{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T05:32:21Z","timestamp":1788845541994,"version":"build-2803163510"},"reference-count":85,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Swedish National Space Agency","award":["2020-00138"],"award-info":[{"award-number":["2020-00138"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m \u00d7 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R2) and a root mean square error (RMSE) of 0.80 and 0.65 m2\/m2, respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production.<\/jats:p>","DOI":"10.3390\/rs15184425","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T07:57:17Z","timestamp":1694159837000},"page":"4425","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6973-6644","authenticated-orcid":false,"given":"El houssaine","family":"Bouras","sequence":"first","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2266-6394","authenticated-orcid":false,"given":"Per-Ola","family":"Olsson","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0006-2960","authenticated-orcid":false,"given":"Shangharsha","family":"Thapa","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jes\u00fas Mallol","family":"D\u00edaz","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0869-5735","authenticated-orcid":false,"given":"Johannes","family":"Albertsson","sequence":"additional","affiliation":[{"name":"Department of Biosystems and Technology, University of Agricultural Sciences (SLU), 234 22 Lomma, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7644-6517","authenticated-orcid":false,"given":"Lars","family":"Eklundh","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/nclimate2470","article-title":"Rising Temperatures Reduce Global Wheat Production","volume":"5","author":"Asseng","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kogan, F. (2019). Remote Sensing for Food Security, Springer.","DOI":"10.1007\/978-3-319-96256-6"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102953","DOI":"10.1016\/j.earscirev.2019.102953","article-title":"A Review of Environmental Droughts: Increased Risk under Global Warming?","volume":"201","author":"Quiring","year":"2020","journal-title":"Earth Sci. Rev."},{"key":"ref_4","unstructured":"FAO (2016). Climate Change and Food Security: Risks and Responses, FAO."},{"key":"ref_5","unstructured":"Basso, B., and Liu, L. (2019). Advances in Agronomy, Academic Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"509","DOI":"10.2134\/agronj2008.0166s","article-title":"Assessment of Aquacrop, Cropsyst, and WOFOST Models in the Simulation of Sunfl Ower Growth under Different Water Regimes","volume":"101","author":"Todorovic","year":"2009","journal-title":"Agron. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.agee.2010.05.007","article-title":"Using a Cropping System Model at Regional Scale: Low-Data Approaches for Crop Management Information and Model Calibration","volume":"142","author":"Therond","year":"2011","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.rse.2013.07.018","article-title":"Assimilation of Remotely Sensed Soil Moisture and Vegetation with a Crop Simulation Model for Maize Yield Prediction","volume":"138","author":"Ines","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote Sensing for Agricultural Applications: A Meta-Review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108553","DOI":"10.1016\/j.agrformet.2021.108553","article-title":"Predicting Within-Field Soybean Yield Variability by Coupling Sentinel-2 Leaf Area Index with a Crop Growth Model","volume":"308\u2013309","author":"Gaso","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Manivasagam, V.S., Sadeh, Y., Kaplan, G., Bonfil, D.J., and Rozenstein, O. (2021). Studying the Feasibility of Assimilating Sentinel-2 and Planetscope Imagery into the SAFY Crop Model to Predict within-Field Wheat Yield. Remote Sens., 13.","DOI":"10.3390\/rs13122395"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.rse.2019.04.005","article-title":"Field-Level Crop Yield Mapping with Landsat Using a Hierarchical Data Assimilation Approach","volume":"228","author":"Kang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"126208","DOI":"10.1016\/j.eja.2020.126208","article-title":"Assimilation of Coupled Microwave\/Thermal Infrared Soil Moisture Profiles into a Crop Model for Robust Maize Yield Estimates over Southeast United States","volume":"123","author":"Mishra","year":"2021","journal-title":"Eur. J. Agron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.agrformet.2007.05.004","article-title":"Crop Model Data Assimilation with the Ensemble Kalman Filter for Improving Regional Crop Yield Forecasts","volume":"146","year":"2007","journal-title":"Agric. For. Meteorol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.eja.2017.11.002","article-title":"A Review of Data Assimilation of Remote Sensing and Crop Models","volume":"92","author":"Jin","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107609","DOI":"10.1016\/j.agrformet.2019.06.008","article-title":"Assimilation of Remote Sensing into Crop Growth Models: Current Status and Perspectives","volume":"276\u2013277","author":"Huang","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Walker, J.P., Pauwels, V.R.N., and Sadeh, Y. (2022). Assimilation of Wheat and Soil States into the Apsim-Wheat Crop Model: A Case Study. Remote Sens., 14.","DOI":"10.3390\/rs14010065"},{"key":"ref_18","first-page":"165","article-title":"A Review on Reflective Remote Sensing and Data Assimilation Techniques for Enhanced Agroecosystem Modeling","volume":"9","author":"Dorigo","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0924-2716(92)90030-D","article-title":"Remote Sensing and Crop Production Models: Present Trends","volume":"47","author":"Maas","year":"1992","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10236-003-0036-9","article-title":"The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation","volume":"53","author":"Evensen","year":"2003","journal-title":"Ocean. Dyn."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Peng, X., Han, W., Ao, J., and Wang, Y. (2021). Assimilation of Lai Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield. Remote Sens., 13.","DOI":"10.3390\/rs13061094"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Silvestro, P.C., Pignatti, S., Pascucci, S., Yang, H., Li, Z., Yang, G., Huang, W., and Casa, R. (2017). Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models. Remote Sens., 9.","DOI":"10.3390\/rs9050509"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhuo, W., Huang, J., Li, L., Zhang, X., Ma, H., Gao, X., Huang, H., Xu, B., and Xiao, X. (2019). Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation. Remote Sens., 11.","DOI":"10.3390\/rs11131618"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bai, T., Wang, S., Meng, W., Zhang, N., Wang, T., Chen, Y., and Mercatoris, B. (2019). Assimilation of Remotely-Sensed LAI into WOFOST Model with the SUBPLEX Algorithm for Improving the Field-Scale Jujube Yield Forecasts. Remote Sens., 11.","DOI":"10.3390\/rs11161945"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.agsy.2018.06.018","article-title":"25 Years of the WOFOST Cropping Systems Model","volume":"168","author":"Boogaard","year":"2019","journal-title":"Agric. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.agee.2009.10.014","article-title":"Validation and Reliability of the EPIC Model to Simulate Maize Production in Small-Holder Farming Systems in Tropical Sub-Humid West Africa and Semi-Arid Brazil","volume":"135","author":"Gaiser","year":"2010","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1016\/j.envsoft.2007.10.003","article-title":"A Simple Algorithm for Yield Estimates: Evaluation for Semi-Arid Irrigated Winter Wheat Monitored with Green Leaf Area Index","volume":"23","author":"Duchemin","year":"2008","journal-title":"Environ. Model. Softw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.mcm.2011.10.038","article-title":"Assimilation of MODIS-LAI into the WOFOST Model for Forecasting Regional Winter Wheat Yield","volume":"58","author":"Ma","year":"2013","journal-title":"Math. Comput. Model."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5473","DOI":"10.1038\/s41598-022-09535-9","article-title":"Wheat Growth Monitoring and Yield Estimation Based on Remote Sensing Data Assimilation into the SAFY Crop Growth Model","volume":"12","author":"Ma","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106884","DOI":"10.1016\/j.agwat.2021.106884","article-title":"Assimilation of Soil Moisture and Canopy Cover Data Improves Maize Simulation Using an Under-Calibrated Crop Model","volume":"252","author":"Lu","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"126501","DOI":"10.1016\/j.eja.2022.126501","article-title":"Assimilating Leaf Area Index Data into a Sugarcane Process-Based Crop Model for Improving Yield Estimation","volume":"136","author":"Marin","year":"2022","journal-title":"Eur. J. Agron."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"126718","DOI":"10.1016\/j.eja.2022.126718","article-title":"Efficiency of Assimilating Leaf Area Index into a Soybean Model to Assess Within-Field Yield Variability","volume":"143","author":"Gaso","year":"2023","journal-title":"Eur. J. Agron."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhuo, W., Huang, H., Gao, X., Li, X., and Huang, J. (2023). An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model. Remote Sens., 15.","DOI":"10.3390\/rs15071825"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.ecolmodel.2013.08.016","article-title":"Assimilating Remote Sensing Information with Crop Model Using Ensemble Kalman Filter for Improving LAI Monitoring and Yield Estimation","volume":"270","author":"Zhao","year":"2013","journal-title":"Ecol. Modell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1016\/j.agrformet.2011.08.002","article-title":"Potential Performances of Remotely Sensed LAI Assimilation in WOFOST Model Based on an OSS Experiment","volume":"151","author":"Curnel","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pasqualotto, N., Delegido, J., Van Wittenberghe, S., Rinaldi, M., and Moreno, J. (2019). Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI). Sensors, 19.","DOI":"10.3390\/s19040904"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the Capabilities of Sentinel-2 for Quantitative Estimation of Biophysical Variables in Vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","first-page":"102697","article-title":"Farming and Earth Observation: Sentinel-2 Data to Estimate within-Field Wheat Grain Yield","volume":"107","author":"Segarra","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gascon, F., Bouzinac, C., Th\u00e9paut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S., and Gaudel-Vacaresse, A. (2017). Copernicus Sentinel-2A Calibration and Products Validation Status. Remote Sens., 9.","DOI":"10.3390\/rs9060584"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.3390\/s90402719","article-title":"Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors","volume":"9","author":"Zheng","year":"2009","journal-title":"Sensors"},{"key":"ref_41","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_42","first-page":"1541","article-title":"Distinguishing Vegetation from Soil Background Information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of Soil-Adjusted Vegetation Indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a Two-Band Enhanced Vegetation Index without a Blue Band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0034-4257(91)90009-U","article-title":"Potentials and Limits of Vegetation Indices for LAI and APAR Assessment","volume":"35","author":"Baret","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1007\/s11119-020-09783-7","article-title":"Upscaling Proximal Sensor N-Uptake Predictions in Winter Wheat (Triticum aestivum L.) with Sentinel-2 Satellite Data for Use in a Decision Support System","volume":"22","author":"Wolters","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_47","unstructured":"Swedish Board of Agriculture (2023, September 03). Use of Agricultural Land 2019, Final Statistics, Available online: www.jordbruksverket.se."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"106858","DOI":"10.1016\/j.agwat.2021.106858","article-title":"Impact of Climate Change on Swedish Agriculture: Growing Season Rain Deficit and Irrigation Need","volume":"251","author":"Grusson","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bouras, E.H., Jarlan, L., Er-Raki, S., Albergel, C., Richard, B., Balaghi, R., and Khabba, S. (2020). Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco. Remote Sens., 12.","DOI":"10.3390\/rs12244018"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1016\/j.agrformet.2010.04.011","article-title":"GAI Estimates of Row Crops from Downward Looking Digital Photos Taken Perpendicular to Rows at 57.5\u00b0 Zenith Angle: Theoretical Considerations Based on 3D Architecture Models and Application to Wheat Crops","volume":"150","author":"Baret","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s11119-013-9336-3","article-title":"Post-Processing Methods to Eliminate Erroneous Grain Yield Measurements: Review and Directions for Future Development","volume":"15","author":"Lyle","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111410","DOI":"10.1016\/j.rse.2019.111410","article-title":"High Resolution Wheat Yield Mapping Using Sentinel-2","volume":"233","author":"Hunt","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"509","DOI":"10.2134\/agronj2018.05.0317","article-title":"Yield Monitor Data Cleaning Is Essential for Accurate Corn Grain and Silage Yield Determination","volume":"111","author":"Kharel","year":"2019","journal-title":"Agron. J."},{"key":"ref_54","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., and Gascon, F. (2016, January 9\u201313). Sentinel-2 SEN2COR: L2A Processor for Users. Proceedings of the European Space Agency, (Special Publication) ESA SP, Prague, Czech Republic."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"108538","DOI":"10.1016\/j.fcr.2022.108538","article-title":"Effective GAI Is Best Estimated from Reflectance Observations as Compared to GAI and LAI: Demonstration for Wheat and Maize Crops Based on 3D Radiative Transfer Simulations","volume":"283","author":"Jiang","year":"2022","journal-title":"Field Crops Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of Leaf-Area Index from Quality of Light on the Forest Floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_58","unstructured":"Rouse, W., Haas, R.H., and Deering, D.W. (1974, January 10\u201314). Monitoring Vegetation Systems in the Great Plains with ERTS, NASA SP-351. Proceedings of the Goddard Space Flight Center Third ERTS-1 Symposium, Washington, DC, USA."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications","volume":"22","author":"Chen","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1098\/rstb.1977.0140","article-title":"Climate and the Efficiency of Crop Production in Britain","volume":"281","author":"Monteith","year":"1977","journal-title":"Philos. Trans. R. Soc. Lond. B Biol. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.rse.2012.04.005","article-title":"Maize and Sunflower Biomass Estimation in Southwest France Using High Spatial and Temporal Resolution Remote Sensing Data","volume":"124","author":"Claverie","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_62","first-page":"63","article-title":"Estimating Winter Wheat Biomass by Assimilating Leaf Area Index Derived from Fusion of Landsat-8 and MODIS Data","volume":"49","author":"Dong","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Silvestro, P.C., Pignatti, S., Yang, H., Yang, G., Pascucci, S., Castaldi, F., and Casa, R. (2017). Sensitivity Analysis of the Aquacrop and SAFYE Crop Models for the Assessment of Water Limited Winter Wheat Yield in Regional Scale Applications. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0187485"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1080\/00031305.2016.1141709","article-title":"Understanding the Ensemble Kalman Filter","volume":"70","author":"Katzfuss","year":"2016","journal-title":"Am. Stat."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0378-4290(93)90118-7","article-title":"Resource Capture and Use in Intercropping: Solar Radiation","volume":"34","author":"Keating","year":"1993","journal-title":"Field Crops Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"747","DOI":"10.2307\/2401901","article-title":"Solar Radiation and Productivity in Tropical Ecosystems","volume":"9","author":"Monteith","year":"1972","journal-title":"J. Appl. Ecol."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Song, Y., Wang, J., Shang, J., and Liao, C. (2020). Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat. Remote Sens., 12.","DOI":"10.3390\/rs12152378"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S1161-0301(98)00047-1","article-title":"Temperatures and the Growth and Development of Wheat: A Review","volume":"10","author":"John","year":"1999","journal-title":"Eur. J. Agron."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.fcr.2004.01.007","article-title":"Genotypic Variation in Specific Leaf Area for Genetic Improvement of Early Vigour in Wheat","volume":"88","author":"Rebetzke","year":"2004","journal-title":"Field Crops Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"348","DOI":"10.2134\/agronj1993.00021962008500020034x","article-title":"Parameterized Model of Gramineous Crop Growth: I. Leaf Area and Dry Mass Simulation","volume":"85","author":"Maas","year":"1993","journal-title":"Agron. J."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Khabba, S., Er-Raki, S., Toumi, J., Ezzahar, J., Hssaine, B.A., Page, M.L., and Chehbouni, A. (2020). A Simple Light-Use-Efficiency Model to Estimate Wheat Yield in the Semi-Arid Areas. Agronomy, 10.","DOI":"10.3390\/agronomy10101524"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.fcr.2012.09.009","article-title":"Yield Gap Analysis with Local to Global Relevance-A Review","volume":"143","author":"Cassman","year":"2013","journal-title":"Field Crops Res."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1080\/01431161.2013.875629","article-title":"Estimation of the Dynamics and Yields of Cereals in a Semi-Arid Area Using Remote Sensing and the SAFY Growth Model","volume":"35","author":"Chahbi","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"108736","DOI":"10.1016\/j.agrformet.2021.108736","article-title":"Early Season Prediction of Within-Field Crop Yield Variability by Assimilating CubeSat Data into a Crop Model","volume":"313","author":"Ziliani","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.eja.2018.06.008","article-title":"Does Remote and Proximal Optical Sensing Successfully Estimate Maize Variables? A Review","volume":"99","author":"Corti","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.fcr.2012.08.008","article-title":"The Use of Satellite Data for Crop Yield Gap Analysis","volume":"143","author":"Lobell","year":"2013","journal-title":"Field Crops Res."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.fcr.2009.11.016","article-title":"Closing the Gap between Actual and Potential Yield of Rainfed Wheat. The Impacts of Environment, Management and Cultivar","volume":"116","author":"Anderson","year":"2010","journal-title":"Field Crops Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"107910","DOI":"10.1016\/j.fcr.2020.107910","article-title":"Estimation of Harvest Index in Wheat Crops Using a Remote Sensing-Based Approach","volume":"256","author":"Campoy","year":"2020","journal-title":"Field Crops Res."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Kganyago, M., Mhangara, P., and Adjorlolo, C. (2021). Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13214314"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.isprsjprs.2019.06.007","article-title":"Estimating Leaf Area Index and Aboveground Biomass of Grazing Pastures Using Sentinel-1, Sentinel-2 and Landsat Images","volume":"154","author":"Wang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_81","first-page":"102727","article-title":"Estimating Species-Specific Leaf Area Index and Basal Area Using Optical and SAR Remote Sensing Data in Acadian Mixed Spruce-Fir Forests, USA","volume":"108","author":"Bhattarai","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"112050","DOI":"10.1016\/j.rse.2020.112050","article-title":"Monitoring of Wheat Crops Using the Backscattering Coefficient and the Interferometric Coherence Derived from Sentinel-1 in Semi-Arid Areas","volume":"251","author":"Ouaadi","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"107290","DOI":"10.1016\/j.agwat.2021.107290","article-title":"Assimilation of SMAP Disaggregated Soil Moisture and Landsat Land Surface Temperature to Improve FAO-56 Estimates of ET in Semi-Arid Regions","volume":"260","author":"Amazirh","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Belaqziz, S., Khabba, S., Kharrou, M.H., Bouras, E.H., Er-Raki, S., and Chehbouni, A. (2021). Optimizing the Sowing Date to Improve Water Management and Wheat Yield in a Large Irrigation Scheme, through a Remote Sensing and an Evolution Strategy-Based Approach. Remote Sens., 13.","DOI":"10.3390\/rs13183789"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"19142","DOI":"10.1038\/s41598-019-55251-2","article-title":"Assessing the Impact of Global Climate Changes on Irrigated Wheat Yields and Water Requirements in a Semi-Arid Environment of Morocco","volume":"9","author":"Bouras","year":"2019","journal-title":"Sci. Rep."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4425\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:47:28Z","timestamp":1760129248000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,8]]},"references-count":85,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184425"],"URL":"https:\/\/doi.org\/10.3390\/rs15184425","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,8]]}}}