{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T14:48:26Z","timestamp":1779202106123,"version":"3.51.4"},"reference-count":66,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,15]],"date-time":"2023-04-15T00:00:00Z","timestamp":1681516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002749","name":"Belgian Science Policy Office (BELSPO)","doi-asserted-by":"publisher","award":["SR\/00\/300"],"award-info":[{"award-number":["SR\/00\/300"]}],"id":[{"id":"10.13039\/501100002749","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002749","name":"Belgian Science Policy Office (BELSPO)","doi-asserted-by":"publisher","award":["SR\/67\/392"],"award-info":[{"award-number":["SR\/67\/392"]}],"id":[{"id":"10.13039\/501100002749","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding crop phenology is crucial for predicting crop yields and identifying potential risks to food security. The objective was to investigate the effectiveness of satellite sensor data, compared to field observations and proximal sensing, in detecting crop phenological stages. Time series data from 122 winter wheat, 99 silage maize, and 77 late potato fields were analyzed during 2015\u20132017. The spectral signals derived from Digital Hemispherical Photographs (DHP), Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent. Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation) demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover), with the best model fits obtained for maize, followed by wheat and potato. S2-derived fAPAR showed decreasing variability as the growing season progressed. The use of a double sigmoid model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid) and senescence (downward sigmoid), while the upward endpoint corresponded to canopy closure and the maximum values to flowering and fruit development. Furthermore, increasing the frequency of sensor revisits is beneficial for detecting short-duration crop phenological stages. The results have implications for data assimilation to improve crop yield forecasting and agri-environmental modeling.<\/jats:p>","DOI":"10.3390\/rs15082090","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T02:02:59Z","timestamp":1681696979000},"page":"2090","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Crop Phenology Modelling Using Proximal and Satellite Sensor Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3742-7062","authenticated-orcid":false,"given":"Anne","family":"Gobin","sequence":"first","affiliation":[{"name":"Department of Earth and Environmental Sciences, Faculty of Bioscience Engineering, Katholieke Universiteit Leuven, 3001 Leuven, Belgium"},{"name":"Remote Sensing Unit, Flemish Institute of Technological Research (VITO), 2400 Mol, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdoul-Hamid Mohamed","family":"Sallah","sequence":"additional","affiliation":[{"name":"SPHERES Research Unit, University of Li\u00e8ge, 6700 Arlon, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yannick","family":"Curnel","sequence":"additional","affiliation":[{"name":"Centre Wallon de Recherches Agronomiques, CRAW, 5030 Gembloux, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cindy","family":"Delvoye","sequence":"additional","affiliation":[{"name":"Earth and Life Institute, Universit\u00e9 Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2341-667X","authenticated-orcid":false,"given":"Marie","family":"Weiss","sequence":"additional","affiliation":[{"name":"EMMAH, Institut National de Recherche pour l\u2019Agriculture, l\u2019alimentation et l\u2019Environnement (INRAE), 84000 Avignon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9444-8206","authenticated-orcid":false,"given":"Joost","family":"Wellens","sequence":"additional","affiliation":[{"name":"SPHERES Research Unit, University of Li\u00e8ge, 6700 Arlon, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1530-8468","authenticated-orcid":false,"given":"Isabelle","family":"Piccard","sequence":"additional","affiliation":[{"name":"Remote Sensing Unit, Flemish Institute of Technological Research (VITO), 2400 Mol, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Viviane","family":"Planchon","sequence":"additional","affiliation":[{"name":"Centre Wallon de Recherches Agronomiques, CRAW, 5030 Gembloux, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernard","family":"Tychon","sequence":"additional","affiliation":[{"name":"SPHERES Research Unit, University of Li\u00e8ge, 6700 Arlon, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7479-796X","authenticated-orcid":false,"given":"Jean-Pierre","family":"Goffart","sequence":"additional","affiliation":[{"name":"Centre Wallon de Recherches Agronomiques, CRAW, 5030 Gembloux, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"Defourny","sequence":"additional","affiliation":[{"name":"Earth and Life Institute, Universit\u00e9 Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1093\/jxb\/erp196","article-title":"Climate Change and the Flowering Time of Annual Crops","volume":"60","author":"Craufurd","year":"2009","journal-title":"J. Exp. Bot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0168-1923(03)00161-8","article-title":"Climate Changes and Trends in Phenology of Fruit Trees and Field Crops in Germany, 1961\u20132000","volume":"121","author":"Chmielewski","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.agrformet.2016.11.003","article-title":"Climate and Management Interaction Cause Diverse Crop Phenology Trends","volume":"233","author":"Siebert","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2599","DOI":"10.1111\/gcb.15000","article-title":"Climate Change Fingerprints in Recent European Plant Phenology","volume":"26","author":"Menzel","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.cois.2019.07.002","article-title":"Prey\u2013Predator Phenological Mismatch under Climate Change","volume":"35","author":"Damien","year":"2019","journal-title":"Curr. Opin. Insect Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1007\/s00484-011-0426-5","article-title":"A Review of Climate-Driven Mismatches between Interdependent Phenophases in Terrestrial and Aquatic Ecosystems","volume":"55","author":"Donnelly","year":"2011","journal-title":"Int. J. Biometeorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.eja.2014.10.003","article-title":"Heat Stress in Cereals: Mechanisms and Modelling","volume":"64","author":"Webber","year":"2015","journal-title":"Eur. J. Agron."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.agsy.2017.06.009","article-title":"Weather Related Risks in Belgian Arable Agriculture","volume":"159","author":"Gobin","year":"2018","journal-title":"Agric. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108565","DOI":"10.1016\/j.agrformet.2021.108565","article-title":"Spatio-Temporal Variability of Dry and Wet Spells and Their Influence on Crop Yields","volume":"308\u2013309","author":"Gobin","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108822","DOI":"10.1016\/j.agrformet.2022.108822","article-title":"Spatio-Temporal Assessment of Frost Risks during the Flowering of Pear Trees in Belgium for 1971\u20132068","volume":"315","author":"Drepper","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tolomio, M., and Casa, R. (2020). Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements. Remote Sens., 12.","DOI":"10.3390\/rs12233945"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13750-021-00247-7","article-title":"Strategies for Managing Spring Frost Risks in Orchards: Effectiveness and Conditionality\u2014A Systematic Review Protocol","volume":"10","author":"Drepper","year":"2021","journal-title":"Environ. Evid."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1017\/S0021859621000216","article-title":"Performance of 13 Crop Simulation Models and Their Ensemble for Simulating Four Field Crops in Central Europe","volume":"159","author":"Hlavinka","year":"2021","journal-title":"J. Agric. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.eja.2018.01.006","article-title":"Towards Improved Calibration of Crop Models\u2014Where Are We Now and Where Should We Go?","volume":"94","author":"Seidel","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kersebaum, K., Kroes, J., Gobin, A., Tak\u00e1\u010d, J., Hlavinka, P., Trnka, M., Ventrella, D., Giglio, L., Ferrise, R., and Moriondo, M. (2016). Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat. Water, 8.","DOI":"10.3390\/w8120571"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1038\/nclimate1916","article-title":"Uncertainty in Simulating Wheat Yields under Climate Change","volume":"3","author":"Asseng","year":"2013","journal-title":"Nat. Clim. Change"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1111\/gcb.12520","article-title":"How Do Various Maize Crop Models Vary in Their Responses to Climate Change Factors?","volume":"20","author":"Bassu","year":"2014","journal-title":"Glob. Change Biol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1038\/s43016-021-00400-y","article-title":"Climate Impacts on Global Agriculture Emerge Earlier in New Generation of Climate and Crop Models","volume":"2","author":"Ruane","year":"2021","journal-title":"Nat. Food"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.eja.2017.12.001","article-title":"Uncertainty in Wheat Phenology Simulation Induced by Cultivar Parameterization under Climate Warming","volume":"94","author":"Liu","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wajid, A., Hussain, K., Ilyas, A., Habib-ur-Rahman, M., Shakil, Q., and Hoogenboom, G. (2021). Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments. Agriculture, 11.","DOI":"10.3390\/agriculture11111166"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.fcr.2014.06.017","article-title":"Potato, Sweet Potato, and Yam Models for Climate Change: A Review","volume":"166","author":"Raymundo","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2005.03.008","article-title":"A Crop Phenology Detection Method Using Time-Series MODIS Data","volume":"96","author":"Sakamoto","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111685","DOI":"10.1016\/j.rse.2020.111685","article-title":"Continental-Scale Land Surface Phenology from Harmonized Landsat 8 and Sentinel-2 Imagery","volume":"240","author":"Bolton","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Durgun, Y., Gobin, A., Van De Kerchove, R., and Tychon, B. (2016). Crop Area Mapping Using 100-m Proba-V Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8070585"},{"key":"ref_25","first-page":"101988","article-title":"A Study on Trade-Offs between Spatial Resolution and Temporal Sampling Density for Wheat Yield Estimation Using Both Thermal and Calendar Time","volume":"86","author":"Durgun","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Misra, G., Cawkwell, F., and Wingler, A. (2020). Status of Phenological Research Using Sentinel-2 Data: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12172760"},{"key":"ref_28","first-page":"102569","article-title":"Disaggregated PROBA-V Data Allows Monitoring Individual Crop Phenology at a Higher Observation Frequency than Sentinel-2","volume":"104","author":"Rivas","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.rse.2005.10.021","article-title":"Improved Monitoring of Vegetation Dynamics at Very High Latitudes: A New Method Using MODIS NDVI","volume":"100","author":"Beck","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111752","DOI":"10.1016\/j.rse.2020.111752","article-title":"A Within-Season Approach for Detecting Early Growth Stages in Corn and Soybean Using High Temporal and Spatial Resolution Imagery","volume":"242","author":"Gao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(02)00135-9","article-title":"Monitoring Vegetation Phenology Using MODIS","volume":"84","author":"Zhang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2016.11.004","article-title":"Toward Mapping Crop Progress at Field Scales through Fusion of Landsat and MODIS Imagery","volume":"188","author":"Gao","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2004.11.017","article-title":"Use of Coupled Canopy Structure Dynamic and Radiative Transfer Models to Estimate Biophysical Canopy Characteristics","volume":"95","author":"Koetz","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Vannoppen, A., Gobin, A., Kotova, L., Top, S., De Cruz, L., V\u012bksna, A., Aniskevich, S., Bobylev, L., Buntemeyer, L., and Caluwaerts, S. (2020). Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia. Remote Sens., 12.","DOI":"10.3390\/rs12142206"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Vannoppen, A., and Gobin, A. (2021). Estimating Farm Wheat Yields from NDVI and Meteorological Data. Agronomy, 11.","DOI":"10.3390\/agronomy11050946"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Vannoppen, A., and Gobin, A. (2022). Estimating Yield from NDVI, Weather Data, and Soil Water Depletion for Sugar Beet and Potato in Northern Belgium. Water, 14.","DOI":"10.3390\/w14081188"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1080\/22797254.2018.1457937","article-title":"Atmospheric Correction of Landsat-8\/OLI and Sentinel-2\/MSI Data Using ICOR Algorithm: Validation for Coastal and Inland Waters","volume":"51","author":"Sterckx","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.14358\/PERS.72.10.1179","article-title":"Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) Algorithm","volume":"72","author":"Irish","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/S0034-4257(02)00034-2","article-title":"An Image Transform to Characterize and Compensate for Spatial Variations in Thin Cloud Contamination of Landsat Images","volume":"82","author":"Zhang","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1051\/agro:2000105","article-title":"Investigation of a Model Inversion Technique to Estimate Canopy Biophysical Variables from Spectral and Directional Reflectance Data","volume":"20","author":"Weiss","year":"2000","journal-title":"Agronomie"},{"key":"ref_43","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","volume":"110","author":"Baret","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.rse.2013.07.027","article-title":"Validation of Coarse Spatial Resolution LAI and FAPAR Time Series over Cropland in Southwest France","volume":"139","author":"Claverie","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"S56","DOI":"10.1016\/j.rse.2008.01.026","article-title":"PROSPECT+SAIL Models: A Review of Use for Vegetation Characterization","volume":"113","author":"Jacquemoud","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_47","unstructured":"Weiss, M., and Baret, F. (2023, April 08). S2ToolBox Level 2 Products: LAI, FAPAR, FCOVER. Version 1.1. Available online: http:\/\/step.esa.int\/docs\/extra\/ATBD_S2ToolBox_L2B_V1.1.pdf."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Vuolo, F., \u017b\u00f3\u0142tak, M., Pipitone, C., Zappa, L., Wenng, H., Immitzer, M., Weiss, M., Baret, F., and Atzberger, C. (2016). Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples. Remote Sens., 8.","DOI":"10.3390\/rs8110938"},{"key":"ref_49","first-page":"41","article-title":"The BBCH System to Coding the Phenological Growth Stages of Plants\u2013History and Publications","volume":"61","author":"Meier","year":"2009","journal-title":"J. F\u00fcr. Kult."},{"key":"ref_50","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_51","unstructured":"Weiss, M., and Baret, F. (2023, April 08). CAN-EYE V6.1 User Manual. 2010. EMMAH Laboratory (Mediterranean Environment and Agro-Hydro System Modelisation). French National Institute of Agricultural Research (INRA). Available online: http:\/\/jecam.org\/wp-content\/uploads\/2018\/07\/CAN_EYE_User_Manual.pdf."},{"key":"ref_52","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A New Look at the Statistical Model Identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. Automat. Contr."},{"key":"ref_54","unstructured":"Zambrano-Bigiarini, M. (2023, April 08). HydroGOF: Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series. R Package Version 0.3-2. Available online: http:\/\/cran.r-project.org\/web\/packages\/hydroGOF\/hydroGOF.pdf."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.compag.2018.11.013","article-title":"Crop Season Planning Tool: Adjusting Sowing Decisions to Reduce the Risk of Extreme Weather Events","volume":"156","author":"Perondi","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s11540-020-09483-9","article-title":"Crop Simulation Models as Decision-Supporting Tools for Sustainable Potato Production: A Review","volume":"64","author":"Divya","year":"2021","journal-title":"Potato Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"109204","DOI":"10.1016\/j.agrformet.2022.109204","article-title":"Predicting Spring Green-up across Diverse North American Grasslands","volume":"327","author":"Post","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.compag.2017.08.026","article-title":"Crowdsourcing for Agricultural Applications: A Review of Uses and Opportunities for a Farmsourcing Approach","volume":"142","author":"Minet","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Durgun, Y., Gobin, A., Gilliams, S., Duveiller, G., and Tychon, B. (2016). Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity. Remote Sens., 8.","DOI":"10.3390\/rs8030170"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Baetens, L., Desjardins, C., and Hagolle, O. (2019). Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. Remote Sens., 11.","DOI":"10.3390\/rs11040433"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Li, D., Miao, Y., Gupta, S.K., Rosen, C.J., Yuan, F., Wang, C., Wang, L., and Huang, Y. (2021). Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13163322"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Van Tricht, K., Gobin, A., Gilliams, S., and Piccard, I. (2018). Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium. Remote Sens., 10.","DOI":"10.20944\/preprints201808.0066.v1"},{"key":"ref_63","first-page":"102720","article-title":"Parcel-Based Summer Maize Mapping and Phenology Estimation Combined Using Sentinel-2 and Time Series Sentinel-1 Data","volume":"108","author":"Wang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_64","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_65","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3354\/cr00925","article-title":"Modelling Climate Impacts on Crop Yields in Belgium","volume":"44","author":"Gobin","year":"2010","journal-title":"Clim. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.5194\/nhess-12-1911-2012","article-title":"Impact of Heat and Drought Stress on Arable Crop Production in Belgium","volume":"12","author":"Gobin","year":"2012","journal-title":"Nat. Hazards Earth Syst. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2090\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:16:36Z","timestamp":1760123796000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2090"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,15]]},"references-count":66,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15082090"],"URL":"https:\/\/doi.org\/10.3390\/rs15082090","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,15]]}}}