EO·Atlas
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
OHC products integrate sparse profile observations with satellite-constrained ocean-state fields into coherent time series.
Operational current analyses combine altimetry, SST, wind, in-situ data, and model constraints into gridded velocity fields.
L4 salinity analyses blend satellite SSS, in-situ salinity, and SST to produce gap-free fields.
- 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.
- [1]Wikipedia: Remote sensingcommunity2026-05-22
Edited from public sources. Last reviewed 2026-05-22 by SpectraWorks editorial. See the data dictionary for field definitions.
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Plain text
Multi-sensor spatiotemporal fusion - gap-filling. EO-Atlas, SpectraWorks B.V., last reviewed 2026-05-22. https://eo-atlas.org/topics/methodologies/multisensor-spatiotemporal-fusion (CC BY 4.0)
BibTeX
@misc{eoatlas:topics:methodologies:multisensor-spatiotemporal-fusion,
title = {Multi-sensor spatiotemporal fusion - gap-filling},
author = {{EO-Atlas}},
publisher = {SpectraWorks B.V.},
year = {2026},
note = {Last reviewed 2026-05-22. CC BY 4.0},
url = {https://eo-atlas.org/topics/methodologies/multisensor-spatiotemporal-fusion}
}