{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:21:12Z","timestamp":1760242872492,"version":"build-2065373602"},"reference-count":77,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2016,10,7]],"date-time":"2016-10-07T00:00:00Z","timestamp":1475798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The China Grains Administration Special Fund for Public Interest","award":["No. 201313009-2","No. 201413003-7"],"award-info":[{"award-number":["No. 201313009-2","No. 201413003-7"]}]},{"name":"The National High Technology Research and Development Program of China (863 program)","award":["No. 2012AA12A307"],"award-info":[{"award-number":["No. 2012AA12A307"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring crop areas and yields is crucial for food security and agriculture management across the world. In this paper, we mapped the biomass and yield of winter wheat using the new Project for On-Board Autonomy-Vegetation (PROBA-V) products in the North China Plain (NCP). First, the daily 100-m land surface reflectance was generated by fusing the PROBA-V 100-m and 300-m S1 products. Our results show that the blended data exhibited high correlations with the referenced data (0.71 \u2264 R2 \u2264 0.94 for the red band, 0.50 \u2264 R2 \u2264 0.95 for the near-infrared band, and  0.88 \u2264 R2 \u2264 0.97 for the shortwave infrared band). The time-series Normalized Difference Vegetation Index (NDVI) derived from the synthetic reflectance was then clustered for winter wheat identification. The overall classification accuracy was between 78% and 87%, with a kappa coefficient above 0.57, which was 10%\u201320% higher than the classification accuracy using the 300-m data. Finally, a light use efficiency model was employed to estimate the biomass and yield. The estimation results were closely related to the field-measured biomass and yield, with high R2 and low root mean square errors (RMSE) (0.864 \u2264 R2 \u2264 0.871 and 168 \u2264 RMSE \u2264 191 g\/m2 for biomass; and  0.631 \u2264 R2 \u2264 0.663 and 41.8 \u2264 RMSE \u2264 62.8 g\/m2 for yield). This paper shows the strong potential of using PROBA-V 100-m data to enhance the spatial resolution of PROBA-V 300-m data and because the proposed framework in this study was based only on the relatively high spatio-temporal resolution PROBA-V data and achieved favorable results, it provides a novel approach for crop areas and yields estimation utilizing the relatively new data set.<\/jats:p>","DOI":"10.3390\/rs8100824","type":"journal-article","created":{"date-parts":[[2016,10,10]],"date-time":"2016-10-10T10:35:19Z","timestamp":1476095719000},"page":"824","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products"],"prefix":"10.3390","volume":"8","author":[{"given":"Yang","family":"Zheng","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Zeng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5546-365X","authenticated-orcid":false,"given":"Bingfang","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1126\/science.1185383","article-title":"Food security: The challenge of feeding 9 billion people","volume":"327","author":"Godfray","year":"2010","journal-title":"Science"},{"key":"ref_3","unstructured":"FAO, IFAD, and WFP (2014). The State of Food Insecurity in the World 2014: Strengthening the Enabling Environment for Food Security and Nutrition, Food and Agriculture Organization of the United Nations (FAO)."},{"key":"ref_4","unstructured":"FAO Regional Office for Asia and the Pacific (2014). FAO statistical Yearbook 2014, Asia and the Pacific, Food and Agriculture, FAO Regional Office for Asia and the Pacific."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1007\/s11707-009-0012-x","article-title":"Estimation of winter wheat biomass based on remote sensing data at various spatial and spectral resolutions","volume":"3","author":"Bao","year":"2009","journal-title":"Front. Earth Sci. China"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13251","DOI":"10.3390\/rs71013251","article-title":"Combined multi-temporal optical and radar parameters for estimating LAI and biomass in winter wheat using HJ and RADARSAT-2 data","volume":"7","author":"Jin","year":"2015","journal-title":"Remote Sens."},{"key":"ref_7","first-page":"235","article-title":"Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops","volume":"34","author":"Kross","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1080\/10106049.2014.937467","article-title":"Winter wheat biomass estimation using high temporal and spatial resolution satellite data combined with a light use efficiency model","volume":"30","author":"Du","year":"2014","journal-title":"Geocarto Int."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5926","DOI":"10.3390\/rs5115926","article-title":"A production efficiency model-based method for satellite estimates of corn and soybean yields in the Midwestern US","volume":"5","author":"Xin","year":"2013","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1098\/rstb.1977.0140","article-title":"Climate and efficiency of crop production in Britain","volume":"281","author":"Monteith","year":"1977","journal-title":"Philos. Trans. R. Soc. Lond. B Biol. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/0034-4257(94)00066-V","article-title":"Global net primary production: Combining ecology and remote sensing","volume":"51","author":"Field","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"747","DOI":"10.2307\/2401901","article-title":"Solar radiation and productivity in tropical ecosystems","volume":"9","author":"Monteith","year":"1972","journal-title":"J. Appl. Ecol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.1016\/j.rse.2009.04.004","article-title":"Evaluation of earth observation based long term vegetation trends\u2014Intercomparing NDVI time series trend analysis consistency of sahel from AVHRR GIMMS, TERRA MODIS and SPOT VGT data","volume":"113","author":"Fensholt","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1080\/0143116031000115265","article-title":"Vegetation\/spot: An operational mission for the earth monitoring; presentation of new standard products","volume":"25","author":"Maisongrande","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","unstructured":"Wolters, E., Dierckx, W., and Swinnen, E. (2015). PROBA-V Products User Manual v1.3, European Space Agency (ESA)."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13843","DOI":"10.3390\/rs71013843","article-title":"Single- and multi-date crop identification using PROBA-V 100 and 300 m S1 products on Zlatia test site, Bulgaria","volume":"7","author":"Roumenina","year":"2015","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"232","DOI":"10.3390\/rs8030232","article-title":"Cropland mapping over Sahelian and Sudanian agrosystems: A knowledge-based approach using PROBA-V time series at 100-m","volume":"8","author":"Defourny","year":"2016","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1109\/TGRS.2015.2466438","article-title":"Evaluating NDVI data continuity between SPOT-VEGETATION and PROBA-V missions for operational yield forecasting in North African countries","volume":"54","author":"Michele","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.rse.2011.10.014","article-title":"Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology","volume":"117","author":"Walker","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2495","DOI":"10.1016\/j.rse.2007.11.012","article-title":"A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin","volume":"112","author":"Hansen","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11518","DOI":"10.3390\/rs61111518","article-title":"Land cover classification of Landsat data with phenological features extracted from time series MODIS NDVI data","volume":"6","author":"Jia","year":"2014","journal-title":"Remote Sens."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1080\/17538947.2011.623189","article-title":"Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation","volume":"6","author":"Meng","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2589","DOI":"10.1080\/01431161.2014.883097","article-title":"PROBA-V mission for global vegetation monitoring: Standard products and image quality","volume":"35","author":"Dierckx","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1080\/01431161.2014.883094","article-title":"The PROBA-V mission: Image processing and calibration","volume":"35","author":"Sterckx","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2548","DOI":"10.1080\/01431161.2014.883098","article-title":"The PROBA-V mission: The space segment","volume":"35","author":"Francois","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","unstructured":"The VITO Product Distribution Portal (PDF). Available online: http:\/\/www.vito-eodata.be\/PDF\/portal\/Application.html#Home."},{"key":"ref_29","unstructured":"SPIRITS Institute for Environment and Sustainability. Available online: http:\/\/spirits.jrc.ec.europa.eu\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.envsoft.2013.10.021","article-title":"Image time series processing for agriculture monitoring","volume":"53","author":"Eerens","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"46","DOI":"10.3389\/fenvs.2015.00046","article-title":"Remote sensing time series analysis for crop monitoring with the SPIRITS software: New functionalities and use examples","volume":"3","author":"Rembold","year":"2015","journal-title":"Front. Environ. Sci."},{"key":"ref_32","unstructured":"China Meteorological Data Sharing Service System. Available online: http:\/\/data.cma.cn."},{"key":"ref_33","unstructured":"Richard, G.A., Luis, S.P., Dirk, R., and Martin, S. (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements, Food and Agriculture Organization of the United Nations (FAO). Irrigation and drainage paper."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2011.06.023","article-title":"Enhancing temporal resolution of satellite imagery for public health studies: A case study of West Nile Virus outbreak in Los Angeles in 2007","volume":"117","author":"Liu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3237","DOI":"10.1080\/01431161.2014.903351","article-title":"Blending MODIS and Landsat images for urban flood mapping","volume":"35","author":"Zhang","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Knauer, K., Gessner, U., Fensholt, R., and Kuenzer, C. (2016). An ESTARFM fusion framework for the generation of large-scale time series in cloud-prone and heterogeneous landscapes. Remote Sens., 8.","DOI":"10.3390\/rs8050425"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huang, C., Chen, Y., Zhang, S., Li, L., Shi, K., and Liu, R. (2016). Surface water mapping from Suomi NPP-VIIRS imagery at 30 m resolution via blending with Landsat data. Remote Sens., 8.","DOI":"10.3390\/rs8080631"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"15244","DOI":"10.3390\/rs71115244","article-title":"Classification of C3 and C4 vegetation types using MODIS and ETM+ blended high spatio-temporal resolution data","volume":"7","author":"Liu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2003.11.008","article-title":"Satellite-based modeling of gross primary production in an evergreen needleleaf forest","volume":"89","author":"Xiao","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2004.08.015","article-title":"Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest","volume":"94","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_41","unstructured":"Rouse, J.W., and Haas, R.H. (1973, January 10\u201314). Monitoring vegetation systems in the great plains with erts. Proceedings of the Third Earth Resources Technology Satellite Symposium, Washington, DC, USA."},{"key":"ref_42","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_43","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_44","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/01431160701250390","article-title":"The use of high-resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco","volume":"29","author":"Simonneaux","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/S0167-8809(02)00021-X","article-title":"Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties","volume":"94","author":"Lobell","year":"2003","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.fcr.2007.06.007","article-title":"A simple method to estimate harvest index in grain crops","volume":"103","author":"Kemanian","year":"2007","journal-title":"Field Crops Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1080\/01621459.1993.10476299","article-title":"Linear model selection by cross-validation","volume":"88","author":"Shao","year":"1993","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6510","DOI":"10.3390\/rs70606510","article-title":"Using RapidEye and MODIS data fusion to monitor vegetation dynamics in semi-arid rangelands in South Africa","volume":"7","author":"Tewes","year":"2015","journal-title":"Remote Sens."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"7610","DOI":"10.3390\/rs6087610","article-title":"The potential of time series merged from Landsat-5 TM and HJ-1 CCD for crop classification: A case study for bole and manas counties in Xinjiang, China","volume":"6","author":"Hao","year":"2014","journal-title":"Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3633","DOI":"10.3390\/rs70403633","article-title":"A hidden Markov models approach for crop classification: Linking crop phenology to time series of multi-sensor remote sensing data","volume":"7","author":"Siachalou","year":"2015","journal-title":"Remote Sens."},{"key":"ref_53","first-page":"S32","article-title":"Potentiality of optical and radar satellite data at high spatio-temporal resolutions for the monitoring of irrigated wheat crops in Morocco","volume":"12","author":"Hadria","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_54","first-page":"63","article-title":"Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data","volume":"49","author":"Dong","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2571","DOI":"10.1109\/JSTARS.2014.2330352","article-title":"Unmixing-based fusion of hyperspatial and hyperspectral airborne imagery for early detection of vegetation stress","volume":"7","author":"Stephanie","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.agwat.2007.11.010","article-title":"Effects of irrigation and planting patterns on radiation use efficiency and yield of winter wheat in North China","volume":"95","author":"Li","year":"2008","journal-title":"Agric. Water Manag."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1093\/aob\/mcg094","article-title":"Estimating photosynthetic radiation use efficiency using incident light and photosynthesis of individual leaves","volume":"91","author":"Rosati","year":"2003","journal-title":"Ann. Bot."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/S0065-2113(08)60914-1","article-title":"Radiation use efficiency","volume":"65","author":"Sinclair","year":"1999","journal-title":"Adv. Agron."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S0378-4290(03)00156-4","article-title":"Interception of photosynthetically active radiation and radiation-use efficiency of wheat, field pea and mustard in a semi-arid environment","volume":"85","author":"Whitfield","year":"2004","journal-title":"Field Crops Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0378-4290(89)90023-3","article-title":"Radiation-use efficiency in biomass accumulation prior to grain-filling for 5 grain-crop species","volume":"20","author":"Kiniry","year":"1989","journal-title":"Field Crops Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1016\/j.envsoft.2007.10.003","article-title":"A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index","volume":"23","author":"Duchemin","year":"2008","journal-title":"Environ. Model. Softw."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/0168-1923(88)90016-0","article-title":"Interception and use efficiency of light in winter-wheat under different nitrogen regimes","volume":"44","author":"Garcia","year":"1988","journal-title":"Agric. For. Meteorol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.ecolmodel.2004.08.023","article-title":"Remote sensing of crop production in China by production efficiency models: Models comparisons, estimates and uncertainties","volume":"183","author":"Tao","year":"2005","journal-title":"Ecol. Model."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5263","DOI":"10.1029\/93JD03221","article-title":"Methodology for the estimation of terrestrial net primary production from remotely sensed data","volume":"99","author":"Ruimy","year":"1994","journal-title":"J. Geophys. Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.fcr.2009.07.008","article-title":"Yield potential and radiation use efficiency of \u201csuper\u201d hybrid rice grown under subtropical conditions","volume":"114","author":"Zhang","year":"2009","journal-title":"Field Crops Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.agrformet.2013.09.006","article-title":"Modelling paddy rice yield using MODIS data","volume":"184","author":"Peng","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1080\/01431161.2012.755276","article-title":"Validation of LAI and assessment of winter wheat status using spectral data and vegetation indices from SPOT VEGETATION and simulated PROBA-V images","volume":"34","author":"Roumenina","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.agrformet.2014.01.006","article-title":"Impacts of light use efficiency and FPAR parameterization on gross primary production modeling","volume":"189\u2013190","author":"Cheng","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_71","unstructured":"The European System for Monitoring the Earth. Available online: http:\/\/www.copernicus.eu\/."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"3097","DOI":"10.1080\/01431161.2015.1042122","article-title":"Modified vegetation indices for estimating crop fraction of absorbed photosynthetically active radiation","volume":"36","author":"Dong","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.rse.2011.10.021","article-title":"Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content","volume":"117","author":"Peng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Vi\u00f1a, A., and Gitelson, A. (2005). New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL023647"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1016\/j.rse.2009.12.009","article-title":"Comparison of four global FPAR datasets over Northern Eurasia for the year 2000","volume":"114","author":"McCallum","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/S0022-1694(96)03128-9","article-title":"Generating surfaces of daily meteorological variables over large regions of complex terrain","volume":"190","author":"Thornton","year":"1997","journal-title":"J. Hydrol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1016\/j.rse.2007.07.021","article-title":"Mapping incident photosynthetically active radiation from MODIS data over China","volume":"112","author":"Liu","year":"2008","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/10\/824\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:32:29Z","timestamp":1760211149000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/10\/824"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,10,7]]},"references-count":77,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2016,10]]}},"alternative-id":["rs8100824"],"URL":"https:\/\/doi.org\/10.3390\/rs8100824","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2016,10,7]]}}}