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.
Determining the seasonality of terrestrial carbon exchange with the atmosphere remains a challenge in tropical forests because of the heterogeneity of ecosystem and climate. The magnitude and spatial variability of this flux are unknown, particularly in Amazonia where empirical upscaling approaches from spatially sparse in situ measurements and simulations from process-based models have been challenged in recent scientific literature. Here, we use satellite proxy observations of canopy structure, skin temperature, water content, and optical properties over a period of 10 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) to constrain and quantify the spatial pattern and seasonality of carbon exchange of Amazonian forests. We identify nine regions through an optimized cluster approach with distinct leaf phenology synchronized with either water or light availability and corresponding seasonal cycles of gross primary production (GPP), covering more than 600 million ha of remaining old growth forests of Amazonia. We find South and Southwestern regions show strong seasonality of GPP with a peak in the wet season; while from Central Western to Northeastern Amazonia cover three regions with rising GPP in the dry season. The remaining four regions have significant but weak seasonality. These patterns agree with satellite florescence observations, a better proxy for photosynthetic activity. Our results suggest that only one-third of the patterns can be explained by the spatial autocorrelation caused by intra-annual variability of climate over Amazonia. The remaining twothirds of variations are due to biogeography of the Amazon basin driven by forest composition, structure, and nutrients. These patterns, for the first time, provide a complex picture of seasonal changes of tropical forests related to photosynthesis and influenced by water, light, and stomatal responses of trees that can improve modeling of regional carbon cycle and future prediction of impacts of climate change.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.