Known Limitations
Every data source has constraints. Understanding these helps you build robust integrations and set accurate expectations with your users.
Cloud Cover Gaps
Optical satellites (Sentinel-2, Landsat) cannot image through clouds. Persistently cloudy regions may have weeks-long data gaps. The API returns the best available cloud-free composite, but some dates may have no usable imagery.
5-Day Revisit Cadence
Sentinel-2 revisits the same location approximately every 5 days (at the equator). Events that occur and resolve between revisits — such as flash floods or short-duration fires — may not appear in the data.
10–30 m Spatial Resolution
Sentinel-2 provides 10 m/pixel for visible bands and 20 m for red-edge/SWIR. Landsat provides 30 m. Objects smaller than one pixel (individual trees, small structures) cannot be resolved.
NDVI Measures Greenness, Not Causation
Normalized Difference Vegetation Index measures chlorophyll reflectance. A drop in NDVI indicates less green vegetation but does not identify the cause — it could be harvest, drought, disease, fire, or seasonal dormancy.
Vegetation Classification Edge Cases
The ESA WorldCover dataset provides 10 m land-cover labels. At biome boundaries (e.g., forest-grassland transitions), the dominant class may not represent the full area. Fallback NDVI thresholds are used if WorldCover data is unavailable for a location.
Anomaly Detection in Arid Regions
Anomaly detection uses NDVI z-scores to compare a current period against a baseline. In arid or semi-arid environments with low NDVI variability, small real changes produce small z-scores that may fall below detection thresholds.
Trend Projection vs. Machine Learning Forecasting
The temporal forecast uses damped linear extrapolation with a seasonal overlay derived from historical observations. This is a statistical projection, not a trained ML model. Accuracy degrades for forecast windows beyond 30–60 days.
Risk Scores Are Indicative, Not Actuarial
Risk scoring combines elevation, flood history, wildfire fuel load, and precipitation data into a composite score. These scores are useful for screening and prioritization but are not a substitute for site-specific actuarial or engineering assessments.
Confidence Scores Are Relative, Not Absolute
Confidence values (0–1) returned alongside detections reflect the model's internal certainty relative to available data quality and cloud-free pixel count. A confidence of 0.85 does not mean "85% probability of being correct" in a statistical sense — it indicates strong internal agreement among spectral bands and temporal composites. Scores below 0.5 warrant manual verification.
Decision-Support Tool, Not Ground Truth
All Satalyse outputs — change maps, anomaly alerts, risk scores, and vegetation indices — are derived from remote-sensing models and should be treated as decision-support inputs. They are not a substitute for ground-truthing, field surveys, or professional assessments. Outputs may contain false positives or miss events that fall below detection thresholds.