{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T20:27:00Z","timestamp":1771273620370,"version":"3.50.1"},"reference-count":81,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,12]],"date-time":"2018-03-12T00:00:00Z","timestamp":1520812800000},"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 an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach.<\/jats:p>","DOI":"10.3390\/rs10030447","type":"journal-article","created":{"date-parts":[[2018,3,12]],"date-time":"2018-03-12T13:13:48Z","timestamp":1520860428000},"page":"447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":137,"title":["Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1865-1050","authenticated-orcid":false,"given":"Seonyoung","family":"Park","sequence":"first","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4506-6877","authenticated-orcid":false,"given":"Jungho","family":"Im","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}]},{"given":"Seohui","family":"Park","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3922-2300","authenticated-orcid":false,"given":"Cheolhee","family":"Yoo","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0414-519X","authenticated-orcid":false,"given":"Hyangsun","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Polar Remote Sensing, Korea Polar Research Institute, Incheon 21990, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5111-601X","authenticated-orcid":false,"given":"Jinyoung","family":"Rhee","sequence":"additional","affiliation":[{"name":"Climate Research Department, APEC Climate Center, Busan 48058, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4090","DOI":"10.3390\/rs6054090","article-title":"Monitoring changes in rice cultivated area from SAR and optical satellite images in Ben Tre and Tra Vinh Provinces in Mekong Delta, Vietnam","volume":"6","author":"Karila","year":"2014","journal-title":"Remote Sens."},{"key":"ref_2","first-page":"1","article-title":"Rice and climate change: Significance for food security and vulnerability","volume":"14","author":"Mohanty","year":"2013","journal-title":"Int. Rice Res. Inst."},{"key":"ref_3","first-page":"250","article-title":"The future of rice production, consumption and seaborne trade: Synthetic prediction method","volume":"36","author":"Purevdorj","year":"2005","journal-title":"J. Food Distrib. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1111\/nyas.12540","article-title":"An overview of global rice production, supply, trade, and consumption","volume":"1324","author":"Muthayya","year":"2014","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"769","DOI":"10.3390\/s150100769","article-title":"Application of remote sensors in mapping rice area and forecasting its production: A review","volume":"15","author":"Mosleh","year":"2015","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yuanshu, J., Gen, L., Jianjun, C., and Yiwen, S. (2013, January 26\u201328). Determination of paddy rice growth indicators with MODIS data and ground-based measurements of LAI. Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013), Nanjing, China.","DOI":"10.2991\/rsete.2013.102"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.rse.2004.12.018","article-title":"Ganges and Indus river basin land use\/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data","volume":"95","author":"Thenkabail","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s11707-015-0518-3","article-title":"Mapping paddy rice distribution using multi-temporal Landsat imagery in the Sanjiang Plain, northeast China","volume":"10","author":"Jin","year":"2016","journal-title":"Front. Earth Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1080\/15481603.2013.817150","article-title":"Object-oriented crop classification using multitemporal ETM+ SLC-off imagery and random forest","volume":"50","author":"Long","year":"2013","journal-title":"GISci. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"053547","DOI":"10.1117\/1.3619838","article-title":"Mapping rice areas of South Asia using MODIS multitemporal data","volume":"5","author":"Gumma","year":"2011","journal-title":"J. Appl. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5402","DOI":"10.1080\/01431161.2012.661091","article-title":"Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia","volume":"33","author":"Nuarsa","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","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_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.isprsjprs.2016.05.010","article-title":"Evolution of regional to global paddy rice mapping methods: A review","volume":"119","author":"Dong","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Clauss, K., Yan, H., and Kuenzer, C. (2016). Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8050434"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.rse.2015.01.004","article-title":"Tracking the dynamics of paddy rice planting area in 1986\u20132010 through time series Landsat images and phenology-based algorithms","volume":"160","author":"Dong","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2011.07.020","article-title":"Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data","volume":"117","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/01431161.2012.700133","article-title":"Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach","volume":"34","author":"Niu","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2532","DOI":"10.1080\/01431161.2011.616552","article-title":"Comparison of multisource image fusion methods and land cover classification","volume":"33","author":"Amarsaikhan","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1080\/15481603.2013.778555","article-title":"Classification of California agriculture using quad polarization radar data and Landsat Thematic Mapper data","volume":"50","author":"Sheoran","year":"2013","journal-title":"GISci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3812","DOI":"10.1109\/JSTARS.2014.2387214","article-title":"Capability of rice mapping using hybrid polarimetric SAR data","volume":"8","author":"Xie","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5279","DOI":"10.3390\/rs6065279","article-title":"Mapping land management regimes in western Ukraine using optical and SAR data","volume":"6","author":"Stefanski","year":"2014","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Torbick, N., Chowdhury, D., Salas, W., and Qi, J. (2017). Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2. Remote Sens., 9.","DOI":"10.3390\/rs9020119"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"10088","DOI":"10.1038\/srep10088","article-title":"Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images","volume":"5","author":"Wang","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2014.11.001","article-title":"Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa","volume":"101","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/10106049.2016.1240719","article-title":"Detection and mapping of bracken fern weeds using multispectral remotely sensed data: A review of progress and challenges","volume":"33","author":"Matongera","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2011.08.026","article-title":"The next Landsat satellite: The Landsat data continuity mission","volume":"122","author":"Irons","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_32","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_33","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_34","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/S0034-4257(00)00212-1","article-title":"Rice monitoring and production estimation using multitemporal RADARSAT","volume":"76","author":"Shao","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1109\/LGRS.2013.2261049","article-title":"Rice-planted area mapping using small sets of multi-temporal SAR data","volume":"10","author":"Miyaoka","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3915","DOI":"10.1109\/TGRS.2009.2023909","article-title":"PALSAR radiometric and geometric calibration","volume":"47","author":"Shimada","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TPAMI.1980.4766994","article-title":"Digital image enhancement and noise filtering by use of local statistics","volume":"2","author":"Lee","year":"1980","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1109\/LGRS.2013.2251453","article-title":"An object-based approach for urban land cover classification: Integrating LiDAR height and intensity data","volume":"10","author":"Zhou","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"14876","DOI":"10.3390\/rs71114876","article-title":"Mapping CORINE land cover from Sentinel-1A SAR and SRTM digital elevation model data using Random Forests","volume":"7","author":"Balzter","year":"2015","journal-title":"Remote Sens."},{"key":"ref_41","first-page":"1201","article-title":"The 2009 Cropland Data Layer","volume":"76","author":"Johnson","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.isprsjprs.2012.03.005","article-title":"Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment","volume":"69","author":"Naidoo","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10021-005-0054-1","article-title":"Newer classification and regression tree techniques: Bagging and random forests for ecological prediction","volume":"9","author":"Prasad","year":"2006","journal-title":"Ecosystems"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1109\/JSTARS.2012.2228167","article-title":"A pixel-based Landsat compositing algorithm for large area land cover mapping","volume":"6","author":"Griffiths","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","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":"GISci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.rse.2005.10.014","article-title":"Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest)","volume":"100","author":"Lawrence","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.isprsjprs.2009.01.003","article-title":"Classifier ensembles for land cover mapping using multitemporal SAR imagery","volume":"64","author":"Waske","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1080\/15481603.2017.1302181","article-title":"A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia, Canada)","volume":"54","author":"Richardson","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1080\/15481603.2017.1331510","article-title":"Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration","volume":"54","author":"Amani","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1080\/15481603.2016.1250328","article-title":"Mining parameter information for building extraction and change detection with very high-resolution imagery and GIS data","volume":"54","author":"Guo","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1007\/s12665-016-5917-6","article-title":"Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches","volume":"75","author":"Im","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.agrformet.2015.10.011","article-title":"Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions","volume":"216","author":"Park","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2004.06.017","article-title":"Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification","volume":"93","author":"Foody","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1080\/01431160512331314083","article-title":"Support vector machines for classification in remote sensing","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.isprsjprs.2015.10.010","article-title":"Semi-supervised SVM for individual tree crown species classification","volume":"110","author":"Dalponte","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2017.02.014","article-title":"Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data","volume":"126","author":"Yang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1080\/15481603.2017.1338389","article-title":"Building block level urban land-use information retrieval based on Google Street View images","volume":"54","author":"Li","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1080\/15481603.2016.1148229","article-title":"Channel bar feature extraction for a mining-contaminated river using high-spatial multispectral remote-sensing imagery","volume":"53","author":"Wang","year":"2016","journal-title":"GISci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1080\/15481603.2016.1177249","article-title":"Integration of full-waveform LiDAR and hyperspectral data to enhance tea and areca classification","volume":"53","author":"Chu","year":"2016","journal-title":"GISci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1109\/TGRS.2004.842022","article-title":"Partially supervised classification of remote sensing images through SVM-based probability density estimation","volume":"43","author":"Mantero","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1080\/01431160110040323","article-title":"An assessment of support vector machines for land cover classification","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","first-page":"352","article-title":"A kernel functions analysis for support vector machines for land cover classification","volume":"11","author":"Kavzoglu","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Joachims, T. (2002). Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms, Kluwer Academic Publishers.","DOI":"10.1007\/978-1-4615-0907-3"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2012.04.001","article-title":"Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points","volume":"70","author":"Shao","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic map comparison","volume":"70","author":"Foody","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.rse.2010.10.005","article-title":"An artificial immune network approach to multi-sensor land use\/land cover classification","volume":"115","author":"Gong","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M.R., Kuemmerle, T., Meyfroidt, P., and Mitchard, E.T. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens., 8.","DOI":"10.3390\/rs8010070"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ecoinf.2012.03.003","article-title":"Support vector machines to map rare and endangered native plants in Pacific islands forests","volume":"9","author":"Pouteau","year":"2012","journal-title":"Ecol. Inf."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1080\/22797254.2017.1299557","article-title":"Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images","volume":"50","author":"Raczko","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.1080\/0143116031000114851","article-title":"The use of backpropagating artificial neural networks in land cover classification","volume":"24","author":"Kavzoglu","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.rse.2006.04.001","article-title":"The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM","volume":"103","author":"Foody","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2014.1001666","article-title":"Sensitivity of vegetation indices to spatial degradation of RapidEye imagery for paddy rice detection: A case study of South Korea","volume":"52","author":"Kim","year":"2015","journal-title":"GISci. Remote Sens."},{"key":"ref_75","first-page":"151","article-title":"Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor","volume":"19","author":"Ramoelo","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1080\/15481603.2017.1291783","article-title":"Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery","volume":"54","author":"Kim","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_77","unstructured":"Bouman, B. (2001). ORYZA2000: Modeling Lowland Rice, IRRI."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature03972","article-title":"Europe-wide reduction in primary productivity caused by the heat and drought in 2003","volume":"437","author":"Ciais","year":"2005","journal-title":"Nature"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Onojeghuo, A., Blackburn, G., Wang, Q., Atkinson, P., Kindred, D., and Miao, Y. (2018). Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series. GISci. Remote Sens., in press.","DOI":"10.1080\/15481603.2018.1423725"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/15481603.2016.1276255","article-title":"Crop classification and acreage estimation in North Korea using phenology features","volume":"54","author":"Zhang","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1080\/15481603.2016.1273438","article-title":"Efficient paddy field mapping using Landsat-8 imagery and object-based image analysis based on advanced fractel net evolution approach","volume":"54","author":"Su","year":"2017","journal-title":"GISci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/3\/447\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:56:46Z","timestamp":1760194606000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/3\/447"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,12]]},"references-count":81,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,3]]}},"alternative-id":["rs10030447"],"URL":"https:\/\/doi.org\/10.3390\/rs10030447","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,12]]}}}