The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert (DIST-ALERT) from Sentinel-1 (S1) validated product maps surface disturbance alerts derived from Sentinel-1 data. The DIST-ALERT-S1 product uses the per-pixel temporal change in radar backscatter to infer surface disturbance events due to changes in vegetation structure (e.g. disappearance of vegetation due to natural disasters such as landslides or earthquake), surface geometry (e.g. new buildings due to urban construction), surface scattering properties (e.g., soil moisture before and after precipitation) and volume scattering properties (e.g. change in tree volume/height before and after wildfires/landslides). The product aims to detect anomalous surface disturbance events that happened relative to a historical timeline. In other words, surface disturbance events that did not happen several years in a row in the past are to be captured by DIST-ALERT-S1, thus seasonal recurring events are not considered surface disturbance events; they are surface recurrence events instead.

Sentinel-1 Single Look Complex (SLC) data products are processed to OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1) and used as input to the DIST-ALERT-S1 algorithm, resulting in output layers similar to those of the optical OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product (Version 1). There are 10 layers contained within the OPERA DIST-ALERT-S1 product for generic disturbances, for example, disturbance status, disturbance confidence, disturbance duration and day of last disturbance detection. The OPERA DIST-ALERT-S1 data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file.

The OPERA DIST-ALERT-S1 product contains modified Copernicus Sentinel data (2026).

The OPERA project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies.