Spectral reflectance behavior of different boreal snow types

Henna-Reetta Hannula,Jouni Pulliainen

doi:10.1017/jog.2019.68

Abstract

AbstractSpectral reflectance of natural snow samples representing various stratigraphies was investigated in a controlled dark laboratory environment. Mean and Std dev. of band specific reflectance values were determined for several satellite sensor bands utilized in remote sensing of snow. The reflectance values for dry, moist, wet and wet and littered snow for different instruments varied between 0.63–0.97 in the visible and near-infrared bands at an incoming light zenith angle of θ = 55°. The results indicate that in MODIS band 4 (545–565 nm), essential to snow mapping, the reflectance of snow drops by 9% when dry snow changes to wet snow and by a further 10% when typical forest litter inclusions exist on the wet snow surface. A separate investigation of individual snow types revealed that they can be grouped either as dry or wet snow based on their spectral behavior. However, some snow types were located between these two distinct groups, such as snow with near-surface melt-freeze crusts, and could not be clearly distinguished. The reflectance statistics collected and analyzed here can be directly used to refine accuracy characterization and parametrization of snow mapping algorithms, such as the SCAmod method, used for the mapping of snow cover area.

Highlights

Seasonal snow cover is linked with the surrounding environment through feedback mechanisms which may intensify or weaken global environmental change
The results indicate that in moderate-resolution imaging spectroradiometer (MODIS) band 4 (545–565 nm), essential to snow mapping, the reflectance of snow drops by 9% when dry snow changes to wet snow and by a further 10% when typical forest litter inclusions exist on the wet snow surface
Some snow types were located between these two distinct groups, such as snow with near-surface melt-freeze crusts, and could not be clearly distinguished

Summary

Introduction

Seasonal snow cover is linked with the surrounding environment through feedback mechanisms which may intensify or weaken global environmental change. Spectral unmixing or inverse model-based methods can be used to retrieve the snow cover area (SCA), or within a satellite pixel, the fractional snow coverage (FSC) from the optical satellite data (Vikhamar and Solberg, 2002, 2003; Painter and others, 2003, 2009; Metsämäki and others, 2005, 2012, 2015; Dozier and others, 2009). These methods describe the scene reflectance as a combination of spectral signatures of model parameters (i.e. end-members), such as snow, forest canopy and snow-free ground. Snow reflectance information gathered in the laboratory or in the field with concurrent in situ measurements, describing the micro- and macro-physical characteristics of snow, can be utilized to further develop the snow algorithms used in satellite remote sensing, and to decrease the existing uncertainties of retrievals

Methods

Results

Discussion

Conclusion