analysis · methodologies

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

Sources

[1]Wikipedia: Remote sensingcommunity2026-05-22

Cite https://eo-atlas.org/methodologies/multisensor-spatiotemporal-fusion Markdown twin → Field definitions →