Seeing snow every day

01

Here's a snow map

This is a crop from MODIS — the imaging spectrometer flying on NASA's Terra and Aqua satellites. Snow is bright where visible light hits it and dark in the shortwave infrared. That gap between two bands, band 4 (visible) and band 6 (1.6 µm), is what separates snow from cloud and bare ground.

The ratio is called NDSI, the normalized difference snow index, and NASA has run a version of it operationally since 2000. Drag the threshold below off the standard cutoff of 0.4 and watch the mask move — turning a continuous quantity into a binary map is never as clean as one number implies.

02

Here's why you can't have one every day

A single satellite crosses any given point roughly once a day at best. Cloud cover blocks the optical view outright — across most regions, a large share of days at a given location are cloud-obstructed. A snow map that can't refresh daily isn't much use for hazard response, hydrology, or reservoir operations, all of which run on daily-to-weekly decisions.

03

Here's the broken sensor in the way

MODIS on Aqua has 20 physical detectors dedicated to band 6, one per scan line. Most failed shortly after launch — in this scene, only 8 of the 20 are still reading real data; the rest are stuck on a constant value. Toggle detectors off below and watch the striping return. This is the real damage pattern on Aqua, not a simulation.

NASA's operational fallback swaps in band 7 (2.1 µm) instead. It's spectrally close, but not close enough — snow/cloud contrast is measurably weaker there, and the standard Aqua snow product eats an accuracy hit as a result.

Worth separating two things that look similar: this is dead-detector failure, not the faint striping every MODIS band has from small calibration differences between otherwise-working detectors. That second kind gets removed with per-detector histogram matching before any of these images are even generated — standard MODIS preprocessing, not specific to this project — so what you're seeing here is the real fault, not leftover noise.

04

Here's how we fixed it

Instead of falling back to a worse band, predict the missing band 6 value directly from a small neighborhood of pixels in the bands that still work — a linear regression fit against the detectors that survived, applied to the ones that didn't. This was work I did my part of at CCNY, and a version of it now runs in NASA's operational Collection 6 Aqua snow product.

It's cheap enough that this runs live, right now, in your browser — not a precomputed image. The wipe below shows the visible effect: every dead-detector gap (red) filled in. (Simplified from the published method: two predictor bands instead of five, one global fit instead of overlapping tiles, same 5x5 window. Close enough to be honest, light enough to ship.)

05

Here's the map you could build on top

Optical still can't see through clouds, restored or not. Passive microwave can — far coarser, tens of kilometers instead of hundreds of meters, but indifferent to cloud cover entirely. Put the two together, colocated in space and reconciled in time, and there's a plausible path to an actual daily snow/sea-ice product: microwave fills the cloudy days, optical sharpens the clear ones.

I specified this. I did not build it. That's worth saying plainly rather than around: a fusion pipeline across two instruments with wildly different resolution, latency, and failure modes is a harder systems problem than the restoration above — and the more interesting one.

06

Here's what stopped me, and how I'd attack it in 2026

[ George: what actually stopped this — time, data access, scope, something else? Say it plainly. ]

[ George: the concrete 2026 attack plan — what's different now (compute, data availability, tooling) that makes this newly tractable? ]