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

Li, Wei; Stamnes, Knut; Chen, Bingquan; Xiong, Xiaozhen

doi:10.1029/2000gl011641

Cited by 79 publications

(67 citation statements)

References 15 publications

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“…The average grain size was observed 161 and 187 µm for this study area, and 183 and 216 µm at station-2 using wavelengths 1240 and 1050 nm, respectively (Tables 3 and 4). The larger grain size retrieved using wavelength 1050 nm compared to 1240 nm can be explained by the fact that the retrieved grain size depends on radiation penetration depth, which is wavelength dependent (Bourdelles and Fily, 1993;Li et al, 2001). The vertical homogeneity of snowpack can also be defined by the ratio of retrieved grain size at the 1050 nm to the 1240 nm wavelength channels, and we found this ratio 1.29 at the station-1 and 1.18 at station-2.…”

Section: Albedomentioning

confidence: 62%

“…Nolin and Dozier (2000) further made a hyperspectral method for snow grain size retrieval using imaging spectrometer AVIRIS data, which integrates across the entire 1.03 µm absorption feature, which is scaled spectrally by its continuum. Li et al (2001) retrieved snow grain size from near-infrared radiances at multiple wavelengths 0.86, 1.05, 1.24 and 1.73 µm from AVIRIS and found the retrieved snow grain size depend on wavelength. Painter et al (2003) estimated and validated the subpixel snow cover and snow grain size using AVIRIS data.…”

Section: Remote Sensing Of Snow Grain Sizementioning

confidence: 99%

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

Negi

Kokhanovsky

2011

The Cryosphere

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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 airborne sensors.

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Section: Albedomentioning

confidence: 62%

Section: Remote Sensing Of Snow Grain Sizementioning

confidence: 99%

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

Negi

Kokhanovsky

2011

The Cryosphere

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“…Using the simple approach outlined in the previous section, we obtained effective diameters equal to 152, 182 and 144 µm for the Grand Mesa, Lake Granby and Derby Peak scene, respectively, when using the RSP band at 2264 nm. Using instead the 1594 nm channel, diameters typically 30 µm smaller are obtained for each scene, which can be explained by the different penetration depths of the two channels, indicating crystal size increasing with depth as expected Li et al, 2001;Aoki et al, 2000;Warren, 1982). These values, at the lower end of common retrieval ranges, are normally associated with fresh snow conditions as found in studies based on other remote sensors in alpine regions (Negi and Kokhanovsky, 2011;Painter et al, 2009;Dozier and Painter, 2004;Nolin and Dozier, 2000) and polar plateaus (Lyapustin et al, 2009;Hori et al, 2007).…”

Section: Resultsmentioning

confidence: 67%

“…For conditions equivalent to those found in snowpacks, Bi et al (2014) showed that the errors in retrieved effective radius attributable to the use of the conventional geometric optics approach are below 5 %. Another important aspect to consider is that the 1594 and 2264 nm channels experience different penetration depths, with the former weighted more towards the top layer, while the latter probes deeper into the medium; as a consequence, the retrieved sizes contain information on the vertical structure of the snowpack Li et al, 2001;Warren, 1982).…”

Section: Methodsmentioning

confidence: 99%

Photopolarimetric retrievals of snow properties

2015

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Abstract. Polarimetric observations of snow surfaces, obtained in the 410-2264 nm range with the Research Scanning Polarimeter onboard the NASA ER-2 high-altitude aircraft, are analyzed and presented. These novel measurements are of interest to the remote sensing community because the overwhelming brightness of snow plagues aerosol and cloud retrievals based on airborne and spaceborne total reflection measurements. The spectral signatures of the polarized reflectance of snow are therefore worthwhile investigating in order to provide guidance for the adaptation of algorithms currently employed for the retrieval of aerosol properties over soil and vegetated surfaces. At the same time, the increased information content of polarimetric measurements allows for a meaningful characterization of the snow medium. In our case, the grains are modeled as hexagonal prisms of variable aspect ratios and microscale roughness, yielding retrievals of the grains' scattering asymmetry parameter, shape and size. The results agree with our previous findings based on a more limited data set, with the majority of retrievals leading to moderately rough crystals of extreme aspect ratios, for each scene corresponding to a single value of the asymmetry parameter.

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“…The former (BT11) can be used as a direct indicator of the thermal condition of snow cover, whereas the latter (Ref1.6) varies depending on snow grain size at the top surface and thus the variation of Ref1.6 can be interpreted as the change of surface snow type. In addition, the penetration depth of light at 1.6 m is within a depth of 1 cm (Li et al, 2001) which is shallower than those at shorter wavelengths and close to the skin depth of TIR radiation. Thus, Ref1.6 was used in this study to assess the change of surface snow type and compared with TIR emissive behaviors of snow cover.…”

Section: Methodsmentioning

confidence: 87%

Dependence of thermal infrared emissive behaviors of snow cover on the surface snow type

Hori

Aoki²,

Tanikawa

et al. 2014

Bulletin of Glacier Research

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The potential of the thermal infrared (TIR) remote sensing for discriminating surface snow types was examined by analyzing TIR radiances acquired from space over the Greenland ice sheet. The brightness temperature difference (BTD) between TIR wavelengths of 11 and 12 m was found to increase in accordance with in situ observed evolutions of surface snow type. Spatial and temporal distributions of BTD over the entire ice sheet indicated that BTD has a sensitivity of about 1.2 K for variations of the possible snow types. The observed behaviors of BTD were coincident with those predicted by a radiative transfer calculation using previous in situ measured snow emissivities, although some biases on the order of 0.1-0.3 K remain. The dependence of BTD on the surface snow type was also consistent with the behaviors of snow reflectance at the shortwave infrared (SWIR) wavelength 1.6 m, which is a measure of snow grain size, except for the case of melting wet snow. The inconsistency in the wet snow case was considered to be due to the different optical responses of the TIR and SWIR signals to wet snow, which suggested the possibility of using TIR signals to discriminate wet/dry conditions of snow cover in an old stage. As a result, it is determined that TIR remote sensing has potential not only as an approach supplementary to the SWIR method for assessing surface snow types in daytime but also as the only method for simultaneous retrieval of snow type and surface temperature in nighttime.

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

Cited by 79 publications

References 15 publications

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

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

Photopolarimetric retrievals of snow properties

Dependence of thermal infrared emissive behaviors of snow cover on the surface snow type

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