[en] 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 2015–2017. 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.
Disciplines :
Agriculture & agronomy
Author, co-author :
Gobin, Anne ; Department of Earth and Environmental Sciences, Faculty of Bioscience Engineering, Katholieke Universiteit Leuven, 3001 Leuven, Belgium ; Remote Sensing Unit, Flemish Institute of Technological Research (VITO), 2400 Mol, Belgium
Sallah, Abdoul-Hamid Mohamed; SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
Curnel, Yannick; Centre Wallon de Recherches Agronomiques, CRAW, 5030 Gembloux, Belgium
Delvoye, Cindy; Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
Weiss, Marie; EMMAH, Institut National de Recherche pour l’Agriculture, l'alimentation et l'Environnement (INRAE), 84000 Avignon, France
Craufurd P.Q. Wheeler T.R. Climate Change and the Flowering Time of Annual Crops J. Exp. Bot. 2009 60 2529 2539 10.1093/jxb/erp196 19505929
Chmielewski F.-M. Müller A. Bruns E. Climate Changes and Trends in Phenology of Fruit Trees and Field Crops in Germany, 1961–2000 Agric. For. Meteorol. 2004 121 69 78 10.1016/S0168-1923(03)00161-8
Eyshi Rezaei E. Siebert S. Ewert F. Climate and Management Interaction Cause Diverse Crop Phenology Trends Agric. For. Meteorol. 2017 233 55 70 10.1016/j.agrformet.2016.11.003
Menzel A. Yuan Y. Matiu M. Sparks T. Scheifinger H. Gehrig R. Estrella N. Climate Change Fingerprints in Recent European Plant Phenology Glob. Chang. Biol. 2020 26 2599 2612 10.1111/gcb.15000 31950538
Damien M. Tougeron K. Prey–Predator Phenological Mismatch under Climate Change Curr. Opin. Insect Sci. 2019 35 60 68 10.1016/j.cois.2019.07.002 31401300
Donnelly A. Caffarra A. O’Neill B.F. A Review of Climate-Driven Mismatches between Interdependent Phenophases in Terrestrial and Aquatic Ecosystems Int. J. Biometeorol. 2011 55 805 817 10.1007/s00484-011-0426-5 21509461
Eyshi Rezaei E. Webber H. Gaiser T. Naab J. Ewert F. Heat Stress in Cereals: Mechanisms and Modelling Eur. J. Agron. 2015 64 98 113 10.1016/j.eja.2014.10.003
Gobin A. Weather Related Risks in Belgian Arable Agriculture Agric. Syst. 2018 159 225 236 10.1016/j.agsy.2017.06.009
Gobin A. Van de Vyver H. Spatio-Temporal Variability of Dry and Wet Spells and Their Influence on Crop Yields Agric. For. Meteorol. 2021 308–309 108565 10.1016/j.agrformet.2021.108565
Drepper B. Gobin A. Van Orshoven J. Spatio-Temporal Assessment of Frost Risks during the Flowering of Pear Trees in Belgium for 1971–2068 Agric. For. Meteorol. 2022 315 108822 10.1016/j.agrformet.2022.108822
Tolomio M. Casa R. Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements Remote Sens. 2020 12 3945 10.3390/rs12233945
Drepper B. Bamps B. Gobin A. Van Orshoven J. Strategies for Managing Spring Frost Risks in Orchards: Effectiveness and Conditionality—A Systematic Review Protocol Environ. Evid. 2021 10 32 10.1186/s13750-021-00247-7
Kostková M. Hlavinka P. Pohanková E. Kersebaum K.C. Nendel C. Gobin A. Olesen J.E. Ferrise R. Dibari C. Takáč J. et al. Performance of 13 Crop Simulation Models and Their Ensemble for Simulating Four Field Crops in Central Europe J. Agric. Sci. 2021 159 69 89 10.1017/S0021859621000216
Seidel S.J. Palosuo T. Thorburn P. Wallach D. Towards Improved Calibration of Crop Models—Where Are We Now and Where Should We Go? Eur. J. Agron. 2018 94 25 35 10.1016/j.eja.2018.01.006
Kersebaum K. Kroes J. Gobin A. Takáč J. Hlavinka P. Trnka M. Ventrella D. Giglio L. Ferrise R. Moriondo M. et al. Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat Water 2016 8 571 10.3390/w8120571
Asseng S. Ewert F. Rosenzweig C. Jones J.W. Hatfield J.L. Ruane A.C. Boote K.J. Thorburn P.J. Rötter R.P. Cammarano D. et al. Uncertainty in Simulating Wheat Yields under Climate Change Nat. Clim. Change 2013 3 827 832 10.1038/nclimate1916
Bassu S. Brisson N. Durand J.-L. Boote K. Lizaso J. Jones J.W. Rosenzweig C. Ruane A.C. Adam M. Baron C. et al. How Do Various Maize Crop Models Vary in Their Responses to Climate Change Factors? Glob. Change Biol. 2014 20 2301 2320 10.1111/gcb.12520 24395589
Jägermeyr J. Müller C. Ruane A.C. Elliott J. Balkovic J. Castillo O. Faye B. Foster I. Folberth C. Franke J.A. et al. Climate Impacts on Global Agriculture Emerge Earlier in New Generation of Climate and Crop Models Nat. Food 2021 2 873 885 10.1038/s43016-021-00400-y
Liu L. Wallach D. Li J. Liu B. Zhang L. Tang L. Zhang Y. Qiu X. Cao W. Zhu Y. Uncertainty in Wheat Phenology Simulation Induced by Cultivar Parameterization under Climate Warming Eur. J. Agron. 2018 94 46 53 10.1016/j.eja.2017.12.001
Wajid A. Hussain K. Ilyas A. Habib-ur-Rahman M. Shakil Q. Hoogenboom G. Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments Agriculture 2021 11 1166 10.3390/agriculture11111166
Raymundo R. Asseng S. Cammarano D. Quiroz R. Potato, Sweet Potato, and Yam Models for Climate Change: A Review Field Crops Res. 2014 166 173 185 10.1016/j.fcr.2014.06.017
Sakamoto T. Yokozawa M. Toritani H. Shibayama M. Ishitsuka N. Ohno H. A Crop Phenology Detection Method Using Time-Series MODIS Data Remote Sens. Environ. 2005 96 366 374 10.1016/j.rse.2005.03.008
Bolton D.K. Gray J.M. Melaas E.K. Moon M. Eklundh L. Friedl M.A. Continental-Scale Land Surface Phenology from Harmonized Landsat 8 and Sentinel-2 Imagery Remote Sens. Environ. 2020 240 111685 10.1016/j.rse.2020.111685
Durgun Y. Gobin A. Van De Kerchove R. Tychon B. Crop Area Mapping Using 100-m Proba-V Time Series Remote Sens. 2016 8 585 10.3390/rs8070585
Durgun Y.Ö. Gobin A. Duveiller G. Tychon B. A Study on Trade-Offs between Spatial Resolution and Temporal Sampling Density for Wheat Yield Estimation Using Both Thermal and Calendar Time Int. J. Appl. Earth Obs. Geoinf. 2020 86 101988 10.1016/j.jag.2019.101988
Atzberger C. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs Remote Sens. 2013 5 949 981 10.3390/rs5020949
Misra G. Cawkwell F. Wingler A. Status of Phenological Research Using Sentinel-2 Data: A Review Remote Sens. 2020 12 2760 10.3390/rs12172760
Rivas H. Delbart N. Ottlé C. Maignan F. Vaudour E. Disaggregated PROBA-V Data Allows Monitoring Individual Crop Phenology at a Higher Observation Frequency than Sentinel-2 Int. J. Appl. Earth Obs. Geoinf. 2021 104 102569 10.1016/j.jag.2021.102569
Zeng L. Wardlow B.D. Xiang D. Hu S. Li D. A Review of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data Remote Sens. Environ. 2020 237 111511 10.1016/j.rse.2019.111511
Beck P.S.A. Atzberger C. Høgda K.A. Johansen B. Skidmore A.K. Improved Monitoring of Vegetation Dynamics at Very High Latitudes: A New Method Using MODIS NDVI Remote Sens. Environ. 2006 100 321 334 10.1016/j.rse.2005.10.021
Gao F. Anderson M. Daughtry C. Karnieli A. Hively D. Kustas W. A Within-Season Approach for Detecting Early Growth Stages in Corn and Soybean Using High Temporal and Spatial Resolution Imagery Remote Sens. Environ. 2020 242 111752 10.1016/j.rse.2020.111752
Zhang X. Friedl M.A. Schaaf C.B. Strahler A.H. Hodges J.C.F. Gao F. Reed B.C. Huete A. Monitoring Vegetation Phenology Using MODIS Remote Sens. Environ. 2003 84 471 475 10.1016/S0034-4257(02)00135-9
Gao F. Anderson M.C. Zhang X. Yang Z. Alfieri J.G. Kustas W.P. Mueller R. Johnson D.M. Prueger J.H. Toward Mapping Crop Progress at Field Scales through Fusion of Landsat and MODIS Imagery Remote Sens. Environ. 2017 188 9 25 10.1016/j.rse.2016.11.004
Duveiller G. Weiss M. Baret F. Defourny P. Retrieving Wheat Green Area Index during the Growing Season from Optical Time Series Measurements Based on Neural Network Radiative Transfer Inversion Remote Sens. Environ. 2011 115 887 896 10.1016/j.rse.2010.11.016
Koetz B. Baret F. Poilvé H. Hill J. Use of Coupled Canopy Structure Dynamic and Radiative Transfer Models to Estimate Biophysical Canopy Characteristics Remote Sens. Environ. 2005 95 115 124 10.1016/j.rse.2004.11.017
Vannoppen A. Gobin A. Kotova L. Top S. De Cruz L. Vīksna A. Aniskevich S. Bobylev L. Buntemeyer L. Caluwaerts S. et al. Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia Remote Sens. 2020 12 2206 10.3390/rs12142206
Vannoppen A. Gobin A. Estimating Farm Wheat Yields from NDVI and Meteorological Data Agronomy 2021 11 946 10.3390/agronomy11050946
Vannoppen A. Gobin A. Estimating Yield from NDVI, Weather Data, and Soil Water Depletion for Sugar Beet and Potato in Northern Belgium Water 2022 14 1188 10.3390/w14081188
De Keukelaere L. Sterckx S. Adriaensen S. Knaeps E. Reusen I. Giardino C. Bresciani M. Hunter P. Neil C. Van der Zande D. et al. Atmospheric Correction of Landsat-8/OLI and Sentinel-2/MSI Data Using ICOR Algorithm: Validation for Coastal and Inland Waters Eur. J. Remote Sens. 2018 51 525 542 10.1080/22797254.2018.1457937
Irish R.R. Barker J.L. Goward S.N. Arvidson T. Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) Algorithm Photogramm. Eng. Remote Sens. 2006 72 1179 1188 10.14358/PERS.72.10.1179
Zhang Y. Guindon B. Cihlar J. An Image Transform to Characterize and Compensate for Spatial Variations in Thin Cloud Contamination of Landsat Images Remote Sens. Environ. 2002 82 173 187 10.1016/S0034-4257(02)00034-2
Weiss M. Baret F. Myneni R.B. Pragnère A. Knyazikhin Y. Investigation of a Model Inversion Technique to Estimate Canopy Biophysical Variables from Spectral and Directional Reflectance Data Agronomie 2000 20 3 22 10.1051/agro:2000105
Baret F. Hagolle O. Geiger B. Bicheron P. Miras B. Huc M. Berthelot B. Niño F. Weiss M. Samain O. et al. LAI, FAPAR and FCover CYCLOPES Global Products Derived from VEGETATION Remote Sens. Environ. 2007 110 275 286 10.1016/j.rse.2007.02.018
Claverie M. Vermote E.F. Weiss M. Baret F. Hagolle O. Demarez V. Validation of Coarse Spatial Resolution LAI and FAPAR Time Series over Cropland in Southwest France Remote Sens. Environ. 2013 139 216 230 10.1016/j.rse.2013.07.027
Li W. Weiss M. Waldner F. Defourny P. Demarez V. Morin D. Hagolle O. Baret F. 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 Remote Sens. 2015 7 15494 15516 10.3390/rs71115494
Jacquemoud S. Verhoef W. Baret F. Bacour C. Zarco-Tejada P.J. Asner G.P. François C. Ustin S.L. PROSPECT+SAIL Models: A Review of Use for Vegetation Characterization Remote Sens. Environ. 2009 113 S56 S66 10.1016/j.rse.2008.01.026
Weiss M. Baret F. S2ToolBox Level 2 Products: LAI, FAPAR, FCOVER. Version 1.1 2016 Available online: http://step.esa.int/docs/extra/ATBD_S2ToolBox_L2B_V1.1.pdf (accessed on 8 April 2023)
Vuolo F. Żółtak M. Pipitone C. Zappa L. Wenng H. Immitzer M. Weiss M. Baret F. Atzberger C. Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples Remote Sens. 2016 8 938 10.3390/rs8110938
Meier U. Bleiholder H. Buhr L. Feller C. Hack H. Heß M. Lancashire P.D. Schnock U. Stauß R. Van Den Boom T. et al. The BBCH System to Coding the Phenological Growth Stages of Plants–History and Publications J. Für. Kult. 2009 61 41 52
Delloye C. Weiss M. Defourny P. Retrieval of the Canopy Chlorophyll Content from Sentinel-2 Spectral Bands to Estimate Nitrogen Uptake in Intensive Winter Wheat Cropping Systems Remote Sens. Environ. 2018 216 245 261 10.1016/j.rse.2018.06.037
Weiss M. Baret F. CAN-EYE V6.1 User Manual. 2010. EMMAH Laboratory (Mediterranean Environment and Agro-Hydro System Modelisation). French National Institute of Agricultural Research (INRA) 2010 Available online: http://jecam.org/wp-content/uploads/2018/07/CAN_EYE_User_Manual.pdf (accessed on 8 April 2023)
R Core Team R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing Vienna, Austria 2021
Akaike H. A New Look at the Statistical Model Identification IEEE Trans. Automat. Contr. 1974 19 716 723 10.1109/TAC.1974.1100705
Zambrano-Bigiarini M. HydroGOF: Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series. R Package Version 0.3-2 2011 Available online: http://cran.r-project.org/web/packages/hydroGOF/hydroGOF.pdf (accessed on 8 April 2023)
Perondi D. Fraisse C.W. Staub C.G. Cerbaro V.A. Barreto D.D. Pequeno D.N.L. Mulvaney M.J. Troy P. Pavan W. Crop Season Planning Tool: Adjusting Sowing Decisions to Reduce the Risk of Extreme Weather Events Comput. Electron. Agric. 2019 156 62 70 10.1016/j.compag.2018.11.013
Divya K.L. Mhatre P.H. Venkatasalam E.P. Sudha R. Crop Simulation Models as Decision-Supporting Tools for Sustainable Potato Production: A Review Potato Res. 2021 64 387 419 10.1007/s11540-020-09483-9
Post A.K. Hufkens K. Richardson A.D. Predicting Spring Green-up across Diverse North American Grasslands Agric. For. Meteorol. 2022 327 109204 10.1016/j.agrformet.2022.109204
Minet J. Curnel Y. Gobin A. Goffart J.-P. Melard F. Tychon B. Wellens J. Defourny P. Crowdsourcing for Agricultural Applications: A Review of Uses and Opportunities for a Farmsourcing Approach Comput. Electron. Agric. 2017 142 126 138 10.1016/j.compag.2017.08.026
Durgun Y. Gobin A. Gilliams S. Duveiller G. Tychon B. Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity Remote Sens. 2016 8 170 10.3390/rs8030170
Baetens L. Desjardins C. Hagolle O. 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. 2019 11 433 10.3390/rs11040433
Li D. Miao Y. Gupta S.K. Rosen C.J. Yuan F. Wang C. Wang L. Huang Y. Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning Remote Sens. 2021 13 3322 10.3390/rs13163322
Van Tricht K. Gobin A. Gilliams S. Piccard I. Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium Remote Sens. 2018 10 1642 10.3390/rs10101642
Wang Y. Fang S. Zhao L. Huang X. Jiang X. Parcel-Based Summer Maize Mapping and Phenology Estimation Combined Using Sentinel-2 and Time Series Sentinel-1 Data Int. J. Appl. Earth Obs. Geoinf. 2022 108 102720 10.1016/j.jag.2022.102720
De Grave C. Verrelst J. Morcillo-Pallarés P. Pipia L. Rivera-Caicedo J.P. Amin E. Belda S. Moreno J. Quantifying Vegetation Biophysical Variables from the Sentinel-3/FLEX Tandem Mission: Evaluation of the Synergy of OLCI and FLORIS Data Sources Remote Sens. Environ. 2020 251 112101 10.1016/j.rse.2020.112101
Gobin A. Modelling Climate Impacts on Crop Yields in Belgium Clim. Res. 2010 44 55 68 10.3354/cr00925
Gobin A. Impact of Heat and Drought Stress on Arable Crop Production in Belgium Nat. Hazards Earth Syst. Sci. 2012 12 1911 1922 10.5194/nhess-12-1911-2012
