Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days after acquisition. To preserve the information content and user freedom, some preprocessing steps (e.g., speckle filtering) are not applied on the ingested Sentinel-1 imagery as they can vary by application. In this technical note, we present a framework for preparing Sentinel-1 SAR backscatter Analysis-Ready-Data in the Google Earth Engine that combines existing and new Google Earth Engine implementations for additional border noise correction, speckle filtering and radiometric terrain normalization. The proposed framework can be used to generate Sentinel-1 Analysis-Ready-Data suitable for a wide range of land and inland water applications. The Analysis Ready Data preparation framework is implemented in the Google Earth Engine JavaScript and Python APIs.

The construction of logging roads has a major ecological impact on tropical forests and leads to large carbon emissions (Kleinschroth & Healy, 2017, Umunay et al. 2019). Negative impacts and emissions of logging roads could potentially be drastically lowered with the adoption of reduced-impact logging practices (Umunay et al. 2019). Accurate, timely, and dynamic logging road maps would help quantify and prioritize opportunities for improved road management and forest conservation across the globe. However, to-date, the limited mapping of logging roads has required time-consuming field data collection or manual digitization from satellite images. The open availability of Sentinel-1 radar and Sentinel-2 optical satellite imagery at high spatiotemporal resolutions now offers a unique opportunity for better automated logging road monitoring in the tropics.In this study, we employ Sentinel-1 and Sentinel-2 data for near real-time mapping of logging roads in the Congo Basin tropical forests. We monitor newly constructed roads based on Sentinel-1 change ratio composites and cloud-masked Sentinel-2 composites. We acquired an extensive reference dataset of manually digitized logging roads to train and test a convolutional neural network for road/non-road classifications.First results indicate promising capacities of Sentinel-1 and -2 data to monitor logging roads especially in forest types in the Republic of Congo and the Democratic Republic of Congo. Forest landscapes in Gabon, Equatorial Guinea and Cameroon appeared to be more challenging for logging road monitoring due to effects of cloud-cover and elevation. Near-future work includes model refinements, the acquisition of more reference data, and a Google Earth Engine-based wall-to-wall application of our model to produce a dynamic Congo Basin logging road dataset. References:Kleinschroth, Fritz, Healy, John R. (2017), Impacts of logging roads on tropical forests, Biotropica 49(5): 620–635 2017Umunay, Peter M., Gregoire, Timothy G., Gopalakrishna, Trisha, Ellis, Peter W., Putz, Francis E. (2019) Selective logging emissions and potential emission reductions from reduced-impact logging in the Congo Basin, Forest Ecology and Management 437 360-37