{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:15:24Z","timestamp":1768781724967,"version":"3.49.0"},"reference-count":62,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,10]],"date-time":"2019-01-10T00:00:00Z","timestamp":1547078400000},"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>Numerous studies have reported the use of multi-spectral and multi-temporal remote sensing images to map irrigated crops. Such maps are useful for water management. The recent availability of optical and radar image time series such as the Sentinel data offers new opportunities to map land cover with high spatial and temporal resolutions. Early identification of irrigated crops is of major importance for irrigation scheduling, but the cloud coverage might significantly reduce the number of available optical images, making crop identification difficult. SAR image time series such as those provided by Sentinel-1 offer the possibility of improving early crop mapping. This paper studies the impact of the Sentinel-1 images when used jointly with optical imagery (Landsat8) and a digital elevation model of the Shuttle Radar Topography Mission (SRTM). The study site is located in a temperate zone (southwest France) with irrigated maize crops. The classifier used is the Random Forest. The combined use of the different data (radar, optical, and SRTM) improves the early classifications of the irrigated crops (k = 0.89) compared to classifications obtained using each type of data separately (k = 0.84). The use of the DEM is significant for the early stages but becomes useless once crops have reached their full development. In conclusion, compared to a \u201cfull optical\u201d approach, the \u201ccombined\u201d method is more robust over time as radar images permit cloudy conditions to be overcome.<\/jats:p>","DOI":"10.3390\/rs11020118","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T04:10:16Z","timestamp":1547179816000},"page":"118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series"],"prefix":"10.3390","volume":"11","author":[{"given":"Val\u00e9rie","family":"Demarez","sequence":"first","affiliation":[{"name":"Centre d\u2019Etudes Spatiales de la Biosph\u00e8re; UMR 5126, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France"}]},{"given":"Florian","family":"Helen","sequence":"additional","affiliation":[{"name":"Airbus-Defense &amp; Space, 31 rue des Cosmonautes, 31400 Toulouse, France"}]},{"given":"Claire","family":"Marais-Sicre","sequence":"additional","affiliation":[{"name":"Centre d\u2019Etudes Spatiales de la Biosph\u00e8re; UMR 5126, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France"}]},{"given":"Fr\u00e9d\u00e9ric","family":"Baup","sequence":"additional","affiliation":[{"name":"Centre d\u2019Etudes Spatiales de la Biosph\u00e8re; UMR 5126, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Loubier, S., Campardon, M., and Morardet, S. (2013). L\u2019irrigation diminue-t-elle en France? Premiers enseignements du recensement agricole de 2010. Sciences Eaux & Territoires, IRSTEA.","DOI":"10.3917\/set.011.0012"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/S0378-3774(00)00080-9","article-title":"Remote sensing for irrigated agriculture: Examples from research and possible applications","volume":"46","author":"Bastiaanssen","year":"2000","journal-title":"Agric. Water Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.3390\/rs2092274","article-title":"Remote Sensing of Irrigated Agriculture: Opportunities and Challenges","volume":"2","author":"Ozdogan","year":"2010","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Goetz, S.J., Varlyguin, D., Smith, A.J., Wright, R.K., Prince, S.D., Mazzacato, M.E., Tringe, J., Jantz, C., and Melchoir, B. (2004). Application of multitemporal Landsat data to map and monitor land cover and land use change in the Chesapeake Bay watershed. Analysis of Multi-temporal Remote Sensing Images, World Scientific.","DOI":"10.1142\/9789812702630_0025"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2747\/1548-1603.43.1.1","article-title":"Regional scale land-cover characterization using MODIS-NDVI 250 m multi-temporal imagery: A phenology based approach","volume":"43","author":"Knight","year":"2006","journal-title":"Gisci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.14358\/PERS.75.12.1383","article-title":"Influence of Resolution in Irrigated Area Mapping and Area Estimations","volume":"75","author":"Velpuri","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.isprsjprs.2009.08.004","article-title":"Irrigated areas of India derived using MODIS 500 m time series for the years 2001\u20132003","volume":"65","author":"Dheeravath","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1016\/j.rse.2010.12.014","article-title":"Use of ENVISAT\/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta","volume":"115","author":"Bouvet","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_10","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_11","first-page":"103","article-title":"A support vector machine to identify irrigated crop types using time-series Landsat NDVI data","volume":"34","author":"Zheng","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2017.05.024","article-title":"Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification","volume":"197","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_13","first-page":"94","article-title":"Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system","volume":"11","author":"Ouzemou","year":"2018","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_14","unstructured":"(2018, July 30). Product User Guide, CCI LC PUGv2. Available online: http:\/\/maps.elie.ucl.ac.be\/CCI\/viewer\/download\/ESACCI-LC-Ph2-PUGv2_2.0.pdf."},{"key":"ref_15","first-page":"9","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ. Sentin. Mission. New Oppor. Sci."},{"key":"ref_16","first-page":"25","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. Sentin. Mission. New Oppor. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3520","DOI":"10.1016\/j.rse.2008.04.010","article-title":"A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US","volume":"112","author":"Ozdogan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_18","first-page":"711","article-title":"Land use classification from multitemporal Landsat imagery using the Yearly Land Cover Dynamics (YLCD) method","volume":"13","author":"Julien","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1080\/01431161.2011.602651","article-title":"Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data","volume":"33","author":"Miao","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Valero, S., Morin, D., Inglada, J., Sepulcre, G., Arias, M., Hagolle, O., Dedieu, G., Bontemps, S., and Defourny, P. (2015, January 26\u201330). Processing Sentinel-2 image time series for developing a real-time cropland mask. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326378"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"12356","DOI":"10.3390\/rs70912356","article-title":"Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery","volume":"7","author":"Inglada","year":"2015","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Valero, S., Morin, D., Inglada, J., Sepulcre, G., Arias, M., Hagolle, O., Dedieu, G., Bontemps, S., Defourny, P., and Koetz, B. (2016). Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions. Remote Sens., 8.","DOI":"10.3390\/rs8010055"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., Atzberger, C., Immitzer, M., Vuolo, F., and Atzberger, E.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_24","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.compag.2016.07.019","article-title":"Crop type mapping in a highly fragmented and heterogeneous agricultural landscape: A case of central Iran using multi-temporal Landsat 8 imagery","volume":"127","author":"Asgarian","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2016.10.010","article-title":"Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas","volume":"187","author":"Pelletier","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pelletier, C., Valero, S., Inglada, J., Champion, N., Marais Sicre, C., Dedieu, G., Pelletier, C., Valero, S., Inglada, J., and Champion, N. (2017). Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9020173"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Moody, D.I., Brumby, S.P., Chartrand, R., Keisler, R., Longbotham, N., Mertes, C., Skillman, S.W., and Warren, M.S. (2017, January 9\u201313). Crop classification using temporal stacks of multispectral satellite imagery. Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, Anaheim, CA, USA.","DOI":"10.1117\/12.2262804"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"026019","DOI":"10.1117\/1.JRS.12.026019","article-title":"Crop classification from Sentinel-2-derived vegetation indices using ensemble learning","volume":"12","author":"Sonobe","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","first-page":"122","article-title":"How much does multi-temporal Sentinel-2 data improve crop type classification?","volume":"72","author":"Vuolo","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., and Marais-Sicre, C. (2016). Improved Early Crop Type Identification by Joint Use of High Temporal Resolution SAR and Optical Image Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8050362"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1080\/15481603.2017.1351149","article-title":"Assessing the suitability of data from Sentinel-1A and 2A for crop classification","volume":"54","author":"Sonobe","year":"2017","journal-title":"Gisci. Remote Sens."},{"key":"ref_33","first-page":"226","article-title":"Field-based rice classification in Wuhua county through integration of multi-temporal Sentinel-1A and Landsat-8 OLI data","volume":"69","author":"Yang","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.envsoft.2018.01.023","article-title":"A new synergistic approach for monitoring wetlands using Sentinels-1 and 2 data with object-based machine learning algorithms","volume":"104","author":"Whyte","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"3495","DOI":"10.1080\/01431161003749485","article-title":"Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security, determined using MODIS 250 m time-series data and spectral matching techniques, in the Krishna River basin (India)","volume":"32","author":"Gumma","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3972","DOI":"10.3390\/rs4123972","article-title":"Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series","volume":"4","author":"Edlinger","year":"2012","journal-title":"Remote Sens."},{"key":"ref_38","first-page":"321","article-title":"Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data","volume":"38","author":"Salmon","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"160118","DOI":"10.1038\/sdata.2016.118","article-title":"Remotely sensed high resolution irrigated area mapping in India for 2000 to 2015","volume":"3","author":"Ambika","year":"2016","journal-title":"Sci. Data"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.rse.2014.04.008","article-title":"Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI","volume":"149","author":"Pervez","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2014.08.016","article-title":"Dynamic identification of summer cropping irrigated areas in a large basin experiencing extreme climatic variability","volume":"154","author":"McVicar","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.apgeog.2017.06.016","article-title":"Mapping and assessing crop diversity in the irrigated Fergana Valley, Uzbekistan","volume":"86","author":"Conrad","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ferrant, S., Selles, A., Le Page, M., Herrault, P.-A., Pelletier, C., Al-Bitar, A., Mermoz, S., Gascoin, S., Bouvet, A., and Saqalli, M. (2017). Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India. Remote Sens., 9.","DOI":"10.3390\/rs9111119"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"421","DOI":"10.5721\/EuJRS20124535","article-title":"Evaluation of random forest method for agricultural crop classification","volume":"45","author":"Ok","year":"2012","journal-title":"Eur. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"623","DOI":"10.2747\/1548-1603.49.5.623","article-title":"An evaluation of bagging, boosting, and Random Forests for land-cover classification in Cape Cod, Massachusetts, USA","volume":"49","author":"Ghimire","year":"2012","journal-title":"Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hao, P., Wang, L., and Niu, Z. (2015). Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0137748"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Song, Q., Hu, Q., Zhou, Q., Hovis, C., Xiang, M., Tang, H., Wu, W., Song, Q., Hu, Q., and Zhou, Q. (2017). In-Season Crop Mapping with GF-1\/WFV Data by Combining Object-Based Image Analysis and Random Forest. Remote Sens., 9.","DOI":"10.3390\/rs9111184"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M., Baghdadi, N., Segui, P., Gao, Q., Zribi, M., Escorihuela, M.J., Baghdadi, N., and Segui, P.Q. (2018). Irrigation Mapping Using Sentinel-1 Time Series at Field Scale. Remote Sens., 10.","DOI":"10.3390\/rs10091495"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.agwat.2017.04.018","article-title":"Modeling water needs and total irrigation depths of maize crop in the south west of France using high spatial and temporal resolution satellite imagery","volume":"189","author":"Battude","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"12242","DOI":"10.3390\/rs70912242","article-title":"SPOT-4 (Take 5): Simulation of Sentinel-2 time series on 45 large sites","volume":"7","author":"Hagolle","year":"2015","journal-title":"Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"255636","DOI":"10.1117\/12.7973877","article-title":"Speckle Suppression and Analysis for Synthetic Aperture Radar Images","volume":"25","author":"Lee","year":"1986","journal-title":"Opt. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_59","unstructured":"Guyenne, T.D., and Hunt, J.J. (1988, January 18\u201322). The Normalisation of a Soil Brightness Index for the Study of Changes in Soil Conditions. Proceedings of the Spectral Signatures of Objects in Remote Sensing, Aussois, France. ESA SP-287."},{"key":"ref_60","first-page":"344","article-title":"The ASTER global DEM","volume":"76","author":"Abrams","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Marais Sicre, C., Inglada, J., Fieuzal, R., Baup, F., Valero, S., Cros, J., Huc, M., Demarez, V., Marais Sicre, C., and Inglada, J. (2016). Early Detection of Summer Crops Using High Spatial Resolution Optical Image Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8070591"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.rse.2017.06.022","article-title":"A new method for crop classification combining time series of radar images and crop phenology information","volume":"198","author":"Bargiel","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/118\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:24:52Z","timestamp":1760185492000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,10]]},"references-count":62,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["rs11020118"],"URL":"https:\/\/doi.org\/10.3390\/rs11020118","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,10]]}}}