Tomonori Tanikawa

Remote sensing of snow grain-size and impurities from Airborne Multispectral Scanner data using a snow bidirectional reflectance distribution function model

Abstract Algorithms to retrieve the snow grain-size and the concentration of snow impurities were developed using a theoretical bidirectional reflectance distribution function (BRDF) model of the snow surface. In this model, snow grains are assumed to be independent spherical ice particles, and the BRDF is calculated with multiple scattering by snow particles. Using these algorithms, the snow grain-size and snow impurities were retrieved from Airborne Multispectral Scanner (AMSS) images at the visible (λ = 0.545 μm) and near-infrared (λ =1.24,1.64 and 2.23 μm) wavelengths observed over the flat snowfield in eastern Hokkaido, Japan, in February 1998. The estimated snow grain-size and impurities were consistent with the results of in situ measurements on the snow surface. For snow grain-size, measured reflectances in the different near-infrared AMSS channels indicated grain-size differences in the vertical profile of the snowpack.

Comparison of discrete ordinate and Monte Carlo simulations of polarized radiative transfer in two coupled slabs with different refractive indices

A comparison is presented of two different methods for polarized radiative transfer in coupled media consisting of two adjacent slabs with different refractive indices, each slab being a stratified medium with no change in optical properties except in the direction of stratification. One of the methods is based on solving the integro-differential radiative transfer equation for the two coupled slabs using the discrete ordinate approximation. The other method is based on probabilistic and statistical concepts and simulates the propagation of polarized light using the Monte Carlo approach. The emphasis is on non-Rayleigh scattering for particles in the Mie regime. Comparisons with benchmark results available for a slab with constant refractive index show that both methods reproduce these benchmark results when the refractive index is set to be the same in the two slabs. Computed results for test cases with coupling (different refractive indices in the two slabs) show that the two methods produce essentially identical results for identical input in terms of absorption and scattering coefficients and scattering phase matrices.

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.

Cloud mask over snow‐/ice‐covered areas for the GCOM‐C1/SGLI cryosphere mission: Validations over Greenland

Cloud detection is a critically important first step required to derive many satellite data products. A novel cloud detection algorithm designed for the cryosphere mission of Global Climate Observation Mission First Climate satellite/Second Generation Global Imager (GCOM-C1/SGLI) is presented. This reflectance-based cloud detection scheme mainly utilizes only two short wavelength infrared channels with dynamic thresholds that depend on Sun-satellite viewing geometry to perform accurate cloud detection over snow/ice surfaces in high latitude as well as high-elevation regions. Profiles of atmospheric absorbing and scattering molecules as well as surface elevation are considered in the determination of the thresholds for the resulting snow/ice cloud mask (SCM) algorithm. Image-based tests and statistical results have been used to validate the performance of the SCM over the Greenland plateau. Statistics using collocated Cloud-Aerosol Lidar with Orthogonal Polarization and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua observations over Greenland in 2007 show that over snow/ice surfaces the performance of the SCM is generally better than that of the MODIS cloud mask.

An overview of the cryosphere products and validation plans for GCOM-C1/SGLI observations

The “Global Change Observation Mission-Climate” (GCOM-C) is a project of Japan Aerospace Exploration Agency (JAXA) for the global and long-term observation of the Earth environment. The GCOM-C is a part of the JAXA’s GCOM mission which consists of two satellite series, GCOM-C and GCOM-W (Water), spanning three generations in order to perform uniform and stable global observations for 13 years. GCOM-C carries a multi-spectral optical radiometer named Second Generation Global Imager (SGLI), which will have special features of wide spectral coverage from 380nm to 12μm, a high spatial resolution of 250m, a field of view exceeding 1000km, two-direction simultaneous observation, and polarization observation. The GCOM-C mission aims to improve our knowledge on the global carbon cycle and radiation budget through high-accuracy observation of global vegetation, ocean color, temperature, cloud, aerosol, and snow and ice. As for the cryosphere products, not only snow and ice cover extent but also snow physical parameters are retrieved from SGLI data such as snow grain sizes at several surface levels (shallow layer, sub-surface layer, and the top surface), temperature, and mass fraction of impurity mixed in snow layer and so on. These snow physical parameters are important factors that determine spectral albedo and radiation budget at the snow surface. Thus it is essential to monitor those parameters from space in order to better understand snow metamorphosis and melting process and also to study the response of snow and sea-ice cover extent in the Polar Regions to a climate forcing such as global warming. This paper will summarize the SGLI cryospheric products and validation plans.

Advantages of Measuring the Q Stokes Parameter in Addition to the Total Radiance I in the Detection of Absorbing Aerosols

A simple but novel study was conducted to investigate whether an imager-type spectroradiometer instrument like MODIS, currently flying on board the Aqua and Terra satellites, or MERIS, which flew on board Envisat, could detect absorbing aerosols if they could measure the

What if MODIS could measure the Q Stokes parameter

Q

Discrete ordinate and Monte Carlo simulations of radiative transfer in coupled atmosphere-ocean systems

Stokes parameter in addition to the total radiance

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

I