{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T19:55:45Z","timestamp":1780516545970,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T00:00:00Z","timestamp":1601424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871344, 41301378"],"award-info":[{"award-number":["41871344, 41301378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Research and Development Plan of China","award":["2017YFD0201501"],"award-info":[{"award-number":["2017YFD0201501"]}]},{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA23100101"],"award-info":[{"award-number":["XDA23100101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Data assimilation is a robust method to predict crop biophysical and biochemical parameters. However, no previous study has attempted to predict grain protein content (GPC) at a regional scale using this method. This study explored the feasibility of designing an assimilation model for wheat GPC estimation using remote sensing, a crop growth model, and a priori knowledge. The data included a field experiment and regional sampling data, and Landsat Operational Land Imager images were employed, with the CERES (Crop Environment REsource Synthesis)-Wheat model used as simulation model. To select an optimal method for data assimilation in GPC prediction, different state variable scenarios and cost function solving algorithm scenarios were compared. Additionally, to determine whether a priori information could improve GPC prediction, the collected leaf area index (LAI) and leaf N content sampling data and the range of GPC in the study region were used to constrain the data assimilation process. Furthermore, the data assimilation method was compared to the use of only the CERES-Wheat model. The results showed that GPC could be predicted by remote sensing observation, a crop growth model, and a priori knowledge at regional scale, where the use of data assimilation improved the GPC prediction compared to using only the CERES-Wheat model.<\/jats:p>","DOI":"10.3390\/rs12193201","type":"journal-article","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T09:04:12Z","timestamp":1601543052000},"page":"3201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Estimation of Winter Wheat Grain Protein Content Based on Multisource Data Assimilation"],"prefix":"10.3390","volume":"12","author":[{"given":"Pengfei","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Science and Natural Resources Research of Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, X., Teng, C., Zhao, Y., Du, Y., Zhao, C., Yang, G., Jin, X., Song, X., Gu, X., and Casa, R. (2020). Prediction of wheat grain protein by coupling multisource remote sensing imagery and ECMWF data. Remote Sens., 12.","DOI":"10.3390\/rs12081349"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1080\/10798587.2014.934593","article-title":"Winter wheat cropland GPC evaluation through remote sensing","volume":"20","author":"Song","year":"2014","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.eja.2006.04.005","article-title":"Simulation of environmental and genetic effects on grain protein concentration in wheat","volume":"25","author":"Asseng","year":"2006","journal-title":"Eur. J. Agron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.2135\/cropsci2014.07.0479","article-title":"Integration of remote sensing and crop modeling for the early assessment of durum wheat harvest at the field scale","volume":"55","author":"Orlando","year":"2015","journal-title":"Crop Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/S1161-0301(01)00116-2","article-title":"Simulation of grain protein content with APSIM-Nwheat","volume":"16","author":"Asseng","year":"2002","journal-title":"Eur. J. Agron."},{"key":"ref_6","unstructured":"Hu, B.G., and Jaeger, M. (2003). Modeling yield and grain protein of Japanese wheat by DSSAT cropping system model. Proceeding of Plant Growth Modeling and Applications, Liama, Chinese Agricultural University."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"322336","DOI":"10.1016\/j.fcr.2005.11.006","article-title":"Modeling plant nitrogen uptake and grain nitrogen accumulation in wheat","volume":"97","author":"Pan","year":"2006","journal-title":"Field Crop. Res."},{"key":"ref_8","unstructured":"Basnet, B.B., Apan, A.A., Kelly, R.M., Jensen, T., Strong, W.M., and Butler, D.G. Relating satellite imagery with gain protein content. Proceedings of the 2003 Spatial Science Institute Biennial Conference: Spatial Knowledge Without Boundaries (SSC2003)."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhao, H., Song, X., Yang, G., Li, Z., Zhang, D., and Feng, H. (2019). Monitoring of nitrogen and GPC in winter wheat based on sentinel-2A data. Remote Sens., 11.","DOI":"10.3390\/rs11141724"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kasampalis, D.A., Alexandridis, T.K., 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"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"107993","DOI":"10.1016\/j.agrformet.2020.107993","article-title":"Improving regional wheat yields estimations by multi-step assimilating of a crop model with multi-source data","volume":"290","author":"Zhang","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"191","DOI":"10.2151\/jmsj1965.75.1B_191","article-title":"Assimilation of observations, an introduction","volume":"75","author":"Talagrand","year":"1997","journal-title":"J. Meteorol. Soc. Jpn."},{"key":"ref_14","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."},{"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":"53","DOI":"10.1016\/j.eja.2015.08.006","article-title":"Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing","volume":"71","author":"Li","year":"2015","journal-title":"Eur. J. Agron."},{"key":"ref_17","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 liked radiative transfer and ecophysiological models","volume":"124","author":"Thorp","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/0304-3800(88)90031-2","article-title":"Use of remotely sensed information in agricultural crop growth models","volume":"41","author":"Mass","year":"1988","journal-title":"Ecol. Model."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"251","DOI":"10.13031\/2013.29490","article-title":"Assimilating leaf area index estimates from remote sensing into the simulations of a cropping systems model","volume":"53","author":"Thorp","year":"2010","journal-title":"Trans. ASABE"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1016\/S1671-2927(11)60156-9","article-title":"Assimilation of remote sensing and crop model for LAI estimation based on ensemble kaiman filter","volume":"10","author":"Li","year":"2011","journal-title":"Agric. Sci. China"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1016\/j.mcm.2012.12.013","article-title":"Integrating a very fast simulated annealing optimization algorithm for crop leaf area index variational assimilation","volume":"58","author":"Dong","year":"2013","journal-title":"Math. Comput. Model."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.pce.2015.08.010","article-title":"Estimation of maize yield by using a process-based model and remote sensing data in the northeast China plain","volume":"87","author":"Yao","year":"2015","journal-title":"Phys. Chem. Earth"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4060","DOI":"10.1109\/JSTARS.2015.2403135","article-title":"Jointly assimilating MODIS LAI and ET products into the SWAP model for winter wheat yield estimation","volume":"8","author":"Huang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"12400","DOI":"10.3390\/rs70912400","article-title":"Assimilation of two variables derived from hyperspectral data into the DSSAT-CERES model for grain yield and quality estimation","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7106","DOI":"10.1016\/j.ijleo.2014.08.089","article-title":"Remote sensing of surface reflective properties: Role of regularization and a priori knowledge","volume":"125","author":"Cui","year":"2014","journal-title":"Optik"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liang, S. (2008). Data Assimilation Methods for Land Surface Variable Estimation, in Advances in Land Remote Sensing: System, Modeling, Inversion and Application, Springer.","DOI":"10.1007\/978-1-4020-6450-0_12"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1016\/j.asr.2016.11.029","article-title":"A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images","volume":"59","author":"Chen","year":"2017","journal-title":"Adv. Space Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.eja.2011.05.001","article-title":"Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models","volume":"35","author":"Palosuo","year":"2011","journal-title":"Eur. J. Agron."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.fcr.2015.12.011","article-title":"Models of grain quality in wheat-A review","volume":"202","author":"Nuttall","year":"2017","journal-title":"Field Crop. Res."},{"key":"ref_30","unstructured":"Teh, C. (2006). Introduction to Mathematical Modeling of Crop Growth, Brown Walker Press."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1080\/00401706.1999.10485594","article-title":"A quantitative model-independent method for global sensitivity analysis of model output","volume":"41","author":"Saltelli","year":"1999","journal-title":"Technometrics"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Carboni, J., Gatelli, D., and Tarantola, S. (2008). Global Sensitivity Analysis: The Primer, John Wiley & Sons Ltd.","DOI":"10.1002\/9780470725184"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.fcr.2018.07.002","article-title":"Parameter sensitivity analysis of the AquaCrop model based on extended Fourier amplitude sensitivity under different agro-meteorological conditions and application","volume":"226","author":"Jin","year":"2018","journal-title":"Field Crop. Res."},{"key":"ref_34","unstructured":"Baret, F. (1986). Contribution au Suivi Radiom\u00e9trique de Cultures de C\u00e9r\u00e9ales. [Ph.D. Thesis, Universit\u00e9 de Paris-Sud Orsay]."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eja.2011.09.004","article-title":"Forcing a wheat crop model with LAI data to access agronomic variables: Evaluation of the impact of model and LAI uncertainties and comparison with an empirical approach","volume":"37","author":"Casa","year":"2012","journal-title":"Eur. J. Agron."},{"key":"ref_36","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., and Harlan, J.C. (1974). Monitoring the Vernal Advancement of Retrogradation (Green Wave Effect) of Natural Vegetation, NASA\/GSFC. Type III, Final Report."},{"key":"ref_37","unstructured":"Pearson, R.L., and Miller, L.D. (1972). Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Short-Grass Prairie, Pawnee National Grasslands, Colorado, ERIM."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/0034-4257(94)90018-3","article-title":"Development of vegetation and soil indices for MODIS","volume":"49","author":"Huete","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.eja.2013.03.005","article-title":"Estimating near future regional corn yields by integrating multi-source observations into a crop growth model","volume":"49","author":"Wang","year":"2013","journal-title":"Eur. J. Agron."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eja.2018.10.008","article-title":"Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST-PROSAIL model","volume":"102","author":"Huang","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.3788\/gzxb20103909.1688","article-title":"Image fusion based on data assimilation and differential evolution algorithm","volume":"39","author":"Shi","year":"2010","journal-title":"Acta Photonica Sin."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/S0034-4257(03)00131-7","article-title":"Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression","volume":"86","author":"Hansen","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"024512","DOI":"10.1117\/1.JRS.14.024512","article-title":"Evaluation of sentinel-1 and-2 time series for estimating LAI and biomass of wheat and rapeseed crop types","volume":"14","author":"Mercier","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_49","first-page":"534","article-title":"Relationship between spring triticale physiological traits and productivity changes as affected by different N rates","volume":"67","author":"Janusauskaite","year":"2017","journal-title":"Acta Agric. Scand. Sect. B Soil Plant Sci."},{"key":"ref_50","first-page":"183","article-title":"Multi-assimilation methods based on AquaCrop model and remote sensing data","volume":"33","author":"Xing","year":"2017","journal-title":"(Trans. Chin. Soc. Agric. Eng.) Trans. Csae."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.agrformet.2016.07.006","article-title":"Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using four-dimensional variational data assimilation","volume":"228\u2013229","author":"Pinnington","year":"2016","journal-title":"Agric. For. Meteorol"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s11430-009-0203-z","article-title":"Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model","volume":"53","author":"Wang","year":"2010","journal-title":"Sci. China Earth Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"483","DOI":"10.2134\/agronj2016.02.0103","article-title":"A stochastic method for crop models: Including uncertainty in a sugarcane model","volume":"109","author":"Marin","year":"2017","journal-title":"Agron. J."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3201\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:15:22Z","timestamp":1760177722000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,30]]},"references-count":53,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12193201"],"URL":"https:\/\/doi.org\/10.3390\/rs12193201","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,30]]}}}