Optimal Estimation of Snow and Ice Surface Parameters from Imaging Spectroscopy Measurements

Abstract

Snow and ice melt processes are a key in Earth's energy-balance and hydrological cycle. Their quantification facilitates predictions of meltwater runoff as well as distribution and availability of fresh water. They control the balance of the Earth's ice sheets and are acutely sensitive to climate change. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), that require imaging spectroscopy to map and measure. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and spaceborne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in an inversion that also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. A validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet gave uncertainties of ±16.4 µg/g ice and less than 3%, respectively. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach's potential and suitability for upcoming orbital imaging spectroscopy missions.

Glacier Ice Surface Properties in South‐West Greenland Ice Sheet: First Estimates From PRISMA Imaging Spectroscopy Data

“…Thus, corresponding retrieval uncertainties are introduced, which was one of the key findings in Bohn et al. (2021). Furthermore, posterior errors for the glacier algae species are strongly negatively correlated since their absorption features are similar (Cook et al., 2020).…”

“…For simplicity, we assume an infinite, horizontal, and isotropic Lambertian surface as well as clear sky and a plane‐parallel atmosphere. Furthermore, surface roughness at subpixel scale tends to make snow reflectance more Lambertian, so that these assumptions ensure validity of using spectral albedo in place of HDRF (Bohn et al., 2021). The atmospheric flux parameters L 0 , E dir , E dif , T ↑ , and S are functions of x ATM , surface elevation as well as solar and observation geometry.…”

“…(2014). To enable the estimation of surface liquid water, we exploit the coupling of BioSNICAR‐GO with a two‐layer coated sphere reflectance model for wet snow (Bohn et al., 2021; Green et al., 2002). The model assumes an increased grain radius attributed to a particular liquid water fraction, and is based on a slight shift between the imaginary parts of the spectral refractive index of liquid water and ice (Dozier & Painter, 2004).…”

“…The algorithm was introduced by Bohn et al. (2021) and is an extended version of the concept presented in Thompson et al. (2018).…”

Joint Characterization of the Cryospheric Spectral Feature Space

“…cryospheric spectra discussed by (Dozier and Painter 2004;Dozier et al, 2007) (Bohn et al, 2021) and (Warren 2019).…”