{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:38:37Z","timestamp":1782452317362,"version":"3.54.5"},"reference-count":51,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Youth Top-Notch Talent Support Program","award":["2015-48"],"award-info":[{"award-number":["2015-48"]}]},{"name":"National Youth Top-Notch Talent Support Program","award":["41925001"],"award-info":[{"award-number":["41925001"]}]},{"name":"National Youth Top-Notch Talent Support Program","award":["19lgjc02"],"award-info":[{"award-number":["19lgjc02"]}]},{"name":"National Youth Top-Notch Talent Support Program","award":["Q2016161"],"award-info":[{"award-number":["Q2016161"]}]},{"name":"China National Funds for Distinguished Young Scientists","award":["2015-48"],"award-info":[{"award-number":["2015-48"]}]},{"name":"China National Funds for Distinguished Young Scientists","award":["41925001"],"award-info":[{"award-number":["41925001"]}]},{"name":"China National Funds for Distinguished Young Scientists","award":["19lgjc02"],"award-info":[{"award-number":["19lgjc02"]}]},{"name":"China National Funds for Distinguished Young Scientists","award":["Q2016161"],"award-info":[{"award-number":["Q2016161"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2015-48"],"award-info":[{"award-number":["2015-48"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["41925001"],"award-info":[{"award-number":["41925001"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["19lgjc02"],"award-info":[{"award-number":["19lgjc02"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["Q2016161"],"award-info":[{"award-number":["Q2016161"]}]},{"name":"Changjiang Young Scholars Program of China","award":["2015-48"],"award-info":[{"award-number":["2015-48"]}]},{"name":"Changjiang Young Scholars Program of China","award":["41925001"],"award-info":[{"award-number":["41925001"]}]},{"name":"Changjiang Young Scholars Program of China","award":["19lgjc02"],"award-info":[{"award-number":["19lgjc02"]}]},{"name":"Changjiang Young Scholars Program of China","award":["Q2016161"],"award-info":[{"award-number":["Q2016161"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Eddy-covariance (EC) measurements are widely used to optimize the terrestrial vegetation gross primary productivity (GPP) model because they provide standardized and high-quality flux data within their footprint areas. However, the extent of flux data taken from a tower site within the EC footprint, represented by the satellite-based grid cell between Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS), and the performance of the model derived from the Normalized Difference Vegetation Index (NDVI) within the EC footprint at different spatial resolutions (e.g., Landsat and MODIS) remain unclear. Here, we first calculated the Landsat-footprint NDVI and MODIS-footprint NDVI and assessed their spatial representativeness at 78 FLUXNET sites at 30 m and 500 m scale, respectively. We then optimized the parameters of the revised Eddy Covariance-Light Use Efficiency (EC-LUE) model using NDVI within the EC-tower footprints that were calculated from the Landsat and MODIS sensor. Finally, we evaluated the performance of the optimized model at 30 m and 500 m scale. Our results showed that matching Landsat data with the flux tower footprint was able to improve the performance of the revised EC-LUE model by 18% for savannas, 14% for croplands, 9% for wetlands. The outperformance of the Landsat-footprint NDVI in driving model relied on the spatial heterogeneity of the flux sites. Our study assessed the advantages of remote sensing data with high spatial resolution in simulating GPP, especially for areas with high heterogeneity of landscapes. This could facilitate a more accurate estimation of global ecosystem carbon sink and a better understanding of plant productivity and carbon climate feedbacks.<\/jats:p>","DOI":"10.3390\/rs14236062","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T05:45:22Z","timestamp":1669787122000},"page":"6062","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["How Well Can Matching High Spatial Resolution Landsat Data with Flux Tower Footprints Improve Estimates of Vegetation Gross Primary Production"],"prefix":"10.3390","volume":"14","author":[{"given":"Xiaojuan","family":"Huang","sequence":"first","affiliation":[{"name":"School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shangrong","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangqian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3783-8363","authenticated-orcid":false,"given":"Mingguo","family":"Ma","sequence":"additional","affiliation":[{"name":"Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoyang","family":"Wu","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenping","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111383","DOI":"10.1016\/j.rse.2019.111383","article-title":"Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years","volume":"233","author":"Xiao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/2017GB005802","article-title":"Influence of Vegetation Growth on the Enhanced Seasonality of Atmospheric CO2","volume":"32","author":"Yuan","year":"2018","journal-title":"Global Biogeochem. Cycles"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.5194\/essd-12-3081-2020","article-title":"Early-season mapping of winter wheat in China based on Landsat and Sentinel images","volume":"12","author":"Dong","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huang, X., Xiao, J., and Ma, M. (2019). Evaluating the Performance of Satellite-Derived Vegetation Indices for Estimating Gross Primary Productivity Using FLUXNET Observations across the Globe. Remote Sens., 11.","DOI":"10.3390\/rs11151823"},{"key":"ref_5","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_6","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_7","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_8","doi-asserted-by":"crossref","first-page":"1","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":"ref_9","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1007\/BF00386231","article-title":"A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species","volume":"149","author":"Farquhar","year":"1980","journal-title":"Planta"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1111\/j.1365-2486.2006.01223.x","article-title":"FLUXNET and modelling the global carbon cycle","volume":"13","author":"Friend","year":"2007","journal-title":"Glob. Chang. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Running, S.W., Thornton, P.E., Nemani, R., and Glassy, J.M. (2000). Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System. Methods in Ecosystem Science, Springer.","DOI":"10.1007\/978-1-4612-1224-9_4"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"170165","DOI":"10.1038\/sdata.2017.165","article-title":"A global moderate resolution dataset of gross primary production of vegetation for 2000-2016","volume":"4","author":"Zhang","year":"2017","journal-title":"Sci. Data"},{"key":"ref_13","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, 1982-2017","volume":"12","author":"Zheng","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100049","DOI":"10.1016\/j.srs.2022.100049","article-title":"High spatial resolution vegetation gross primary production product: Algorithm and validation","volume":"5","author":"Huang","year":"2022","journal-title":"Sci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lin, S., Huang, X., Zheng, Y., Zhang, X., and Yuan, W. (2022). An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution. Remote Sens.","DOI":"10.3390\/rs14112651"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., and Loveland, T.R. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 2013 342, 850\u2013853.","DOI":"10.1126\/science.1244693"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"151335","DOI":"10.1016\/j.scitotenv.2021.151335","article-title":"Diverse biosphere influence on carbon and heat in mixed urban Mediterranean landscape revealed by high resolution thermal and optical remote sensing","volume":"806","author":"Parazoo","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.isprsjprs.2019.12.005","article-title":"Estimating winter wheat yield based on a light use efficiency model and wheat variety data","volume":"160","author":"Dong","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1002\/rse2.74","article-title":"Terrestrial primary production for the conterminous United States derived from Landsat 30 m and MODIS 250 m","volume":"4","author":"Robinson","year":"2018","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108350","DOI":"10.1016\/j.agrformet.2021.108350","article-title":"Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites","volume":"301\u2013302","author":"Chu","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1016\/j.rse.2012.06.007","article-title":"Characterizing spatial representativeness of flux tower eddy-covariance measurements across the Canadian Carbon Program Network using remote sensing and footprint analysis","volume":"124","author":"Chen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_22","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\u2013215","author":"Verma","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"G04021","DOI":"10.1029\/2008JG000781","article-title":"Remote sensing data assimilation for a prognostic phenology model","volume":"113","author":"Stockli","year":"2008","journal-title":"J. Geophys. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"136407","DOI":"10.1016\/j.scitotenv.2019.136407","article-title":"Integrating eddy fl uxes and remote sensing products in a rotational grazing native tallgrass prairie pasture","volume":"712","author":"Wagle","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.rse.2012.02.017","article-title":"Remote estimation of crop gross primary production with Landsat data","volume":"121","author":"Gitelson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_26","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\u2013238","author":"Knox","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1016\/j.rse.2010.01.022","article-title":"Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data","volume":"114","author":"Yuan","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108314","DOI":"10.1016\/j.agrformet.2020.108314","article-title":"Improving the global MODIS GPP model by optimizing parameters with FLUXNET data","volume":"300","author":"Huang","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2018.12.031","article-title":"Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002\u20132017","volume":"222","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1021\/ac034173t","article-title":"A Perfect Smoother","volume":"75","author":"Eilers","year":"2003","journal-title":"Anal. Chem."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1080\/17538947.2010.505664","article-title":"A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America","volume":"4","author":"Atzberger","year":"2011","journal-title":"Int. J. Digit. Earth"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1177\/1471082X14549288","article-title":"L- and V-curves for optimal smoothing","volume":"15","author":"Frasso","year":"2015","journal-title":"Stat. Modelling"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2019.06.014","article-title":"A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine","volume":"155","author":"Kong","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3695","DOI":"10.5194\/gmd-8-3695-2015","article-title":"A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP)","volume":"8","author":"Kljun","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1175\/1520-0450(1994)033<0435:ASMTDO>2.0.CO;2","article-title":"A simple method to determine Obukhov lengths for suburban areas","volume":"33","author":"Grimmond","year":"1994","journal-title":"J. Appl. Meteorol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.agrformet.2018.02.004","article-title":"Seasonal variation of source contributions to eddy-covariance CO2 measurements in a mixed hardwood-conifer forest","volume":"253\u2013254","author":"Kim","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"108905","DOI":"10.1016\/j.agrformet.2022.108905","article-title":"Evolution of light use efficiency models: Improvement, uncertainties, and implications","volume":"317","author":"Pei","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.agrformet.2017.01.018","article-title":"Examining the short-term impacts of diverse management practices on plant phenology and carbon fluxes of Old World bluestems pasture","volume":"237\u2013238","author":"Zhou","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_39","first-page":"43","article-title":"Effect of spatial heterogeneity on the validation of remote sensing based GPP estimations","volume":"174\u2013175","author":"Barcza","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.agrformet.2018.06.004","article-title":"Contributions of landscape heterogeneity within the footprint of eddy-covariance towers to flux measurements","volume":"260","author":"Giannico","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xie, X., Li, A., Jin, H., Yin, G., and Bian, J. (2018). Spatial downscaling of gross primary productivity using topographic and vegetation heterogeneity information: A case study in the Gongga Mountain region of China. Remote Sens., 10.","DOI":"10.3390\/rs10040647"},{"key":"ref_42","first-page":"52","article-title":"Intercomparison and validation of MODIS and GLASS leaf area index (LAI) products over mountain areas: A case study in southwestern China","volume":"55","author":"Jin","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1002\/ecy.1730","article-title":"A survival guide to Landsat preprocessing","volume":"98","author":"Young","year":"2017","journal-title":"Ecology"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2020JG005848","article-title":"Spatial Scaling of Gross Primary Productivity Over Sixteen Mountainous Watersheds Using Vegetation Heterogeneity and Surface Topography","volume":"126","author":"Xie","year":"2021","journal-title":"J. Geophys. Res. Biogeosciences"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.1016\/j.rse.2011.05.012","article-title":"Global evaluation of four AVHRR\u2013NDVI data sets: Intercomparison and assessment against Landsat imagery","volume":"115","author":"Beck","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.rse.2007.08.011","article-title":"The availability of cloud-free Landsat ETM plus data over the conterminous United States and globally","volume":"112","author":"Ju","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"108878","DOI":"10.1016\/j.agrformet.2022.108878","article-title":"Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates","volume":"316","author":"Kong","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/MGRS.2015.2434351","article-title":"Fusing Landsat and MODIS Data for Vegetation Monitoring","volume":"3","author":"Gao","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2013.02.007","article-title":"Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection","volume":"133","author":"Emelyanova","year":"2013","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6062\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:29:52Z","timestamp":1760146192000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,29]]},"references-count":51,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14236062"],"URL":"https:\/\/doi.org\/10.3390\/rs14236062","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,29]]}}}