Counting Snowflakes from Above (Part 2)
By: Bernhard Sassik
Tags: Remote Sensing, Satellite Imagery, Earth Observation, Snow, Weather

Counting Snowflakes from Above (Part 2)

„The time lag between snowfall and snow melt enables a sun synchronous satellite, re-visiting the same point on earth in intervals that are measured in days, to catch the bright white carpet that laid on the surface’s relief. [Part 1]“

Classifying snow in satellite images is nothing very innovative at it‘s core. Like the widely used Normalised Difference Vegetation Index (NDVI), there is an index based approach for snow detection as well. The NDSI (for Snow) is the quick-and-easy way of generating a decent snow map from a multispectral image, a picture that not only contains the three spectral parts of the light humans can see (RGB), but also, for example, infrared wavelengths. Multispectral earth observation satellites commonly have eight or more of those spectral parts, also called bands. These bands help us to identify what‘s on the ground by putting them in relation to each other. A pixel reflecting little red light and lots of infrared is a proxy for photosynthetic activity in the area. Another pixel reflecting lots of the visible light wavelengths (a white pixel) but absorbing shortwave infrared waves is an indicator for a white and cold surface – Snow!

The NDSI is not enough

For many applications it might be sufficient to separate white pixels based on their shortwave infrared response in order to differentiate between snow and clouds, which tend to reflect even these wavelengths, but a specialised snow product cannot afford to leave out the many exceptions that come along with that approach. A pixel area the size of 10x10 or 500x500 meters can contain snow patches and one or more other land cover classes in combination, partly or completely shaded by clouds or a mountain.

For ExoLabs‘ snow products, we developed our own scalable classifier, that is especially sensible to snow in such edge conditions.

COSMOS Cloud Detection Example

Harnessing the Power of Satellite Images

It is useful to combine the best of two worlds for reliable daily global snow mapping: Daily medium resolution images and high resolution images that come in every couple of days.

At the moment, we digest a selection of freely available multispectral imagery into our system, which recording satellites are listed below, but with ESA‘s Sentinel family in orbit and old sensors starting to report issues, other kinds of datasets recently became interesting and obvious.

Landsat 7 and Landsat 8: 30x30 m/px

MODIS Terra and Aqua: 500x500 m/px

Sentinel-2 A and B: 10x10 m/px

MODIS | Landsat 8 | Sentinel 2. Spatial Resolution Differences Example

For every image that comes in and all pixels in its view, we weigh the generated information against two factors, space and time. A large pixel provides less information per square-meter than a pixel with small footprint, but an older image is obviously less valuable than a newer one. By considering that, we calculate probabilities of a respective area being snowy. We further integrate attributes like altitude, regional connections or previous states, to refine these results.

In a nutshell, we download and classify newly available images of considered satellites every day and compare respective results to previous observations. The final resampled snow cover product has a spatial resolution of 20x20 m/px.

From Snow Cover to Snow Depth

The height of the snow blanket is the next obvious level of detail. Such deductions cannot be done with satellite imagery alone, but remote sensing and geospatial modelling definitively play a vital role in it.

At the moment, our snow depth data is available for the United States and the European Alps. At its core, it is based on weather station data of snow, temperature and precipitation, which is interpolated in a sophisticated approach and considers main characteristics like topography, wind and sun exposure and regionality.

We validated our product based on 61 airborne snow depth surveys, since these represent the costly standard of retrieving snow depth data in catchments that we want to make more efficient. Basically, the difference of surface heights between one observation in summer and one in winter is measured. Oftentimes, the results contain negative or unrealistically high snow depth values, that we clipped or masked for comparison. Overall, the average correlation coefficients and RMSE is 0.32 and 0.85 m in the US and 0.22 and 0.53 m in Switzerland, respectively. Given the highly variable nature of snow depths, high spatial resolution and limitations of the compared-to datasets, these numbers indicate an important milestone towards improved snow monitoring in alpine regions with versatile applications.

COSMOS-SD Accuracy Assessment Example

From Snow Depth to Snow-Water-Equivalent

A block of snow can be evaluated a bit like a tree with its rings. Sequences of snowfall events accumulate snow on top of each other and put pressure on existing layers that maybe already developed a crust from sun exposure. Old layers might had already started to melt and changed from champagne powder that off-piste skiers seek to slushy mixes of ice crystals and water.

National Avalanche Services developed physical models that try to keep track of the energy influxes and outputs in a system approach, but up to this day, field trips and manual sample collections are essential for them.

Our model to generate snow densities and therefore snow-water-equivalents (SWE) is based on an empirical approach with an estimated RMSE of 53 kg/m2. It leaves out the many details mentioned above but rather gives a general estimation at larger scales, in order to derive the one parameter most valuable for hydro power plants, the amount of water they can expect from snow melt.

In the future, we will regionally optimise the results with in-situ data from our partners. It will contain vertical layering information at point locations that could enable us to derive interpolated maps.