Next Article in Journal
Different Patterns in Daytime and Nighttime Thermal Effects of Urbanization in Beijing-Tianjin-Hebei Urban Agglomeration
Next Article in Special Issue
Mapping the Expansion of Boom Crops in Mainland Southeast Asia Using Dense Time Stacks of Landsat Data
Previous Article in Journal
Detection of Archaeological Residues in Vegetated Areas Using Satellite Synthetic Aperture Radar
Previous Article in Special Issue
Remote Sensing-Based Assessment of the 2005–2011 Bamboo Reproductive Event in the Arakan Mountain Range and Its Relation with Wildfires
Article

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

1
Applied Geosolutions, Newmarket, NH 03857, USA
2
Michigan State University, East Lansing, MI 48823, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(2), 119; https://doi.org/10.3390/rs9020119
Received: 22 December 2016 / Accepted: 24 January 2017 / Published: 1 February 2017
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. View Full-Text
Keywords: rice; Sentinel-1; food security; Myanmar; PALSAR-2; Landsat-8; random forest; classification; time series phenology rice; Sentinel-1; food security; Myanmar; PALSAR-2; Landsat-8; random forest; classification; time series phenology
Show Figures

Graphical abstract

MDPI and ACS Style

Torbick, N.; Chowdhury, D.; Salas, W.; Qi, J. Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2. Remote Sens. 2017, 9, 119. https://doi.org/10.3390/rs9020119

AMA Style

Torbick N, Chowdhury D, Salas W, Qi J. Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2. Remote Sensing. 2017; 9(2):119. https://doi.org/10.3390/rs9020119

Chicago/Turabian Style

Torbick, Nathan, Diya Chowdhury, William Salas, and Jiaguo Qi. 2017. "Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2" Remote Sensing 9, no. 2: 119. https://doi.org/10.3390/rs9020119

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop
{"cc_re__cc_re__cc_by_3_0": ["PALSAR-2\">\n<meta name=\"dc.creator\" content=\"Nathan Torbick\">\n<meta name=\"dc.creator\" content=\"Diya Chowdhury\">\n<meta name=\"dc.creator\" content=\"William Salas\">\n<meta name=\"dc.creator\" content=\"Jiaguo Qi\">\n<meta name=\"dc.type\" content=\"Article\">\n<meta name=\"dc.source\" content=\"Remote Sensing 2017, Vol. 9, Page 119\">\n<meta name=\"dc.date\" content=\"2017-02-01\">\n<meta name=\"dc.identifier\" content=\"10.3390/rs9020119\">\n<meta name=\"dc.publisher\" content=\"Multidisciplinary Digital Publishing Institute\">\n<meta name=\"dc.rights\" content=\"http://creativecommons.org/licenses/by/3.0/\">\n<meta name=\"dc.format\" content=\"application/pdf\">\n<meta name=\"dc.language\" content=\"en\">\n<meta name=\"dc.description\" content=\"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 S"], "cc_re__cc_re__cc_by_4_0": ["rong>Figure 15</strong><br/>\n                                            &lt;p&gt;Scatterplot between 2015 census statistics and 2015 Sentinel-1A total harvested rice area shows moderate agreement with discrepancies due to Cyclone Komen disaster flooding and uncertainty in re-planting and accurate reporting.&lt;/p&gt;\n                                                            \"></a></div>\n<div class=\"info-box info-box__copyright top-margin\">\nThis is an open access article distributed under the <a href=\"https://creativecommons.org/licenses/by/4.0/\" target=\"_blank\" rel=\"noopener noreferrer\">Creative Commons Attribution License</a> which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited\n</div>\n</div>\n</div>\n<div class=\"content__container content__container__combined-for-large content__container__combined-for-large__no-padding\">\n<a href=\"/2072-4292/9/2/119/scifeed_display\" data-reveal-id=\"scifeed-modal\" data-reveal-ajax=\"true\">\n<img width=\"100%\" src=\"/bundles/mdpimain/img/scifeed_large.png\" id=\"feed_option\" alt=\"SciFe"], "copyright__copyright__copyright": ["https://doi.org/10.3390/rs9020119\n\nChicago/Turabian Style\n\nTorbick, Nathan, Diya Chowdhury, William Salas, and Jiaguo Qi. 2017. \"Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2\" Remote Sensing 9, no. 2: 119. https://doi.org/10.3390/rs9020119\n\nFind Other Styles\nNote that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.\n\nArticle Access Map by Country/Region\n\n1\nBack to TopTop", "https://doi.org/10.3390/rs9020119\n\nChicago/Turabian Style\n\nTorbick, Nathan, Diya Chowdhury, William Salas, and Jiaguo Qi. 2017. \"Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2\" Remote Sensing 9, no. 2: 119. https://doi.org/10.3390/rs9020119\n\nFind Other Styles\nNote that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.\n\nArticle Access Map by Country/Region\n\n1\nBack to TopTop", "https://doi.org/10.3390/rs9020119\n\nChicago/Turabian Style\n\nTorbick, Nathan, Diya Chowdhury, William Salas, and Jiaguo Qi. 2017. \"Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2\" Remote Sensing 9, no. 2: 119. https://doi.org/10.3390/rs9020119\n\nFind Other Styles\nNote that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.\n\nArticle Access Map by Country/Region\n\n1\nBack to TopTop", "https://doi.org/10.3390/rs9020119\n\nChicago/Turabian Style\n\nTorbick, Nathan, Diya Chowdhury, William Salas, and Jiaguo Qi. 2017. \"Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2\" Remote Sensing 9, no. 2: 119. https://doi.org/10.3390/rs9020119\n\nFind Other Styles\nNote that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.\n\nArticle Access Map by Country/Region\n\n1\nBack to TopTop"]}