EO·Atlas
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Analysis methodology ยท Fusion

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.

Algorithms such as STARFM or ESTARFM predict fine-resolution reflectance at unobserved dates by learning spatial-temporal relationships from paired image sets. Wikipedia: Remote sensing

Topic
Fit
Crop phenologyfirst choice
Ocean heat contentfirst choice

OHC products integrate sparse profile observations with satellite-constrained ocean-state fields into coherent time series.

Wind energy sitingfirst choice
Droughtsuitable
Ocean currentssuitable

Operational current analyses combine altimetry, SST, wind, in-situ data, and model constraints into gridded velocity fields.

Ocean salinitysuitable

L4 salinity analyses blend satellite SSS, in-situ salinity, and SST to produce gap-free fields.

Subsidencesuitable
Urban changesuitable
Wildfiresuitable
Air qualityadequate
Disaster damage assessmentadequate

time-series-continuity

Floodingadequate
Oil spillsadequate
Demonstrated
  • Verde

    Verde applies multi-sensor spatiotemporal gap-filling (de-clouding) to produce consistent vegetation time series regardless of cloud cover, using SPOT, Pleiades, and open imagery in combination

  • 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.

  • 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.

  • 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.

  • Integrates passive-microwave precipitation estimates with microwave-calibrated infrared observations and interpolation to produce near-global rainfall fields, exemplified by GPM IMERG.

Sources
Methodology

Edited from public sources. Last reviewed date pending by SpectraWorks editorial. See the data dictionary for field definitions.

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