How to effectively combine remote sensing data with the eddy covariance (EC) technique to accurately quantify gross primary production (GPP) in coastal wetlands has been a challenge and is also important and necessary for carbon (C) budgets assessment and climate change studies at larger scales. In this study, a satellite-based Vegetation Photosynthesis Model (VPM) combined with EC measurement and Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to evaluate the phenological characteristics and the biophysical performance of MODIS-based vegetation indices (VIs) and the feasibility of the model for simulating GPP of coastal wetland ecosystems. The results showed that greenness-related and water-related VIs can better identify the green-up and the senescence phases of coastal wetland vegetation, corresponds well with the C uptake period and the phenological patterns that were delineated by GPP from EC tower (GPPEC). Temperature can explain most of the seasonal variation in VIs and GPPEC fluxes. Both enhanced vegetation index (EVI) and water-sensitive land surface water index (LSWI) have a higher predictive power for simulating GPP in this coastal wetland. The comparisons between modeled GPP (GPPVPM) and GPPEC indicated that VPM model can commendably simulate the trajectories of the seasonal dynamics of GPPEC fluxes in terms of patterns and magnitudes, explaining about 85% of GPPEC changes over the study years (p &lt; 0.0001). The results also demonstrate the potential of satellite-driven VPM model for modeling C uptake at large spatial and temporal scales in coastal wetlands, which can provide valuable production data for the assessment of global wetland C sink\/source.<\/jats:p>","DOI":"10.3390\/rs10050708","type":"journal-article","created":{"date-parts":[[2018,5,7]],"date-time":"2018-05-07T03:12:21Z","timestamp":1525662741000},"page":"708","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Xiaoming","family":"Kang","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Wetland Services and Restoration, Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Sichuan Zoige Wetland Ecosystem Research Station, Tibetan Autonomous Prefecture of Aba 624500, China"},{"name":"Department of Biology Science, Institute of Environment Sciences, University of Quebec at Montreal, Montreal, QC C3H 3P8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Yan","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Wetland Services and Restoration, Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Sichuan Zoige Wetland Ecosystem Research Station, Tibetan Autonomous Prefecture of Aba 624500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Wetland Services and Restoration, Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Sichuan Zoige Wetland Ecosystem Research Station, Tibetan Autonomous Prefecture of Aba 624500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Wetland Services and Restoration, Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Sichuan Zoige Wetland Ecosystem Research Station, Tibetan Autonomous Prefecture of Aba 624500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dashuan","family":"Tian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changhui","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Biology Science, Institute of Environment Sciences, University of Quebec at Montreal, Montreal, QC C3H 3P8, Canada"},{"name":"Center for Ecological Forecasting and Global Change, College of Forestry, Northwest Agriculture and Forest University, Yangling 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haidong","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Wetland Services and Restoration, Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Sichuan Zoige Wetland Ecosystem Research Station, Tibetan Autonomous Prefecture of Aba 624500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Wetland Services and Restoration, Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Sichuan Zoige Wetland Ecosystem Research Station, Tibetan Autonomous Prefecture of Aba 624500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Environmental Science and Engineering, Tianjin University, China-Australia Centre for Sustainable Urban Development, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,4]]},"reference":[{"key":"ref_1","first-page":"311","article-title":"Vegetation succession and carbon sequestration in a coastal wetland in northwest Florida: Evidence from carbon isotopes","volume":"15","author":"Choi","year":"2001","journal-title":"Glob. Chang. Biol."},{"key":"ref_2","unstructured":"Cicin-Sain, B., Knecht, R.W., Jang, D., and Fisk, G.W. (1998). Integrated Coastal and Ocean Management: Concepts and Practices, Island Press."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/BF02393837","article-title":"Greenhouse effect and coastal wetland policy: How Americans could abandon an area the size of Massachusetts at minimum cost","volume":"15","author":"Titus","year":"1991","journal-title":"Environ. Manag."},{"key":"ref_4","first-page":"77","article-title":"Terms of reference towards coastal management and sustainable development in Latin America: Introduction to Special Issue on progress and experiences","volume":"42","year":"1999","journal-title":"Ocean Coast. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1690","DOI":"10.1111\/j.1365-2486.2008.01589.x","article-title":"Closing the carbon budget of estuarine wetlands with tower-based measurements and MODIS time series","volume":"14","author":"Yan","year":"2008","journal-title":"Glob. Chang. Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1885","DOI":"10.1007\/s12237-014-9927-x","article-title":"Carbon sequestration and soil accretion in coastal wetland communities of the Yellow River Delta and Liaohe Delta, China","volume":"38","author":"Ye","year":"2015","journal-title":"Estuar. Coast."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.3390\/rs6043321","article-title":"Evaluating parameter adjustment in the MODIS gross primary production algorithm based on eddy covariance tower measurements","volume":"6","author":"Chen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1641\/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2","article-title":"A continuous satellite-derived measure of global terrestrial primary production","volume":"54","author":"Running","year":"2004","journal-title":"Bioscience"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.ecolmodel.2004.08.023","article-title":"Remote sensing of crop production in China by production efficiency models: Models comparisons, estimates and uncertainties","volume":"183","author":"Tao","year":"2005","journal-title":"Ecol. Model."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.agee.2008.10.017","article-title":"Modeling gross primary productivity for winter wheat-maize double cropping system using MODIS time series and CO2 eddy flux tower data","volume":"129","author":"Yan","year":"2009","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1038\/nature22030","article-title":"Large historical growth in global terrestrial gross primary production","volume":"544","author":"Campbell","year":"2017","journal-title":"Nature"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1007\/s12237-016-0177-y","article-title":"A landscape-scale assessment of above-and belowground primary production in coastal wetlands: Implications for climate change-induced community shifts","volume":"40","author":"Stagg","year":"2017","journal-title":"Estuar. Coast."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10215","DOI":"10.3390\/rs61010215","article-title":"Comparison of different GPP models in China using MODIS image and ChinaFLUX data","volume":"6","author":"Liu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_14","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 densities","volume":"82","author":"Baldocchi","year":"2001","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s13157-014-0529-y","article-title":"Modeling carbon fluxes using multi-temporal MODIS imagery and CO2 eddy flux tower data in Zoige Alpine Wetland, South-West China","volume":"34","author":"Kang","year":"2014","journal-title":"Wetlands"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.agrformet.2004.12.004","article-title":"Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet\/dry savanna in Australia: Measurements and comparison with MODIS remote sensing estimates","volume":"129","author":"Leuning","year":"2005","journal-title":"Agric. For. Meteorol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.scitotenv.2011.09.067","article-title":"The fluxes of CO2 from grazed and fenced temperate steppe during two drought years on the Inner Mongolia Plateau, China","volume":"410","author":"Wang","year":"2011","journal-title":"Sci. Total Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1046\/j.1365-2486.2003.00629.x","article-title":"Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: Past, present and future","volume":"9","author":"Baldocchi","year":"2003","journal-title":"Glob. Chang. Biol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1007\/s11368-011-0339-2","article-title":"Modeling impacts of climate change on carbon dynamics in a steppe ecosystem in Inner Mongolia, China","volume":"11","author":"Kang","year":"2011","journal-title":"J. Soil. Sediment."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1016\/j.agrformet.2011.04.001","article-title":"Carbon exchange in a freshwater marsh in the Sanjiang Plain, Northeastern China","volume":"151","author":"Song","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"G01026","DOI":"10.1029\/2011JG001836","article-title":"Incorporating spatial heterogeneity created by permafrost thaw into a landscape carbon estimate","volume":"117","author":"Belshe","year":"2012","journal-title":"J. Geophys. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kang, X.M., Hao, Y.B., Cui, X.Y., Chen, H., Huang, S.X., Du, Y.G., Li, W., Kardol, P., Xiao, X.M., and Cui, L.J. (2016). Variability and changes in climate, phenology, and gross primary production of an alpine wetland ecosystem. Remote Sens., 8.","DOI":"10.3390\/rs8050391"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.rse.2006.10.003","article-title":"Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data","volume":"107","author":"Li","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1111\/j.1365-2745.2011.01916.x","article-title":"The productivity, metabolism and carbon cycle of tropical forest vegetation","volume":"100","author":"Malhi","year":"2012","journal-title":"J. Ecol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.agrformet.2017.02.020","article-title":"Using digital camera and Landsat imagery with eddy covariance data to model gross primary production in restored wetlands","volume":"237","author":"Knox","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.rse.2012.10.030","article-title":"Spatial and temporal variation in primary productivity (NDVI) of coastal Alaskan tundra: Decreased vegetation growth following earlier snowmelt","volume":"129","author":"Gamon","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2013.02.023","article-title":"Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data","volume":"134","author":"Zhang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_28","first-page":"177","article-title":"Modelling the gross primary productivity of West Africa with the Regional Biomass Model RBM plus, using optimized 250 m MODIS FPAR and fractional vegetation cover information","volume":"43","author":"Machwitz","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1029\/93GB02725","article-title":"Terrestrial ecosystem production\u2014A process model-based on global satellite and surface data","volume":"7","author":"Potter","year":"1993","journal-title":"Glob. Biogeochem. Cycle"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.rse.2003.06.005","article-title":"Scaling gross primary production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation","volume":"88","author":"Turner","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_31","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_32","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_33","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_34","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1890\/04-0470","article-title":"Modeling gross primary production of an everygreen needleleaf forest using satellite images and climate data","volume":"15","author":"Xiao","year":"2005","journal-title":"Ecol. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2004.08.015","article-title":"Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest","volume":"94","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.rse.2004.03.010","article-title":"Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data","volume":"91","author":"Xiao","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A comparison of vegetation indices over a global set of TM images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.agrformet.2015.08.246","article-title":"Seasonal variations in phenology and productivity of a tropical dry deciduous forest from MODIS and Hyperion","volume":"214","author":"Christian","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.rse.2013.03.033","article-title":"Phenology and gross primary production of two dominant savanna woodland ecosystems in Southern Africa","volume":"135","author":"Jin","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2016.11.025","article-title":"Modeling gross primary production of paddy rice cropland through analyses of data from CO2 eddy flux tower sites and MODIS images","volume":"190","author":"Xin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1016\/j.ecoleng.2009.03.022","article-title":"Evaluating the ecological performance of wetland restoration in the Yellow River Delta, China","volume":"35","author":"Cui","year":"2009","journal-title":"Ecol. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1016\/j.jenvman.2011.05.030","article-title":"Soil organic carbon of degraded wetlands treated with freshwater in the Yellow River Delta, China","volume":"92","author":"Wang","year":"2011","journal-title":"J. Environ. Manag."},{"key":"ref_43","first-page":"1745","article-title":"Study on the wetland resource and biodiversity in the Yellow River Delta","volume":"35","author":"Wang","year":"2007","journal-title":"J. Anhui Agric. Sci."},{"key":"ref_44","unstructured":"Zhao, T., and Song, C. (1995). Scientific Survey of the Yellow River Delta Nature Reserve, China Forestry Publishing House."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1002\/clen.201200059","article-title":"Spatial and temporal distributions of soil organic carbon and total nitrogen in two marsh wetlands with different flooding frequencies of the Yellow River Delta, China","volume":"40","author":"Bai","year":"2012","journal-title":"Clean Soil Air Water"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0168-1923(00)00225-2","article-title":"Gap filling strategies for defensible annual sums of net ecosystem exchange","volume":"107","author":"Falge","year":"2001","journal-title":"Agric. For. Meteorol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/S0168-1923(02)00104-1","article-title":"Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation","volume":"113","author":"Law","year":"2002","journal-title":"Agric. For. Meteorol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1016\/0038-0717(94)00242-S","article-title":"The Temperature-Dependence of Soil Organic-Matter Decomposition, and the Effect of Global Warming on Soil Organic-C Storage","volume":"27","author":"Kirschbaum","year":"1995","journal-title":"Soil Biol. Biochem."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0034-4257(02)00129-3","article-title":"Sensitivity of vegetation indices to atmospheric aerosols: Continental- scale observations in northern Asia","volume":"84","author":"Xiao","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"399","DOI":"10.2307\/1941899","article-title":"Potential net primary productivity in South-America-application of a global-model","volume":"1","author":"Raich","year":"1991","journal-title":"Ecol. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0065-2504(08)60063-X","article-title":"CO2 fluxes over plant canopies and solar radiation: A review","volume":"26","author":"Ruimy","year":"1995","journal-title":"Adv. Ecol. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"28987","DOI":"10.1029\/97JD01111","article-title":"Physiological responses of a black spruce forest to weather","volume":"102","author":"Goulden","year":"1997","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1016\/j.agrformet.2010.04.015","article-title":"Modeling gross primary production of maize cropland and degraded grassland in northeastern China","volume":"150","author":"Wang","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/S0016-7061(97)00087-6","article-title":"A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments","volume":"81","author":"Smith","year":"1997","journal-title":"Geoderma"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.agrformet.2008.07.013","article-title":"Agro-C: A biogeophysical model for simulating the carbon budget of agroecosystems","volume":"149","author":"Huang","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_57","first-page":"12","article-title":"R: A language and environment for statistical computing","volume":"1","author":"Coreteam","year":"2015","journal-title":"Computing"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Karkauskaite, P., Tagesson, T., and Fensholt, R. (2017). Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone. Remote Sens., 9.","DOI":"10.3390\/rs9050485"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.rse.2015.03.031","article-title":"Evaluating temporal consistency of long-term global NDVI datasets for trend analysis","volume":"163","author":"Tian","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S0034-4257(02)00051-2","article-title":"Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data","volume":"82","author":"Xiao","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1111\/j.1365-2486.2011.02405.x","article-title":"Relationships between phenology, radiation and precipitation in the Amazon region","volume":"17","author":"Bradley","year":"2011","journal-title":"Glob. Chang. Biol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2013.01.010","article-title":"Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements","volume":"132","author":"Hmimina","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1514","DOI":"10.1016\/j.agrformet.2011.06.007","article-title":"Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO2 flux tower data","volume":"151","author":"Kalfas","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_64","unstructured":"Reed, B.C., Schwartz, M.D., and Xiao, X.M. (2009). Phenology of Ecosystem Processes, Springer."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1016\/j.proenv.2012.01.068","article-title":"Modeling gross primary production of two steppes in Northern China using MODIS time series and climate data","volume":"13","author":"Liu","year":"2012","journal-title":"Proc. Environ. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1007\/s11430-008-0113-5","article-title":"Modeling gross primary production of a temperate grassland ecosystem in Inner Mongolia, China, using MODIS imagery and climate data","volume":"51","author":"Wu","year":"2008","journal-title":"Sci. China Ser. D Earth Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.agrformet.2014.03.007","article-title":"Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database","volume":"192","author":"Yuan","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.rse.2004.12.011","article-title":"Improvements of the MODIS terrestrial gross and net primary production global data set","volume":"95","author":"Zhao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_69","unstructured":"ORNL DAAC (2017, October 16). MODIS Collection 6 Land Products Global Subsetting and Visualization Tool. Available online: https:\/\/doi.org\/10.3334\/ORNLDAAC\/1379."},{"key":"ref_70","unstructured":"Running, S., Mu, Q., and Zhao, M. (2017, October 16). MOD17A2H MODIS\/Terra Gross Primary Productivity 8-Day L4 Global 500 m SIN Grid V006. NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MOD17A2H.006."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"7183","DOI":"10.1029\/2000JD900719","article-title":"Summarizing multiple aspects of model performance in a single diagram","volume":"106","author":"Taylor","year":"2001","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rse.2014.01.004","article-title":"Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production","volume":"144","author":"Gitelson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Niu, B., He, Y., and Zhang, X. (2016). Tower-based validation and improvement of MODIS gross primary production in an alpine swamp meadow on the Tibetan Plateau. Remote Sens., 8.","DOI":"10.3390\/rs8070592"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1016\/j.rse.2007.08.004","article-title":"A newmodel of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS","volume":"112","author":"Sims","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0304-3800(99)00140-4","article-title":"Satellite remote sensing of primary production: An improved production efficiency modeling approach","volume":"122","author":"Goetz","year":"1999","journal-title":"Ecol. Model."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/S0034-4257(99)00061-9","article-title":"A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data","volume":"70","author":"Running","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"053551","DOI":"10.1117\/1.3624519","article-title":"Site-level evaluation of MODIS-based primary production in an old-growth forest in Northeast China","volume":"5","author":"Wu","year":"2011","journal-title":"J. Appl. Remote. Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1076\/iaij.4.1.5.16466","article-title":"Defining uncertainty: A conceptual basis for uncertainty management in model-based decision support","volume":"4","author":"Walker","year":"2003","journal-title":"Integr. Assess."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1016\/j.envsoft.2007.02.004","article-title":"Uncertainty in the environmental modelling process\u2013a framework and guidance","volume":"22","author":"Refsgaard","year":"2007","journal-title":"Environ. Model. Softw."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2014.05.010","article-title":"Sensitivity of vegetation indices and gross primary production of tallgrass prairie to severe drought","volume":"152","author":"Wagle","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_81","first-page":"444","article-title":"GPP and maximum light use efficiency estimates using different approaches over a rotating biodiesel crop","volume":"214","author":"Pardo","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1016\/j.agrformet.2008.12.002","article-title":"Satellite-based estimation of evapotranspiration of an old-growth temperate mixed forest","volume":"149","author":"Zhang","year":"2009","journal-title":"Agric. For. Meteorol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/708\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:03:19Z","timestamp":1760194999000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/708"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,4]]},"references-count":82,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["rs10050708"],"URL":"https:\/\/doi.org\/10.3390\/rs10050708","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,4]]}}}