Snow Cover Distribution Research Articles

Investigating spatial-temporal trend of snow cover over the three provinces of Northeast China based on a cloud-free MODIS snow cover product

Snow serves as a pivotal water resource in the three provinces of Northeast China (TPNC), the investigation of spatial and temporal distribution changes of snow cover is essential for the region’s efficient water resource management. The Moderate Resolution Spectroradiometer (MODIS) snow cover product has proven effective in providing detailed spatial–temporal distribution and identification of snow cover. However, its accuracy and reliability are significantly hampered by cloud obscuration. To address this issue, we have developed a novel four-step cloud removal approach, which includes Terra and Aqua combination, 5-day temporal combination, snowline method (IMS) as well as 8-pixel and 24-pixel adjacent pixel method. Results indicate that the proposed approach successfully eliminates all cloud cover, yielding a highly accurate cloud-free snow cover product with validation accuracies of 92% and 97% when using in situ snow depth measurements and cloud masking method, respectively. We investigated the spatial and temporal variations of snow cover for the TPNC during 2003–2018 with snow coverage and snow cover days (SCD) and analyzed the correlation between snow cover and climate factors. The results showed that the annual basin-averaged snow coverage increased at a rate of 1.27% yr−1 before 2009 and decreased at a rate of 1.80% yr−1 after 2009. The Mann-Kendall tests indicated that about 72% of the total area showed an increasing trend of annual SCD during 2003–2018. Conversely, 94% of the total area exhibited a decreasing trend of annual SCD for the period of 2009–2018 due to the increasing trend of temperature and decreasing trend of precipitation. Our approach shows a promising application of generating accurate cloud-free products and its potential to examine spatial–temporal variations of snow cover under climate change for other snow-covered regions globally.

Snow Cover Extraction from Landsat 8 OLI Based on Deep Learning with Cross-Scale Edge-Aware and Attention Mechanism

Snow cover distribution is of great significance for climate change and water resource management. Current deep learning-based methods for extracting snow cover from remote sensing images face challenges such as insufficient local detail awareness and inadequate utilization of global semantic information. In this study, a snow cover extraction algorithm integrating cross-scale edge perception and an attention mechanism on the U-net model architecture is proposed. The cross-scale edge perception module replaces the original jump connection of U-net, enhances the low-level image features by introducing edge detection on the shallow feature scale, and enhances the detail perception via branch separation and fusion features on the deep feature scale. Meanwhile, parallel channel and spatial attention mechanisms are introduced in the model encoding stage to adaptively enhance the model’s attention to key features and improve the efficiency of utilizing global semantic information. The method was evaluated on the publicly available CSWV_S6 optical remote sensing dataset, and the accuracy of 98.14% indicates that the method has significant advantages over existing methods. Snow extraction from Landsat 8 OLI images of the upper reaches of the Irtysh River was achieved with satisfactory accuracy rates of 95.57% (using two, three, and four bands) and 96.65% (using two, three, four, and six bands), indicating its strong potential for automated snow cover extraction over larger areas.

Development of a Daily Cloud-Free Snow-Cover Dataset Using MODIS-Based Snow-Cover Probability for High Mountain Asia during 2000–2020

Investigating the changes in snow cover caused by climate change is extremely important and has attracted increasing attention in cryosphere and climate research. Optimal remote sensing-based snow datasets can provide long-term daily and global spatial-temporal snow-cover distribution at regional and global scales. However, the application of these snow-cover products is inevitably limited because of the space–time discontinuities caused by cloud obscuration, which poses a significant challenge in snowpack-related studies, especially in High Mountain Asia (HMA), an area that has high-elevation mountains, complex terrain, and harsh environments and has fewer observation stations. To address this issue, we developed an improved five-step hybrid cloud removal strategy by integrating the daily merged snow-cover probability (SCP) algorithm, eight-day merged SCP algorithm, decision tree algorithm, temporal downscaling algorithm, and optimal threshold segmentation algorithm to produce a 21-year, daily cloud-free snow-cover dataset using two daily MODIS snow-cover products over the HMA. The accuracy assessment demonstrated that the newly developed cloud-free snow-cover product achieved a mean overall accuracy of 93.80%, based on daily classified snow depth observations from 86 meteorological stations over 10 years. The time series of the daily percentage of binary snow-cover over HMA was analyzed during this period, indicating that the maximum snow cover tended to change more dramatically than the minimum snow cover. The annual snow-cover duration (SCD) experienced an insignificantly increasing trend over most of the northeastern and southwestern HMA (e.g., Qilian, eastern Kun Lun, the east of Inner Tibet, the western Himalayas, the central Himalayas, and the Hindu Kush) and an insignificant declining trend over most of the northwestern and southeastern HMA (e.g., the eastern Himalayas, Hengduan, the west of Inner Tibet, Pamir, Hissar Alay, and Tien). This new high-quality snow-cover dataset will promote studies on climate systems, hydrological modeling, and water resource management in this remote and cold region.

Numerical simulation of wind–snow flow on a roof considering time-varying snow phase boundaries

Large-span roofs can be damaged by uneven snow loads caused by snow drifting. To predict wind-induced snow loads more accurately, this paper proposes a numerical simulation method. The method considers the interaction of wind and snow, the changing characteristics of snow phase boundaries over time, and corrects for snow deposition and erosion flux. The effectiveness of the method was confirmed by simulating wind–snow flow on a three-centered cylindrical shell and comparing the results with wind tunnel measurements. The simulation considered different snowfall conditions and time-varying snow phase boundaries on a large-span shell-shaped roof. The study investigated the influence of snowfall conditions and dynamic changes in snow phase boundaries on snow cover distribution by comparing simulation results.

Distribution and Composition Patterns of Snow Cover within the Landscapes of Chashnikovo

Distribution and Composition Patterns of Snow Cover within the Landscapes of Chashnikovo

Monitoring Snow Cover in Typical Forested Areas Using a Multi-Spectral Feature Fusion Approach

Accurate snow cover monitoring is greatly significant for research on the hydrology model and regional climate variation, especially in Northeast China where forests cover almost forty percent of the total area. However, effectively monitoring snow cover under the forest canopy is still challenging with either in situ or remote sensing observations. The global SNOWMAP algorithm pertinent to the fixed normalized difference snow index (NDSI) threshold is, therefore, no longer applicable in a typical forested region of Northeast China. In order to achieve the goal of improving the accuracy of monitoring snow cover in areas with forest, utilizing MOD09GA and MOD13A1 products, a new approach of snow mapping was developed in this study, and it exploits the fusion and coupling of spectral features by integrating and analyzing the normalized difference forest snow index (NDFSI), the normalized difference vegetation index (NDVI), and the NDSI index. Then, Landsat 8 OLI images of high resolution were used to evaluate snow cover mapping precision. The experimental results indicated that the NDFSI index combined with the NDVI index showed great potential for extracting the snow cover distribution in forested regions. Compared with the snow distribution obtained from the Landsat 8 images, the average bias and FAR (false alarm ratio) values of snow cover mapping obtained by this algorithm were 1.23 and 13.54%, which were reduced by 1.98 and 29.36%, respectively. The overall accuracy of 81.31% was reached, which is improved by 20.19%. Thus, the snow classification scheme combining multiple spectral features from MODIS data works effectively in improving the precision of automatic snow cover mapping in typical forested areas of Northeast China, which provides essential support and significant perspectives for the next step of establishing a runoff model and rationally regulating forest water resources.

Reconstructing Snow Cover under Clouds and Cloud Shadows by Combining Sentinel-2 and Landsat 8 Images in a Mountainous Region

Snow cover is a sensitive indicator of global climate change, and optical images are an important means for monitoring its spatiotemporal changes. Due to the high reflectivity, rapid change, and intense spatial heterogeneity of mountainous snow cover, Sentinel-2 (S2) and Landsat 8 (L8) satellite imagery with both high spatial resolution and spectral resolution have become major data sources. However, optical sensors are more susceptible to cloud cover, and the two satellite images have significant spectral differences, making it challenging to obtain snow cover beneath clouds and cloud shadows (CCSs). Based on our previously published approach for snow reconstruction on S2 images using the Google Earth Engine (GEE), this study introduces two main innovations to reconstruct snow cover: (1) combining S2 and L8 images and choosing different CCS detection methods, and (2) improving the cloud shadow detection algorithm by considering land cover types, thus further improving the mountainous-snow-monitoring ability. The Babao River Basin of the Qilian Mountains in China is chosen as the study area; 399 scenes of S2 and 35 scenes of L8 are selected to analyze the spatiotemporal variations of snow cover from September 2019 to August 2022 in GEE. The results indicate that the snow reconstruction accuracies of both images are relatively high, and the overall accuracies for S2 and L8 are 80.74% and 88.81%, respectively. According to the time-series analysis of three hydrological years, it is found that there is a marked difference in the spatial distribution of snow cover in different hydrological years within the basin, with fluctuations observed overall.

Ground-penetrating radar as a tool for determining the interface between temperate and cold ice, and snow depth: a case study for Hurd-Johnsons glaciers, Livingston Island, Antarctica

Abstract We analyze the internal structure of two polythermal glaciers, Hurd and Johnsons, located on Livingston Island, Antarctica, using 200 and 750 MHz GPR data collected in 2003/04, 2008/09 and 2016/17 field campaigns. Based on the different permittivities of snow and ice, we determined the thickness distribution of the end-of winter snow cover and of the cold ice layer. Their knowledge is fundamental for mass balance and glacier dynamics studies due to the different densities and rheological properties of such media. The average measured thicknesses for the snow and cold ice layers (the latter including the snow layer) were of 1.44 ± 0.09 and 29.1 ± 1.5 m, and their corresponding maxima were of 2.45 ± 0.21 and 80.8 ± 2.5 m. GPR snow profiling allowed for extension of the coverage of the snow thickness survey, but added little information to that supplied by snow pits, stake readings and manual snow probing, because of the multiplicity of reflections within the seasonal snowpack caused by internal ice layers and lenses. The polythermal structure determined for Hurd Glacier fits into the so-called Scandinavian type, seldom reported for the Antarctic region.

Climate Change Impact on Water Resources in the Republic of Srpska

Global warming, in addition to population growth and intensive industrialization, is one of the most significant pressures on water resources worldwide. The most significant impacts of climate change that directly affect water resources are changes in the distribution of precipitation and snow cover, as well as the increased frequency of floods and droughts. Analyzes of average annual temperatures in the last 60 years have shown that the trend of increasing the average annual air temperature in the Republic of Srpska already exists, and precipitation regimes have changed. The period after 2000 is characterized by shifts of very or extremely dry years and years in which extreme floods have been recorded. In order to determine the impact of climate change on water resources in the Republic of Srpska, a comparison of average annual and monthly discharge of watercourses for the period from 1960 until today was performed, depending on the available data for individual watercourses. After 1980, the rivers Bosna, Vrbas and Vrbanja are characterized by reduced water discharge compared to the previous period, i.e. they have lower values of the average annual discharge with significantly higher oscillations. In the West of the Republic of Srpska, the situation is different: rivers Una and Sana also have significant variations in the average annual discharge, but the river Una has a higher annual discharge after 2000 compared to the average for previous twenty-year period, while the average annual discharge of the Sana River has not changed in relation to the reference period.

Understanding the Snow Cover Climatology over Turkey from ERA5-Land Reanalysis Data and MODIS Snow Cover Frequency Product

Understanding the distribution, patterns, and characteristics of snowfall and snow cover within a given region over extended periods is important. Snow climatology provides valuable insights into the seasonal and long-term variations in snowfall, helping researchers and meteorologists understand the impacts of climate change on snow accumulation, melt rates, and snowmelt runoff. In this study, in order to understand the spatial and temporal variation in snow cover in Turkey, the temporal and spatial dynamics of snow cover in the country were analyzed during the latest and longest period from 1970 to 2022 using ERA5-Land reanalysis product. It is aimed (1) to show snow-covered area (SCA), snow duration, and snow depth trends over the country; (2) to examine the altitudinal difference of snow phenology response to climate change; and (3) to evaluate the Snow Cover Frequency Maps from MODIS Snow Cover Products with the reanalysis snow depth data. It is found that the “false snow” mapping problem still exists in the MOD10C1_CGF Snow Cover Frequency maps over Turkey, especially in the melting period. We found that an increasing trend of 0.4 °C/decade and snow duration have a decreasing trend due to the early melting between 1970 and 2022. This trend is even more noticeable at elevations below 2000 m. Another important finding is the decreasing trend in snow duration at altitudes below 500 m, indicating a shift from snow to rain for precipitation types.

MODELING OF SNOW LOAD ON ROOFS OF UNIQUE BUILDINGS

Introduction: The purpose of the work is to study the processes of snow transfer and snow deposition on the roofing of a unique building. Currently, the problem of assigning snow loads to the roofs of buildings and structures remains relevant, since cases of roof collapses in winter are still recorded annually, including those in the Russian Federation. A circus building with the roof in the form of a suspended reinforced concrete shell was chosen as the object of study. Methods: The design diagram for this type of roof is contained in SP 20.13330.2016; the snow load distribution diagrams for this roof were adopted according to Appendix B to SP 20.13330.2016. The author considered several options of snow load distribution and chose the most unfavorable one, when an increased value of the snow load is observed on one half of the roofing. During one winter period, field measurements of the thickness of the snow cover were carried out at the site. Results:It was established that in general, with the exception of local zones, the actual distribution of snow cover coincides with the adopted design solution, while the actual value of the weight of the snow cover for the current winter season was significantly lower than the calculated one. Discussion: The obtained result demonstrates that when developing design solutions for certain types of suspended roofing, it is permissible, without conducting specialized experimental studies, to use the data given in the scientific and technical literature, based on the results of monitoring the thickness of the snow cover on the roofing of the building.

Effective Improvement of the Accuracy of Snow Cover Discrimination Using a Random Forests Algorithm Considering Multiple Factors: A Case Study of the Three-Rivers Headwater Region, Tibet Plateau

Accurate information on snow cover extent plays a crucial role in understanding regional and global climate change, as well as the water cycle, and supports the sustainable development of socioeconomic systems. Remote sensing technology is a vital tool for monitoring snow cover’ extent, but accurate identification of shallow snow cover on the Tibetan Plateau has remained challenging. Focusing on the Three-Rivers Headwater Region (THR), this study addressed this issue by developing a snow cover discrimination model (SCDM) using a random forests (RF) algorithm. Using daily observed snow depth (SD) data from 15 stations in the THR during the period 2001–2013, a comprehensive analysis was conducted, considering various factors influencing regional snow cover distribution, such as land surface reflectance, land surface temperature (LST), Normalized Difference Snow Index (NDSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Forest Snow Index (NDFSI). The key results were as follows: (1) Optimal model performance was achieved with the parameters Ntree, Mtry, and ratio set to 1000, 2, and 19, respectively. The SCDM outperformed other snow cover products in both pixel-scale and local spatial-scale discrimination. (2) Spectral information of snow cover proved to be the most influential auxiliary variable in discrimination, and the combined inclusion of NDVI and LST improved model performance. (3) The SCDM achieved accuracy of 99.04% for thick snow cover (SD > 4 cm) and 98.54% for shallow snow cover (SD ≤ 4 cm), significantly (p < 0.01) surpassing the traditional dynamic threshold method. This study can offer valuable reference for monitoring snow cover dynamics in regions with limited data availability.

Possible impacts of vegetation cover increment on the relationship between winter snow cover anomalies over the Third Pole and summer precipitation in East Asia

Snow cover over the Tibetan Plateau (TP)–the Third Pole of the earth has been recognized as a reliable signal of summer floods or droughts in East Asia (EA). The distribution of snow cover can be influenced by vegetation, however, the impacts of changes in cover of non-growing season vegetation–withered grass stem over TP on the climatic effects of snow cover remains poorly understood. Here, we showed that the relationship between TP winter snow cover (TPWSC) and EA summer precipitation (EASP) strengthened starting in the early 1990s but weakened after the early 2000s. The weakening of the TPWSC–EASP relationship was linked to the effects of vegetation cover increment (VCI) on winter and spring snow cover over the TP. A possible mechanism behind this linkage is that VCI leads to a shortened persistence of TPWSC anomalies and weakened surface diabatic heating anomalies in spring. Consequently, the influences of TP thermal forcing on the downstream atmospheric circulation in summer were altered, resulting in a different pattern of EASP anomalies. These findings highlight the importance of snow—vegetation feedback and its potential to alter the effectiveness of snow cover in seasonal climate prediction.

Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment

Abstract. The assimilation of data from Earth observation satellites into numerical models is considered to be the path forward to estimate snow cover distribution in mountain catchments, providing accurate information on the mountainous snow water equivalent (SWE). The land surface temperature (LST) can be observed from space, but its potential to improve SWE simulations remains underexplored. This is likely due to the insufficient temporal or spatial resolution offered by the current thermal infrared (TIR) missions. However, three planned missions will provide global-scale TIR data at much higher spatiotemporal resolution in the coming years. To investigate the value of TIR data to improve SWE estimation, we developed a synthetic data assimilation (DA) experiment at five snow-dominated sites covering a latitudinal gradient in the Northern Hemisphere. We generated synthetic true LST and SWE series by forcing an energy balance snowpack model with the ERA5-Land reanalysis. We used this synthetic true LST to recover the synthetic true SWE from a degraded version of ERA5-Land. We defined different observation scenarios to emulate the revisiting times of Landsat 8 (16 d) and the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) (3 d) while accounting for cloud cover. We replicated the experiments 100 times at each experimental site to assess the robustness of the assimilation process with respect to cloud cover under both revisiting scenarios. We performed the assimilation using two different approaches: a sequential scheme (particle filter) and a smoother (particle batch smoother). The results show that LST DA using the smoother reduced the normalized root mean square error (nRMSE) of the SWE simulations from 61 % (open loop) to 17 % and 13 % for 16 d revisit and 3 d revisit respectively in the absence of clouds. We found similar but higher nRMSE values by removing observations due to cloud cover but with a substantial increase in the standard deviation of the nRMSE of the replicates, highlighting the importance of revisiting times in the stability of the assimilation performance. The smoother largely outperformed the particle filter algorithm, suggesting that the capability of a smoother to propagate the information along the season is key to exploit LST information for snow modelling. Finally, we have compared the benefit of assimilating LST with synthetic observations of fractional snow cover area (FSCA). LST DA performed better than FSCA DA in all the study sites, suggesting that the information provided by LST is not limited to the duration of the snow season. These results suggest that the LST data assimilation has an underappreciated potential to improve snowpack simulations and highlight the value of upcoming TIR missions to advance snow hydrology.

Snow Cover on the Tibetan Plateau and Topographic Controls

Snow cover plays a critical role in global energy and water cycles. Snow cover on the Tibetan Plateau (TP) provides vital water sources in western China and Himalayan regions, in addition to its weather and climate significance. The massive high mountain topography of the TP is the main condition for the presence and persistence of snow cover on the plateau at the mid-low latitudes of the Northern Hemisphere (NH). However, how the mountain topography controls snow-cover distribution on the TP remains largely unclear, and the relationship is not well quantified. Here, the spatial distribution and the topographic controls of snow cover on the TP are examined based on snow cover frequency (SCF) derived from MODIS snow cover product (MOD10A2 v005) and digital elevation model (DEM) data. The results show that snow cover on the TP is spatially unevenly distributed, and that it is characterized by rich snow and high SCF on the interior and the surrounding high mountain ranges, with less snow and low SCF in inland basins and river valleys. Snow cover on the TP presents elevation dependence: the higher the altitude, the higher the SCF, the longer the snow cover duration, and the more stable the intra-annual variation. The annual mean SCF below 3000 m above sea level (m a.s.l) is less than 4%, and it reaches 77% above 6000 m a.s.l. The intra-annual snow cover variation below 4000 m a.s.l features a unimodal distribution, while above 4000 m a.s.l it presents a bimodal distribution. The mean minimum SCF below 6000 m a.s.l occurs in summer, while above 6000 m a.s.l it occurs in winter. Due to differences in solar radiation and moisture condition caused by the mountain slope and aspect, the mean SCF generally increases with mountain slopes, and it is the highest on the north-facing aspect and the lowest on the south-facing aspect.

Spatial Distribution of Snow Cover in Tibet and Topographic Dependence

Many major river systems in Asia, such as the Yangtze, Yarlung Zangbo, Indus, Ganges and Salween originate in the Tibetan mountains and snow cover in Tibet provides substantial water resources for these rivers, in addition to its weather-related and climatic significance. The high mountain terrain of Tibet is the main condition that snow cover exists and persists at mid–low altitudes. However, the relationships between snow cover and topographic factors of the plateau have not been fully addressed. In this study, the overall spatial distribution of snow cover and the impacts of topography (elevation, aspect and slope) on snow cover distribution in Tibet were analyzed based on the MODIS snow cover product and digital elevation model (DEM) using GIS spatial analysis techniques. The results showed that (1) snow cover in Tibet is spatially very uneven and is characterized by rich snow and high SCF (snow cover frequency) on Nyainqentanglha mountain and the surrounding high mountains, with less snow and a low SCF in the southern Tibetan valley and central part of northern Tibet. (2) Snow cover in Tibet has a strong elevation dependence and a higher SCF corresponds well with high mountain ranges. The mean SCF below 2000 m above sea level (m a.s.l) was less than 4%, while above 6000 m a.s.l, it reached 75%. (3) Intra-annual snow cover distribution below 4000 m a.s.l was characterized by unimodal patterns, while above 4000 m a.s.l, it was characterized by bimodal patterns. The lowest SCF below 6000 m a.s.l occurred in summer, while above 6000 m it occurred in winter. (4) The mountain slope and aspect affect snow cover distribution through changing radiation and energy balances in the mountain regions. The mean SCF generally increased with mountain slopes, with the highest on the north-facing aspect and the lowest on the south-facing aspect.

Characteristics of snow cover distribution along railway subgrade and the protective effect of snow fences

Characteristics of snow cover distribution along railway subgrade and the protective effect of snow fences

Snow Cover Mapping Based on SNPP-VIIRS Day/Night Band: A Case Study in Xinjiang, China

Detailed snow cover maps are essential for estimating the earth’s energy balance and hydrological cycle. Mapping the snow cover across spatially extensive and topographically complex areas with less or no cloud obscuration is challenging, but the SNPP-VIIRS Day/Night Band (DNB) nighttime light data offers a potential solution. This paper aims to map snow cover distribution at 750 m resolution across the diverse 1,664,900 km2 of Xinjiang, China, based on SNPP-VIIRS DNB radiance. We implemented a swarm intelligent optimization technique Krill Herd algorithm, which finds the optimal threshold value by taking Otsu’s method as the objective function. We derived SNPP-VIIRS DNB snow maps of 14 consecutive scenes in December 2021, compared our snow-covered area estimations with those from MODIS and AMSR2 standard snow cover products, and generated composite snow maps by merging MODIS and SNPP-VIIRS DNB data. Results show that SNPP-VIIRS DNB snow maps are capable of providing reliable snow cover maps superior to MODIS and AMSR2, with an overall accuracy level of 84.66%. The composite snow maps at 500 m spatial resolution provided 55.85% more information on snow cover distribution than standard MODIS products and achieved an overall accuracy of 84.69%. Our study demonstrated the feasibility of snow cover detection in Xinjiang based on SNPP-VIIRS DNB, which can serve as a supplementary dataset for MODIS estimations where clouded pixels are present.

Understanding the spatiotemporal distribution of snow refugia in the rain-snow transition zone of north-central Idaho

Knowledge of snow cover distribution and disappearance dates over a wide range of scales is imperative for understanding hydrological dynamics and for habitat management of wildlife species that rely on snow cover. Identification of snow refugia, or places with relatively late snow disappearance dates (SDDs) compared to surrounding areas, is especially important as climate change alters snow cover timing and duration. The purpose of this study was to increase understanding of snow refugia in complex terrain spanning the rain-snow transition zone at fine spatial and temporal scales. To accomplish this objective, we used remote cameras to provide relatively high temporal and spatial resolution measurements on snowpack conditions. We built linear models to relate SDDs at the monitoring sites to topoclimatic and canopy cover metrics. One model to quantify SDDs included elevation, aspect, and an interaction between canopy cover and cold-air pooling potential. High-elevation, north-facing sites in cold-air pools (CAPs) had the latest SDDs, but isolated lower-elevation points also exhibited relatively late potential SDDs. Importantly, canopy cover had a much stronger effect on SDDs in CAPs than in non-CAPs, indicating that best practices in forest management for snow refugia could vary across microtopography. A second model that included in situ hydroclimate observations (December–February (DJF) temperature and March 1 snow depth) indicated that March 1 snow depth had little impact on SDD at the coldest winter temperatures, and that DJF temperatures had a stronger effect on SDD at lower snow depths, implying that the relative importance of snowfall and temperature could vary across hydroclimatic contexts in their impact on snow refugia. This new understanding of factors influencing snow refugia can guide forest management actions to increase snow retention and inform management of snow-dependent wildlife species in complex terrain.

Improved Landsat-based snow cover mapping accuracy using a spatiotemporal NDSI and generalized linear mixed model

Snow cover extent and distribution over the years have a significant impact on hydrological, terrestrial, and climatologic processes. Snow cover mapping accuracy using remote sensing data is then particularly important. This study analyses Landsat-8 NDSI snow cover datasets over time and space using different NDSI-based approach. The objectives are (i) to investigate the relation snow-NDSI with different environmental variables, (ii) to evaluate the accuracy of the common NDSI threshold of 0.4 against in-situ snow depth measurement and (iii) to develop a method that optimises snow cover mapping accuracy and minimises snow cover detection errors of omission and commission. Landsat-8 snow cover datasets were compared to ground snow depth measurements of climate stations over Switzerland for the period 2014–2020. It was found that there is a consistent relationship between NDSI values and land cover type, elevation, seasons, and snow depth measurements. The global NDSI threshold of 0.4 may not be always optimal for the Swiss territory and tends to underestimate the snow cover extent. Best NDSI thresholds vary spatially and are generally lower than 0.4 for the three snow depth threshold tested. We therefore propose a new spatiotemporal NDSI method to maximize snow cover mapping accuracy by using a generalized linear mixed model (GLMM). This model uses three environmental variables (i.e., elevation, land cover type and seasons) and raw NDSI values and improves snow cover mapping accuracy by 24% compared to the fixed threshold of 0.4. By using this method omissions errors decrease considerably while keeping a very low value of commission errors. This method will then be integrated in the Snow Observation from Space (SOfS) algorithm used for snow detection in Switzerland.