{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:21:58Z","timestamp":1775229718234,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"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":["41671418"],"award-info":[{"award-number":["41671418"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Facilities Council of UK-Newton Agritech Programme","award":["Sentinels of Wheat"],"award-info":[{"award-number":["Sentinels of Wheat"]}]},{"name":"National Natural Science Foundation of China \u2013 Science and Technology Facilities Council Joint Project","award":["6151101278"],"award-info":[{"award-number":["6151101278"]}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["201806350193"],"award-info":[{"award-number":["201806350193"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg\/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg\/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types.<\/jats:p>","DOI":"10.3390\/rs11131618","type":"journal-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T11:02:37Z","timestamp":1562583757000},"page":"1618","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":112,"title":["Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation"],"prefix":"10.3390","volume":"11","author":[{"given":"Wen","family":"Zhuo","sequence":"first","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}]},{"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\u2013Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1381-0654","authenticated-orcid":false,"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri\u2013Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6347-4973","authenticated-orcid":false,"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri\u2013Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"}]},{"given":"Hongyuan","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Geography, University College London, and National Centre for Earth Observation, London WC1E 6BT, UK"}]},{"given":"Xinran","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}]},{"given":"Hai","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}]},{"given":"Baodong","family":"Xu","sequence":"additional","affiliation":[{"name":"Macro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, Wuhan 430000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0956-7428","authenticated-orcid":false,"given":"Xiangming","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,8]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"1","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_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2016.2628809","article-title":"Assimilation of leaf area index and surface soil moisture with the ceres-wheat model for winter wheat yield estimation using a particle filter algorithm","volume":"10","author":"Xie","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.agrformet.2007.05.004","article-title":"Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts","volume":"146","year":"2007","journal-title":"Agric. For. Meteorol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Meng, J., Shang, J., Liu, J., Qiao, Y., Qian, B., Jing, Q., and Dong, T. (2018). Improving soil available nutrient estimation by integrating modified WOFOST model and time-series earth observations. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2018.2878382"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1016\/j.rse.2007.02.040","article-title":"Data assimilation: From photon counts to Earth System forecasts","volume":"112","author":"Mathieu","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.agrformet.2012.04.011","article-title":"Estimating regional winter wheat yield with WOFOST through the assimilation of green area index retrieved from MODIS observations","volume":"164","author":"Duveiller","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8033","DOI":"10.1080\/01431161.2010.532170","article-title":"Soil hydraulic parameters estimated from satellite information through data assimilation","volume":"32","author":"Charoenhirunyingyos","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5737","DOI":"10.1080\/01431161.2015.1103920","article-title":"Potential of Dubois model for soil moisture retrieval in prairie areas using SAR and optical data","volume":"36","author":"Bai","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.jhydrol.2012.10.044","article-title":"Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications","volume":"476","author":"Kornelsen","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.jhydrol.2013.12.008","article-title":"Soil moisture at watershed scale: Remote sensing techniques","volume":"516","author":"Fang","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JSTARS.2019.2891583","article-title":"Soil moisture retrieval from SAR and optical data using a combined model","volume":"12","author":"Tao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/0034-4257(95)00151-4","article-title":"A fully polarimetric multiple scattering model for crops","volume":"54","author":"Bracaglia","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1080\/01431169008955090","article-title":"Michigan microwave canopy scattering model","volume":"11","author":"Ulaby","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1029\/RS013i002p00357","article-title":"Vegetation modeled as a water cloud","volume":"13","author":"Attema","year":"1978","journal-title":"Radio Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/S0034-4257(00)00200-5","article-title":"Parameterization of vegetation backscatter in radar-based, soil moisture estimation","volume":"76","author":"Bindlish","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1109\/TGRS.2003.813351","article-title":"Herbaceous biomass retrieval in habitats of complex composition: A model merging SAR images with unmixed Landsat TM data","volume":"41","author":"Svoray","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1109\/LGRS.2010.2099641","article-title":"Soil moisture estimation using high-resolution spotlight TerraSAR-X data","volume":"8","author":"Kseneman","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3885","DOI":"10.1109\/TGRS.2012.2228486","article-title":"Soil moisture estimation from X-band data using Tikhonov regularization and neural net","volume":"51","author":"Kseneman","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2802","DOI":"10.1109\/TGRS.2014.2364914","article-title":"A modified water-cloud model with leaf angle parameters for microwave backscattering from agricultural fields","volume":"53","author":"Kweon","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2015.2442999","article-title":"A Bayesian network-based method to alleviate the ill-posed inverse problem: A case study on leaf area index and canopy water content retrieval","volume":"53","author":"Quan","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bai, X., He, B., Li, X., Zeng, J., Wang, X., Wang, Z., Zeng, Y., and Su, Z. (2017). First assessment of sentinel-1a data for surface soil moisture estimations using a coupled water cloud model and advanced integral equation model over the Tibetan plateau. Remote Sens., 9.","DOI":"10.3390\/rs9070714"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.pce.2015.02.009","article-title":"Surface soil moisture retrievals from remote sensing: Current status, products & future trends","volume":"83","author":"Petropoulos","year":"2015","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"3867","DOI":"10.1109\/JSTARS.2014.2315999","article-title":"Assimilation of SMOS soil moisture for quantifying drought impacts on crop yield in agricultural regions","volume":"7","author":"Chakrabarti","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","first-page":"390","article-title":"The sustainable evaluations, the development potentialities and the countermeasures of water and land resources use in the Huang-Huai-Hai plain","volume":"5","author":"Wu","year":"2001","journal-title":"Sci. Geogr. Sin."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4422","DOI":"10.1109\/JSTARS.2014.2316012","article-title":"Application of crop model data assimilation with a particle filter for estimating regional winter wheat yields","volume":"7","author":"Jiang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","first-page":"482","article-title":"Modeling winter wheat leaf area index and canopy water content with three different approaches using Sentinel-2 multispectral instrument data","volume":"22","author":"Pan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","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_31","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_32","unstructured":"Louis, J., Debaecker, V., Pflug, B., and Main-Knorn, M. (2016, January 9\u201313). Sentinel-2 Sen2Cor: L2A processor for users. Proceedings of the Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1191\/0309133303pp378ra","article-title":"Extracting biophysical parameters from remotely sensed radar data: a review of the water cloud model","volume":"27","author":"Graham","year":"2003","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3401","DOI":"10.1080\/01431169608949158","article-title":"Monitoring leaf area of sugar beet using ERS-1 SAR data","volume":"17","author":"Xu","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","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":"2010","journal-title":"Soil Use Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/S1161-0301(02)00106-5","article-title":"On approaches and applications of the Wageningen crop models","volume":"18","author":"Leffelaar","year":"2003","journal-title":"Eur. J. Agron."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.fcr.2017.06.004","article-title":"Integrating WOFOST and Noah LSM for modeling maize production and soil moisture with sensitivity analysis, in the east of The Netherlands","volume":"210","author":"Eweys","year":"2017","journal-title":"Field Crop. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.2136\/sssaj2005.0117","article-title":"Soil water characteristic estimates by texture and organic matter for hydrologic solutions","volume":"70","author":"Saxton","year":"2006","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"10143","DOI":"10.1029\/94JC00572","article-title":"Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics","volume":"99","author":"Evensen","year":"1994","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1175\/1520-0493(1998)126<1719:ASITEK>2.0.CO;2","article-title":"Analysis scheme in the ensemble Kalman filter","volume":"126","author":"Burgers","year":"1998","journal-title":"Mon. Weather. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10236-003-0036-9","article-title":"The ensemble Kalman filter: Theoretical formulation and practical implementation","volume":"53","author":"Evensen","year":"2003","journal-title":"Ocean Dyn."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Evensen, G. (2009). Data Assimilation: The Ensemble Kalman Filter, Springer.","DOI":"10.1007\/978-3-642-03711-5"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1080\/00031305.2016.1141709","article-title":"Understanding the ensemble Kalman filter","volume":"70","author":"Katzfuss","year":"2016","journal-title":"Am. Stat."},{"key":"ref_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","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_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2016.2541169","article-title":"Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield","volume":"9","author":"Betbeder","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"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":"297","DOI":"10.1051\/agro:2003003","article-title":"Assimilating optical and radar data into the STICS crop model for wheat","volume":"23","author":"Chauki","year":"2003","journal-title":"Agronomie"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.ecolmodel.2014.07.013","article-title":"Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions","volume":"291","author":"Li","year":"2014","journal-title":"Ecol. Model."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/JSTARS.2009.2037163","article-title":"Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring","volume":"3","author":"Bolten","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1109\/TGRS.1989.1398243","article-title":"Multitemporal and dual-polarization observations of agricultural vegetation covers by X-band SAR images","volume":"27","author":"Laur","year":"1989","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1016\/j.rse.2007.07.008","article-title":"An evaluation of the nonlinear\/non-Gaussian filters for the sequential data assimilation","volume":"112","author":"Han","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1002\/qj.443","article-title":"Covariance localisation and balance in an ensemble Kalman filter","volume":"135","author":"Kepert","year":"2009","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.ecolmodel.2003.08.012","article-title":"Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions","volume":"171","author":"Eitzinger","year":"2004","journal-title":"Ecol. Model."},{"key":"ref_55","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_56","first-page":"1","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1618\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:03:36Z","timestamp":1760187816000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1618"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,8]]},"references-count":56,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11131618"],"URL":"https:\/\/doi.org\/10.3390\/rs11131618","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,8]]}}}