EO·Atlas
14 methods · 5 families
Analysis methodologies
Methods applied to the data after it is acquired, such as multi-sensor fusion, change detection, and spectral analysis. They combine or transform the outputs of one or more sensing methods, so they sit above the technology tree rather than under any single technology — a peer axis to the sensing technologies.
Change detection
4 methods- Multi-temporal burned-area mapping Maps the extent and timing of fire-affected area by tracking persistent post-fire changes in surface reflectance across a time-series, often anchored by coincident active-fire detections. Produces systematic burned-area records. →
- Optical time-series change detection Flags surface change by testing each new optical observation against a per-pixel statistical baseline built from the historical time-series, accumulating evidence across dates before confirming a change. Underlies near-real-time deforestation and disturbance alert systems. →
- SAR backscatter - change detection Detects land-cover or structural change by comparing backscatter intensity across dates; good for flood mapping, deforestation alerts, and disaster damage assessment. →
- SAR coherent change detection (CCD) Detects fine-scale surface disturbance by measuring the loss of interferometric phase coherence between two SAR acquisitions, sensitive to changes too subtle to alter backscatter amplitude. Used for damage assessment and disturbed-ground mapping. →
Classification
1 methodFusion
5 methods- Cross-sensor harmonization and calibration transfer Adjusts observations from different instruments onto a common radiometric and geometric reference so they can be used interchangeably in one time-series, correcting for differences in band response, view geometry, and calibration. The basis of harmonised multi-mission surface-reflectance products. →
- Multi-sensor active-passive blending Merges measurements from active (radar or scatterometer) and passive (radiometer) sensors that observe the same geophysical variable, combining their complementary error and sampling characteristics into a single harmonised record. The basis of long-term blended soil moisture climate data records. →
- Multi-sensor spatiotemporal fusion - gap-filling Reconstructs dense time-series by blending high-frequency coarse-resolution and low-frequency fine-resolution observations; good for crop phenology tracking and cloud-gap filling. →
- Optical-SAR data fusion Combines optical reflectance and SAR backscatter to exploit complementary information; good for cloud-persistent monitoring, crop classification under cloud cover, and flood mapping in vegetated areas. →
- Satellite precipitation retrieval Integrates passive-microwave precipitation estimates with microwave-calibrated infrared observations and interpolation to produce near-global rainfall fields, exemplified by GPM IMERG. →
Spectral analysis
3 methods- Matched-filter trace-gas plume detection Detects and quantifies localised trace-gas enhancements by applying a matched filter tuned to the target gas absorption signature across an imaging-spectrometer scene, isolating plumes against a variable surface background. Used for methane and carbon dioxide point-source mapping. →
- Spectral unmixing Decomposes a mixed-pixel spectrum into the fractional abundances of its constituent endmember materials, resolving sub-pixel composition that a single hard class label cannot capture. Used to estimate vegetation, soil, and impervious-surface fractions. →
- Spectral-library matching Identifies surface materials by matching an observed reflectance spectrum against a reference library of known signatures, typically after continuum removal to isolate diagnostic absorption features. Used for mineral and surface-composition mapping from imaging spectrometers. →