Since 2015, the world's tropical forests can be observed regularly at an unprecedented 6/12-day interval with the satellites of the Sentinel-1 mission. Millions of gigabytes of C-band synthetic aperture radar (SAR) scenes are acquired day and night, regardless of cloud cover, haze, smoke or aerosols, potentially allowing deforestation and forest degradation to be monitored at least biweekly. The challenge, however, lies in finding adequate methods to extract meaningful indicators of forest loss from the vast amount of incoming SAR data, such that anomalies in the time-series can be regularly and consistently detected across tropical forests. Such forest-monitoring methods should be transparent and easily understandable to the wider public, hence enabling confidence in their use across various public and private sectors.

The Sentinel-1 for Science Amazonas project presents a simple space-time data cube design (referred to as StatCubes), where statistical information relevant to identify deforestation is extracted at each point in the SAR backscatter time-series. In addition, the algorithm uses an AI approach with a U-net architecture, including the creation of training data,

training the model, and finally prediction of areas of forest loss. With this approach, the project demonstrates the use of Sentinel-1 data to create a dynamic deforestation product over the Amazon basin. Billions of pixels from the Sentinel-1 satellites from 2015 to December 2021, each representing a 20 x 20 m of forest, are harmonised under the StatCubes-and-AI design, and an approach to detect forest loss is demonstrated in the final version of the results. The largest challenge in the project was the vast amount of data handling and processing; a number of user-friendly software tools were developed to access the data efficiently, processing over 450 TB of data to create the forest loss maps. A specialised production system was set up to manage and monitor the processing of data. Go to Explorer

As a follow on of the project, the next scientific goal was to achieve a product of carbon maps from the Brazilian National Forest Inventory (NFI), when integrated together with products of the ESA Climate Change Initiative (CCI) and Global Ecosystem Dynamics Investigation (GEDI). Go to Story