{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T19:24:37Z","timestamp":1791314677263,"version":"4.3.1"},"reference-count":59,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2017,2,1]],"date-time":"2017-02-01T00:00:00Z","timestamp":1485907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NASA LCLUC","award":["NNX11AJ35G"],"award-info":[{"award-number":["NNX11AJ35G"]}]},{"name":"NASA SBIR","award":["NNX14CS01C"],"award-info":[{"award-number":["NNX14CS01C"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Assessment and monitoring of rice agriculture over large areas has been limited by cloud cover, optical sensor spatial and temporal resolutions, and lack of systematic or open access radar. Dense time series of open access Sentinel-1 C-band data at moderate spatial resolution offers new opportunities for monitoring agriculture. This is especially pertinent in South and Southeast Asia where rice is critical to food security and mostly grown during the rainy seasons when high cloud cover is present. In this research application, time series Sentinel-1A Interferometric Wide images (632) were utilized to map rice extent, crop calendar, inundation, and cropping intensity across Myanmar. An updated (2015) land use land cover map fusing Sentinel-1, Landsat-8 OLI, and PALSAR-2 were integrated and classified using a randomforest algorithm. Time series phenological analyses of the dense Sentinel-1 data were then executed to assess rice information across all of Myanmar. The broad land use land cover map identified 186,701 km2 of cropland across Myanmar with mean out-of-sample kappa of over 90%. A phenological time series analysis refined the cropland class to create a rice mask by extrapolating unique indicators tied to the rice life cycle (dynamic range, inundation, growth stages) from the dense time series Sentinel-1 to map rice paddy characteristics in an automated approach. Analyses show that the harvested rice area was 6,652,111 ha with general (R2 = 0.78) agreement with government census statistics. The outcomes show strong ability to assess and monitor rice production at moderate scales over a large cloud-prone region. In countries such as Myanmar with large populations and governments dependent upon rice production, more robust and transparent monitoring and assessment tools can help support better decision making. These results indicate that systematic and open access Synthetic Aperture Radar (SAR) can help scale information required by food security initiatives and Monitoring, Reporting, and Verification programs.<\/jats:p>","DOI":"10.3390\/rs9020119","type":"journal-article","created":{"date-parts":[[2017,2,1]],"date-time":"2017-02-01T10:53:53Z","timestamp":1485946433000},"page":"119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":251,"title":["Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2"],"prefix":"10.3390","volume":"9","author":[{"given":"Nathan","family":"Torbick","sequence":"first","affiliation":[{"name":"Applied Geosolutions, Newmarket, NH 03857, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diya","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Applied Geosolutions, Newmarket, NH 03857, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Salas","sequence":"additional","affiliation":[{"name":"Applied Geosolutions, Newmarket, NH 03857, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8183-0297","authenticated-orcid":false,"given":"Jiaguo","family":"Qi","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, MI 48823, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,2,1]]},"reference":[{"key":"ref_1","unstructured":"Food and Agriculture Organization of the United Nations. Available online: http:\/\/www.fao.org\/home\/en."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.3390\/rs2061589","article-title":"Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project","volume":"2","author":"Justice","year":"2010","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.3390\/rs70201461","article-title":"A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)","volume":"7","author":"Whitcraft","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2005.09.007","article-title":"Spatio\u2013temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers","volume":"100","author":"Sakamoto","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"053547","DOI":"10.1117\/1.3619838","article-title":"Mapping rice areas in South Asia using MODIS multi temporal data","volume":"5","author":"Gumma","year":"2011","journal-title":"J. Appl. Remote Sens."},{"key":"ref_7","first-page":"451","article-title":"Mapping rice agriculture in the Sacramento Valley, USA with multitemporal PALSAR and MODIS imagery","volume":"4","author":"Torbick","year":"2010","journal-title":"IEEE J. Sel. Top. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17","DOI":"10.5589\/m11-020","article-title":"Integrating SAR and optical imagery for regional mapping of paddy rice attributes in the Poyang Lake Watershed, China","volume":"37","author":"Torbick","year":"2011","journal-title":"Can. J. Remote Sens."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Guan, X., Huang, C., Liu, G., Meng, X., and Liu, Q. (2016). Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance. Remote Sens., 8.","DOI":"10.3390\/rs8010019"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1080\/01431160903464179","article-title":"Quantifying the area and spatial distribution of double- and triple-cropping croplands in India with multi-temporal MODIS imagery in 2005","volume":"32","author":"Biradar","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1080\/01431160802698919","article-title":"Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium","volume":"30","author":"Thenkabail","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.isprsjprs.2014.02.007","article-title":"Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010","volume":"91","author":"Gumma","year":"2014","journal-title":"ISPRS J. Photogramm."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.rse.2010.09.006","article-title":"Assessing drought probability for agricultureal areas in Africa with coarse resolution remote sensing imagery","volume":"115","author":"Rojas","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s11273-014-9342-x","article-title":"Mapping agricultural wetlands in the Sacramento Valley, USA with satellite remote sensing","volume":"23","author":"Torbick","year":"2014","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_17","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_18","first-page":"1","article-title":"Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images","volume":"46","author":"Zhou","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1080\/01431160500421507","article-title":"A neural network integrated approach for rice crop monitoring","volume":"27","author":"Chen","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/S0034-4257(01)00343-1","article-title":"Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarizaiton backscatter signatures over paddy rice field and their relationship with biological variables","volume":"81","author":"Inoue","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_21","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"},{"key":"ref_22","unstructured":"Ribbes, F., and Le Toan, T. (1999). Towards Digital Earth, International Symposium in Digital Earth."},{"key":"ref_23","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"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.rse.2013.09.001","article-title":"Capability of C-band backscattering coefficients from high-resolution satellite SAR sensors to assess biophysical variables in paddy rice","volume":"140","author":"Inoue","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"10773","DOI":"10.3390\/rs61110773","article-title":"Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project","volume":"6","author":"Nelson","year":"2014","journal-title":"Remote Sens."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Torbick, N., Salas, W., Chowdhury, D., and Ingraham, P. (2017). Mapping rice greenhouse gas emissions in the Red River Delta Vietnam. Carb. Manag., in press.","DOI":"10.1080\/17583004.2016.1275816"},{"key":"ref_29","unstructured":"United States Department of Agriculture Foreign Agricultural Service, Available online: http:\/\/www.fas.usda.gov."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat surface reflectance dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Soc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"17131","DOI":"10.1029\/97JD00201","article-title":"Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation","volume":"102","author":"Vermote","year":"1997","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Vermote, E.F., and Kotchenova, S. (2008). Atmospheric correction for the monitoring of land surfaces. J. Geophys. Res.-Atmos., 113.","DOI":"10.1029\/2007JD009662"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.14358\/PERS.72.10.1179","article-title":"Characterization of the Landsat 7 ETM+ automated cloud cover assessment (ACCA) algorithm","volume":"72","author":"Irish","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","unstructured":"Freden, S.C., Mercanti, E.P., and Becker, M.A. (1974, January 10). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third ERTS Symposium, Washington, DC, USA."},{"key":"ref_38","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_39","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_40","doi-asserted-by":"crossref","first-page":"456","DOI":"10.2111\/REM-D-11-00188.1","article-title":"Mapping total vegetation cover across western rangelands with MODIS data","volume":"65","author":"Hagen","year":"2012","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_41","first-page":"453","article-title":"A library of georeferenced photos from the field","volume":"92","author":"Xiao","year":"2011","journal-title":"EOS Earth Space Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Torbick, N., Ledoux, L., Salas, W., and Zhao, M. (2016). Regional Mapping of Plantation Extent Using Multisensor Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8030236"},{"key":"ref_43","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_44","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_45","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1016\/j.rse.2009.04.015","article-title":"Monitoring of cropland practices for carbon sequestration purposes in north central Montana by Landsat remote sensing","volume":"113","author":"Watts","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_46","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 southwestern Brazil","volume":"7","author":"Schultz","year":"2015","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"54","DOI":"10.5589\/m08-080","article-title":"Mapping vegetated wetlands of Alaska using L-band radar satellite imagery","volume":"35","author":"Whitcomb","year":"2009","journal-title":"Can. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1974","DOI":"10.3390\/rs4071974","article-title":"High resolution mapping of peatland hydroperiod at a high-latitude Swedish mire","volume":"4","author":"Torbick","year":"2012","journal-title":"Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"12563","DOI":"10.3390\/rs70912563","article-title":"Mapping forest canopy height over large areas by upscaling ALS estimates with freely available satellite data","volume":"7","author":"Wilkes","year":"2015","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"13564","DOI":"10.3390\/rs71013564","article-title":"Leanring-based algal bloom event recognition for oceanographic decision support system using remote sensing data","volume":"7","author":"Song","year":"2015","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1080\/15481603.2015.1076561","article-title":"Mapping urban sprawl and impervious surfaces in the northeast United States for the past four decades","volume":"52","author":"Torbick","year":"2015","journal-title":"GISci Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"10017","DOI":"10.3390\/rs70810017","article-title":"Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using Landsat 8 and random forest","volume":"7","author":"Karlson","year":"2015","journal-title":"Remote Sens."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_55","first-page":"1924","article-title":"The Calculus of Observations","volume":"10","author":"Whittaker","year":"1924","journal-title":"Trans. Fac. Act."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1080\/17538947.2010.505664","article-title":"A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America","volume":"4","author":"Atzberger","year":"2011","journal-title":"Int. J. Dig. Earth"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.rse.2012.04.001","article-title":"Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology","volume":"123","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1021\/ac034173t","article-title":"A Perfect Smoother","volume":"75","author":"Eilers","year":"2003","journal-title":"Anal. Chem."},{"key":"ref_59","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/2\/119\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:27:19Z","timestamp":1760207239000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/2\/119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,1]]},"references-count":59,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2017,2]]}},"alternative-id":["rs9020119"],"URL":"https:\/\/doi.org\/10.3390\/rs9020119","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,2,1]]}}}