RIASSUNTO
Rice accounts for over 20% of the global calorie supply, yet no maps exist that quantify rice paddy acreage with a regular cadence. Efforts to date have focused on classifying paddy fields either locally or regionally, and methods to do this have varied, with no agreed upon best approach. Here, we aim to generate a map of lowland rice paddy across Asia, with an accurate, easy-to-update method so that changes in area can be understood and quantified. To do so, we exploit the ability of synthetic aperture radar (SAR) to penetrate cloud cover since most rice is grown in the seasonally-cloudy monsoonal tropics. With the launch of the first Sentinel-1 satellite in 2014 by the European Space Agency, scientists have access to freely-available, global coverage SAR data for the first time. To leverage this data, we rely on the Descartes Labs platform, which provides the entire archive of topographically-corrected Sentinel-1 imagery. The unique phenology of rice paddies can be captured using statistics from SAR backscatter values, and these insights can be used to estimate rice paddy area across Asia. The ability to efficiently scale this method will provide scientists and policy-makers with the first comprehensive, easily-updated map of rice paddy acreage.