Abstract
Improving the estimation of snow density is a key task in current snow research. Characterization of the variability of density in time and space is essential for the estimation of water equivalent, hydroelectric power production, assessment of natural hazards (avalanches, floods, etc.). Hyperspectral imaging is proving to be a promising and reliable tool for monitoring and estimating this physical property. Indeed, the spectral reflectance of snow is partly controlled by changes in its physical properties, particularly in the near-infrared (NIR) part of the spectrum. For this purpose, several models have been designed to estimate snow density from spectral information. However, none has yet achieved significant performance. One of the major difficulties is that the relationship between snow density and spectral reflectance is non-bijective (surjective). Indeed, several reflectance amplitudes can be associated with the same density and vice versa, so the correlation between density and spectral reflectance can be very poor. To resolve this issue, a hybrid snow density estimation model based on spectral data is proposed in this work. The principle behind this model is to classify the snow density prior to its estimation by means of a specific estimator corresponding to a predetermined snow density class. These additional steps eliminate the surjective relation by converting it into three bijective relations between density and spectral reflectance. The calibration step showed that the densities included within the three classes are sensitive to different spectral regions, with R2 > 0.80. The results of the cross-validation for the specific estimators were also satisfactory with R2 > 0.78 and RMSE < 36.36 kg m−3. The overall performance of the hybrid model (HM), when tested with independent data, demonstrated the effectiveness of using proximal NIR hyperspectral imagery to estimate snow density (R2 = NASH = 0.93).
Highlights
7), it is difficult todensification, evaluate the extent toiswhich be attributedrelating to the physical cause of and this wherethe thedecrease difficultyshould of experimentally underlying physical cause of densification, and this is where the difficulty of experimenspectral reflectance to snow density lies
The hybrid model was evaluated at two levels: using the leave-one-out cross-validation (LOOCV) algorithm and using the systematic division validation technique (SSV)
The LOOCV technique was used to assess the three specific estimators, and the SSV data were used to assess the performance of the hybrid model (HM)
Summary
Quantifying the variability of density in time and space is essential for estimating the water equivalent [3,4], hydroelectric power production [5,6], and assessing natural hazards (avalanches, floods, etc.) [7,8]. This variable varies with changes in other physical properties such as grain size, grain shape, and liquid water content during the metamorphic transformation of the snowpack [9]. According to Pomeroy et al [10], the typical seasonal density of the snowpack ranges between 80 kg m−3 and 600 kg m−3
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
