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Article

Estimating Snowpack Density from Near-Infrared Spectral Reflectance Using a Hybrid Model

Institut National de la Recherche Scientifique (INRS), Québec, QC G1K 9A9, Canada
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Academic Editors: Alexander Kokhanovsky and Gareth Rees
Remote Sens. 2021, 13(20), 4089; https://doi.org/10.3390/rs13204089
Received: 6 August 2021 / Revised: 4 October 2021 / Accepted: 7 October 2021 / Published: 13 October 2021
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). View Full-Text
Keywords: snowpack; snow density; spectral reflectance; near-infrared; hybrid model; hyperspectral imagery snowpack; snow density; spectral reflectance; near-infrared; hybrid model; hyperspectral imagery
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MDPI and ACS Style

El Oufir, M.K.; Chokmani, K.; El Alem, A.; Bernier, M. Estimating Snowpack Density from Near-Infrared Spectral Reflectance Using a Hybrid Model. Remote Sens. 2021, 13, 4089. https://doi.org/10.3390/rs13204089

AMA Style

El Oufir MK, Chokmani K, El Alem A, Bernier M. Estimating Snowpack Density from Near-Infrared Spectral Reflectance Using a Hybrid Model. Remote Sensing. 2021; 13(20):4089. https://doi.org/10.3390/rs13204089

Chicago/Turabian Style

El Oufir, Mohamed Karim, Karem Chokmani, Anas El Alem, and Monique Bernier. 2021. "Estimating Snowpack Density from Near-Infrared Spectral Reflectance Using a Hybrid Model" Remote Sensing 13, no. 20: 4089. https://doi.org/10.3390/rs13204089

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