{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:44:06Z","timestamp":1771047846205,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2016,6,7]],"date-time":"2016-06-07T00:00:00Z","timestamp":1465257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rice is the staple food for half of the world\u2019s population. Therefore, accurate information of rice area is vital for food security. This study investigates the effect of phenology for rice mapping using an object-based image analysis (OBIA) approach. Crop phenology is combined with high spatial resolution multispectral data to accurately classify the rice. Phenology was used to capture the seasonal dynamics of the crops, while multispectral data provided the spatial variation patterns. Phenology was extracted from MODIS NDVI time series, and the distribution of rice was mapped from China\u2019s Environmental Satellite (HJ-1A\/B) data. Classification results were evaluated by a confusion matrix using 100 sample points. The overall accuracy of the resulting map of rice area generated by both spectral and phenology is 93%. The results indicate that the use of phenology improved the overall classification accuracy from 2%\u20134%. The comparison between the estimated rice areas and the State\u2019s statistics shows underestimated values with a percentage difference of \u221234.53%. The results highlight the potential of the combined use of crop phenology and multispectral satellite data for accurate rice classification in a large area.<\/jats:p>","DOI":"10.3390\/rs8060479","type":"journal-article","created":{"date-parts":[[2016,6,7]],"date-time":"2016-06-07T11:17:28Z","timestamp":1465298248000},"page":"479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India"],"prefix":"10.3390","volume":"8","author":[{"given":"Mrinal","family":"Singha","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Division for Digital Agriculture, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, West Beichen Road, Chaoyang District, 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":"Division for Digital Agriculture, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, West Beichen Road, Chaoyang District, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Division for Digital Agriculture, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, West Beichen Road, Chaoyang District, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,6,7]]},"reference":[{"key":"ref_1","unstructured":"Maclean, J., Hardy, B., and Hettel, G. (2013). Rice Almanac: Source Book for One of the Most Important Economic Activities on Earth, IRRI."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/S0065-2113(04)92004-4","article-title":"Rice and water","volume":"92","author":"Bouman","year":"2007","journal-title":"Adv. Agron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1080\/01431161.2012.738946","article-title":"Remote sensing of rice crop areas","volume":"34","author":"Kuenzer","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","unstructured":"Nguyen, N.V. (2008). Global Climate Changes and Rice Food Security, FAO."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1016\/j.mcm.2010.11.033","article-title":"Mapping rice planting areas in southern China using the China environment satellite data","volume":"54","author":"Chen","year":"2011","journal-title":"Math. Comput. Model."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1080\/014311698216134","article-title":"Using NOAA AVHRR and Landsat TM to estimate rice area year-by-year","volume":"19","author":"Fang","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.isprsjprs.2014.02.007","article-title":"Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010","volume":"91","author":"Gumma","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.jenvman.2013.11.039","article-title":"Remote sensing based change analysis of rice environments in Odisha, India","volume":"148","author":"Gumma","year":"2015","journal-title":"J. Environ. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/36.551933","article-title":"Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results","volume":"35","author":"Ribbes","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1109\/TGRS.2014.2330377","article-title":"Paddy-rice monitoring using TanDEM-X","volume":"53","author":"Rossi","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3390\/rs6010135","article-title":"A phenology-based classification of time-series MODIS data for rice crop monitoring in Mekong Delta, Vietnam","volume":"6","author":"Son","year":"2013","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1080\/014311698216404","article-title":"Classification of multi-temporal SPOT-XS satellite data for mapping rice fields on a West African floodplain","volume":"19","author":"Turner","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, J., Xiao, X., Qin, Y., Dong, J., Zhang, G., Kou, W., Jin, C., Zhou, Y., and Zhang, Y. (2015). Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images. Sci. Rep., 5.","DOI":"10.1038\/srep10088"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3467","DOI":"10.3390\/rs70403467","article-title":"Rice fields mapping in fragmented area using multi-temporal HJ-1A\/B CCD images","volume":"7","author":"Wang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_16","unstructured":"Lam-Dao, N. (2009). Rice Crop Monitoring Using New Generation Synthetic Aperture Radar (SAR) Imagery. [Ph.D. Thesis, University of Southern Queensland]."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2005.10.004","article-title":"Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images","volume":"100","author":"Xiao","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gumma, M.K., Nelson, A., Thenkabail, P.S., and Singh, A.N. (2011). Mapping rice areas of South Asia using MODIS multitemporal data. J. Appl. Remote Sens., 5.","DOI":"10.1117\/1.3619838"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.agee.2011.10.016","article-title":"Estimation of Southeast Asian rice paddy areas with different ecosystems from moderate-resolution satellite imagery","volume":"146","author":"Bridhikitti","year":"2012","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1080\/13658810802587709","article-title":"An enhanced supervised spatial decision support system of image classification: Consideration on the ancillary information of paddy rice area","volume":"24","author":"Wan","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.agee.2012.09.005","article-title":"Delineating rice cropping activities from MODIS data using wavelet transform and artificial neural networks in the Lower Mekong countries","volume":"162","author":"Chen","year":"2012","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ecolind.2015.03.039","article-title":"Mapping paddy rice areas based on vegetation phenology and surface moisture conditions","volume":"56","author":"Qiu","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","first-page":"7046","article-title":"Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data","volume":"35","author":"Kim","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"221","DOI":"10.5558\/tfc84221-2","article-title":"Towards automated segmentation of forest inventory polygons on high spatial resolution satellite imagery","volume":"84","author":"Wulder","year":"2008","journal-title":"For. Chron."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3220","DOI":"10.1016\/j.rse.2011.07.006","article-title":"Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China","volume":"115","author":"Dronova","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"558","DOI":"10.3390\/rs5020558","article-title":"An object-based approach for mapping shrub and tree cover on grassland habitats by use of LiDAR and CIR orthoimages","volume":"5","author":"Hellesen","year":"2013","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"117","DOI":"10.5721\/EuJRS20144708","article-title":"Forest mapping through object-based image analysis of multispectral and LiDAR aerial data","volume":"47","author":"Machala","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.landusepol.2012.08.005","article-title":"Impact of land fragmentation, farm size, land ownership and crop diversity on profit and efficiency of irrigated farms in India","volume":"31","author":"Manjunatha","year":"2013","journal-title":"Land Use Policy"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Schmidt, M., Udelhoven, T., Gill, T., and R\u00f6der, A. (2012). Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian Savanna. J. Appl. Remote Sens., 6.","DOI":"10.1117\/1.JRS.6.063512"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1078\/1433-8319-00053","article-title":"The phenology of growth and reproduction in plants","volume":"1","author":"Fenner","year":"1998","journal-title":"Perspect. Plant Ecol. Evol. Syst."},{"key":"ref_33","first-page":"230","article-title":"Combined use of multi-seasonal high and medium resolution satellite imagery for parcel-related mapping of cropland and grassland","volume":"28","author":"Esch","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7777","DOI":"10.1080\/01431161.2010.527397","article-title":"A phenology-based approach to map crop types in the San Joaquin Valley, California","volume":"32","author":"Zhong","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","unstructured":"Ahmed, T., Chetia, S.K., Chowdhury, R., and Ali, S. (2011). Status Paper on Rice in Assam: Rice Knowledge Management Portal, Regional Agricultural Research Station."},{"key":"ref_36","unstructured":"Data Pool | LP DAAC: NASA Land Data Products and Services, Available online: https:\/\/lpdaac.usgs.gov\/data_access\/data_pool."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jia, K., Wu, B., and Li, Q. (2013). Crop classification using HJ satellite multispectral data in the North China Plain. J. Appl. Remote Sens., 7.","DOI":"10.1117\/1.JRS.7.073576"},{"key":"ref_38","unstructured":"China Resources Satellite Application Center. Available online: http:\/\/cresda.com.cn\/EN\/."},{"key":"ref_39","unstructured":"Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH) (Using ENVI) | Exelis VIS Docs Center. Available online: http:\/\/www.harrisgeospatial.com\/docs\/FLAASH.html."},{"key":"ref_40","first-page":"570","article-title":"GVG, a crop type proportion sampling instrument","volume":"8","author":"Wu","year":"2004","journal-title":"J. Remote Sens."},{"key":"ref_41","first-page":"101","article-title":"Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes","volume":"16","author":"Wu","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","unstructured":"Directorate of Economics and Statistics, Assam. Available online: http:\/\/ecostatassam.nic.in\/."},{"key":"ref_43","unstructured":"Welcome to Bhuvan | ISRO\u2019s Geoportal | Gateway to Indian Earth Observation, Available online: http:\/\/bhuvan.nrsc.gov.in\/bhuvan_links.php."},{"key":"ref_44","unstructured":"Welcome to the QGIS Project!. Available online: http:\/\/qgis.org\/en\/site\/."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"9213","DOI":"10.3390\/rs6109213","article-title":"Blending Landsat and MODIS data to generate multispectral indices: A comparison of \u201cIndex-then-Blend\u201d and \u201cBlend-then-Index\u201d approaches","volume":"6","author":"Jarihani","year":"2014","journal-title":"Remote Sens."},{"key":"ref_47","unstructured":"Definiens, A.G. (2009). Definiens eCognition Developer 8 User Guide, Definiens AG."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.rse.2012.04.011","article-title":"Object based image analysis and data mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas","volume":"123","author":"Vieira","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_49","unstructured":"Baatz, M., and Sch\u00e4pe, A. (, January January). Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation. Proceedings of the Angewandte Geographische Informationsverarbeitung XII, Heidelberg, Germany."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1016\/0031-3203(95)00169-7","article-title":"A survey on evaluation methods for image segmentation","volume":"29","author":"Zhang","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.cageo.2004.05.006","article-title":"TIMESAT\u2014A program for analyzing time-series of satellite sensor data","volume":"30","author":"Eklundh","year":"2004","journal-title":"Comput. Geosci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality extraction by function fitting to time-series of satellite sensor data","volume":"40","author":"Jonsson","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"703","DOI":"10.2307\/3235884","article-title":"Measuring phenological variability from satellite imagery","volume":"5","author":"Reed","year":"1994","journal-title":"J. Veg. Sci."},{"key":"ref_54","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. Atmos."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.","DOI":"10.1201\/9781420055139"},{"key":"ref_56","unstructured":"Tso, B., and Mather, P.M. (2009). Classification Methods for Remotely Sensed Data, CRC. [2nd ed.]."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.rse.2013.05.004","article-title":"Characterisation of land surface phenology and land cover based on moderate resolution satellite data in cloud prone areas\u2014A novel product for the Mekong Basin","volume":"136","author":"Leinenkugel","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_58","unstructured":"Directorate of Economics and Statistics, and Govt. of India (2015). Agricultural Statistics at a Glance 2014."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"4255","DOI":"10.3390\/rs5094255","article-title":"Mapping and evaluation of NDVI trends from synthetic time series obtained by blending Landsat and MODIS data around a coalfield on the Loess Plateau","volume":"5","author":"Tian","year":"2013","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2014.04.004","article-title":"Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data","volume":"93","author":"Jia","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","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_62","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_63","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/01431161.2010.532826","article-title":"Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis","volume":"33","author":"Nguyen","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1080\/01431160902894442","article-title":"Discriminating different landuse types by using multitemporal NDXI in a rice planting area","volume":"31","author":"Pan","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0273-1177(01)00345-3","article-title":"Comparison of SAR and optical sensor data for monitoring of rice plant around Hiroshima","volume":"28","author":"Oguro","year":"2001","journal-title":"Adv. Space Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"6301","DOI":"10.1080\/01431160902842391","article-title":"Mapping paddy rice with multitemporal ALOS\/PALSAR imagery in southeast China","volume":"30","author":"Zhang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"50","DOI":"10.3390\/rs1020050","article-title":"Irrigated area maps and statistics of India using remote sensing and national statistics","volume":"1","author":"Thenkabail","year":"2009","journal-title":"Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.isprsjprs.2015.04.008","article-title":"Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery","volume":"105","author":"Qin","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.isprsjprs.2015.05.011","article-title":"Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data","volume":"106","author":"Zhang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"8858","DOI":"10.3390\/rs70708858","article-title":"Mapping Flooded rice paddies using time series of MODIS imagery in the Krishna River Basin, India","volume":"7","author":"Teluguntla","year":"2015","journal-title":"Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Dong, J., Xiao, X., Menarguez, M.A., Zhang, G., Qin, Y., Thau, D., Biradar, C., and Moore, B. (2016). Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2016.02.016"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"3855","DOI":"10.1080\/01431160010006926","article-title":"Cloud cover in Landsat observations of the Brazilian Amazon","volume":"22","author":"Asner","year":"2001","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/6\/479\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:25:09Z","timestamp":1760210709000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/6\/479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,7]]},"references-count":72,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2016,6]]}},"alternative-id":["rs8060479"],"URL":"https:\/\/doi.org\/10.3390\/rs8060479","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,6,7]]}}}