{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:50:12Z","timestamp":1775069412038,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T00:00:00Z","timestamp":1678320000000},"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 a globally significant staple food crop. Therefore, it is crucial to have adequate tools for monitoring changes in the extent of rice paddy cultivation. Such a system would require a sustainable and operational workflow that employs open-source medium to high spatial and temporal resolution satellite imagery and efficient classification techniques. This study used similar phenological data from Sentinel-2 (S2) optical and Sentinel-1 (S1) Synthetic Aperture Radar (SAR) satellite imagery to identify paddy rice distribution with deep learning (DL) techniques. Using Google Earth Engine (GEE) and U-Net Convolutional Neural Networks (CNN) segmentation, a workflow that accurately delineates smallholder paddy rice fields using multi-temporal S1 SAR and S2 optical imagery was investigated. The study\u2032s accuracy assessment results showed that the optimal dataset for paddy rice mapping was a fusion of S2 multispectral bands (visible and near infra-red (VNIR), red edge (RE) and short-wave infrared (SWIR)), and S1-SAR dual polarization bands (VH and VV) captured within the crop growing season (i.e., vegetative, reproductive, and ripening). Compared to the random forest (RF) classification, the DL model (i.e., ResU-Net) had an overall accuracy of 94% (three percent higher than the RF prediction). The ResU-Net paddy rice prediction had an F1-Score of 0.92 compared to 0.84 for the RF classification generated using 500 trees in the model. Using the optimal U-Net classified paddy rice maps for the dates analyzed (i.e., 2016\u20132020), a change detection analysis over two epochs (2016 to 2018 and 2018 to 2020) provided a better understanding of the spatial\u2013temporal dynamics of paddy rice agriculture in the study area. The results indicated that 377,895 and 8551 hectares of paddy rice fields were converted to other land-use over the first (2016\u20132018) and second (2018\u20132020) epochs. These statistics provided valuable insight into the paddy rice field distribution changes across the selected districts analyzed. The proposed DL framework has the potential to be upscaled and transferred to other regions. The results indicated that the approach could accurately identify paddy rice fields locally, improve decision making, and support food security in the region.<\/jats:p>","DOI":"10.3390\/rs15061517","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T01:31:41Z","timestamp":1678411901000},"page":"1517","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Deep ResU-Net Convolutional Neural Networks Segmentation for Smallholder Paddy Rice Mapping Using Sentinel 1 SAR and Sentinel 2 Optical Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2944-3967","authenticated-orcid":false,"given":"Alex Okiemute","family":"Onojeghuo","sequence":"first","affiliation":[{"name":"Research and Development Unit, Jolexy Environmental Services Ltd., Edmonton, AB T5T7L1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8419-6511","authenticated-orcid":false,"given":"Yuxin","family":"Miao","sequence":"additional","affiliation":[{"name":"Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN 55108, USA"}]},{"given":"George Alan","family":"Blackburn","sequence":"additional","affiliation":[{"name":"Lancaster Environment Centre, Library Avenue, Bailrigg, Lancaster LA1 4YQ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"ref_1","unstructured":"FAOSTAT (2022, December 24). FAO Statistical Databases (Food and Agriculture Organization of the United Nations) Databases. Available online: http:\/\/digital.library.wisc.edu\/1711.web\/faostat."},{"key":"ref_2","unstructured":"Shahbandeh, M. (2020, July 09). Paddy Rice Production Worldwide 2017\u20132018, by Country. Statista. Available online: https:\/\/www.statista.com\/statistics\/255937\/leading-rice-producers-worldwide\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"S50","DOI":"10.1038\/514S50a","article-title":"Rice by the numbers: A good grain","volume":"514","author":"Elert","year":"2014","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/s41597-019-0036-3","article-title":"High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data","volume":"6","author":"Singha","year":"2019","journal-title":"Sci. Data"},{"key":"ref_5","first-page":"711","article-title":"Crop Yield Prediction Using Multi Sensors Remote Sensing","volume":"25","author":"Abdelraouf","year":"2022","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Vadrevu, K.P., Toan, T.L., Ray, S.S., and Justice, C. (2022). Remote Sensing of Agriculture and Land Cover\/Land Use Changes in South and Southeast Asian Countries, Springer International Publishing.","DOI":"10.1007\/978-3-030-92365-5"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.isprsjprs.2022.06.012","article-title":"Mapping corn dynamics using limited but representative samples with adaptive strategies","volume":"190","author":"Wen","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.worlddev.2015.10.041","article-title":"The Number, Size, and Distribution of Farms, Smallholder Farms, and Family Farms Worldwide","volume":"87","author":"Lowder","year":"2016","journal-title":"World Dev."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, L., Zhang, H., Wang, C., Wei, S., Zhang, B., Wu, F., and Tang, Y. (2021). Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model. Remote Sens., 13.","DOI":"10.3390\/rs13193994"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, A.-X., Zhao, F.-H., Pan, H.-B., and Liu, J.-Z. (2021). Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics. Remote Sens., 13.","DOI":"10.3390\/rs13071360"},{"key":"ref_11","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_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.09.013","article-title":"Big Remotely Sensed Data: Tools, applications and experiences","volume":"202","author":"Casu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gul\u00e1csi, A., and Kov\u00e1cs, F. (2020). Sentinel-1-imagery-based high-resolution water cover detection on wetlands, Aided by Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12101614"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.isprsjprs.2018.10.008","article-title":"An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support","volume":"146","author":"Hu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","first-page":"110","article-title":"Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine cloud","volume":"81","author":"Oliphant","year":"2019","journal-title":"Int. J.App. Earth Observ. Geoinf."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1080\/01431161.2017.1395969","article-title":"Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data","volume":"39","author":"Onojeghuo","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A comparison of vegetation indices over a global set of TM images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3987","DOI":"10.1080\/01431160802575653","article-title":"Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product","volume":"31","author":"Chandrasekar","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1080\/15481603.2018.1423725","article-title":"Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series","volume":"55","author":"Onojeghuo","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Boschetti, M., Nutini, F., Manfron, G., Brivio, P.A., and Nelson, A. (2014). Comparative Analysis of Normalised Difference Spectral Indices Derived from MODIS for Detecting Surface Water in Flooded Rice Cropping Systems. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0088741"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, R., Li, Y., and Ma, M. (2021). Mapping Paddy Rice with Satellite Remote Sensing: A Review. Sustainability, 13.","DOI":"10.3390\/su13020503"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112679","DOI":"10.1016\/j.rse.2021.112679","article-title":"Deep machine learning with Sentinel satellite data to map paddy rice production stages across West Java, Indonesia","volume":"265","author":"Thorp","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Sepp","year":"1997","journal-title":"Neural Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.isprsjprs.2021.06.018","article-title":"An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine","volume":"178","author":"Ni","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2022.10.005","article-title":"A full resolution deep learning network for paddy rice mapping using Landsat data","volume":"194","author":"Xia","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1007\/s41324-019-00246-4","article-title":"Paddy acreage mapping and yield prediction using sentinel-based optical and SAR data in Sahibganj district, Jharkhand (India)","volume":"27","author":"Ranjan","year":"2019","journal-title":"Spat. Inf. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.fcr.2013.09.023","article-title":"Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages","volume":"155","author":"Gnyp","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1111\/j.1744-7909.2007.00619.x","article-title":"Identification of Optimal Hyperspectral Bands for Estimation of Rice Biophysical Parameters","volume":"50","author":"Wang","year":"2008","journal-title":"J. Integr. Plant Biol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s11442-014-1082-6","article-title":"Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s","volume":"24","author":"Liu","year":"2014","journal-title":"J. Geogr. Sci."},{"key":"ref_36","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_37","unstructured":"ESA (2020, July 12). ESA Step\u2014Science Toolbox Exploitation Platform. Available online: http:\/\/step.esa.int\/main\/doc\/tutorials\/."},{"key":"ref_38","unstructured":"Kau, L.J., and Lee, T.L. (2013, January 13\u201316). An HSV model-based approach for the sharpening of color images. Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). Medical Image Computing and Computer-Assisted Intervention 2015, Springer International Publishing.","DOI":"10.1007\/978-3-319-24553-9"},{"key":"ref_40","unstructured":"ESRI (2022, December 29). How U-Net works?. Available online: https:\/\/developers.arcgis.com\/python\/guide\/how-unet-works\/?rsource=https%3A%2F%2Flinks.esri.com%2FDevHelp_HowUNetWorks."},{"key":"ref_41","first-page":"1","article-title":"Semantic Segmentation Based on Temporal Features: Learning of Temporal-Spatial Information from Time-Series SAR Images for Paddy Rice Mapping","volume":"60","author":"Yang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dang, K.B., Nguyen, M.H., Nguyen, D.A., Phan, T.T.H., Giang, T.L., Pham, H.H., Nguyen, T.N., Tran, T.T.V., and Bui, D.T. (2020). Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam. Remote Sens., 12.","DOI":"10.3390\/rs12193270"},{"key":"ref_43","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","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."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_48","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_49","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_50","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_51","doi-asserted-by":"crossref","unstructured":"Kussul, N., Skakun, S., Shelestov, A., and Kussul, O. (2014, January 13\u201318). The use of satellite SAR imagery to crop classification in Ukraine within JECAM project. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6946721"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Park, S., Im, J., Park, S., Yoo, C., Han, H., and Rhee, J. (2018). Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data. Remote Sens., 10.","DOI":"10.3390\/rs10030447"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1109\/TGRS.2008.2007963","article-title":"Monitoring of the Rice Cropping System in the Mekong Delta Using ENVISAT\/ASAR Dual Polarization Data","volume":"47","author":"Bouvet","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","unstructured":"Saadat, M., Seydi, S.T., Hasanlou, M., and Homayouni, S. (2022). A Convolutional Neural Network Method for Rice Mapping Using Time-Series of Sentinel-1 and Sentinel-2 Imagery. Agriculture, 12.","DOI":"10.3390\/agriculture12122083"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhai, P., Li, S., He, Z., Deng, Y., and Hu, Y. (2021, January 17\u201322). Collaborative mapping rice planting areas using multisource remote sensing data. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Kuala Kumpur, Malaysia.","DOI":"10.1109\/IGARSS47720.2021.9553245"},{"key":"ref_57","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_58","doi-asserted-by":"crossref","unstructured":"Du, M., Huang, J., Wei, P., Yang, L., Chai, D., Peng, D., Sha, J., Sun, W., and Huang, R. (2022). Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model. Agronomy, 12.","DOI":"10.3390\/agronomy12071583"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"241","DOI":"10.54386\/jam.v24i3.1587","article-title":"Assessing rice blast disease severity through hyperspectral remote sensing","volume":"24","author":"Mandal","year":"2022","journal-title":"J. Agrometeorol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/15481603.2017.1370169","article-title":"Landsat-8 vs. Sentinel-2: Examining the added value of sentinel-2\u2032s red-edge bands to land-use and land-cover mapping in Burkina Faso","volume":"55","author":"Forkuor","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Jiang, X., Fang, S., Huang, X., Liu, Y., and Guo, L. (2021). Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands. Remote Sens., 13.","DOI":"10.3390\/rs13040579"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"112789","DOI":"10.1016\/j.rse.2021.112789","article-title":"The influence of surface canopy water on the relationship between L-band backscatter and biophysical variables in agricultural monitoring","volume":"268","author":"Khabbazan","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"525","DOI":"10.5589\/m03-069","article-title":"The application of C-band polarimetric SAR for agriculture: A review","volume":"30","author":"McNairn","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_64","first-page":"102683","article-title":"Multi-temporal phenological indices derived from time series Sentinel-1 images to country-wide crop classification","volume":"107","author":"Rybicki","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Mansaray, L.R., Huang, W., Zhang, D., Huang, J., and Li, J. (2017). Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets. Remote Sens., 9.","DOI":"10.3390\/rs9030257"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1080\/2150704X.2016.1225172","article-title":"Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data","volume":"7","author":"Nguyen","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Xiao, W., Xu, S., and He, T. (2021). Mapping Paddy Rice with Sentinel-1\/2 and Phenology-, Object-Based Algorithm\u2014A Implementation in Hangjiahu Plain in China Using GEE Platform. Remote Sens., 13.","DOI":"10.3390\/rs13050990"},{"key":"ref_68","first-page":"100627","article-title":"Irrigated rice crop identification in Southern Brazil using convolutional neural networks and Sentinel-1 time series","volume":"24","author":"Gomes","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.isprsjprs.2021.02.011","article-title":"Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model","volume":"174","author":"Wei","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1016\/S2095-3119(19)62592-X","article-title":"Science and Technology Backyard: A novel approach to empower smallholder farmers for sustainable intensification of agriculture in China","volume":"18","author":"Jiao","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"135183","DOI":"10.1016\/j.scitotenv.2019.135183","article-title":"Large increases of paddy rice area, gross primary production, and grain production in Northeast China during 2000\u20132017","volume":"711","author":"Xin","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1007\/s10333-015-0516-9","article-title":"Differences in rice water consumption and yield under four irrigation schedules in central Jilin Province, China","volume":"14","author":"Lu","year":"2016","journal-title":"Paddy Water Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1517\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:51:32Z","timestamp":1760122292000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1517"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,9]]},"references-count":72,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061517"],"URL":"https:\/\/doi.org\/10.3390\/rs15061517","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,9]]}}}