{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:15:06Z","timestamp":1783570506390,"version":"3.55.0"},"reference-count":118,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T00:00:00Z","timestamp":1646870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["755617"],"award-info":[{"award-number":["755617"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor. Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016\u20132020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.<\/jats:p>","DOI":"10.3390\/rs14061347","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T20:19:10Z","timestamp":1646943550000},"page":"1347","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6957-0269","authenticated-orcid":false,"given":"Pablo","family":"Reyes-Mu\u00f1oz","sequence":"first","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0759-4422","authenticated-orcid":false,"given":"Luca","family":"Pipia","sequence":"additional","affiliation":[{"name":"Institut Cartografic i Geologic de Catalunya (ICGC), Parc de Montj\u00fcic, 08038 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5010-0179","authenticated-orcid":false,"given":"Mat\u00edas","family":"Salinero-Delgado","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3739-6056","authenticated-orcid":false,"given":"Santiago","family":"Belda","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, 46980 Paterna, Spain"},{"name":"Department of Applied Mathematics, University of Alicante, 03690 Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0784-7717","authenticated-orcid":false,"given":"Katja","family":"Berger","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, 46980 Paterna, Spain"},{"name":"Department of Geography, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen (LMU), Luisenstr. 37, 80333 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4290-3542","authenticated-orcid":false,"given":"Jos\u00e9","family":"Est\u00e9vez","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0537-6803","authenticated-orcid":false,"given":"Miguel","family":"Morata","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3188-1448","authenticated-orcid":false,"given":"Juan Pablo","family":"Rivera-Caicedo","sequence":"additional","affiliation":[{"name":"Secretary of Research and Graduate Studies, CONACYT-UAN, Tepic 63155, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-2081","authenticated-orcid":false,"given":"Jochem","family":"Verrelst","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1038\/s42003-021-01985-7","article-title":"Global climate and nutrient controls of photosynthetic capacity\u2014Communications Biology","volume":"4","author":"Peng","year":"2021","journal-title":"Commun. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13717-020-00255-4","article-title":"Current and near-term advances in Earth observation for ecological applications","volume":"10","author":"Ustin","year":"2021","journal-title":"Ecol. Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.rse.2011.07.024","article-title":"The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission","volume":"120","author":"Donlon","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1109\/TGRS.2016.2621820","article-title":"The FLuorescence EXplorer Mission Concept-ESA\u2019s Earth Explorer 8","volume":"55","author":"Drusch","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","unstructured":"ESA (European Space Agency) (2015). Report for Mission Selection: FLEX. ESA SP-1330\/2 (2 Volume Series), ESA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112101","DOI":"10.1016\/j.rse.2020.112101","article-title":"Quantifying vegetation biophysical variables from the Sentinel-3\/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources","volume":"251","author":"Verrelst","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111177","DOI":"10.1016\/j.rse.2019.04.030","article-title":"Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress","volume":"231","author":"Mohammed","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"15494","DOI":"10.3390\/rs71115494","article-title":"A Generic Algorithm to Estimate LAI, FAPAR and FCOVER Variables from SPOT4 HRVIR and Landsat Sensors: Evaluation of the Consistency and Comparison with Ground Measurements","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1111\/j.1365-3040.1992.tb00992.x","article-title":"Defining leaf area index for non-flat leaves","volume":"15","author":"Chen","year":"1992","journal-title":"Plant Cell Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.agrformet.2003.08.001","article-title":"Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling","volume":"121","author":"Weiss","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.agrformet.2004.09.006","article-title":"Methodology comparison for canopy structure parameters extraction from digital hemispherical photography in boreal forests","volume":"129","author":"Leblanc","year":"2005","journal-title":"Agric. For. Meteorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1016\/j.rse.2010.11.016","article-title":"Retrieving wheat Green Area Index during the growing season from optical time series measurements based on neural network radiative transfer inversion","volume":"115","author":"Duveiller","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112168","DOI":"10.1016\/j.rse.2020.112168","article-title":"Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring","volume":"255","author":"Amin","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.rse.2008.08.017","article-title":"On the need to observe vegetation canopies in the near-infrared to estimate visible light absorption","volume":"113","author":"Pinty","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Knorr, W., Kaminski, T., Scholze, M., Gobron, N., Pinty, B., Giering, R., and Mathieu, P.P. (2010). Carbon cycle data assimilation with a generic phenology model. J. Geophys. Res. Biogeosci., 115.","DOI":"10.1029\/2009JG001119"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, S., Liu, L., He, X., Liu, Z., and Peng, D. (2020). Upscaling from Instantaneous to Daily Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for Satellite Products. Remote Sens., 12.","DOI":"10.3390\/rs12132083"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.5194\/bg-9-3173-2012","article-title":"Consistent assimilation of MERIS FAPAR and atmospheric CO2 into a terrestrial vegetation model and interactive mission benefit analysis","volume":"9","author":"Kaminski","year":"2012","journal-title":"Biogeosciences"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.agrformet.2015.02.021","article-title":"Towards regional grain yield forecasting with 1km-resolution EO biophysical products: Strengths and limitations at pan-European level","volume":"206","author":"Duveiller","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_20","unstructured":"Liang, S., and Wang, J. (2020). Fractional vegetation cover. Advanced Remote Sensing, Academic Press. [2nd ed.]."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3357","DOI":"10.1080\/01431160010020100","article-title":"A procedure for obtaining green plant cover: Relation to NDVI in a case study for barley","volume":"22","author":"Calera","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1080\/01431161.2015.1041174","article-title":"Influence of acquisition time and resolution on wheat yield estimation at the field scale from canopy biophysical variables retrieved from SPOT satellite data","volume":"36","author":"Castaldi","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pastor-Guzman, J., Brown, L., Morris, H., Bourg, L., Goryl, P., Dransfeld, S., and Dash, J. (2020). The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI): Algorithm Improvements, Spatiotemporal Consistency and Continuity with the MERIS Archive. Remote Sens., 12.","DOI":"10.3390\/rs12162652"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2136","DOI":"10.3390\/s8042136","article-title":"Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us about the Landscape","volume":"8","author":"Glenn","year":"2008","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.isprsjprs.2015.05.005","article-title":"Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties\u2014A review","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","first-page":"263","article-title":"Why confining to vegetation indices? Exploiting the potential of improved spectral observations using radiative transfer models","volume":"Volume 8174","author":"Atzberger","year":"2011","journal-title":"Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1007\/s10712-018-9478-y","article-title":"Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods","volume":"40","author":"Verrelst","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_29","first-page":"71","article-title":"Applying different inversion techniques to retrieve stand variables of summer barley with PROSPECT+SAIL","volume":"12","author":"Vohland","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/02757250009532396","article-title":"Inversion methods for physically-based models","volume":"18","author":"Kimes","year":"2000","journal-title":"Remote Sens. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TGRS.2013.2238242","article-title":"Optimizing LUT-Based RTM Inversion for Semiautomatic Mapping of Crop Biophysical Parameters from Sentinel-2 and -3 Data: Role of Cost Functions","volume":"52","author":"Verrelst","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2011.11.002","article-title":"Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3","volume":"118","author":"Verrelst","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Verrelst, J., Vicent, J., Rivera-Caicedo, J., Lumbierres, M., Morcillo-Pallar\u00e9s, P., and Moreno, J. (2019). Global sensitivity analysis of leaf-canopy-atmosphere RTMs: Implications for biophysical variables retrieval from top-of-atmosphere radiance data. Remote Sens., 11.","DOI":"10.3390\/rs11161923"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.isprsjprs.2015.04.013","article-title":"Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods\u2014A comparison","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1002\/2014JG002713","article-title":"Models of fluorescence and photosynthesis for interpreting measurements of solar-induced chlorophyll fluorescence","volume":"119","author":"Berry","year":"2014","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K.I. (2006). Gaussian Processes for Machine Learning, The MIT Press.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"20170660","DOI":"10.1098\/rsbl.2017.0660","article-title":"Mechanistic models versus machine learning, a fight worth fighting for the biological community?","volume":"14","author":"Baker","year":"2018","journal-title":"Biol. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1109\/TGRS.2011.2168962","article-title":"Retrieval of vegetation biophysical parameters using Gaussian process techniques","volume":"50","author":"Verrelst","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","unstructured":"(2021, November 19). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/."},{"key":"ref_40","first-page":"A3-1","article-title":"Validation of OGVI (OLCI Global Vegetation Index) and OTCI (OLCI Terrestrial Chlorophyll Index) provided by the OLCI (Ocean and Land Color Instrument) sensor at the Valencia Anchor Station","volume":"42","year":"2018","journal-title":"42nd COSPAR Sci. Assem."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"112850","DOI":"10.1016\/j.rse.2021.112850","article-title":"Evaluation of Sentinel-3A and Sentinel-3B ocean land colour instrument green instantaneous fraction of absorbed photosynthetically active radiation","volume":"270","author":"Gobron","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"De Grave, C., Pipia, L., Siegmann, B., Morcillo-Pallar\u00e9s, P., Rivera-Caicedo, J.P., Moreno, J., and Verrelst, J. (2021). Retrieving and Validating Leaf and Canopy Chlorophyll Content at Moderate Resolution: A Multiscale Analysis with the Sentinel-3 OLCI Sensor. Remote Sens., 13.","DOI":"10.3390\/rs13081419"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1080\/15481603.2019.1690780","article-title":"Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud","volume":"57","author":"Gumma","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1007\/s10661-016-5664-x","article-title":"Assessing Nebraska playa wetland inundation status during 1985\u20132015 using Landsat data and Google Earth Engine","volume":"188","author":"Tang","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Campos-Taberner, M., Moreno-Mart\u00ednez, \u00c1., Garc\u00eda-Haro, F.J., Camps-Valls, G., Robinson, N.P., Kattge, J., and Running, S.W. (2018). Global Estimation of Biophysical Variables from Google Earth Engine Platform. Remote Sens., 10.","DOI":"10.3390\/rs10081167"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pipia, L., Amin, E., Belda, S., Salinero-Delgado, M., and Verrelst, J. (2021). Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13030403"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Est\u00e9vez, J., Berger, K., Vicent, J., Rivera-Caicedo, J.P., Wocher, M., and Verrelst, J. (2021). Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow. Remote Sens., 13.","DOI":"10.3390\/rs13081589"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.isprsjprs.2020.07.004","article-title":"Gaussian processes retrieval of LAI from Sentinel-2 top-of-atmosphere radiance data","volume":"167","author":"Vicent","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1111\/j.1420-9101.2009.01775.x","article-title":"The common patterns of nature","volume":"22","author":"Frank","year":"2009","journal-title":"J. Evol. Biol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Berger, K., Atzberger, C., Danner, M., D\u2019Urso, G., Mauser, W., Vuolo, F., and Hank, T. (2018). Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study. Remote Sens., 10.","DOI":"10.3390\/rs10010085"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111362","DOI":"10.1016\/j.rse.2019.111362","article-title":"Multiple-constraint inversion of SCOPE. Evaluating the potential of GPP and SIF for the retrieval of plant functional traits","volume":"234","author":"Julitta","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/36.581987","article-title":"Second simulation of the satellite signal in the solar spectrum, 6S: An overview","volume":"35","author":"Vermote","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.5194\/gmd-13-1945-2020","article-title":"Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)","volume":"13","author":"Vicent","year":"2020","journal-title":"Geosci. Model Dev."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2866","DOI":"10.3390\/rs4092866","article-title":"Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS\/PROBA Data","volume":"4","author":"Verrelst","year":"2012","journal-title":"Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1080\/01431160802438555","article-title":"On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing","volume":"30","author":"Guanter","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","unstructured":"Myneni, R., Knyazikhin, Y., and Park, T. (2015). MCD15A3H MODIS\/Terra+Aqua Leaf Area Index\/FPAR 4-Day L4 Global 500m SIN Grid V006 [Data Set], NASA EOSDIS Land Processes DAAC, USGS-EROS."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1109\/JSTARS.2014.2298752","article-title":"Toward a semiautomatic machine learning retrieval of biophysical parameters","volume":"7","author":"Verrelst","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/MGRS.2015.2510084","article-title":"A survey on Gaussian processes for earth-observation data analysis: A comprehensive investigation","volume":"4","author":"Verrelst","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Belda, S., Pipia, L., Morcillo-Pallar\u00e9s, P., and Verrelst, J. (2020). Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring. Agronomy, 10.","DOI":"10.3390\/agronomy10050618"},{"key":"ref_61","unstructured":"The European Space Agency (ESA) Sentinel-3 OLCI Technical Guide, ESA. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/user-guides\/sentinel-3-olci."},{"key":"ref_62","unstructured":"The European Environment Agency (EEA) (2018). Copernicus Land Monitoring Service, European Commission. Available online: https:\/\/land.copernicus.eu\/."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"32257","DOI":"10.1029\/98JD02462","article-title":"Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data","volume":"103","author":"Knyazikhin","year":"1998","journal-title":"J. Geophys. Res. D Atmos."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Fuster, B., S\u00e1nchez-Zapero, J., Camacho, F., Garc\u00eda-Santos, V., Verger, A., Lacaze, R., Weiss, M., Baret, F., and Smets, B. (2020). Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service. Remote Sens., 12.","DOI":"10.3390\/rs12061017"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1080\/01431169408954055","article-title":"SMAC: A simplified method for the atmospheric correction of satellite measurements in the solar spectrum","volume":"15","author":"Rahman","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","unstructured":"Baret, F., Weiss, M., Allard, D., Garrigue, S., Leroy, M., Jeanjean, H., Fernandes, R., Myneni, R., Privette, J., and Morisette, J. (2022, January 08). VALERI: A Network of Sites and a Methodology for the Validation of Medium Spatial Resolution Land Satellite Products. Available online: http:\/\/w3.avignon.inra.fr\/valeri\/."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TGRS.2006.872529","article-title":"Validation of Global Moderate-Resolution LAI Products: A Framework Proposed within the CEOS Land Product Validation Subgroup","volume":"44","author":"Morisette","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1109\/TGRS.2006.876030","article-title":"Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: Proposition of the CEOS-BELMANIP","volume":"44","author":"Baret","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.rse.2013.02.030","article-title":"GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products","volume":"137","author":"Camacho","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.rse.2006.07.014","article-title":"Neural network estimation of LAI, fAPAR, fCover and LAI\u00d7Cab, from top of canopy MERIS reflectance data: Principles and validation","volume":"105","author":"Bacour","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rse.2007.02.018","article-title":"LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: Principles of the algorithm","volume":"110","author":"Baret","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1046\/j.1466-822X.2003.00026.x","article-title":"Global synthesis of leaf area index observations: Implications for ecological and remote sensing studies","volume":"12","author":"Asner","year":"2003","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1127\/0941-2948\/2006\/0130","article-title":"World map of the K\u00f6ppen-Geiger climate classification updated","volume":"15","author":"Kottek","year":"2006","journal-title":"Meteorol. Z."},{"key":"ref_74","unstructured":"Subsecretar\u00eda de Agricultura, Pesca y Alimentaci\u00f3n (2019). Calendario de Siembra, Recolecci\u00f3n y Comercializaci\u00f3n 2014\u20132016, Ministerio de Agricultura, Pesca y Alimentaci\u00f3n."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1109\/JSTARS.2012.2222356","article-title":"Gaussian Process Retrieval of Chlorophyll Content From Imaging Spectroscopy Data","volume":"6","author":"Verrelst","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Dans, J.L., Lewis, P.E., and Disney, M. (2016). Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences. Remote Sens., 8.","DOI":"10.3390\/rs8020119"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1109\/LGRS.2009.2039191","article-title":"Gaussian Process Regression for Estimating Chlorophyll Concentration in Subsurface Waters From Remote Sensing Data","volume":"7","author":"Pasolli","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the relation between NDVI, fractional vegetation cover, and leaf area index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.isprsjprs.2021.06.017","article-title":"Mapping landscape canopy nitrogen content from space using PRISMA data","volume":"178","author":"Verrelst","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Berger, K., Hank, T., Halabuk, A., Rivera-Caicedo, J.P., Wocher, M., Mojses, M., Gerh\u00e1tov\u00e1, K., Tagliabue, G., Dolz, M.M., and Venteo, A.B.P. (2021). Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13224711"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Wardlow, B.D., Anderson, M.C., and Verdin, J.P. (2012). Remote Sensing of Drought: Innovative Monitoring Approaches, CRC Press.","DOI":"10.1201\/b11863"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.japb.2017.05.004","article-title":"Why does Quercus suber species decline in Mediterranean areas?","volume":"10","author":"Kim","year":"2017","journal-title":"J. Asia-Pac. Biodivers."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s13595-015-0534-1","article-title":"Cork oak pests: A review of insect damage and management","volume":"73","author":"Tiberi","year":"2015","journal-title":"Ann. For. Sci."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"112240","DOI":"10.1016\/j.rse.2020.112240","article-title":"Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS)","volume":"255","author":"Huo","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.rse.2018.11.041","article-title":"Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options","volume":"221","author":"Meroni","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2009.08.014","article-title":"Detecting trend and seasonal changes in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhang, C., Hao, G., Chen, W., Yun, W., Gao, L., and Wang, H. (2020). Analyzing Ecological Vulnerability and Vegetation Phenology Response Using NDVI Time Series Data and the BFAST Algorithm. Remote Sens., 12.","DOI":"10.3390\/rs12203371"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"103804","DOI":"10.1016\/j.actao.2021.103804","article-title":"Multi-year monitoring land surface phenology in relation to climatic variables using MODIS-NDVI time-series in Mediterranean forest, Northeast Tunisia","volume":"114","author":"Touhami","year":"2022","journal-title":"Acta Oecologica"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"111511","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":"ref_92","doi-asserted-by":"crossref","unstructured":"Salinero-Delgado, M., Est\u00e9vez, J., Pipia, L., Belda, S., Berger, K., Paredes G\u00f3mez, V., and Verrelst, J. (2021). Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression. Remote Sens., 14.","DOI":"10.3390\/rs14010146"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"3109","DOI":"10.5194\/bg-6-3109-2009","article-title":"An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance","volume":"6","author":"Verhoef","year":"2009","journal-title":"Biogeosciences"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1109\/TGRS.2005.853718","article-title":"Using 1-D models to interpret the reflectance anisotropy of 3-D canopy targets: Issues and caveats","volume":"43","author":"Widlowski","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"112328","DOI":"10.1016\/j.rse.2021.112328","article-title":"Improved retrieval of land surface biophysical variables from time series of Sentinel-3 OLCI TOA spectral observations by considering the temporal autocorrelation of surface and atmospheric properties","volume":"256","author":"Yang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Chen, J. (2017). Remote Sensing of Leaf Area Index and Clumping Index, Elsevier.","DOI":"10.1016\/B978-0-12-409548-9.10540-8"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"2791","DOI":"10.1016\/j.rse.2010.06.013","article-title":"Assessment of biases in MODIS surface reflectance due to Lambertian approximation","volume":"114","author":"Wang","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1109\/36.701026","article-title":"Atmospheric correction of ASTER","volume":"36","author":"Thome","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.rse.2004.01.001","article-title":"On the dimensionality of multi-view hyperspectral measurements of vegetation","volume":"90","author":"Settle","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"112440","DOI":"10.1016\/j.rse.2021.112440","article-title":"The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem","volume":"260","author":"Nieto","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0034-4257(99)00081-4","article-title":"Evaluating the Effects of Subpixel Heterogeneity on Pixel Average Fluxes","volume":"74","author":"Kustas","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0022-1694(96)03133-2","article-title":"The scaling characteristics of remotely-sensed variables for sparsely-vegetated heterogeneous landscapes","volume":"190","author":"Humes","year":"1997","journal-title":"J. Hydrol."},{"key":"ref_103","unstructured":"North, G.R., Pyle, J., and Zhang, F. (2015). CLOUDS AND FOG | Classification of Clouds. Encyclopedia of Atmospheric Sciences, Academic Press. [2nd ed.]."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Holton, J.R., and Hakim, G.J. (2013). Chapter 10\u2014The General Circulation. An Introduction to Dynamic Meteorology, Academic Press. [5th ed.].","DOI":"10.1016\/B978-0-12-384866-6.00010-6"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"104666","DOI":"10.1016\/j.envsoft.2020.104666","article-title":"DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection","volume":"127","author":"Belda","year":"2020","journal-title":"Environ. Model. Softw."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Prikaziuk, E., Yang, P., and van der Tol, C. (2021). Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences. Remote Sens., 13.","DOI":"10.3390\/rs13061098"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2016.01.018","article-title":"Evaluating the predictive power of sun-induced chlorophyll fluorescence to estimate net photosynthesis of vegetation canopies: A SCOPE modeling study","volume":"176","author":"Verrelst","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Berger, K., Rivera Caicedo, J.P., Martino, L., Wocher, M., Hank, T., and Verrelst, J. (2021). A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data. Remote Sens., 13.","DOI":"10.3390\/rs13020287"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1016\/j.agrformet.2009.06.022","article-title":"Assessment of forest fire seasonality using MODIS fire potential: A time series approach","volume":"149","author":"Huesca","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"4473","DOI":"10.3390\/rs6054473","article-title":"Time Series Analysis of Land Cover Change: Developing Statistical Tools to Determine Significance of Land Cover Changes in Persistence Analyses","volume":"6","author":"Waylen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"111452","DOI":"10.1016\/j.rse.2019.111452","article-title":"Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes","volume":"235","author":"Pipia","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1908","DOI":"10.1109\/TGRS.2005.853936","article-title":"Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations","volume":"44","author":"Heinsch","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"3069","DOI":"10.5194\/bg-16-3069-2019","article-title":"Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model","volume":"16","author":"Norton","year":"2019","journal-title":"Biogeosciences"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"438","DOI":"10.32615\/ps.2021.034","article-title":"Towards the quantitative and physically-based interpretation of solar-induced vegetation fluorescence retrieved from global imaging","volume":"59","author":"Sabater","year":"2021","journal-title":"Photosynthetica"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.5194\/bg-14-2343-2017","article-title":"Reviews and syntheses: Flying the satellite into your model: On the role of observation operators in constraining models of the Earth system and the carbon cycle","volume":"14","author":"Kaminski","year":"2017","journal-title":"Biogeosciences"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1414","DOI":"10.1002\/jgrg.20118","article-title":"The BETHY\/JSBACH Carbon Cycle Data Assimilation System: Experiences and challenges","volume":"118","author":"Kaminski","year":"2013","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Wu, M., Scholze, M., Vo\u00dfbeck, M., Kaminski, T., and Hoffmann, G. (2019). Simultaneous assimilation of remotely sensed soil moisture and FAPAR for improving terrestrial carbon fluxes at multiple sites using CCDAS. Remote Sens., 11.","DOI":"10.3390\/rs11010027"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"619818","DOI":"10.3389\/frsen.2021.619818","article-title":"Grand Challenges in Satellite Remote Sensing","volume":"2","author":"Dubovik","year":"2021","journal-title":"Front. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1347\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:34:24Z","timestamp":1760135664000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,10]]},"references-count":118,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14061347"],"URL":"https:\/\/doi.org\/10.3390\/rs14061347","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,10]]}}}