{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:11:54Z","timestamp":1785420714124,"version":"3.56.0"},"reference-count":113,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,17]],"date-time":"2019-08-17T00:00:00Z","timestamp":1566000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["80NSSC17K0339"],"award-info":[{"award-number":["80NSSC17K0339"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G \u00d7 E \u00d7 M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype \u00d7 management (G \u00d7 M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI\/satellite measurement regression coefficients lead to large uncertainty in the G \u00d7 M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.<\/jats:p>","DOI":"10.3390\/rs11161928","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T06:10:14Z","timestamp":1566195014000},"page":"1928","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models"],"prefix":"10.3390","volume":"11","author":[{"given":"Nathaniel","family":"Levitan","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, City College of New York, 160 Convent Ave., New York, NY 10031, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8563-1503","authenticated-orcid":false,"given":"Yanghui","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Wisconsin-Madison, 550 N. Park St., Madison, WI 53706, USA"},{"name":"Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mutlu","family":"\u00d6zdo\u011fan","sequence":"additional","affiliation":[{"name":"Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, USA"},{"name":"Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5505-6552","authenticated-orcid":false,"given":"Vincenzo","family":"Magliulo","sequence":"additional","affiliation":[{"name":"CNR-Institute of Mediterranean Forest and Agricultural Systems, 85 Via Patacca, 80040-Ercolano (Napoli), Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paulo","family":"Castillo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering Technology, Farmingdale State College, 2350 Broadhollow Road, Farmingdale, NY 11735-1021, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fred","family":"Moshary","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, City College of New York, 160 Convent Ave., New York, NY 10031, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barry","family":"Gross","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, City College of New York, 160 Convent Ave., New York, NY 10031, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.eja.2016.05.005","article-title":"The interactions between genotype, management and environment in regional crop modeling","volume":"88","author":"Teixeira","year":"2017","journal-title":"Eur. J. Agron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.2134\/agronj2013.0421","article-title":"Evaluating APSIM maize, soil water, soil nitrogen, manure, and soil temperature modules in the Midwestern United States","volume":"106","author":"Archontoulis","year":"2014","journal-title":"Agron. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S1161-0301(02)00108-9","article-title":"An overview of APSIM, a model designed for farming systems simulation","volume":"18","author":"Keating","year":"2003","journal-title":"Eur. J. Agron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.agsy.2017.01.019","article-title":"Modeling the impacts of pests and diseases on agricultural systems","volume":"155","author":"Donatelli","year":"2017","journal-title":"Agric. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.fcr.2010.09.012","article-title":"High-yield irrigated maize in the Western, U.S. Corn Belt: I. On-farm yield, yield potential, and impact of agronomic practices","volume":"120","author":"Grassini","year":"2011","journal-title":"Field Crops Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.fcr.2016.04.004","article-title":"Can crop simulation models be used to predict local to regional maize yields and total production in the U.S. Corn Belt?","volume":"192","author":"Morell","year":"2016","journal-title":"Field Crops Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1071\/CP09052","article-title":"Re-inventing model-based decision support with Australian dryland farmers. 3. Relevance of APSIM to commercial crops","volume":"60","author":"Carberry","year":"2009","journal-title":"Crop Pasture Sci."},{"key":"ref_8","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_9","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_10","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0167-8809(00)00168-7","article-title":"Adjustment procedures of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation","volume":"81","author":"Duke","year":"2000","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.agrformet.2019.05.013","article-title":"Management and spatial resolution effects on yield and water balance at regional scale in crop models","volume":"275","author":"Constantin","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.agsy.2010.01.006","article-title":"Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method","volume":"103","author":"He","year":"2010","journal-title":"Agric. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.agrformet.2016.12.015","article-title":"Data requirement for effective calibration of process-based crop models","volume":"234\u2013235","author":"He","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.ecolmodel.2016.02.013","article-title":"Quantifying uncertainty in crop model predictions due to the uncertainty in the observations used for calibration","volume":"328","author":"Confalonieri","year":"2016","journal-title":"Ecol. Model."},{"key":"ref_15","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_16","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_17","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_18","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.fcr.2012.02.012","article-title":"Using Leaf Area Index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of field crops","volume":"131","author":"Pattey","year":"2012","journal-title":"Field Crops Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.fcr.2016.10.004","article-title":"Inter-comparison of performance of soybean crop simulation models and their ensemble in southern Brazil","volume":"200","author":"Battisti","year":"2017","journal-title":"Field Crops Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.agrformet.2012.11.017","article-title":"Implication of crop model calibration strategies for assessing regional impacts of climate change in Europe","volume":"170","author":"Angulo","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3268","DOI":"10.1073\/pnas.1222463110","article-title":"Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison","volume":"111","author":"Rosenzweig","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.agsy.2018.03.002","article-title":"Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe","volume":"168","author":"Lecerf","year":"2019","journal-title":"Agric. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.envsci.2016.08.011","article-title":"Integrating high resolution soil data into federal crop insurance policy: Implications for policy and conservation","volume":"66","author":"Woodard","year":"2016","journal-title":"Environ. Sci. Policy"},{"key":"ref_24","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\u2014A review","volume":"143","author":"Grassini","year":"2013","journal-title":"Field Crop. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.eja.2015.11.021","article-title":"Assessing uncertainty and complexity in regional-scale crop model simulations","volume":"88","author":"Koehler","year":"2017","journal-title":"Eur. J. Agron."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Reynolds, M., Kropff, M., Crossa, J., Koo, J., Kruseman, G., Molero, A.M., Rutkoski, J., Schulthess, U., and Sonder, K. (2018). Role of Modeling in International Crop Research: Overview and Some Case Studies. Agronomy, 8.","DOI":"10.3390\/agronomy8120291"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kang, Y., \u00d6zdo\u011fan, M., Zipper, S., Rom\u00e1n, M., Walker, J., Hong, S., Marshall, M., Magliulo, V., Moreno, J., and Alonso, L. (2016). How Universal Is the Relationship between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment. Remote Sens., 8.","DOI":"10.3390\/rs8070597"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, K., Tang, C., Cao, Q., Tian, Y., Zhu, Y., Cao, W., Liu, X., Wang, Y., and Zhang, K. (2019). Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles. Remote Sens., 11.","DOI":"10.3390\/rs11111371"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Clevers, J., Kooistra, L., van den Brande, M., Clevers, J.G.P.W., Kooistra, L., and Van den Brande, M.M.M. (2017). Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop. Remote Sens., 9.","DOI":"10.3390\/rs9050405"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/TGRS.2013.2247405","article-title":"Improved LAI Algorithm Implementation to MODIS Data by Incorporating Background, Topography, and Foliage Clumping Information","volume":"52","author":"Gonsamo","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.rse.2012.04.002","article-title":"Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons","volume":"123","author":"Liu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, Z., Jin, X., Yang, G., Drummond, J., Yang, H., Clark, B., Li, Z., Zhao, C., Li, Z., and Jin, X. (2018). Remote Sensing of Leaf and Canopy Nitrogen Status in Winter Wheat (Triticum aestivum L.) Based on N-PROSAIL Model. Remote Sens., 10.","DOI":"10.3390\/rs10091463"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Boren, E.J., Boschetti, L., Johnson, D.M., Boren, E.J., Boschetti, L., and Johnson, D.M. (2019). Characterizing the Variability of the Structure Parameter in the PROSPECT Leaf Optical Properties Model. Remote Sens., 11.","DOI":"10.3390\/rs11101236"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rse.2014.01.004","article-title":"Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production","volume":"144","author":"Gitelson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1080\/01431168508948330","article-title":"Sun-angle and canopy-architecture effects on the spectral reflectance of six wheat cultivars","volume":"6","author":"Pinter","year":"1985","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(02)00035-4","article-title":"Retrieval of canopy biophysical variables from bidirectional reflectance: Using prior information to solve the ill-posed inverse problem","volume":"84","author":"Combal","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2016.08.017","article-title":"Adapting a regularized canopy reflectance model (REGFLEC) for the retrieval challenges of dryland agricultural systems","volume":"186","author":"Houborg","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.rse.2014.12.008","article-title":"Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)","volume":"159","author":"Houborg","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2536","DOI":"10.1109\/TGRS.2009.2015656","article-title":"A Temporally Integrated Inversion Method for Estimating Leaf Area Index From MODIS Data","volume":"47","author":"Xiao","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.rse.2004.06.016","article-title":"Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models","volume":"93","author":"Atzberger","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.3390\/rs1041139","article-title":"Enhanced automated canopy characterization from hyperspectral data by a novel two step radiative transfer model inversion approach","volume":"1","author":"Dorigo","year":"2009","journal-title":"Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4589","DOI":"10.1109\/JSTARS.2014.2360069","article-title":"Newly Combined Spectral Indices to Improve Estimation of Total Leaf Chlorophyll Content in Cotton","volume":"7","author":"Jin","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4014","DOI":"10.1109\/TGRS.2013.2278838","article-title":"Sensitivity Analysis of Vegetation Reflectance to Biochemical and Biophysical Variables at Leaf, Canopy, and Regional Scales","volume":"52","author":"Xiao","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1016\/j.isprsjprs.2011.09.013","article-title":"Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models","volume":"66","author":"Darvishzadeh","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.compag.2010.05.006","article-title":"Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat","volume":"73","author":"Atzberger","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"036015","DOI":"10.1117\/1.JRS.10.036015","article-title":"Comparison of partial least squares and support vector regressions for predicting leaf area index on a tropical grassland using hyperspectral data","volume":"10","author":"Kiala","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, L., Chang, Q., Li, F., Yan, L., Huang, Y., Wang, Q., Luo, L., Wang, L., Chang, Q., and Li, F. (2019). Effects of growth stage development on paddy rice leaf area index prediction models. Remote Sens., 11.","DOI":"10.3390\/rs11030361"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.envsoft.2014.08.010","article-title":"Enhanced biomass prediction by assimilating satellite data into a crop growth model","volume":"62","author":"Machwitz","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0168-1923(01)00234-9","article-title":"Coupling canopy functioning and radiative transfer models for remote sensing data assimilation","volume":"108","author":"Weiss","year":"2001","journal-title":"Agric. For. Meteorol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.fcr.2016.04.014","article-title":"Estimating wheat yield by integrating the WheatGrow and PROSAIL models","volume":"192","author":"Zhang","year":"2016","journal-title":"Field Crops Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.rse.2012.05.013","article-title":"Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models","volume":"124","author":"Thorp","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1007\/s11119-016-9488-z","article-title":"Crop model- and satellite imagery-based recommendation tool for variable rate N fertilizer application for the US Corn system","volume":"18","author":"Jin","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1002\/2016MS000625","article-title":"Calibration-induced uncertainty of the EPIC model to estimate climate change impact on global maize yield","volume":"8","author":"Xiong","year":"2016","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.ecolmodel.2015.11.006","article-title":"Effects of automatic multi-objective optimization of crop models on corn yield reproducibility in the U.S.A","volume":"322","author":"Tatsumi","year":"2016","journal-title":"Ecol. Model."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"eaat4517","DOI":"10.1126\/sciadv.aat4517","article-title":"Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat yields","volume":"4","author":"Frieler","year":"2018","journal-title":"Sci. Adv."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.5194\/gmd-10-1403-2017","article-title":"Global gridded crop model evaluation: Benchmarking, skills, deficiencies and implications","volume":"10","author":"Elliott","year":"2017","journal-title":"Geosci. Model Dev."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1038\/nclimate2117","article-title":"Making the most of climate impacts ensembles","volume":"4","author":"Challinor","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.eja.2017.12.007","article-title":"Modeling the nitrogen dynamics of maize crops\u2014Enhancing the APSIM maize model","volume":"100","author":"Soufizadeh","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"488","DOI":"10.2134\/agronj2008.0029xs","article-title":"Validating the FAO AquaCrop Model for Irrigated and Water Deficient Field Maize","volume":"101","author":"Heng","year":"2009","journal-title":"Agron. J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.fcr.2003.10.003","article-title":"Hybrid-Maize\u2014A maize simulation model that combines two crop modeling approaches","volume":"87","author":"Yang","year":"2004","journal-title":"Field Crops Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/S1161-0301(00)00063-0","article-title":"Using the CERES-Maize model in a semi-arid Mediterranean environment. Evaluation of model performance","volume":"13","author":"Katerji","year":"2000","journal-title":"Eur. J. Agron."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/0308-521X(94)00018-M","article-title":"Uncertainties in crop, soil and weather inputs used in growth models: Implications for simulated outputs and their applications","volume":"48","author":"Aggarwal","year":"1995","journal-title":"Agric. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Levitan, N., and Gross, B. (2018). Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms. Remote Sens., 10.","DOI":"10.3390\/rs10121968"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.agsy.2017.01.023","article-title":"Big Data in Smart Farming\u2014A review","volume":"153","author":"Wolfert","year":"2017","journal-title":"Agric. Syst."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2415","DOI":"10.1175\/1520-0477(2001)082<2415:FANTTS>2.3.CO;2","article-title":"FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem\u2013Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities","volume":"82","author":"Baldocchi","year":"2001","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Pastorello, G., Papale, D., Chu, H., Trotta, C., Agarwal, D., Canfora, E., Baldocchi, D., and Torn, M. (Eos, 2017). A new data set to keep a sharper eye on land-air exchanges, Eos.","DOI":"10.1029\/2017EO071597"},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"5015","DOI":"10.5194\/bg-15-5015-2018","article-title":"Basic and extensible post-processing of eddy covariance flux data with REddyProc","volume":"15","author":"Wutzler","year":"2018","journal-title":"Biogeosciences"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.agrformet.2003.08.027","article-title":"Review of methods for in situ leaf area index determination: Part, I. Theories, sensors and hemispherical photography","volume":"121","author":"Jonckheere","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Schmidt, G., Jenkerson, C.B., Masek, J., Vermote, E., and Gao, F. (2013). Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) Algorithm Description.","DOI":"10.3133\/ofr20131057"},{"key":"ref_75","unstructured":"Schaaf, C., and Wang, Z. (2015). MODIS\/Terra and Aqua Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid V006."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1029\/2003EO480001","article-title":"New analysis reveals representativeness of the AmeriFlux network","volume":"84","author":"Hargrove","year":"2003","journal-title":"EOS Trans. Am. Geophys. Union"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agrformet.2005.06.005","article-title":"Seasonal dynamics and partitioning of isotopic CO2 exchange in a C3\/C4 managed ecosystem","volume":"132","author":"Griffis","year":"2005","journal-title":"Agric. For. Meteorol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.agrformet.2019.01.017","article-title":"Assessing the carbon and climate benefit of restoring degraded agricultural peat soils to managed wetlands","volume":"268","author":"Hemes","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.agee.2015.07.021","article-title":"Vulnerability of crops and native grasses to summer drying in the U.S. Southern Great Plains","volume":"213","author":"Billesbach","year":"2015","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1016\/j.agrformet.2010.03.008","article-title":"Land use regulates carbon budgets in eastern Germany: From NEE to NBP","volume":"150","author":"Prescher","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s11104-011-0751-9","article-title":"Carbon, nitrogen and Greenhouse gases budgets over a four years crop rotation in northern France","volume":"343","author":"Loubet","year":"2011","journal-title":"Plant Soil"},{"key":"ref_82","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_83","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s11738-007-0041-6","article-title":"Effects of water stress on gas exchange of field grown Zea mays L. in Southern Italy: An analysis at canopy and leaf level","volume":"29","author":"Vitale","year":"2007","journal-title":"Acta Physiol. Plant."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.agee.2010.04.013","article-title":"Variability in carbon exchange of European croplands","volume":"139","author":"Moors","year":"2010","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_85","unstructured":"(2013, September 02). European Space Agency Earth Observation Campaigns Data. Available online: https:\/\/earth.esa.int\/web\/guest\/campaigns."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"808","DOI":"10.3390\/rs70100808","article-title":"Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing","volume":"7","author":"Marshall","year":"2015","journal-title":"Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"597","DOI":"10.7780\/kjrs.2012.28.6.1","article-title":"Comparing LAI Estimates of Corn and Soybean from Vegetation Indices of Multi-resolution Satellite Images","volume":"28","author":"Kim","year":"2012","journal-title":"Korean J. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1016\/j.advwatres.2008.01.018","article-title":"The NAFE\u201906 data set: Towards soil moisture retrieval at intermediate resolution","volume":"31","author":"Merlin","year":"2008","journal-title":"Adv. Water Resour."},{"key":"ref_89","unstructured":"Anderson, M. (2003). SMEX02 Regional Vegetation Sampling Data, Iowa, National Snow and Ice Data Center."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"49","DOI":"10.2134\/agronj2018.02.0086","article-title":"Modeled and Measured Ecosystem Respiration in Maize\u2013Soybean Systems Over 10 Years","volume":"111","author":"Zhan","year":"2019","journal-title":"Agron. J."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Zhan, M., Liska, A.J., Nguy-Robertson, A.L., Suyker, A.E., Pelton, M.P., and Yang, H. (2018). Data from: Modeled and measured ecosystem respiration in maize\u2013soybean systems over 10 years. Dryad Digit. Repos.","DOI":"10.2134\/agronj2018.02.0086"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., and Lyon, J.G. (2016). Remote Sensing Estimation of Crop Biophysical Characteristics at Various Scales. Hyperspectral Remote Sensing of Vegetation, CRC Press.","DOI":"10.1201\/b11222"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"D08S11","DOI":"10.1029\/2005JD006017","article-title":"Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity","volume":"111","author":"Gitelson","year":"2006","journal-title":"J. Geophys. Res."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2014.09.017","article-title":"The need for a common basis for defining light-use efficiency: Implications for productivity estimation","volume":"156","author":"Gitelson","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1109\/JSTARS.2018.2799955","article-title":"Scaling Correction of Remotely Sensed Leaf Area Index for Farmland Landscape Pattern With Multitype Spatial Heterogeneities Using Fractal Dimension and Contextural Parameters","volume":"11","author":"Wu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.jenvman.2006.08.016","article-title":"Spatial scaling between leaf area index maps of different resolutions","volume":"85","author":"Jin","year":"2007","journal-title":"J. Environ. Manag."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1016\/j.rse.2010.01.006","article-title":"The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis","volume":"114","author":"Ozdogan","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.envpol.2007.06.062","article-title":"Application and test of a simple tool for operational footprint evaluations","volume":"152","author":"Neftel","year":"2008","journal-title":"Environ. Pollut."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.envsoft.2015.05.009","article-title":"Analysis and classification of data sets for calibration and validation of agro-ecosystem models","volume":"72","author":"Kersebaum","year":"2015","journal-title":"Environ. Model. Softw."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"B\u00e9gu\u00e9, A., Arvor, D., Bellon, B., Betbeder, J., de Abelleyra, D., Ferraz, R.P.D., Lebourgeois, V., Lelong, C., Sim\u00f5es, M.R., and Ver\u00f3n, S. (2018). Remote Sensing and Cropping Practices: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10010099"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"9034","DOI":"10.3390\/rs6099034","article-title":"Defining the spatial resolution requirements for crop identification using optical remote sensing","volume":"6","author":"Duveiller","year":"2014","journal-title":"Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.2134\/agronj2013.0242","article-title":"Continuous monitoring of crop reflectance, vegetation fraction, and identification of developmental stages using a four band radiometer","volume":"105","author":"Gitelson","year":"2013","journal-title":"Agron. J."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Wu, B., Zhang, M., Zeng, H., Zheng, Y., Wu, B., Zhang, M., and Zeng, H. (2016). Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products. Sensors, 16.","DOI":"10.3390\/s16122099"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1080\/15481603.2018.1423725","article-title":"Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series","volume":"55","author":"Onojeghuo","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1007\/s11119-010-9210-5","article-title":"Variable nitrogen rate determination from plant spectral reflectance in soft red winter wheat","volume":"12","author":"Thomason","year":"2011","journal-title":"Precis. Agric."},{"key":"ref_107","unstructured":"Robert, P.C., Rust, R.H., and Larson, W.E. (2000). Coincident Detection of Crop Water Stress, Nitrogen Status and Canopy Density Using Ground-Based Multispectral Data. Proceedings of the Fifth International Conference on Precision Agriculture, American Society of Agronomy, Crop Science Society of America, Soil Science Society of America."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11119-007-9036-y","article-title":"Multi-temporal wheat disease detection by multi-spectral remote sensing","volume":"8","author":"Franke","year":"2007","journal-title":"Precis. Agric."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Holman, F., Riche, A., Michalski, A., Castle, M., Wooster, M., Hawkesford, M., Holman, F.H., Riche, A.B., Michalski, A., and Castle, M. (2016). High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing. Remote Sens., 8.","DOI":"10.3390\/rs8121031"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Zhang, C., Walters, D., and Kovacs, J.M. (2014). Applications of Low Altitude Remote Sensing in Agriculture upon Farmers\u2019 Requests\u2014A Case Study in Northeastern Ontario, Canada. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0112894"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.agsy.2010.02.004","article-title":"The yield gap of global grain production: A spatial analysis","volume":"103","author":"Neumann","year":"2010","journal-title":"Agric. Syst."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Kasampalis, D., Alexandridis, T., Deva, C., Challinor, A., Moshou, D., and Zalidis, G. (2018). Contribution of Remote Sensing on Crop Models: A Review. J. Imaging, 4.","DOI":"10.3390\/jimaging4040052"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/16\/1928\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:11:53Z","timestamp":1760188313000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/16\/1928"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,17]]},"references-count":113,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11161928"],"URL":"https:\/\/doi.org\/10.3390\/rs11161928","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,17]]}}}