{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:17:29Z","timestamp":1783570649352,"version":"3.55.0"},"reference-count":76,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,3,14]],"date-time":"2019-03-14T00:00:00Z","timestamp":1552521600000},"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>The distribution of corn cultivation areas is crucial for ensuring food security, eradicating hunger, adjusting crop structures, and managing water resources. The emergence of high-resolution images, such as Sentinel-1 and Sentinel-2, enables the identification of corn at the field scale, and these images can be applied on a large scale with the support of cloud computing technology. Hebei Province is the major production area of corn in China, and faces serious groundwater overexploitation due to irrigation. Corn was mapped using multitemporal synthetic aperture radar (SAR) and optical images in the Google Earth Engine (GEE) cloud platform. A total of 1712 scenes of Sentinel-2 data and 206 scenes of Sentinel-1 data acquired from June to October 2017 were processed to composite image metrics as input to a random forest (RF) classifier. To avoid speckle noise in the classification results, the pixel-based classification result was integrated with the object segmentation boundary completed in eCognition software to generate an object-based corn map according to crop intensity. The results indicated that the approach using multitemporal SAR and optical images in the GEE cloud platform is reliable for corn mapping. The corn map had a high F1-Score of 90.08% and overall accuracy of 89.89% according to the test dataset, which was not involved in model training. The corn area estimated from optical and SAR images was well correlated with the census data, with an R2 = 0.91 and a root mean square error (RMSE) of 470.90 km2. The results of the corn map are expected to provide detailed information for optimizing crop structure and water management, which are critical issues in this region.<\/jats:p>","DOI":"10.3390\/rs11060629","type":"journal-article","created":{"date-parts":[[2019,3,15]],"date-time":"2019-03-15T04:12:09Z","timestamp":1552623129000},"page":"629","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform"],"prefix":"10.3390","volume":"11","author":[{"given":"Fuyou","family":"Tian","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, W. Beichen Road, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5546-365X","authenticated-orcid":false,"given":"Bingfang","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, W. Beichen Road, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Zeng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, W. Beichen Road, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7844-593X","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, W. Beichen Road, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3273-9806","authenticated-orcid":false,"given":"Jiaming","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, W. 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Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/495305a","article-title":"Policy: Sustainable development goals for people and planet","volume":"495","author":"Griggs","year":"2013","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.jhydrol.2012.02.043","article-title":"Validation of ETWatch using field measurements at diverse landscapes: A case study in Hai basin of china","volume":"436\u2013437","author":"Wu","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.agwat.2015.02.003","article-title":"Assessing potential water savings in agriculture on the Hai basin plain, china","volume":"154","author":"Yan","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2014.04.023","article-title":"Improved maize cultivated area estimation over a large scale combining modis\u2013evi time series data and crop phenological information","volume":"94","author":"Zhang","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.agwat.2004.04.007","article-title":"Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize","volume":"69","author":"Zwart","year":"2004","journal-title":"Agric. Water Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.rse.2017.01.008","article-title":"National-scale soybean mapping and area estimation in the united states using medium resolution satellite imagery and field survey","volume":"190","author":"Song","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2016.11.004","article-title":"Toward mapping crop progress at field scales through fusion of landsat and MODIS imagery","volume":"188","author":"Gao","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","article-title":"Mapping paddy rice planting area in northeastern Asia with landsat 8 images, phenology-based algorithm and google earth engine","volume":"185","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2006.11.021","article-title":"Analysis of time-series Modis 250 m vegetation index data for crop classification in the U.S. Central great plains","volume":"108","author":"Wardlow","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.rse.2016.03.039","article-title":"A hybrid approach for detecting corn and soybean phenology with time-series MODIS data","volume":"181","author":"Zeng","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_15","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."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.rse.2011.01.009","article-title":"Object-based crop identification using multiple vegetation indices, textural features and crop phenology","volume":"115","author":"Ngugi","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Larra\u00f1aga, A., and \u00c1lvarez-Mozos, J. (2016). On the added value of Quad-Pol Data in a multi-temporal crop classification framework based on RADARSAT-2 imagery. Remote Sens., 8.","DOI":"10.3390\/rs8040335"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6505","DOI":"10.1109\/TGRS.2016.2585744","article-title":"A complete procedure for crop phenology estimation with PolSAR data based on the complex Wishart classifier","volume":"54","author":"Mascolo","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.rse.2007.07.019","article-title":"Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central great plains","volume":"112","author":"Wardlow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.rse.2015.03.028","article-title":"Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series","volume":"163","author":"Estel","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_21","first-page":"188","article-title":"Mapping crop phenology using NDVI time-series derived from HJ-1 A\/B data","volume":"34","author":"Pan","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"024015","DOI":"10.1088\/1748-9326\/11\/2\/024015","article-title":"Mapping cropland-use intensity across Europe using MODIS NDVI time series","volume":"11","author":"Estel","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"7847","DOI":"10.1080\/01431161.2010.531783","article-title":"Classification of modis evi time series for crop mapping in the state of Mato Grosso, brazil","volume":"32","author":"Arvor","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2017.04.026","article-title":"Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a gaussian mixture model","volume":"195","author":"Skakun","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/01431161.2015.1125556","article-title":"Investigating collection 4 versus collection 5 MODIS 250 m NDVI time-series data for crop separability in Kansas, USA","volume":"37","author":"Lee","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.rse.2017.06.033","article-title":"Modis phenology-derived, multi-year distribution of conterminous U.S. Crop types","volume":"198","author":"Massey","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"L\u00f6w, F., Prishchepov, A., Waldner, F., Dubovyk, O., Akramkhanov, A., Biradar, C., and Lamers, J. (2018). Mapping cropland abandonment in the Aral Sea Basin with MODIS time series. Remote Sens., 10.","DOI":"10.3390\/rs10020159"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4091","DOI":"10.1080\/01431160310001619580","article-title":"Efficiency and accuracy of per-field classification for operational crop mapping","volume":"25","author":"Clevers","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","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_31","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_32","doi-asserted-by":"crossref","first-page":"14482","DOI":"10.3390\/rs71114482","article-title":"Self-guided segmentation and classification of multi-temporal Landsat 8 images for crop type mapping in southeastern brazil","volume":"7","author":"Schultz","year":"2015","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2015.08.004","article-title":"Mapping rice paddy extent and intensification in the vietnamese mekong river delta with dense time stacks of Landsat data","volume":"169","author":"Kontgis","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"16062","DOI":"10.3390\/rs71215815","article-title":"Building a data set over 12 globally distributed sites to support the development of agriculture monitoring applications with sentinel-2","volume":"7","author":"Bontemps","year":"2015","journal-title":"Remote Sens."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"13208","DOI":"10.3390\/rs71013208","article-title":"An automated method for annual cropland mapping along the season for various globally-distributed agrosystems using high spatial and temporal resolution time series","volume":"7","author":"Matton","year":"2015","journal-title":"Remote Sens."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1109\/TGRS.2011.2172994","article-title":"Crop classification by multitemporal C- and L-band single- and dual-polarization and fully polarimetric SAR","volume":"50","author":"Skriver","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1080\/01431169408954210","article-title":"On the use of multi-frequency and polarimetric radar backscatter features for classification of agricultural crops","volume":"15","author":"Freeman","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Schmullius, C., and Schrage, T. (1998, January 6\u201310). Classification, Crop Parameter Estimation and Synergy Effects Using Airborne DLR E-SAR and DAEDALUS Images. Proceedings of the 1998 IEEE International Geoscience and Remote Sensing Symposium, IGARSS\u201998, Seattle, WA, USA.","DOI":"10.1109\/IGARSS.1998.702810"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.1109\/36.964970","article-title":"Quantitative comparison of classification capability: Fully polarimetric versus dual and single-polarization SAR","volume":"39","author":"Lee","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1109\/TGRS.2003.813530","article-title":"Crop classification using multiconfiguration C-band SAR data","volume":"41","author":"Schiavon","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1080\/01431161.2011.587844","article-title":"Crop classification using multi-configuration SAR data in the north china plain","volume":"33","author":"Jia","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1109\/TGRS.2009.2026052","article-title":"The contribution of ALOS PALSAR multipolarization and polarimetric data to crop classification","volume":"47","author":"McNairn","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google earth engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_47","first-page":"199","article-title":"Multitemporal settlement and population mapping from Landsat using google earth engine","volume":"35","author":"Patel","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2017.01.019","article-title":"Automated cropland mapping of continental Africa using google earth engine cloud computing","volume":"126","author":"Xiong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Goldblatt, R., You, W., Hanson, G., and Khandelwal, A. (2016). Detecting the boundaries of urban areas in india: A dataset for pixel-based image classification in google earth engine. Remote Sens., 8.","DOI":"10.3390\/rs8080634"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2017.05.025","article-title":"Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring","volume":"202","author":"Azzari","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in google earth engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_52","unstructured":"National Bureau of Statistics of China (2018, October 22). National Data, Available online: http:\/\/data.stats.gov.cn\/easyquery.htm?cn=E0103&zb=A0D0P&reg=130000&sj=2016."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2018.02.045","article-title":"A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach","volume":"210","author":"Cai","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sen. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S0034-4257(02)00051-2","article-title":"Characterization of forest types in Northeastern China, using multi-temporal spot-4 vegetation sensor data","volume":"82","author":"Xiao","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/0034-4257(92)90102-P","article-title":"Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme","volume":"42","author":"Sellers","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_58","unstructured":"Veci, L. (2016). Sentinel-1 Toolbox, Array Systems Computing Inc.. SAR Basics Tutoria."},{"key":"ref_59","unstructured":"RADI (2018, October 29). Gvg for Android. Available online: https:\/\/play.google.com\/store\/apps\/details?id=com.sysapk.gvg&hl=en_US."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Benediktsson, J.A., Swain, P.H., and Ersoy, O.K. (1990, January 6\u201310). Neural network approaches versus statistical methods in classification of multisource remote sensing data. Proceedings of the 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/TGRS.1990.572944"},{"key":"ref_61","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_62","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2011.08.027","article-title":"Quantifying forest cover loss in democratic republic of the Congo, 2000\u20132010, with Landsat ETM+ data","volume":"122","author":"Potapov","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_63","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_64","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_65","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2015.11.033","article-title":"A novel approach for quantifying particulate matter distribution on leaf surface by combining SEM and object-based image analysis","volume":"173","author":"Yan","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.rse.2016.07.028","article-title":"Land cover change detection by integrating object-based data blending model of Landsat and MODIS","volume":"184","author":"Lu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"Esp: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Tiede","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2013.11.018","article-title":"Automated parameterisation for multi-scale image segmentation on multiple layers","volume":"88","author":"Dragut","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_69","unstructured":"National Bureau of Statistics of China (2018, March 11). National Data, Available online: http:\/\/data.stats.gov.cn\/english\/easyquery.htm?cn=E0103."},{"key":"ref_70","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1016\/j.agrformet.2008.05.020","article-title":"Estimating crop water stress with ETM+ NIR and SWIR data","volume":"148","author":"Ghulam","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"You, J., Li, X., Low, M., Lobell, D., and Ermon, S. (2017, January 4\u20139). Deep gaussian process for crop yield prediction based on remote sensing data. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11172"},{"key":"ref_73","unstructured":"ESA (2018, December 11). First Sentinel-2B Images Delivered by Laser. Available online: https:\/\/www.esa.int\/Our_Activities\/Observing_the_Earth\/Copernicus\/Sentinel-2\/First_Sentinel-2B_images_delivered_by_laser."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.rse.2018.10.013","article-title":"Integrating cloud-based workflows in continental-scale cropland extent classification","volume":"219","author":"Massey","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Sundermeyer, M., Schl\u00fcter, R., and Ney, H. (2012, January 9\u201313). Lstm neural networks for language modeling. Proceedings of the Thirteenth Annual Conference of the International Speech Communication Association, Portland, OR, USA.","DOI":"10.21437\/Interspeech.2012-65"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"RuBwurm, M., and K\u00f6rner, M. (2017, January 21\u201326). Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satellite images. 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