{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T09:59:27Z","timestamp":1771235967499,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"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"}]},{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["RFP\/3-15477\/18\/NL\/NA"],"award-info":[{"award-number":["RFP\/3-15477\/18\/NL\/NA"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>ESA\u2019s Eighth Earth Explorer mission \u201cFLuorescence EXplorer\u201d (FLEX) will be dedicated to the global monitoring of the chlorophyll fluorescence emitted by vegetation. In order to properly interpret the measured fluorescence signal, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem formation with Sentinel-3 (S3), which conveys the Ocean and Land Color Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In support of FLEX\u2019s preparatory activities, this paper presents a first validation exercise of OLCI vegetation products against in situ data coming from the 2018 FLEXSense campaign. During this campaign, leaf chlorophyll content (LCC) and leaf area index (LAI) measurements were collected over croplands, while HyPlant DUAL images of the area were acquired at a 3 m spatial resolution. A multiscale validation strategy was pursued. First, estimates of these two variables, together with the combined canopy chlorophyll content (CCC = LCC \u00d7 LAI), were obtained at the HyPlant spatial resolution and were compared against the in situ measurements. Second, the fine-scale retrieval maps from HyPlant were coarsened to the S3 spatial scale as a reference to assess the quality of the OLCI vegetation products. As an intermediary step, vegetation products extracted from Sentinel-2 data were used to compare retrievals at the in-between spatial resolution of 20 m. For all spatial scales, CCC delivered the most accurate estimates with the smallest prediction error obtained at the 300 m resolution (R2 of 0.74 and RMSE = 26.8 \u03bcg cm\u22122). Results of a scaling analysis suggest that CCC performs well at the different tested spatial resolutions since it presents a linear behavior across scales. LCC, on the other hand, was poorly retrieved at the 300 m scale, showing overestimated values over heterogeneous pixels. The introduction of a new LCC model integrating mixed reflectance spectra in its training enabled to improve by 16% the retrieval accuracy for this variable (RMSE = 10 \u03bcg cm\u22122 for the new model versus RMSE = 11.9 \u03bcg cm\u22122 for the former model).<\/jats:p>","DOI":"10.3390\/rs13081419","type":"journal-article","created":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T11:31:59Z","timestamp":1617795119000},"page":"1419","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Retrieving and Validating Leaf and Canopy Chlorophyll Content at Moderate Resolution: A Multiscale Analysis with the Sentinel-3 OLCI Sensor"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9327-9420","authenticated-orcid":false,"given":"Charlotte","family":"De Grave","sequence":"first","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0759-4422","authenticated-orcid":false,"given":"Luca","family":"Pipia","sequence":"additional","affiliation":[{"name":"Institut Cartogr\u00e0fic i Geol\u00f2gic de Catalunya (ICGC), Parc de Montj\u00fcic, 08038 Barcelona, Spain"}]},{"given":"Bastian","family":"Siegmann","sequence":"additional","affiliation":[{"name":"Forschungszentrum J\u00fclich GmbH, Institute of Bio- and Geosciences, Plant Sciences (IBG-2), D-52425 J\u00fclich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0970-9492","authenticated-orcid":false,"given":"Pablo","family":"Morcillo-Pallar\u00e9s","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"},{"name":"Instituto ITACA, Universidad Polit\u00e9cnica de Valencia, Camino de Vera s\/n, 46022 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3188-1448","authenticated-orcid":false,"given":"Juan Pablo","family":"Rivera-Caicedo","sequence":"additional","affiliation":[{"name":"CONACyT-UAN, Secretar\u00eda de Investigaci\u00f3n y Posgrado, Universidad Aut\u00f3noma de Nayarit, Ciudad de la Cultura Amado Nervo, 63155 Tepic, Nayarit, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5283-3333","authenticated-orcid":false,"given":"Jos\u00e9","family":"Moreno","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-2081","authenticated-orcid":false,"given":"Jochem","family":"Verrelst","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fr\u00f3na, D., Szender\u00e1k, J., and Harangi-R\u00e1kos, M. (2019). The challenge of feeding the world. Sustainability, 11.","DOI":"10.3390\/su11205816"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2125","DOI":"10.1098\/rstb.2005.1751","article-title":"Climate change, global food supply and risk of hunger","volume":"360","author":"Parry","year":"2005","journal-title":"Philos. Trans. R. Soc. B Biol. Sci."},{"key":"ref_3","first-page":"6","article-title":"Food web: Concept and applications","volume":"3","author":"Hui","year":"2012","journal-title":"Nat. Educ. Knowl."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","unstructured":"Croft, H., Chen, J., Wang, R., Mo, G., Luo, S., Luo, X., He, L., Gonsamo, A., Arabian, J., and Zhang, Y. (2020). The global distribution of leaf chlorophyll content. Remote Sens. Environ., 236.","DOI":"10.1016\/j.rse.2019.111479"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1111\/gcb.14624","article-title":"Improved estimates of global terrestrial photosynthesis using information on leaf chlorophyll content","volume":"25","author":"Luo","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gitelson, A., Vi\u00f1a, A., Ciganda, V., Rundquist, D., and Arkebauer, T. (2005). Remote estimation of canopy chlorophyll in crops. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL022688"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/0034-4257(95)00018-V","article-title":"Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors","volume":"52","author":"Jacquemoud","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_9","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 LAIxCab, from top of canopy MERIS reflectance data: Principles and validation","volume":"105","author":"Bacour","year":"2006","journal-title":"Remote Sens. Environ."},{"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":"2592","DOI":"10.1016\/j.rse.2007.12.003","article-title":"Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland","volume":"112","author":"Darvishzadeh","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Clevers, J., Kooistra, L., and van den Brande, M. (2017). Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sens., 9.","DOI":"10.3390\/rs9050405"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.fcr.2017.05.005","article-title":"Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping","volume":"210","author":"Jay","year":"2017","journal-title":"Field Crop. Res."},{"key":"ref_14","first-page":"5","article-title":"Sentinel-3 OLCI Radiometric and Spectral Performance Activities","volume":"Volume 734","author":"Ouwehand","year":"2015","journal-title":"Sentinel-3 for Science Workshop"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1109\/TGRS.2004.840643","article-title":"Terra MODIS on-orbit spatial characterization and performance","volume":"43","author":"Xiong","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2589","DOI":"10.1080\/01431161.2014.883097","article-title":"PROBA-V mission for global vegetation monitoring: Standard products and image quality","volume":"35","author":"Dierckx","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.rse.2006.03.013","article-title":"Quantifying spatial heterogeneity at the landscape scale using variogram models","volume":"103","author":"Garrigues","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"De Grave, C., Verrelst, J., Morcillo-Pallar\u00e9s, P., Pipia, L., Rivera-Caicedo, J., Amin, E., Belda, S., and Moreno, J. (2020). Quantifying vegetation biophysical variables from the Sentinel-3\/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources. Remote Sens. Environ., 251.","DOI":"10.1016\/j.rse.2020.112101"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhao, L., and Lin, A. (2020). Evaluating the performance of Sentinel-3A OLCI land products for gross primary productivity estimation using AmeriFlux data. Remote Sens., 12.","DOI":"10.3390\/rs12121927"},{"key":"ref_21","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_22","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_23","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K.I. (2006). Gaussian Processes for Machine Learning, MIT Press.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_24","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_25","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_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2016.2538300","article-title":"FLEX End-to-End Mission Performance Simulator","volume":"54","author":"Vicent","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","unstructured":"Zuhlke, M., Fomferra, N., Brockmann, C., Peters, M., Veci, L., Malik, J., and Regner, P. (2015, January 2\u20135). SNAP (sentinel application platform) and the ESA Sentinel 3 toolbox. Proceedings of the Sentinel-3 for Science Workshop, Lido Palazzo del Casin\u00f2, Italy."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s10712-019-09534-y","article-title":"Variability and Uncertainty Challenges in Scaling Imaging Spectroscopy Retrievals and Validations from Leaves up to Vegetation Canopies","volume":"40","author":"Buddenbaum","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1080\/014311600750020000","article-title":"Developments in the \u2019validation\u2019 of satellite sensor products for the study of the land surface","volume":"21","author":"Justice","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","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_31","unstructured":"Fernandes, R., Plummer, S., Nightingale, J., Baret, F., Camacho de Coca, F., Fang, H., Garrigues, S., Gobron, N., Lang, M., and Lacaze, R. (2014). CEOS Global LAI Product Validation Good Practices, CEOS."},{"key":"ref_32","first-page":"36","article-title":"VALERI: A network of sites and methodology for the validation of medium spatial resolution land products","volume":"76","author":"Baret","year":"2013","journal-title":"Remote. Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2004JD004860","article-title":"Validation of Moderate Resolution Imaging Spectroradiometer leaf area index product in croplands of Alpilles, France","volume":"110","author":"Tan","year":"2005","journal-title":"J. Geophys. Res. D Atmos."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Brown, L., Meier, C., Morris, H., Pastor-Guzman, J., Bai, G., Lerebourg, C., Gobron, N., Lanconelli, C., Clerici, M., and Dash, J. (2020). Evaluation of global leaf area index and fraction of absorbed photosynthetically active radiation products over North America using Copernicus Ground Based Observations for Validation data. Remote Sens. Environ., 247.","DOI":"10.1016\/j.rse.2020.111935"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/S0034-4257(02)00047-0","article-title":"Multiscale analysis and validation of the MODIS LAI product I. Uncertainty assessment","volume":"83","author":"Tian","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Siegmann, B., Alonso, L., Celesti, M., Cogliati, S., Colombo, R., Damm, A., Douglas, S., Guanter, L., Hanu\u0161, J., and Kataja, K. (2019). The High-Performance Airborne Imaging Spectrometer HyPlant\u2014From Raw Images to Top-of-Canopy Reflectance and Fluorescence Products: Introduction of an Automatized Processing Chain. Remote Sens., 11.","DOI":"10.3390\/rs11232760"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Henocq, C., North, P., Heckel, A., Ferron, S., Lamquin, N., Dransfeld, S., Bourg, L., Tote, C., and Ramon, D. (2018, January 22\u201327). OLCI\/SLSTR SYN L2 algorithm and products overview. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517420"},{"key":"ref_39","unstructured":"ESA (2020, May 10). Sentinel-3 User Handbook, Sentinel-3 Team, 1.0 ed. Available online: https:\/\/sentinel.esa.int."},{"key":"ref_40","unstructured":"Weiss, M., and Baret, F. (2020, June 15). S2 ToolBox Level 2 Products: LAI, FAPAR, FCOVER Version 1.1. Available online: https:\/\/step.esa.int\/docs\/extra\/ATBD_S2ToolBox_L2B_V1.1.pdf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.rse.2018.06.037","article-title":"Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems","volume":"216","author":"Delloye","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2010.09.012","article-title":"Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS\/PROBA observations","volume":"115","author":"Verger","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.isprsjprs.2018.03.005","article-title":"Derivation of global vegetation biophysical parameters from EUMETSAT Polar System","volume":"139","author":"Laparra","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","unstructured":"QGIS Development Team (2020, July 25). QGIS Geographic Information System. Open Source Geospatial Foundation. Available online: http:\/\/qgis.org."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/S0176-1617(96)80283-5","article-title":"Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements","volume":"148","author":"Lichtenthaler","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2006.03.014","article-title":"Horizontal radiation transport in 3-D forest canopies at multiple spatial resolutions: Simulated impact on canopy absorption","volume":"103","author":"Widlowski","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.3390\/s90301768","article-title":"Scale issues in remote sensing: A review on analysis, processing and modeling","volume":"9","author":"Wu","year":"2009","journal-title":"Sensors"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hardiman, B., LaRue, E., Atkins, J., Fahey, R., Wagner, F., and Gough, C. (2018). Spatial variation in canopy structure across forest landscapes. Forests, 9.","DOI":"10.20944\/preprints201806.0351.v1"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1007\/BF00328580","article-title":"Estimation of deciduous forest leaf area index using direct and indirect methods","volume":"104","year":"1995","journal-title":"Oecologia"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.agrformet.2018.11.033","article-title":"Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives","volume":"265","author":"Yan","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/S0034-4257(99)00006-1","article-title":"Spatial scaling of a remotely sensed surface parameter by contexture","volume":"69","author":"Chen","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1109\/LGRS.2014.2313592","article-title":"Mapping biophysical variables from solar and thermal infrared remote sensing: Focus on agricultural landscapes with spatial heterogeneity","volume":"11","author":"Jacob","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.rse.2006.07.013","article-title":"Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data","volume":"105","author":"Garrigues","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_57","unstructured":"Sentinel-3 MPC (2020, April 20). Sentinel-3 Mission Performance Center Optical Annual Performance Report\u2014Year 2018. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/user-guides\/sentinel-3-olci\/document-library\/-\/asset_publisher\/hkf7sg9Ny1d5\/content\/sentinel-3-optical-annual-performance-report-year-2."},{"key":"ref_58","unstructured":"Bell, S.A. (2001). A Beginner\u2019s Guide to Uncertainty of Measurement, National Physical Laboratory."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.isprsjprs.2013.09.012","article-title":"Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval","volume":"86","author":"Verrelst","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1109\/LGRS.2013.2279695","article-title":"Retrieval of biophysical parameters with heteroscedastic Gaussian processes","volume":"11","author":"Titsias","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_61","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_62","first-page":"102174","article-title":"Retrieval of aboveground crop nitrogen content with a hybrid machine learning method","volume":"92","author":"Berger","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1016\/j.rse.2017.08.006","article-title":"Hyperspectral radiative transfer modeling to explore the combined retrieval of biophysical parameters and canopy fluorescence from FLEX\u2013Sentinel-3 tandem mission multi-sensor data","volume":"204","author":"Verhoef","year":"2018","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1419\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:58:47Z","timestamp":1760363927000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1419"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,7]]},"references-count":63,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081419"],"URL":"https:\/\/doi.org\/10.3390\/rs13081419","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,7]]}}}