{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T17:02:51Z","timestamp":1775840571156,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of China","award":["Y8J0390059"],"award-info":[{"award-number":["Y8J0390059"]}]},{"name":"the National Key Research and Development 477 Project of China","award":["019YFE0126900"],"award-info":[{"award-number":["019YFE0126900"]}]},{"name":"the GEF Integrated Water Resources and Water Environment Management Exten- 479 sion (Mainstreaming) Project","award":["MWR-C-3-11"],"award-info":[{"award-number":["MWR-C-3-11"]}]},{"name":"the Natural Science Foundation of Qinghai Prov- 480 ince","award":["2020-ZJ-927"],"award-info":[{"award-number":["2020-ZJ-927"]}]},{"name":"CAS &quot;Light of West China&quot; Program","award":["1 _5"],"award-info":[{"award-number":["1 _5"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop type classification is critical for crop production estimation and optimal water allocation. Crop type data are challenging to generate if crop reference data are lacking, especially for target years with reference data missed in collection. Is it possible to transfer a trained crop type classification model to retrace the historical spatial distribution of crop types? Taking the Hetao Irrigation District (HID) in China as the study area, this study first designed a 10 m crop type classification framework based on the Google Earth Engine (GEE) for crop type mapping in the current season. Then, its interannual transferability to accurately retrace historical crop distributions was tested. The framework used Sentinel-1\/2 data as the satellite data source, combined percentile, and monthly composite approaches to generate classification metrics and employed a random forest classifier with 300 trees for crop classification. Based on the proposed framework, this study first developed a 10 m crop type map of the HID for 2020 with an overall accuracy (OA) of 0.89 and then obtained a 10 m crop type map of the HID for 2019 with an OA of 0.92 by transferring the trained model for 2020 without crop reference samples. The results indicated that the designed framework could effectively identify HID crop types and have good transferability to obtain historical crop type data with acceptable accuracy. Our results found that SWIR1, Green, and Red Edge2 were the top three reflectance bands for crop classification. The land surface water index (LSWI), normalized difference water index (NDWI), and enhanced vegetation index (EVI) were the top three vegetation indices for crop classification. April to August was the most suitable time window for crop type classification in the HID. Sentinel-1 information played a positive role in the interannual transfer of the trained model, increasing the OA from 90.73% with Sentinel 2 alone to 91.58% with Sentinel-1 and Sentinel-2 together.<\/jats:p>","DOI":"10.3390\/rs14051208","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T21:25:14Z","timestamp":1646169914000},"page":"1208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China"],"prefix":"10.3390","volume":"14","author":[{"given":"Yueran","family":"Hu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9558-7304","authenticated-orcid":false,"given":"Hongwei","family":"Zeng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1758-8763","authenticated-orcid":false,"given":"Fuyou","family":"Tian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Miao","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"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, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Sven","family":"Gilliams","sequence":"additional","affiliation":[{"name":"Vlaamse Instelling voor Technologisch Onderzoek (VITO), Boeretang 200, 2400 Mol, Belgium"}]},{"given":"Sen","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Yuanchao","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8418-5193","authenticated-orcid":false,"given":"Yuming","family":"Lu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Honghai","family":"Yang","sequence":"additional","affiliation":[{"name":"Big Data Center of Geospatial and Nature Resources of Qinghai Province, Xining 810001, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,1]]},"reference":[{"key":"ref_1","unstructured":"UN (2017). Transforming Our World: The 2030 Agenda for Sustainable Development. A New Era in Global Health, Springer Publishing Company."},{"key":"ref_2","unstructured":"FAO, IFAD, UNICEF, WFP, and WHO (2021). The State of Food Security and Nutrition in the World 2021. Transforming Food Systems for Food Security, Improved Nutrition and Affordable Healthy Diets for All, FAO."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"3829","DOI":"10.5194\/hess-19-3829-2015","article-title":"GlobWat\u2014A global water balance model to assess water use in irrigated agriculture","volume":"19","author":"Hoogeveen","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8805","DOI":"10.1038\/s41598-017-08952-5","article-title":"The contribution of human agricultural activities to increasing evapotranspiration is significantly greater than climate change effect over Heihe agricultural region","volume":"7","author":"Zou","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1016\/j.jclepro.2018.12.284","article-title":"A trade-off method between environment restoration and human water consumption: A case study in Ebinur Lake","volume":"217","author":"Zeng","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1111\/jiec.12447","article-title":"Crop Planting Structure Optimization for Water Scarcity Alleviation in China","volume":"20","author":"Zhang","year":"2016","journal-title":"J. Ind. Ecol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2992","DOI":"10.1002\/jsfa.6645","article-title":"Impacts of changing cropping pattern on virtual water flows related to crops transfer: A case study for the Hetao irrigation district, China","volume":"94","author":"Liu","year":"2014","journal-title":"J. Sci. Food Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Teluguntla, P.G., Xiong, J., Oliphant, A., Congalton, R.G., Ozdogan, M., Gumma, M.K., Tilton, J.C., Giri, C., and Milesi, C. (2021). Global Cropland-Extent Product at 30-m Resolution (GCEP30) Derived from Landsat Satellite Time-Series Data for the Year 2015 Using Multiple Machine-Learning Algorithms on Google Earth Engine Cloud.","DOI":"10.3133\/pp1868"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"d\u2019Andrimont, R., Verhegghen, A., Lemoine, G., Kempeneers, P., Meroni, M., and van der Velde, M. (2021). From parcel to continental scale\u2014A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations. Remote Sens. Environ., 266.","DOI":"10.1016\/j.rse.2021.112708"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Van Tricht, K., Gobin, A., Gilliams, S., and Piccard, I. (2018). Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium. Remote Sens., 10.","DOI":"10.20944\/preprints201808.0066.v1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41597-021-00827-9","article-title":"The 10-m crop type maps in Northeast China during 2017\u20132019","volume":"8","author":"You","year":"2021","journal-title":"Sci. Data"},{"key":"ref_16","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_17","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_18","doi-asserted-by":"crossref","unstructured":"Yang, G., Yu, W., Yao, X., Zheng, H., Cao, Q., Zhu, Y., Cao, W., and Cheng, T. (2021). AGTOC: A novel approach to winter wheat mapping by automatic generation of training samples and one-class classification on Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf., 102.","DOI":"10.1016\/j.jag.2021.102446"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tian, F., Wu, B., Zeng, H., Zhang, X., and Xu, J. (2019). Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform. Remote Sens., 11.","DOI":"10.3390\/rs11060629"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.rse.2018.06.036","article-title":"Estimating smallholder crops production at village level from Sentinel-2 time series in Mali\u2019s cotton belt","volume":"216","author":"Lambert","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.rse.2018.11.007","article-title":"Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world","volume":"221","author":"Defourny","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2021.08.021","article-title":"A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery","volume":"181","author":"Xun","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kussul, N., Kolotii, A., Shelestov, A., Lavrenyuk, M., Bellemans, N., Bontemps, S., Defourny, P., Koetz, B., and Symposium, R.S. (2017, January 23\u201328). Sentinel-2 for agriculture national demonstration in ukraine: Results and further steps. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128337"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Moumni, A., Sebbar, B.E., Simonneaux, V., Ezzahar, J., and Lahrouni, A. (2020, January 9\u201311). Evaluation of Sen2agri System over Semi-Arid Conditions: A Case Study of The Haouz Plain in Central Morocco. Proceedings of the 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia.","DOI":"10.1109\/M2GARSS47143.2020.9105233"},{"key":"ref_28","unstructured":"Cintas, R.J., Franch, B., Becker-Reshef, I., Skakun, S., Sobrino, J.A., van Tricht, K., Degerickx, J., and Gilliams, S. (2021, January 11\u201316). Generating Winter Wheat Global Crop Calendars in the Framework of Worldcereal. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.isprsjprs.2020.01.001","article-title":"Examining earliest identifiable timing of crops using all available Sentinel 1\/2 imagery and Google Earth Engine","volume":"161","author":"You","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","first-page":"102692","article-title":"10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product","volume":"107","author":"Tran","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wu, B., Ponce-Campos, G.E., Zhang, M., Chang, S., and Tian, F. (2018). Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images. Remote Sens., 10.","DOI":"10.3390\/rs10081200"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.rse.2013.08.014","article-title":"Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)","volume":"140","author":"Hansen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Dell\u2019Acqua, F., Iannelli, G.C., Torres, M.A., and Martina, M.L.V. (2018). A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data. Sensors, 18.","DOI":"10.3390\/s18020591"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gallego, J., and Delinc\u00e9, J. (2010). The European land use and cover area-frame statistical survey. Agric. Surv. Methods, 149\u2013168.","DOI":"10.1002\/9780470665480.ch10"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.geosus.2020.03.006","article-title":"Cloud services with big data provide a solution for monitoring and tracking sustainable development goals","volume":"1","author":"Bingfang","year":"2020","journal-title":"Geogr. Sustain."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.envsoft.2011.11.015","article-title":"Geo-Wiki: An online platform for improving global land cover","volume":"31","author":"Fritz","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"345","DOI":"10.3390\/rs1030345","article-title":"Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover","volume":"1","author":"Fritz","year":"2009","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Laso Bayas, J.C., Lesiv, M., Waldner, F., Schucknecht, A., Duerauer, M., See, L., Fritz, S., Fraisl, D., Moorthy, I., and McCallum, I. (2017). A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform. Sci. Data, 4.","DOI":"10.1038\/sdata.2017.136"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yu, B., and Shang, S. (2017). Multi-Year Mapping of Maize and Sunflower in Hetao Irrigation District of China with High Spatial and Temporal Resolution Vegetation Index Series. Remote Sens., 9.","DOI":"10.3390\/rs9080855"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"107612","DOI":"10.1016\/j.agrformet.2019.06.011","article-title":"Mapping daily evapotranspiration over a large irrigation district from MODIS data using a novel hybrid dual-source coupling model","volume":"276\u2013277","author":"Yu","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.agwat.2010.08.025","article-title":"Assessing the groundwater dynamics and impacts of water saving in the Hetao Irrigation District, Yellow River basin","volume":"98","author":"Xu","year":"2010","journal-title":"Agric. Water Manag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1016\/j.envpol.2019.04.119","article-title":"Occurrence and spatial variation of antibiotic resistance genes (ARGs) in the Hetao Irrigation District, China","volume":"251","author":"Shi","year":"2019","journal-title":"Environ. Pollut."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.compag.2016.03.008","article-title":"Mapping interannual variability of maize cover in a large irrigation district using a vegetation index\u2014Phenological index classifier","volume":"123","author":"Jiang","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wen, Y., Shang, S., and Rahman, K.U. (2019). Pre-Constrained Machine Learning Method for Multi-Year Mapping of Three Major Crops in a Large Irrigation District. Remote Sens., 11.","DOI":"10.3390\/rs11030242"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.jaridenv.2015.03.009","article-title":"Inter-county virtual water flows of the Hetao irrigation district, China: A new perspective for water scarcity","volume":"119","author":"Liu","year":"2015","journal-title":"J. Arid. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"106670","DOI":"10.1016\/j.agwat.2020.106670","article-title":"Optimal irrigation water allocation in Hetao Irrigation District considering decision makers\u2019 preference under uncertainties","volume":"246","author":"Zhang","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Nie, W.-B., Dong, S.-X., Li, Y.-B., and Ma, X.-Y. (2021). Optimization of the border size on the irrigation district scale\u2014Example of the Hetao irrigation district. Agric. Water Manag., 248.","DOI":"10.1016\/j.agwat.2021.106768"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2019.04.016","article-title":"Smallholder maize area and yield mapping at national scales with Google Earth Engine","volume":"228","author":"Jin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_51","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_52","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 Sens. Environ."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"L08403","DOI":"10.1029\/2005GL022688","article-title":"Remote estimation of canopy chlorophyll content in crops","volume":"32","author":"Gitelson","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.rse.2017.12.001","article-title":"A sub-pixel method for estimating planting fraction of paddy rice in Northeast China","volume":"205","author":"Liu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.compag.2018.07.039","article-title":"Mapping spatiotemporal dynamics of maize in China from 2005 to 2017 through designing leaf moisture based indicator from Normalized Multi-band Drought Index","volume":"153","author":"Qiu","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_59","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/S0146-664X(81)80018-4","article-title":"Refined filtering of image noise using local statistics","volume":"15","author":"Lee","year":"1981","journal-title":"Comput. Graph. Image Processing"},{"key":"ref_61","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_62","doi-asserted-by":"crossref","first-page":"112576","DOI":"10.1016\/j.rse.2021.112576","article-title":"Pre- and within-season crop type classification trained with archival land cover information","volume":"264","author":"Johnson","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/s11769-020-1119-y","article-title":"A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa","volume":"30","author":"Zeng","year":"2020","journal-title":"Chin. Geogr. Sci."},{"key":"ref_64","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_65","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.5194\/essd-12-1217-2020","article-title":"Annual dynamics of global land cover and its long-term changes from 1982 to 2015","volume":"12","author":"Liu","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_66","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_67","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2018.12.026","article-title":"Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques","volume":"222","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.patcog.2009.05.010","article-title":"Out-of-bag estimation of the optimal sample size in bagging","volume":"43","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0034-4257(97)00083-7","article-title":"Selecting and interpreting measures of thematic classification accuracy","volume":"62","author":"Stehman","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1868","DOI":"10.1080\/17538947.2021.1980125","article-title":"Deep neural network ensembles for remote sensing land cover and land use classification","volume":"14","author":"Ekim","year":"2021","journal-title":"Int. J. Digit. Earth"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2695","DOI":"10.1109\/TGRS.2011.2176740","article-title":"Rice Phenology Monitoring by Means of SAR Polarimetry at X-Band","volume":"50","author":"Cloude","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/S2095-3119(18)62016-7","article-title":"Research advances of SAR remote sensing for agriculture applications: A review","volume":"18","author":"Liu","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s12145-020-00531-z","article-title":"Object-based crop classification in Hetao plain using random forest","volume":"14","author":"Su","year":"2021","journal-title":"Earth Sci. Inform."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"6805","DOI":"10.1080\/01431161.2018.1466076","article-title":"Characterizing the spatiotemporal evolution of soil salinization in Hetao Irrigation District (China) using a remote sensing approach","volume":"39","author":"Guo","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"6472","DOI":"10.3390\/rs6076472","article-title":"Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa","volume":"6","author":"Forkuor","year":"2014","journal-title":"Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1109\/36.297984","article-title":"Detection of forests using mid-IR reflectance: An application for aerosol studies","volume":"32","author":"Kaufman","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.isprsjprs.2020.09.009","article-title":"Mapping plastic materials in an urban area: Development of the normalized difference plastic index using WorldView-3 superspectral data","volume":"169","author":"Guo","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.isprsjprs.2018.07.017","article-title":"A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","volume":"144","author":"Teluguntla","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1208\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:30:13Z","timestamp":1760135413000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,1]]},"references-count":78,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051208"],"URL":"https:\/\/doi.org\/10.3390\/rs14051208","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,1]]}}}