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

Himalayan alpine ecohydrology: An urgent scientific concern in a changing climate

Climate change is projected to have important impacts on snow and vegetation distribution in global mountains. Despite this, the coupling of ecological shifts and hydrological processes within alpine zones has not attracted significant scientific attention. As the largest and one of the most climatically sensitive mountain systems, we argue that Himalayan alpine ecohydrological processes require urgent scientific attention because up to 1.6 billion people rely on water supplies from the mountains. We review studies from global mountain systems to highlight the importance of considering ecohydrological impacts within Himalayan alpine zones (4100-6000 m.a.s.l), explaining mechanisms for interactions between snow and dwarf plants. Our findings highlight the paucity of monitoring stations within Himalayan alpine systems. We suggest that it is likely that alpine ecological shifts will impact hydrological processes, but we found that specific mechanisms and functional relationships are missing for Himalayan systems, so the strength and direction of ecohydrological relationships is currently unknown. We advocate for more purposeful and widespread monitoring efforts below glaciers and above the treeline, calling for new experiments to query the role of small plants within the Himalayan alpine hydrological system. We outline the need for community engagement with alpine ecohydrological experiments, and we explain how new snow and vegetation products derived from remote sensing observations have the potential to improve scientific understanding of the interacting effects of warming and ecohydrological factors in this sensitive region.

The Potential of Sentinel-1A Data for Identification of Debris-Covered Alpine Glacier Based on Machine Learning Approach

Microwave remote sensing is one of the main approaches to glacier monitoring. This paper provides a comparative analysis of how different types of radar information differ in identifying debris-covered alpine glaciers using machine learning algorithms. Based on Sentinel-1A data, three data suites were designed: A backscattering coefficient (BC)-based data suite, a polarization decomposition parameter (PDP)-based data suite, and an interference coherence coefficient (ICC)-based data suite. Four glaciers with very different orientations in different climatic zones of the Tibetan Plateau were selected and classified using an integrated machine learning classification approach. The results showed that: (1) The boosted trees and subspace k-nearest neighbor algorithms were optimal and robust; and (2) the PDP suite (63.41–99.57%) and BC suite (55.85–99.94%) both had good recognition accuracy for all glaciers; notably, the PDP suite exhibited better rock and debris recognition accuracy. We also analyzed the influence of the distribution of glacier surface aspect on the classification accuracy and found that the more asymmetric it was about the sensor orbital plane, the more difficult it was for the BC and PDP suites to recognize the glacier, and a large slope could further reduce the accuracy. Our results suggested that during the inventory or classification of large-scale debris-covered alpine glaciers, priority should be given to polarization decomposition features and elevation information, and it is best to divide the glaciers into multiple subregions based on the spatial relationship between glacier surface aspect and radar beams.

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

The warming climate is threatening to alter inland water resources on a global scale. Within all waterbody types, lake and river systems are vital not only for natural ecosystems but, also, for human society. Snowmelt phenology is also altered by global warming, and snowmelt is the primary water supply source for many river and lake systems around the globe. Hence, (1) monitoring snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced river and lake systems, and (3) quantifying the causal effect of snowmelt conditions on these waterbodies are critical to understand the cryo-hydrosphere interactions under climate change. Previous studies utilized in-situ or multispectral sensors to track either the surface areas or water levels of waterbodies, which are constrained to small-scale regions and limited by cloud cover, respectively. On the contrary, in the present study, we employed the latest Sentinel-1 synthetic aperture radar (SAR) and Sentinel-3 altimetry data to grant a high-resolution, cloud-free, and illumination-independent comprehensive inland water dynamics monitoring strategy. Moreover, in contrast to previous studies utilizing in-house algorithms, we employed freely available cloud-based services to ensure a broad applicability with high efficiency. Based on altimetry and SAR data, the water level and the water-covered extent (WCE) (surface area of lakes and the flooded area of rivers) can be successfully measured. Furthermore, by fusing the water level and surface area information, for Lake Urmia, we can estimate the hypsometry and derive the water volume change. Additionally, for the Brahmaputra River, the variations of both the water level and the flooded area can be tracked. Last, but not least, together with the wet snow cover extent (WSCE) mapped with SAR imagery, we can analyze the influence of snowmelt conditions on water resource variations. The distributed lag model (DLM) initially developed in the econometrics discipline was employed, and the lagged causal effect of snowmelt conditions on inland water resources was eventually assessed.