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.
How it works
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](https://en.wikipedia.org/wiki/Remote_sensing)
Topics this serves
- Crop phenology well suited
- Drought adequate
- Land cover change adequate
Existing implementations
Demonstrated
Capable, undemonstrated
None on record.
Related methodologies
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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.
Sources
- [wikipedia]Wikipedia: Remote sensingcommunityaccessed 2026-05-22
Methodology
Edited from public sources. Last reviewed date pending by SpectraWorks editorial. See the data dictionary for field definitions.