Outputs

super_resolve returns a Result.

Attribute	Shape / type	Meaning
image	(4, 5H, 5W) float32	surface reflectance, B04 B03 B02 B08, 2.0 m grid
confidence	(4, 5H, 5W) float32	predicted absolute error scale (reflectance); learned, not calibrated
support	(5H, 5W) uint8	2 HIGH, 1 MEDIUM, 0 LOW or invalid; see Support
valid	(5H, 5W) bool	False where the input was NoData, cloud, shadow, cirrus or saturated
consistency	dict	per-band RMS of A(image) - y in noise units
x_base	(4, 5H, 5W)	the observation-determined baseline
prior	(4, 5H, 5W)	structure added by the learned prior; x_base + prior == image
gsd	float	output grid spacing in metres
metadata	dict	scan backend, precision, tiling, offset, SCL source, regularisation weights

Helpers

import synapse_sr
result = synapse_sr.super_resolve("scene.tif")

result.rgb()          # (5H, 5W, 3) uint8 true-colour quicklook, 2-98 % stretch
result.ndvi()         # (5H, 5W) NDVI, NaN where invalid
result.to_xarray()    # DataArray (band, y, x) with map coordinates (needs xarray)
result.save("out.tif")

Saved GeoTIFF

Band	Name	Content
1 to 4	B04 B03 B02 B08	reflectance, float32, NaN where invalid
5 to 8	ERRSCALE_B04 ... ERRSCALE_B08	predicted error scale
9	SUPPORT	2 / 1 / 0

save(path, with_confidence=False) (CLI --no-confidence) writes bands 1 to 4 only. For array inputs without georeferencing, save writes a compressed .npz.

The CRS is copied from the input. The geotransform keeps the input origin and divides the pixel size by five, so the output covers exactly the input bounds. Output pixel 5i + r lies inside input pixel i, and r = 2 is centred on it.