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\begin{document}

\title{Machine Learning Classification and Derived Snow Metrics from
Very-high-resolution Multispectral Satellite Imagery in Complex Terrain}



\author[1]{Michelle Hu}%
\author[1]{David Shean}%
\affil[1]{University of Washington}%


\vspace{-1em}



  
  \date{May 19, 2024}


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\begin{abstract}
Satellite remote sensing often requires a compromise between spatial
resolution and spatial coverage for timely and accurate measurements of
earth-system processes. But in recent years, increased availability of
submeter-scale imagery dramatically altered this balance. Commercial
satellite imagery from DigitalGlobe and Planet offer on-demand, very
high-resolution panchromatic stereo and multispectral (MS) image
collection over snow-covered landscapes, with individual image coverage
of up to \textasciitilde{}1900 km2. Repeat stereo-derived digital
elevation models can be used to accurately estimate snow depth.
Integration of contemporaneous \textasciitilde{}1--2 m land cover
classification maps can provide precise snow-covered area (SCA) products
and improved processing, analysis, and interpretation of these snow
depth estimates. We are developing machine learning classification
algorithms to identify snow, vegetation, water, and exposed rock using
varying combinations of available bands (panchromatic, 4/8-band
multispectral, SWIR) and band ratios (e.g. NDVI, NDSI) from these
products. We present findings for NASA SnowEx campaign sites (Grand Mesa
and Senator Beck Basin, CO) and other snow monitoring sites in the
Western U.S. using WorldView-3, PlanetScope, and Landsat 8 imagery.
Preliminary results show that a tuned random forest algorithm using
WorldView-3 MS and SWIR bands yielded the most accurate estimates of SCA
of all band combinations and imagery products. With the power to resolve
individual trees, these products offer direct measurements of SCA,
without the need to account for mixed pixels and fractional SCA as with
lower-resolution products. This open-source workflow will be used to
process longer time-series and larger areas in a semi-automated fashion,
allowing for rapid analysis, increased portability, and broader utility
for the community.%
\end{abstract}%



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