{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T02:03:05Z","timestamp":1772848985648,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T00:00:00Z","timestamp":1599782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The global increase in food demand in the context of climate change requires a clear understanding of cropland function and of its impact on biogeochemical cycles. However, although gas exchange between croplands and the atmosphere is measurable in the field, it is difficult to quantify at the plot scale over relatively large areas because of the heterogeneous character of landscapes and differences in crop management. However, assessing accurate carbon and water budgets over croplands is essential to promote sustainable agronomic practices and reduce the water demand and the climatic impacts of croplands while maintaining sufficient yields. From this perspective, we developed a crop model, SAFYE-CO2, that assimilates high spatial- and temporal-resolution (HSTR) remote sensing products to estimate daily crop biomass, water and CO2 fluxes, annual yields, and carbon budgets at the parcel level over large areas. This modeling approach was evaluated for sunflower against two in situ datasets. First, the model\u2019s output was compared to data acquired during two cropping seasons at the Aurad\u00e9 integrated carbon observation system (ICOS) instrumented site in southwestern France. The model accurately simulated the daily net CO2 flux (root mean square error (RMSE) = 0.97 gC\u00b7m\u22122\u00b7d\u22121 and determination coefficient (R2) = 0.83) and water flux (RMSE = 0.68 mm\u00b7d\u22121 and R2 = 0.79). The model\u2019s performance was then evaluated against biomass and yield data collected from 80 plots located in southwestern France. The model was able to satisfactorily estimate biomass dynamics and yield (RMSE = 66 and 54 g\u00b7m\u22122, respectively). To investigate the potential application of the proposed approach at a large scale, given that soil properties are important factors affecting the model, a sensitivity analysis of two existing soil products (GlobalSoilMap and SoilGrids) was carried out. Our results show that these products are not sufficiently accurate for inclusion as inputs to the model, which requires more accurate information on soil water retention capacity to assess water fluxes. Additionally, we argue that no water stress should be considered in the crop growth computation since this stress is already present because of remote sensing information in the proposed approach. This study should be considered a first step to fulfill the existing gap in quantifying carbon budgets at the plot scale over large areas and to accurately estimate the effects of management practices, such as the use of cover crops or specific crop rotations on cropland C and water budgets.<\/jats:p>","DOI":"10.3390\/rs12182967","type":"journal-article","created":{"date-parts":[[2020,9,13]],"date-time":"2020-09-13T21:11:32Z","timestamp":1600031492000},"page":"2967","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to Sunflower"],"prefix":"10.3390","volume":"12","author":[{"given":"Ga\u00e9tan","family":"Pique","sequence":"first","affiliation":[{"name":"CESBIO, Universit\u00e9 de Toulouse, CNES\/CNRS\/INRAE\/IRD\/UPS, 31400 Toulouse, France"},{"name":"Agence De l\u2019Environnement et de Ma\u00eetrise de l\u2019Energie (ADEME), CEDEX 1, 49004 Angers, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R\u00e9my","family":"Fieuzal","sequence":"additional","affiliation":[{"name":"CESBIO, Universit\u00e9 de Toulouse, CNES\/CNRS\/INRAE\/IRD\/UPS, 31400 Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philippe","family":"Debaeke","sequence":"additional","affiliation":[{"name":"INRAE, UMR 1248 AGIR, 31326 Castanet-Tolosan, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1756-1096","authenticated-orcid":false,"given":"Ahmad","family":"Al Bitar","sequence":"additional","affiliation":[{"name":"CESBIO, Universit\u00e9 de Toulouse, CNES\/CNRS\/INRAE\/IRD\/UPS, 31400 Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9893-1123","authenticated-orcid":false,"given":"Tiphaine","family":"Tallec","sequence":"additional","affiliation":[{"name":"CESBIO, Universit\u00e9 de Toulouse, CNES\/CNRS\/INRAE\/IRD\/UPS, 31400 Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5941-752X","authenticated-orcid":false,"given":"Eric","family":"Ceschia","sequence":"additional","affiliation":[{"name":"CESBIO, Universit\u00e9 de Toulouse, CNES\/CNRS\/INRAE\/IRD\/UPS, 31400 Toulouse, France"},{"name":"INRAE, USC 1439 CESBIO, 31100 Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.agrformet.2012.07.008","article-title":"Crops\u2019 water use efficiencies in temperate climate: Comparison of stand, ecosystem and agronomical approaches","volume":"168","author":"Tallec","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_2","unstructured":"De Witt, T., and Brouwer, R. (1969, January 14\u201321). The simulation of photosynthetic systems. Proceedings of the IBP\/PP Technical Meeting, Trebon, Czech Republic."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"622","DOI":"10.13031\/2013.36082","article-title":"A Dynamic Grain Sorghum Growth Model","volume":"19","author":"Arkin","year":"1976","journal-title":"Trans. ASAE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"63","DOI":"10.13031\/2013.33877","article-title":"Modeling Soybean Growth for Crop Management","volume":"26","author":"Wilkerson","year":"1983","journal-title":"Trans. ASAE"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"562","DOI":"10.13031\/2013.33979","article-title":"Real-Time Irrigation Decision Analysis Using Simulation","volume":"26","author":"Swaney","year":"1983","journal-title":"Trans. ASAE"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/0308-521X(89)90012-7","article-title":"Application of the GOSSYM\/COMAX system to cotton crop management","volume":"31","author":"McKinion","year":"1989","journal-title":"Agric. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/0308-521X(96)00011-X","article-title":"The \u201cSchool of de Wit\u201d crop growth simulation models: A pedigree and historical overview","volume":"52","author":"Bouman","year":"1996","journal-title":"Agric. Syst."},{"key":"ref_8","unstructured":"Van Laar, H.H. (2020, September 10). Simulation of Crop Growth for Potential and Water-Limited Production Situations: As Applied to Spring Wheat. Available online: https:\/\/library.wur.nl\/WebQuery\/wurpubs\/fulltext\/359573."},{"key":"ref_9","unstructured":"Yin, X., and van Laar, H.H. (2005). Crop Systems Dynamics: An Ecophysiological Model of Genotype-by-Environment Interactions (GECROS), Wageningen Academic Publishers."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"426","DOI":"10.2134\/agronj2008.0139s","article-title":"AquaCrop\u2014The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles","volume":"101","author":"Steduto","year":"2009","journal-title":"Agron. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.agrformet.2015.02.011","article-title":"The soil-crop models STICS and AqYield predict yield and soil water content for irrigated crops equally well with limited data","volume":"206","author":"Constantin","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_12","first-page":"277","article-title":"Climate and the Efficiency of Crop Production in Britain and Discussion","volume":"281","author":"Monteith","year":"1977","journal-title":"Philos. Trans. R. Soc. B Biol. Sci."},{"key":"ref_13","unstructured":"Jones, C.A., Kiniry, J.R., and Dyke, P.T. (1986). CERES-Maize: A Simulation Model of Maize Growth and Development, Texas A&M University Press."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"497","DOI":"10.13031\/2013.31032","article-title":"The EPIC Crop Growth Model","volume":"32","author":"Williams","year":"1989","journal-title":"Trans. ASAE"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/S1161-0301(02)00110-7","article-title":"An overview of the crop model stics","volume":"18","author":"Brisson","year":"2003","journal-title":"Eur. J. Agron."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"5951","DOI":"10.3390\/rs70505951","article-title":"Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and FORMOSAT-2 Images","volume":"7","author":"Duchemin","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"114428","DOI":"10.1016\/j.geoderma.2020.114428","article-title":"Estimation of daily CO2 fluxes and of the components of the carbon budget for winter wheat by the assimilation of Sentinel 2-like remote sensing data into a crop model","volume":"376","author":"Pique","year":"2020","journal-title":"Geoderma"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S1161-0301(02)00109-0","article-title":"CropSyst, a cropping systems simulation model","volume":"18","author":"Donatelli","year":"2003","journal-title":"Eur. J. Agron."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1111\/j.1475-2743.1989.tb00755.x","article-title":"WOFOST: A simulation model of crop production","volume":"5","author":"Diepen","year":"1989","journal-title":"Soil Use Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"403","DOI":"10.2134\/agronj1996.00021962008800030008x","article-title":"OILCROP-SUN: A Development, Growth, and Yield Model of the Sunflower Crop","volume":"88","author":"Villalobos","year":"1996","journal-title":"Agron. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.agrformet.2010.09.012","article-title":"SUNFLO, a model to simulate genotype-specific performance of the sunflower crop in contrasting environments","volume":"151","author":"Casadebaig","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.rse.2016.07.030","article-title":"Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data","volume":"184","author":"Battude","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"24","DOI":"10.2134\/agronj2013.0314","article-title":"Testing Remote Sensing Approaches for Assessing Yield Variability among Maize Fields","volume":"106","author":"Sibley","year":"2014","journal-title":"Agron. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.5194\/bg-10-2451-2013","article-title":"A data assimilation framework for constraining upscaled cropland carbon flux seasonality and biometry with MODIS","volume":"10","author":"Sus","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_26","first-page":"410","article-title":"Assimilating MODIS-LAI into Crop Growth Model with EnKF to Predict Regional Crop Yield","volume":"Volume 370","author":"Li","year":"2012","journal-title":"Computer and Computing Technologies in Agriculture V"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sala, O.E., Jackson, R.B., Mooney, H.A., and Howarth, R.W. (2000). Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System. Methods in Ecosystem Science, Springer.","DOI":"10.1007\/978-1-4612-1224-9"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.agwat.2010.08.019","article-title":"Using vegetation indices from satellite remote sensing to assess corn and soybean response to controlled tile drainage","volume":"98","author":"Cicek","year":"2010","journal-title":"Agric. Water Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3390\/rs2030673","article-title":"Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques","volume":"2","author":"Panda","year":"2010","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"583","DOI":"10.2134\/agronj2001.933583x","article-title":"Use of Remote-Sensing Imagery to Estimate Corn Grain Yield","volume":"93","author":"Shanahan","year":"2001","journal-title":"Agron. J."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"5219","DOI":"10.5194\/hess-18-5219-2014","article-title":"Agro-hydrology and multi-temporal high-resolution remote sensing: Toward an explicit spatial processes calibration","volume":"18","author":"Ferrant","year":"2014","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_33","unstructured":"Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (1998). Crop Evapotranspiration\u2014Guidelines for Computing Crop Water Requirements\u2014FAO Irrigation and Drainage Paper 56, FAO."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.agwat.2017.04.018","article-title":"Modeling water needs and total irrigation depths of maize crop in the south west of France using high spatial and temporal resolution satellite imagery","volume":"189","author":"Battude","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_35","first-page":"15","article-title":"Outils, r\u00e9f\u00e9rences et m\u00e9thodes pour la construction d\u2019un simulateur pour la pr\u00e9vision du rendement et de la qualit\u00e9 du tournesol \u00e0 l\u2019\u00e9chelle territoriale mobilisant la t\u00e9l\u00e9d\u00e9tection satellitaire","volume":"71","author":"Champolivier","year":"2019","journal-title":"Innov. Agron."},{"key":"ref_36","first-page":"151","article-title":"Pourquoi irriguer le tournesol, une culture r\u00e9put\u00e9e tol\u00e9rante \u00e0 la s\u00e9cheresse?","volume":"14","author":"Champolivier","year":"2011","journal-title":"Innov. Agron."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1016\/j.agrformet.2009.05.004","article-title":"Carbon balance of a three crop succession over two cropland sites in South West France","volume":"149","author":"Ceschia","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"65","DOI":"10.3189\/S0260305500011277","article-title":"A meteorological estimation of relevant parameters for snow models","volume":"18","author":"Durand","year":"1993","journal-title":"Ann. Glaciol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Aubinet, M., Vesala, T., and Papale, D. (2012). Eddy Covariance: A Practical Guide to Measurement and Data Analysis, Springer.","DOI":"10.1007\/978-94-007-2351-1"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1046\/j.1365-2486.2003.00629.x","article-title":"Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: Past, present and future","volume":"9","author":"Baldocchi","year":"2003","journal-title":"Glob. Chang. Biol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/S0022-1694(96)03194-0","article-title":"A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide","volume":"188\u2013189","author":"Moncrieff","year":"1997","journal-title":"J. Hydrol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1111\/j.1365-2486.2005.001002.x","article-title":"On the separation of net ecosystem exchange into assimilation and ecosystem respiration: Review and improved algorithm","volume":"11","author":"Reichstein","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.geoderma.2019.02.036","article-title":"Uncertainty assessment of GlobalSoilMap soil available water capacity products: A French case study","volume":"344","author":"Dobarco","year":"2019","journal-title":"Geoderma"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hengl, T., Mendes de Jesus, J., Heuvelink, G.B., Ruiperez Gonzalez, M., Kilibarda, M., Blagoti\u0107, A., Shangguan, W., Wright, M.N., Geng, X., and Guevara, M.A. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Boettinger, J.L., Howell, D.W., Moore, A.C., Hartemink, A.E., and Kienast-Brown, S. (2010). Methodologies for Global Soil Mapping. Digital Soil Mapping, Springer.","DOI":"10.1007\/978-90-481-8863-5"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.geoderma.2018.08.022","article-title":"Pedotransfer functions for predicting available water capacity in French soils, their applicability domain and associated uncertainty","volume":"336","author":"Dobarco","year":"2019","journal-title":"Geoderma"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1016\/j.rse.2010.01.004","article-title":"Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model","volume":"114","author":"Liu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/S0167-8809(02)00021-X","article-title":"Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties","volume":"94","author":"Lobell","year":"2003","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"12242","DOI":"10.3390\/rs70912242","article-title":"SPOT-4 (Take 5): Simulation of Sentinel-2 Time Series on 45 Large Sites","volume":"7","author":"Hagolle","year":"2015","journal-title":"Remote Sens."},{"key":"ref_50","first-page":"10","article-title":"Outils de pr\u00e9traitements des images optiques KALIDEOS","volume":"197","author":"Lafrance","year":"2012","journal-title":"Rev. Fr. Photogramm. T\u00e9l\u00e9d\u00e9tect."},{"key":"ref_51","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_52","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_53","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.agrformet.2007.11.015","article-title":"Estimation of leaf area and clumping indexes of crops with hemispherical photographs","volume":"148","author":"Demarez","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"509","DOI":"10.2135\/cropsci1974.0011183X001400040005x","article-title":"Equations for the Rate of Dark Respiration of White Clover and Grain Sorghum, as Functions of Dry Weight, Photosynthetic Rate, and Temperature1","volume":"14","author":"McCree","year":"1974","journal-title":"Crop Sci."},{"key":"ref_55","unstructured":"Van\u2019t Hoff, J.H. (1898). Lectures on Theoretical and Physical Chemistry. Part I. Chemical Dynamics, Edward Arnold."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Amthor, J.S. (1989). Respiration and Crop Productivity, Springer.","DOI":"10.1007\/978-1-4615-9667-7"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/S0168-1923(99)00156-2","article-title":"A sensitivity analysis of the radiation use efficiency for gross photosynthesis and net carbon accumulation by wheat","volume":"101","author":"Choudhury","year":"2000","journal-title":"Agric. For. Meteorol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/BF00012815","article-title":"Root biomass fraction as a function of growth degree days in wheat","volume":"140","author":"Baret","year":"1992","journal-title":"Plant Soil"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.2134\/agronj2017.04.0194","article-title":"Shoot and Root Biomass Allocation of Sunflower Varying with Soil Salinity and Nitrogen Applications","volume":"109","author":"Ma","year":"2017","journal-title":"Agron. J."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/0022-1694(88)90054-6","article-title":"Estimation of bare soil evaporation from airborne measurements","volume":"99","author":"Soares","year":"1988","journal-title":"J. Hydrol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1137\/S1052623496303470","article-title":"Convergence Properties of the Nelder\u2013Mead Simplex Method in Low Dimensions","volume":"9","author":"Lagarias","year":"1998","journal-title":"SIAM J. Optim."},{"key":"ref_62","unstructured":"Delogu, E. (2017). Mod\u00e9lisation de la Respiration du Sol dans les Agrosyst\u00e8mes, Universit\u00e9 Paul Sabatier."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"324","DOI":"10.2134\/agronj1998.00021962009000030002x","article-title":"Temperature and Sowing Date Affect the Linear Increase of Sunflower Harvest Index","volume":"90","author":"Bange","year":"1998","journal-title":"Agron. J."},{"key":"ref_64","first-page":"85","article-title":"Study of the Effect of Drought Stress on Yield, Yield Components and Harvest Index of Sunflower Hybrid Iroflor at Different Levels of Nitrogen and Plant Population","volume":"37","author":"Gholinezhad","year":"2009","journal-title":"Not. Bot. Horti Agrobot."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1051\/ocl.2010.0308","article-title":"Simulation de la r\u00e9ponse vari\u00e9tale du tournesol \u00e0 l\u2019environnement \u00e0 l\u2019aide du mod\u00e8le SUNFLO","volume":"17","author":"Debaeke","year":"2010","journal-title":"Ol. Corps Gras Lipides"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.agee.2010.09.020","article-title":"Management effects on net ecosystem carbon and GHG budgets at European crop sites","volume":"139","author":"Ceschia","year":"2010","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_67","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 Sunflower Growth under Different Water Regimes","volume":"101","author":"Todorovic","year":"2009","journal-title":"Agron. J."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.agee.2010.06.011","article-title":"Predicting the net carbon exchanges of crop rotations in Europe with an agro-ecosystem model","volume":"139","author":"Lehuger","year":"2010","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1016\/j.soilbio.2007.08.022","article-title":"Modeling consequences of straw residues export on soil organic carbon","volume":"40","author":"Mary","year":"2008","journal-title":"Soil Biol. Biochem."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Powlson, D.S., Smith, P., and Smith, J.U. (1996). RothC-26.3\u2014A Model for the turnover of carbon in soil. Evaluation of Soil Organic Matter Models, Springer.","DOI":"10.1007\/978-3-642-61094-3_1"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4747","DOI":"10.1109\/JSTARS.2018.2878502","article-title":"Estimation of Corn Yield by Assimilating SAR and Optical Time Series into a Simplified Agro-Meteorological Model: From Diagnostic to Forecast","volume":"11","author":"Ameline","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Baup, F., Ameline, M., Fieuzal, R., Frappart, F., Corgne, S., and Berthoumieu, J.-F. (2019). Temporal Evolution of Corn Mass Production Based on Agro-Meteorological Modelling Controlled by Satellite Optical and SAR Images. Remote Sens., 11.","DOI":"10.3390\/rs11171978"},{"key":"ref_73","first-page":"14","article-title":"Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks","volume":"57","author":"Fieuzal","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.rse.2013.06.002","article-title":"Carbon cycling of European croplands: A framework for the assimilation of optical and microwave Earth observation data","volume":"137","author":"Revill","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_75","first-page":"175","article-title":"Thermal infra-red remote sensing for water stress estimation in agriculture","volume":"67","author":"Lebourgeois","year":"2012","journal-title":"Options M\u00e9diterr. S\u00e9r. 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