A Combination of PROBA-V/MODIS-Based Products with Sentinel-1 SAR Data for Detecting Wet and Dry Snow Cover in Mountainous Areas

Abstract

In the present study, we explore the value of employing both vegetation indexes as well as land surface temperature derived from Project for On-Board Autonomy – Vegetation (PROBA-V) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, respectively, to support the detection of total (wet + dry) snow cover extent (SCE) based on a simple tuning machine learning approach and provide reliability maps for further analysis. We utilize Sentinel-1-based synthetic aperture radar (SAR) observations, including backscatter coefficient, interferometric coherence, and polarimetric parameters, and four topographical factors as well as vegetation and temperature information to detect the total SCE with a land cover-dependent random forest-based approach. Our results show that the overall accuracy and F-measure are over 90% with an ’Area Under the receiver operating characteristic Curve (ROC)’ (AUC) score of approximately 80% over five study areas located in different mountain ranges, continents, and hemispheres. These accuracies are also confirmed by a comprehensive validation approach with different data sources, attesting the robustness and global transferability. Additionally, based on the reliability maps, we find an inversely proportional relationship between classification reliability and vegetation density. In conclusion, comparing to a previous study only utilizing SAR-based observations, the method proposed in the present study provides a complementary approach to achieve a higher total SCE mapping accuracy while maintaining global applicability with reliable accuracy and corresponding uncertainty information.

Sentinel-1 time series for mapping snow cover depletion and timing of snowmelt in Arctic periglacial environments: case study from Zackenberg and Kobbefjord, Greenland

“…According to Tsai et al (2019b), SAR time series have rarely been investigated for SC mapping as most studies apply their method only on a few scenes and mostly for one single year, hence not taking advantage of the abundant data available. Only Tsai et al (2019a, c) developed a SC monitoring approach using the entire temporal information of a S-1 SAR time series by incorporating interferometric, polarimetric, and backscatter features as well as elevation and land cover information into a supervised classification approach. On the contrary, recent studies developed methods for snow depth estimation (Lievens et al, 2019) and snowmelt phase detection (Marin et al, 2020) based only on the distinct seasonal SAR backscatter signal of snow described in the following (Fig.…”

Sentinel-1 time series for mapping snow cover and timing of snowmelt in Arctic periglacial environments: Case study from the Zackenberg Valley, Greenland

“…According to Tsai et al (2019b), SAR time series have rarely been investigated for SC mapping as most studies apply their method only on a few scenes and mostly for one single year, hence not taking advantage of the abundant data available. So far, only Tsai et al (2019a, c) developed a SC monitoring approach using the entire temporal information of a S-1 SAR time series by incorporating interferometric, polarimetric and backscatter intensity features as well as elevation and land cover information into a supervised classification approach. On the contrary, recent studies developed methods for snow depth estimation (Lievens et al, 2019) and snowmelt phase detection (Marin et al, 2020) based only on the distinct seasonal SAR backscatter signal of snow described in the following (Fig.…”

“…With this new approach multiple advances compared to other recent studies on SAR-based SC detection and the current standard, Nagler's method, are made: (i) we use the entire time series instead of only a few images per year unlike most previous studies (according to Tsai et al, 2019b); (ii) we avoid the manual selection of reference images for Nagler's method (Nagler and Rott, 2000) omits challenges like finding a completely snow-free or dry snow scene as well as a potential deterioration of the reference caused by altered backscatter signals due to perennial snow and firn. Due to the simple backscatter threshold approach, we keep analysis fast and reduce processing capacity compared to the supervised classification approach by Tsai et al (2019a, c), who additionally calculated interferometric and polarimetric features. Even though the spatial resolution is lower in their approach (100 m), resulting overall accuracies for likewise low vegetated areas are similar to the observed ones here (Tsai et al, 2019c).…”

Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt

“…It utilizes two SAR scenes (one is a wet snow-covered period image, and the other is a referenced snow-free image) and calculates their ratio image, which is then thresholded using a fixed value to derive the binary WSCE. For a detailed description of the processing steps and value setting, refer to [9,76,77].…”

“…Secondly, in our study, we used the WSCE% for representing the snowmelt condition. Although it is efficient and straightforward to depict the whole snowmelt dynamics [9,11,76,77], it cannot provide the information of how much snowmelt water is actually generated during the snowmelt period. Instead, the measurement of either SWE or LWC should be used.…”