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

Abstract

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 km 2 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 (R 2 = 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.

Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets

“…These data have already been tested in rice and land-cover monitoring [38,39,43]. However, specific applications of these data in mapping subsistence or smallholder commercial rice agriculture typical in urban landscapes are yet to be undertaken.…”

“…Land-cover classification accuracies have been improved with the conjugative use of optical and microwave datasets [39,43]. In the current study, it is proposed that the integration of the aforementioned optical indices with temporal microwave data could provide additional information that would improve overall classification accuracy.…”

Improved estimates of mangrove cover and change reveal catastrophic deforestation in Myanmar

“…Underlying the proximate causes of mangrove loss are drivers unique to Myanmar. Rice expansion is smallholder-driven to enhance livelihoods and employment (Okamoto 2007, Matsuda 2009, Stokke et al 2018; interventions dating back to the 1980s include capital intensification, development of irrigation infrastructure, agricultural mechanisation, crop diversification, and improvement of agricultural management practices; and market liberalisation and reforms in 2003 further incentivised rice expansion (Okamoto 2007, Matsuda 2009, Torbick et al 2017. Mangrove conversion to oil palm in Myanmar (Richards and Friess 2015), in contrast, was driven by large-scale agribusiness concessions, particularly targeting Tanintharyi (Connette et al 2016), to meet domestic and industrial demands for palm oil and achieve self-sufficiency in edible oils (Donald et al 2015).…”

Contribution of the new satellites (Sentinel-1, Sentinel-2 and SPOT-6) to the coastal vegetation monitoring in the Pays de Brest (France)

“…Another similar study monitoring rice fields revealed that here the temporal backscatter change was higher. Torbick et al (Torbick et al, 2017) also used the radar backscatter coefficient of the S1 series to monitor rice crops in order to map the rice growing areas of Myanmar, in South-Eastern Asia, their cropping calendar, flooding and crop intensification. Pathier et al also showed the high importance of high spatial resolution of the Sentinel-1 series.…”