Retrieved Snow Grain Research Articles

Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements

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/gice 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.

Retrieval of Dust Properties From Spectral Snow Reflectance Measurements

In this paper, we present a simplified algorithm to retrieve snow grain size, dust mass absorption coefficient and dust mass concentration from spectral diffuse reflectance measurements at three wavelengths located in the visible and near infrared. The theoretical model is then compared with field spectroscopy data collected in different days from an automated spectrometer in the Alps.

Simulated reflectance above snow constrained by airborne measurements of solar radiation: implications for the snow grain morphology in the Arctic

Abstract. Accurate knowledge of the reflectance from snow/ice-covered surfaces is of fundamental importance for the retrieval of snow parameters and atmospheric constituents from space-based and airborne observations. In this paper, we simulate the reflectance in a snow–atmosphere system, using the phenomenological radiative transfer model SCIATRAN, and compare the results with that of airborne measurements. To minimize the differences between measurements and simulation, we determine and employ the key atmospheric and surface parameters, such as snow grain morphologies (or habits). First, we report on a sensitivity study. This addresses the requirement for adequate a priori knowledge about snow models and ancillary information about the atmosphere. For this aim, we use the well-validated phenomenological radiative transfer model, SCIATRAN. Second, we present and apply a two-stage snow grain morphology (i.e., size and shape of ice crystals in the snow) retrieval algorithm. We then describe the use of this new retrieval for estimating the most representative snow model, using different types of snow morphologies, for the airborne observation conditions performed by NASA's Cloud Absorption Radiometer (CAR). Third, we present a comprehensive comparison of the simulated reflectance (using retrieved snow grain size and shape and independent atmospheric data) with that from airborne CAR measurements in the visible (0.670 µm) and near infrared (NIR; 0.870 and 1.6 µm) wavelength range. The results of this comparison are used to assess the quality and accuracy of the radiative transfer model in the simulation of the reflectance in a coupled snow–atmosphere system. Assuming that the snow layer consists of ice crystals with aggregates of eight column ice habit and having an effective radius of ∼99 µm, we find that, for a surface covered by old snow, the Pearson correlation coefficient, R, between measurements and simulations is 0.98 (R2∼0.96). For freshly fallen snow, assuming that the snow layer consists of the aggregate of five plates ice habit with an effective radius of ∼83 µm and having surface inhomogeneity, the correlation is ∼0.97 (R2∼0.94) in the infrared and 0.88 (R2∼0.77) in the visible wavelengths. The largest differences between simulated and measured values are observed in the glint area (i.e., in the angular regions of specular and near-specular reflection), with relative azimuth angles <±40∘ in the forward-scattering direction. The absolute difference between the modeled results and measurements in off-glint regions, with a viewing zenith angle of less than 50∘, is generally small ∼±0.025 and does not exceed ±0.05. These results will help to improve the calculation of snow surface reflectance and relevant assumptions in the snow–atmosphere system algorithms (e.g., aerosol optical thickness retrieval algorithms in the polar regions).

Snow grain size retrieval over the polar ice sheets with the Ice, Cloud, and land Elevation Satellite (ICESat) observations

Snow grain size is an important parameter for cryosphere studies. As a proof of concept, this paper presents an approach to retrieve this parameter over Greenland, East and West Antarctica ice sheets from surface reflectances observed with the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat) at 1064nm. Spaceborne lidar observations overcome many of the disadvantages in passive remote sensing, including difficulties in cloud screening and low sun angle limitations and hence tend to provide more accurate and stable retrievals. Results from the GLAS L2A campaign, which began on 25 September and lasted until 19 November, 2003, show that the mode of the grain size distribution over Greenland is the largest (~300µm) among the three, West Antarctica is the second (~220µm) and East Antarctica is the smallest (~190µm). Snow grain sizes are larger over the coastal regions compared to inland the ice sheets. These results are consistent with previous studies. Applying the broadband snow surface albedo parameterization scheme developed by Garder and Sharp (2010) to the retrieved snow grain size, ice sheet surface albedo is also derived. In the future, more accurate retrievals can be achieved with multiple wavelengths lidar observations.

Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model

Abstract. DMRT-ML is a physically based numerical model designed to compute the thermal microwave emission of a given snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1–200 GHz similar to those acquired routinely by space-based microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the model to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large ice-sheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software. A convenient user interface is provided in Python.

Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data

Abstract. In the present study we describe the retrievals of snow grain size and spectral albedo (plane and spherical albedo) for western Himalayan snow cover using Hyperion sensor data. The asymptotic radiative transfer (ART) theory was explored for the snow retrievals. To make the methodology operational only five spectral bands (440, 500, 1050, 1240 and 1650 nm) of Hyperion were used for snow parameters retrieval. The bi-spectral method (440 nm in the visible and 1050/1240 nm in the NIR region) was used to retrieve snow grain size. Spectral albedos were retrieved using satellite reflectances and estimated grain size. A good agreement was observed between retrieved snow parameters and ground observed snow-meteorological conditions. The satellite retrieved grain sizes were compared with field spectroradiometer retrieved grain sizes and close results were found for lower Himalayan snow. The wavelength 1240 nm was found to be more suitable compared to 1050 nm for grain size retrieval along the steep slopes. The methodology was able to retrieve the spatial variations in snow parameters in different parts of western Himalaya which are due to snow climatic and terrain conditions of Himalaya. This methodology is of importance for operational snow cover and glacier monitoring in Himalayan region using space-borne and air-borne sensors.

Retrieval of snow albedo and grain size using reflectance measurements in Himalayan basin

Abstract. In the present paper, spectral reflectance measurements of Himalayan seasonal snow were carried out and analysed to retrieve the snow albedo and effective grain size. The asymptotic radiative transfer (ART) theory was applied to retrieve the plane and spherical albedo. The retrieved plane albedo was compared with the measured spectral albedo and a good agreement was observed with ±10% differences. Retrieved integrated albedo was found within ±6% difference with ground observed broadband albedo. The retrieved snow grain sizes using different models based on the ART theory were compared for various snow types and it was observed that the grain size model using two channel method (one in visible and another in NIR region) can work well for the Himalayan seasonal snow and it was found consistent with temporal changes in grain size. This method can work very well for clean, dry snow as in the upper Himalaya, but sometimes, due to the low reflectances (<20%) using wavelength 1.24 μm, the ART theory cannot be applied, which is common in lower and middle Himalayan old snow. This study is important for monitoring the Himalayan cryosphere using air-borne or space-borne sensors.

Snow optical properties for different particle shapes with application to snow grain size retrieval and MODIS/CERES radiance comparison over Antarctica

We investigated the single scattering optical properties of snow for different ice particle shapes and degrees of microscopic scale roughness. These optical properties were implemented and tested in a coupled atmosphere-snow radiative transfer model. The modeled surface spectral albedo and radiance distribution were compared with surface measurements. The results show that the reflected radiance and irradiance over snow are sensitive to the snow grain size and its vertical profile. When inhomogeneity of the particle size distribution in the vertical is taken into account, the measured spectral albedo can be matched, regardless of the particle shapes. But this is not true for the modeled radiance distribution, which depends a lot on the particle shape. The usual “equivalent spheres” assumption significantly overestimates forward reflected radiances, and underestimates backscattering radiances, around the principal plane. On average, the aggregate shape assumption has the best agreement with the measured radiances to a mean bias within 2%. The snow optical properties with the aggregate assumption were applied to the retrieval of snow grain size over the Antarctic plateau. The retrieved grain sizes of the top layer showed similar and large seasonal variation in all years, but only small year to year variation. Using the retrieved snow grain sizes, the modeled spectral and broadband radiances showed good agreements with MODIS and CERES measurements over the Antarctic plateau. Except for the MODIS 2.13 μm channel, the mean relative model-observation differences are within few percent. The modeled MODIS radiances using measured surface reflectance at Dome C also showed good agreement in visible channels, where radiation is not sensitive to snow grain size and the measured surface bidirectional reflectance is applicable over the Antarctic plateau. But modeled radiances using local, surface-measured reflectance in the near infrared yielded large errors because of the high sensitivity to the snow grain size, which varies spatially and temporally. The CERES broadband shortwave radiance is moderately sensitive to the snow grain size, comparable to the MODIS 0.86 μm channel. The variation of broadband snow reflectance due to the seasonal variation in snow grain size is about 5% in a year over the Antarctic plateau. CERES broadband radiances simulated with grain sizes retrieved using MODIS are about 2% larger than those observed.

ADEOS-II/GLI snow/ice products — Part III: Retrieved results

Spatial and temporal distributions of snow grain size, mass fraction of snow impurity, and snow surface temperature around the northern polar region were retrieved together with snow and sea ice cover extents from radiance data observed by the Global Imager (GLI), an optical sensor onboard Japan's Advanced Earth Observing Satellite-II (ADEOS-II). The sensor acquired global radiance data from April 2 to October 24 in 2003. Two grain sizes, one for the shallow (0–20 cm) layer and the other for the top surface (0–2 cm) layer, were retrieved using channels with different light penetration depths. Snow and sea ice related products derived from the Advanced Microwave Scanning Radiometer (AMSR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) were used for comparison. Retrieved seasonal variations of snow and sea ice cover extents were consistent with the MODIS and AMSR products. GLI derived snow surface temperatures were also correlated with the MODIS surface temperatures and exhibited latitudinal dependence over the northern hemisphere. Retrieved snow impurities were considered to be partly inaccurate over the Arctic sea ice in April and over inland areas of the continents possibly due to thin snow and/or dense vegetation. However, the snow cover around the northern Canadian Arctic tundra and Archipelago regions and the Greenland ice sheet in April to June was found to be very clean with a soot-equivalent impurity fraction of around 0.05 ppmw or less. Thus, these areas can be used as reference sites for monitoring pollution levels of the Arctic snow cover. Retrieved snow grain radii in the shallow layer seemed erroneous over thin snow covers, but indicated drastic spatial and seasonal evolution of the snow cover over the northern hemisphere with radius ranging from 50 μm to over 1000 μm synchronized with variations in surface temperature. The spatial variability of the top surface grain radius was partly different from that of the grain size in the shallow layer suggesting the possibility of detecting vertical inhomogeneity of the top 10 cm of the snow cover.

Infrared Radiative Properties of the Antarctic Plateau from AVHRR Data. Part I: Effect of the Snow Surface

The effective scene temperature, or “brightness temperature,” measured in channel 3 (3.5–3.9 μm) of the Advanced Very High Resolution Radiometer (AVHRR) is shown to be sensitive, in principle, to the effective particle size of snow grains on the Antarctic plateau, over the range of snow grain sizes reported in field studies. In conjunction with a discrete ordinate method radiative transfer model that couples the polar atmosphere with a scattering and absorbing snowpack, the thermal infrared channels of the AVHRR instrument can, therefore, be used to estimate effective grain size at the snow surface over Antarctica. This is subject to uncertainties related to the modeled top-of-atmosphere bidirectional reflectance distribution function resulting from the possible presence of sastrugi and to lack of complete knowledge of snow crystal shapes and habits as they influence the scattering phase function. However, when applied to NOAA-11 and NOAA-12 AVHRR data from 1992, the snow grain effective radii of order 50 μm are retrieved, consistent with field observations, with no apparent discontinuity between two spacecraft having different viewing geometries. Retrieved snow grain effective radii are 10–20-μm larger when the snow grains are modeled as hexagonal solid columns rather than as spheres with a Henyey–Greenstein phase function. Despite the above-mentioned uncertainties, the retrievals are consistent enough that one should be able to monitor climatically significant changes in surface snow grain size due to major precipitation events. It is also shown that a realistic representation of the surface snow grain size is critical when retrieving the optical depth and effective particle radius of clouds for the optically thin clouds most frequently encountered over the Antarctic plateau.

Snow grain size retrieved from near‐infrared radiances at multiple wavelengths

A comprehensive forward radiative transfer model is used to construct a snow grain size retrieval algorithm that relies on the use of NIR radiances. Data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) at the wavelengths 0.86, 1.05, 1.24 and 1.73 µm are used to retrieve snow grain size. Based on a single‐layer (homogeneous) snow model, the retrieved snow grain size appears to depend on wavelength. The photon penetration depth defined as the e‐folding flux attenuation depth has be computed for different snow grain sizes and different wavelengths. It reveals that this apparent wavelength dependence occurs because (i) the snow grain size generally increases with depth, and (ii) the photon penetration depth decreases with increasing wavelength. The results show that the wavelength dependence of the photon penetration depth can be used to retrieve the depth dependence of the snow grain size.