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The main objective of this article is to develop a physically constrained support vector machine (SVM) to predict C-band backscatter over snow-covered terrain as a function of geophysical inputs that reasonably represent the relevant characteristics of the snowpack. Sentinel-1 observations, in conjunction with geophysical variables from the Noah-MP land surface model, were used as training targets and input datasets, respectively. Robustness of the SVM prediction was analyzed in terms of training targets, training windows, and physical constraints related to snow liquid water content. The results showed that a combination of ascending and descending overpasses yielded the highest coverage of prediction (15.2%) while root mean square error (RMSE) ranged from 2.06 to 2.54 dB and unbiased RMSE ranged from 1.54 to 2.08 dB, but that the combined overpasses were degraded compared with ascending-only and descending-only training target sets due to the mixture of distinctive microwave signals during different times of the day (i.e., 6 A.M. versus 6 P.M. local time). Elongation of the training window length also increased the spatial coverage of prediction (given the sparsity of the training sets), but resulted in introducing more random errors. Finally, delineation of dry versus wet snow pixels for SVM training resulted in improving the accuracy of predicted backscatter relative to training on a mixture of dry and wet snow conditions. The overall results suggest that the prediction accuracy of the SVM was strongly linked with the first-order physics of the electromagnetic response of different snow conditions. ispartof: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING vol:14 pages:2403-2417 ispartof: location:United States status: published

Seasonal snow is an essential water resource in many mountain regions. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) at regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We investigate the ability of the Sentinel-1 mission to monitor snow depth at sub-kilometer (100 m, 500 m, and 1 km) resolutions over the European Alps for 2017–2019. The Sentinel-1 backscatter observations, especially in cross-polarization, show a high correlation with regional model simulations of snow depth over Austria and Switzerland. The observed changes in radar backscatter with the accumulation or ablation of snow are used in an empirical change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. Compared to in situ measurements at 743 sites in the European Alps, dry snow depth retrievals at 500 m and 1 km resolution have a spatio-temporal correlation of 0.89. The mean absolute error equals 20 %–30 % of the measured values for snow depths between 1.5 and 3 m. The performance slightly degrades for retrievals at the finer 100 m spatial resolution as well as for retrievals of shallower and deeper snow. The results demonstrate the ability of Sentinel-1 to provide snow estimates in mountainous regions where satellite-based estimates of snow mass are currently lacking. The retrievals can improve our knowledge of seasonal snow mass in areas with complex topography and benefit a number of applications, such as water resource management, flood forecasting, and numerical weather prediction. However, future research is recommended to further investigate the physical basis of the sensitivity of Sentinel-1 backscatter observations to snow accumulation.

Monitoring of snow cover over large areal extent requires the use of remotely sensed satellite data. Conventional snow cover mapping algorithms using SAR data have seldom utilized target scattering information for land cover characterization. In this paper, an approach is proposed for snow cover mapping that utilizes the Touzi eigenvalue-eigenvector-based decomposition parameters viz., the scattering helicities $(\tau _{m}; m = 1, 2)$ , the scattering magnitude $(\alpha _{s})$ , and the entropy $(H)$ . The seasonal variation of these parameters is used as an indicator of scattering mechanism dominance in the proposed algorithm. The C -band RADARSAT-2 (FQ20) winter–summer image pairs over the Manali-Dhundi region of Himachal Pradesh, India, located in the western Hindu-Kush Himalayas are used in this paper. The proposed methodology is able to delineate the snow-covered areas suitably. The temporal extent of the cover is also quantified and validated with field campaigns and weather data. Furthermore, the results obtained from the proposed methodology are compared with NDSI-based snow cover maps derived from LANDSAT-8 images.

Remote sensing is an indispensable tool for Earth observation over large areal extent. Snow cover extent monitoring has been one such application where microwave sensors have been a popular choice due to their sensitivity toward the dielectric property. Snow is a dynamic matter since its dielectric state is dependent on climatic factors prevailing locally around it. In the literature, the polarization fraction (PF) has been used for landcover characterization. In this paper, we utilize the seasonal variation of the PF for mapping snow cover over the Himalayan terrain. Temporal variation of the strength in the polarized return due to change in landcover and season is the prime motivation behind this approach. In addition, the effect of SAR data acquisition time on mapping algorithms is considered in this work which is seldom discussed in the literature. Furthermore, a study is conducted to analyze the seasonal variation in the entropy ( $H$ ) and the scattering type ( $\alpha$ ) parameters over the bare snow-covered ground and the forested areas. The seasonal response of the terrain in the $H/\alpha$ plane corresponding to specific scattering characteristics is analyzed. The applicability of the approach is tested with RADARSAT-2 (FQ28) C-band full polarimetric image pairs over the Manali–Dhundi region, Himachal Pradesh, India, located in the western Hindu-Kush Himalayas. The snow cover map is explicitly validated with in situ observatory measurements and optical satellite imagery.