{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T11:00:41Z","timestamp":1766401241081,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,7]],"date-time":"2020-09-07T00:00:00Z","timestamp":1599436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFE0122700"],"award-info":[{"award-number":["2018YFE0122700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671418","41971383"],"award-info":[{"award-number":["41671418","41971383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Predicting crop maturity dates is important for improving crop harvest planning and grain quality. The prediction of crop maturity dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the maturity dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the maturity dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional maturity date prediction with determination coefficient (R2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat maturity date prediction.<\/jats:p>","DOI":"10.3390\/rs12182896","type":"journal-article","created":{"date-parts":[[2020,9,7]],"date-time":"2020-09-07T09:18:16Z","timestamp":1599470296000},"page":"2896","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model"],"prefix":"10.3390","volume":"12","author":[{"given":"Wen","family":"Zhuo","sequence":"first","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0341-1983","authenticated-orcid":false,"given":"Jianxi","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinran","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyuan","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Geography, University College London, and National Centre for Earth Observation, London WC1E 6BT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4099-8675","authenticated-orcid":false,"given":"Hai","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8726-5858","authenticated-orcid":false,"given":"Wei","family":"Su","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihua","family":"Meng","sequence":"additional","affiliation":[{"name":"Key Lab of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"China Meteorological Administration\u00b7Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou 450003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huailiang","family":"Chen","sequence":"additional","affiliation":[{"name":"China Meteorological Administration\u00b7Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou 450003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2080-3463","authenticated-orcid":false,"given":"Dongqin","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,7]]},"reference":[{"key":"ref_1","unstructured":"FAO (2017). The Future of Food and Agriculture\u2013Trends and Challenges, FAO. Annual Report."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1002\/fes3.48","article-title":"Food security and food self-sufficiency in China: From past to 2050","volume":"3","author":"Ghose","year":"2014","journal-title":"Food Energy Secur."},{"key":"ref_3","unstructured":"Hoque, M.M., and Hoffmann, V. (2019). Safety and Quality of Food (Rice and Wheat) Distributed through Public Food Distribution System (PFDS) in Bangladesh: Results from Laboratory Tests for Selected Contaminants, International Food Policy Research Institute."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"476","DOI":"10.3389\/fpls.2015.00476","article-title":"Seed shattering: From models to crops","volume":"6","author":"Dong","year":"2015","journal-title":"Front. Plant Sci."},{"key":"ref_5","first-page":"226","article-title":"Application of Phenological Observation Data to Forecast of Wheat Harvest Period","volume":"27","author":"Lian","year":"2006","journal-title":"China Agric. Meteorol."},{"key":"ref_6","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","author":"Huang","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Meng, J., and Wang, Y. (2016). Improving spring maize yield estimation at field scale by assimilating time-series HJ-1 CCD data into the WOFOST model using a new method with fast algorithms. Remote Sens., 8.","DOI":"10.3390\/rs8040303"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Su, W., Huang, J.X., Liu, D.S., and Zhang, M.Z. (2019). Retrieving Corn Canopy Leaf Area Index from Multitemporal Landsat Imagery and Terrestrial LiDAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11050572"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4055","DOI":"10.1080\/01431160110115988","article-title":"Monitoring phenological cycles of desert ecosystems using NDVI and LST data derived from NOAA-AVHRR imagery","volume":"23","author":"Karnieli","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2005.03.008","article-title":"A crop phenology detection method using time-series MODIS data","volume":"96","author":"Sakamoto","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1006\/anbo.2001.1512","article-title":"Biomass Accumulation and Main Stem Elongation of Durum Wheat Grown under Mediterranean Conditions","volume":"88","author":"Villegas","year":"2001","journal-title":"Ann. Bot."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1002\/joc.819","article-title":"Assessing satellite-derived start-of-season measures in the conterminous USA","volume":"22","author":"Schwartz","year":"2002","journal-title":"Int. J. Climatol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1175\/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2","article-title":"A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation","volume":"9","author":"Sellers","year":"1996","journal-title":"J. Clim."},{"key":"ref_14","unstructured":"Han-ya, I., Ishii, K., and Noguchi, N. (2009, January 14\u201317). Acquisition and analysis of wheat growth information using satellite and aerial vehicle imageries. Proceedings of the 3rd Asian Conference on Precision Agriculture, Beijing, China."},{"key":"ref_15","first-page":"225","article-title":"Predicting mature date of winter wheat with HJ-1A\/1B data","volume":"27","author":"Matisoo","year":"2011","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.postharvbio.2003.08.007","article-title":"Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy","volume":"31","author":"Saranwong","year":"2004","journal-title":"Postharvest Biol. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.postharvbio.2004.05.010","article-title":"Effect of natural variability among apples on the accuracy of VIS-NIR calibration models for optimal harvest date predictions","volume":"35","author":"Peirs","year":"2005","journal-title":"Postharvest Biol. Technol."},{"key":"ref_18","first-page":"131","article-title":"Nondestructive internal quality assessment of kiwifruit using near-infrared spectroscopy","volume":"3","author":"Slaughter","year":"1998","journal-title":"Semin. Food Anal."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.02.057","article-title":"Vegetation baseline phenology from kilometric global LAI satellite products","volume":"178","author":"Verger","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.agrformet.2009.09.012","article-title":"Linking weather generators and crop models for assessment of climate forecast outcomes","volume":"150","author":"Apipattanavis","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_22","unstructured":"Li, Z. (2016). Winter Wheat Yield and Quality Prediction Based on DSSAT Model Based on Remote Sensing Data and Weather Forecast Data. [Ph.D. Thesis, Zhejiang University]. (In Chinese)."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, N., Liu, D., Feng, Q., Xiong, Q., Zhang, L., Ren, T., Zhao, Y., Zhu, D., and Huang, J. (2019). Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids. Remote Sens., 11.","DOI":"10.3390\/rs11121500"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, X., Huang, J., Feng, Q., and Yin, D. (2020). Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-producing Regions of China with Deep Learning Approaches. Remote Sens., 12.","DOI":"10.3390\/rs12111744"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.agrformet.2009.08.004","article-title":"On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau","volume":"150","author":"Yang","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_28","first-page":"706","article-title":"Multi-model super-ensemble prediction of surface temperature based on TIGGE data","volume":"20","author":"Lin","year":"2009","journal-title":"J. Appl. Meteorol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yang, K., and He, J. (2011). Improving land surface temperature modeling for dry land of China. J. Geophys. Res. Atmos., 116.","DOI":"10.1029\/2011JD015921"},{"key":"ref_30","unstructured":"Li, S. (2016). Research on Theory and Application of Multi-center Aggregate Forecast Based on Tigge, Nanjing University. (In Chinese)."},{"key":"ref_31","first-page":"257","article-title":"Regional yield forecast based on historical meteorological data and WOFOST model","volume":"49","author":"Ma","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_32","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_33","first-page":"16","article-title":"WOFOST: A simulation model of crop production","volume":"5","author":"Wolf","year":"2010","journal-title":"Soil Use Manag."},{"key":"ref_34","first-page":"142","article-title":"Modelling of agricultural production: Weather, soils and crops","volume":"30","author":"Keulen","year":"1986","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1016\/j.mcm.2012.12.028","article-title":"Estimating regional winter wheat yield by assimilation of time series of HJ-1 CCD NDVI into WOFOST\u2013ACRM model with Ensemble Kalman Filter","volume":"58","author":"Ma","year":"2013","journal-title":"Math. Comput. Model."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.agrformet.2015.10.013","article-title":"Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation","volume":"216","author":"Huang","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/BF00939380","article-title":"Shuffled complex evolution approach for effective and efficient global minimization","volume":"76","author":"Duan","year":"1993","journal-title":"J. Optim. Theory Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/0022-1694(94)90057-4","article-title":"Optimal Use of the SCE-UA Global Optimization Method for Calibrating Watershed Models","volume":"158","author":"Duan","year":"1994","journal-title":"J. Hydrol."},{"key":"ref_40","first-page":"6","article-title":"Application of SCE-UA, Genetic Algorithm and Simplex Optimization Algorithm","volume":"42","author":"Song","year":"2009","journal-title":"J. Wuhan Univ."},{"key":"ref_41","first-page":"0160","article-title":"Multidimensional joint distribution calculation method and its application in hydrology","volume":"37","author":"Dai","year":"2006","journal-title":"J. Hydraul. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1080\/02626660009492327","article-title":"Joint probability distribution of annual maximum storm peaks and amounts as represented by daily rainfalls","volume":"45","author":"Yue","year":"2000","journal-title":"Hydrol. Sci. J."},{"key":"ref_43","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\u2013PROSAIL model","volume":"102","author":"Huang","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_44","first-page":"222","article-title":"Dynamic simulation of winter wheat growth process in main producing areas of China based on WOFOST model","volume":"33","author":"Huang","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Liang, S. (2008). Data assimilation methods for land surface variable estimation. Advances in Land Remote Sensing: System, Modeling, Inversion and Application, Springer.","DOI":"10.1007\/978-1-4020-6450-0_12"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1016\/j.rse.2007.05.023","article-title":"Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield","volume":"112","author":"Dente","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.agrformet.2015.02.001","article-title":"Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model","volume":"204","author":"Huang","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.agrformet.2012.12.008","article-title":"Climate change impacts on regional winter wheat production in main wheat production regions of China","volume":"171","author":"Lv","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","first-page":"1","article-title":"Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield","volume":"3","author":"Huang","year":"2018","journal-title":"Int. J. Digit. Earth"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/S2095-3119(19)62657-2","article-title":"Soil temperature estimation at different depths, using remotely-sensed data","volume":"19","author":"Huang","year":"2020","journal-title":"J. Integr. Agric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2896\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:07:35Z","timestamp":1760177255000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2896"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,7]]},"references-count":51,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12182896"],"URL":"https:\/\/doi.org\/10.3390\/rs12182896","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,9,7]]}}}