Blog
Tutorials, case studies, and technical insights on satellite imagery analysis.
How to Choose the Right Satellite Index for Your Monitoring Goal
Choosing between NDVI, SAR VV, NDWI, NDBI, NBR, DNB and other indices depends on what you want to monitor. This decision guide matches monitoring goals — vegetation health, floods, urban growth, fires, economic activity — to the best index and satellite sensor.
Environmental Compliance Monitoring with Satellite Imagery
Satellite time series monitoring allows companies, NGOs, and regulators to objectively verify deforestation commitments, track ESG land use pledges, and monitor supply chain sourcing areas. Learn how NDVI and SAR time series create an always-on compliance monitoring system.
Multi-Index Satellite Monitoring: Why One Sensor Is Never Enough
Monitoring a site with a single index misses events that only appear in other data streams. Combining NDVI, SAR backscatter, NDWI, and nighttime lights for the same polygon creates a complete picture that no single sensor can provide.
Flood Monitoring with Sentinel-1 SAR Time Series: Tracking Inundation from Space
SAR backscatter time series from Sentinel-1 detects flood onset, peak inundation, and recession through clouds. Learn how VV backscatter drops indicate flooding, how to set up continuous flood monitoring zones, and how to interpret SAR time series for disaster response.
Wildfire Recovery Monitoring: From Burn Scar to Regrowth
Post-fire vegetation recovery is one of the most revealing applications of satellite time series monitoring. NBR and NDVI together track the complete trajectory from fire damage through initial regrowth to mature vegetation recovery, revealing ecological resilience and identifying areas at risk of degradation.
Coastal Change Monitoring with NDWI and SAR Time Series
Coastal zones are among the most dynamic and threatened environments on Earth. NDWI time series from Sentinel-2 and SAR from Sentinel-1 together provide continuous monitoring of shoreline change, mangrove extent, coral reefs, and tidal dynamics. Learn how to set up multi-index coastal monitoring.
Seasonal Crop Monitoring with NDVI Time Series: From Planting to Harvest
NDVI time series from Sentinel-2 reveals the complete crop growth cycle — planting date, peak canopy, stress events, and harvest timing. Learn how to set up crop monitoring, identify the key phenological stages, and detect anomalies that signal crop failure or irrigation problems.
Post-Disaster Recovery Monitoring with Satellite Time Series
Satellite time series monitoring tracks the recovery trajectory after floods, earthquakes, wildfires, and other disasters. Learn how NDVI, SAR backscatter, NDWI, and nighttime lights collectively reveal what has recovered, what has not, and where recovery is lagging.
Forest Health Monitoring with NDVI and SAR Vegetation Indices
Combining Sentinel-2 NDVI with Sentinel-1 SAR indices like RVI and RFDI provides comprehensive forest health monitoring that works through clouds. Learn how optical and radar time series complement each other to detect deforestation, degradation, and stress before it becomes visible to the naked eye.
Monitoring Water Surface Changes with NDWI and MNDWI Time Series
NDWI and MNDWI time series from Sentinel-2 track changes in open water extent, reservoir levels, and flood dynamics. Learn how to set up water body monitoring, interpret seasonal patterns, and detect anomalies like drought drawdown or sudden flooding.
Monitoring Economic Activity and Infrastructure with VIIRS Nighttime Light Time Series
VIIRS nighttime light (DNB) time series from NASA Black Marble tracks economic activity, electrification, conflict impacts, and post-disaster recovery. Learn how to monitor any area's nighttime brightness over time and interpret what changes mean.
Continuous Maritime Monitoring with SAR Ship Detection Time Series
SAR-based ship detection from Sentinel-1 provides vessel counts for any ocean area, updated every 6–12 days. Learn how continuous maritime monitoring reveals port activity, fishing effort, traffic patterns, and anomalies without AIS data dependency.