Katsuyuki Kuchiki

Retrieval of snow, physical parameters using a ground-based spectral radiometer

A ground-based spectral radiometer system for albedo and flux (GSAF) was developed to retrieve a mass concentration of snow impurities and effective snow grain size automatically. The GSAF measures spectral albedo and diffuse fraction with a single sensor to omit a radiometric calibration. The deviation from an ideal cosine response of the sensor to insolation is precisely corrected. The snow physical parameters can be retrieved with the GSAF even under cloudy conditions, because the effect of illumination conditions on albedo is considered in a retrieval algorithm. Continuous measurements with the GSAF at two snowfields in Hokkaido, Japan, showed the correlations between the retrieved parameters and in situ measurements (R=0.595 to 0.940).

Numerical simulation of spectral albedos of glacier surfaces covered with glacial microbes in Northwestern Greenland

To clarify the effect of light absorbing impurities including glacial microbes spectral albedo measurements using a spectrometer for spectral domains of the ultraviolet, visible and near-infrared have been carried out on ablation area in Qaanaaq Glacier in northwestern Greenland in July 2011. The almost glacier surfaces in the ablation area were covered with cryoconite (biogenic dust) on thin ice grain layer above bare ice. There were also snow-covered surfaces including red snow (snow algae). The measured spectral albedos had a remarkable contrast between red snow surface and cryoconite-covered ice surface in the spectral domain from the ultraviolet to the visible, where red snow albedo increased rapidly with the wavelength, while the cryoconite albedo was relatively flat to the wavelength. We simulated the spectral albedos of these surfaces with a radiative transfer model for the atmosphere-snow system. The single scattering properties are calculated with Mie theory by assuming red snow gains to be sphe...

In situ measurements of polarization properties of snow surface under the Brewster geometry in Hokkaido, Japan, and northwest Greenland ice sheet

Ground-based measurements of spectral degree of linear polarization (DLP) of various snow types were made during intensive field campaigns in a snowfield in Hokkaido, Japan, and on the northwest Greenland ice sheet in 2012. Spectral measurements were conducted under the solar zenith angle of approximately the Brewster angle in order to quantify the polarization properties of light reflected from snow. We obtained spectral DLPs for five different snow types in both field campaigns including precipitation particles, needles, surface hoar, melt forms, and melt freeze crust covering the snow surface. The measurements showed that in the visible region the spectral dependence of the DLP was small while in the near infrared region it increased with increasing snow grain size with some distinct local peaks. The angular dependence indicated that the DLP exhibited small angular dependence in the visible region while in the near-infrared region it exhibited large and broad peaks in the forward direction. Especially for the melt-freeze crust, the DLP approached 1.0 at wavelengths close to λ = 1.5 and 2.0 μm. These features can be explained by (1) the relative contribution of surface versus volume scattering to the reflected light, (2) the incident angle (solar zenith angle) of approximately the Brewster angle, and (3) the ratio between direct and diffuse components of the solar radiation incident on the snow surface. The spectral DLP was found to be quiet sensitive to the incident solar radiation and solar elevation as well as snow optical properties. Comparison between the spectral DLP and snow grain size obtained by snow pit work shows that the DLP for λ > 1.5 μm was very sensitive to large snow grains close to the surface. This finding suggests that polarization measurements obtained from airborne/satellite polarimeters will be useful for surface snow grain size retrievals and help improve the accuracy of such retrievals based on the intensity-only measurements, especially for the large snow grain sizes.

Retrieval of snow physical parameters by neural networks and optimal estimation: case study for ground-based spectral radiometer system.

A new retrieval algorithm for estimation of snow grain size and impurity concentration from spectral radiation data is developed for remote sensing applications. A radiative transfer (RT) model for the coupled atmosphere-snow system is used as a forward model. This model simulates spectral radiant quantities for visible and near-infrared channels. The forward RT calculation is, however, the most time-consuming part of the forward-inverse modeling. Therefore, we replaced it with a neural network (NN) function for fast computation of radiances and Jacobians. The retrieval scheme is based on an optimal estimation method with a priori constraints. The NN function was also employed to obtain an accurate first guess in the retrieval scheme. Validation with simulation data shows that a combination of NN techniques and optimal estimation method can provide more accurate retrievals than by using only NN techniques. In addition, validation with in-situ measurements conducted by using ground-based spectral radiometer system shows that comparison between retrieved snow parameters with in-situ measurements is acceptable with satisfactory accuracy. The algorithm provides simultaneous, accurate and fast retrieval of the snow properties. The algorithm presented here is useful for airborne/satellite remote sensing.

Possibility to discriminate snow types using brightness temperatures in the thermal infrared wavelength region

Spectral emissivity of snow surface in the thermal infrared (TIR) wavelength region is an important parameter for monitoring snow surface temperature in cold climate regions and also for discriminating clouds and underlying snow surfaces in polar nights using satellite observed brightness temperature data. Past in-situ observations of snow emissivity revealed that the emissivity of snow surfaces varies depending on snow type [1]. Fine dendrite snow exhibits high emissivity over 0.98 in TIR at all exiting angles (θ). As ice granules of snow surface become large, the snow emissivity in TIR decreases and exhibits a wavelength dependence due to enhanced Fresnel reflectance at a wavelength around 12μm. Reduced snow emissivity is further enhanced as exiting angle increases. For example, emissivities of coarse grain snow at wavelengths of 11μm and 12μm are 0.99 and 0.975 for the zenith direction (θ=0°) but 0.965 and 0.93 for the slant direction of θ=75°. For sun crust snow, wavelength and directional dependences...