Abstract
It is difficult and time consuming to use traditional measurement methods to estimate the physical properties of snow. However, the emergence of hyperspectral imagery for estimating the physical properties of snow provides a powerful tool. Snow albedo, grain size, and temperature are important factors for evaluating the surface energy balance. Using the spectrum-reflection curves of the different grain sizes of snow measured in the fields of the Binggou watershed of the Heihe River Basin, China, we analyzed the spectral reflection characteristics of snow. A statistical detection method was used to choose the most sensitive bands in the field spectra and find the corresponding band (band 89) in the Hyperion imagery. The bands near 1033 nm were sensitive to the snow grain size. According to the relationship between the snow grain size and the measured spectrum, we built a snow grain-size estimation model. The results showed that the snow reflectance had a good linear and exponential relationship with the snow grain size. The correlation coefficients of the two models were 0.81 and 0.84, respectively. We obtained the location of the absorption valley at the near-infrared wavelength, and the results showed that 6.9% of the pixels were affected by the snow water content. The locations of the absorption valley moved 1–4 bands from band 89 to shorter wavelengths. The accuracy of the snow grain size estimates based on the Hyperion imagery was relatively high.
Highlights
Snow is an important subject of cryosphere-environment science and is a useful environmental indicator of global changes in terms of long-term monitoring [1]
Dozier et al used the theoretical model of Wiscombe and Warren and NOAA-6 AVHRR data to highlight that using remote sensing tools to measure the snow grain size was a potentially effective method [4], but their result was difficult to interpret because the authors did not address near-infrared bands, which were sensitive to grain size in the NOAA-6 AVHRR data
The wavelength near 1,030 nm is most sensitive to snow grain size, which was found by implementing the variogram algorithm based on the analysis of field spectral and snow grain size measurements in the paper
Summary
Snow is an important subject of cryosphere-environment science and is a useful environmental indicator of global changes in terms of long-term monitoring [1]. Nolin and Dozier used an airborne visible/infrared imaging spectrometer (AVIRIS) and discrete-ordinate model to calculate directional reflectance as a function of snow grain size [7] Their inversion method was validated using measured data and had a higher accuracy for solar incidence angles between 0° and 30°. The authors improved their model by using a radiative transfer model based on the near-infrared reflectance characteristics of snow to obtain a quantitative retrieval of snow grain size [8]. Hemisphere-directed reflections of snow cover at the wavelengths of 1.8 μm and 2.2 μm have been used to estimate snow grain size based on MODIS/ASTER data [9] This approach caused large errors when the grain size was large, and the method was greatly impacted by the atmosphere and solar radiation. The study aimed to estimate the snow grain-size distribution of the Heihe River Basin region in the Binggou watershed and evaluate the estimates using ground-measured data
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