Normalized Difference Snow Index Research Articles

Remote Sensing Data Assimilation to Improve the Seasonal Snow Cover Simulations Over the Heihe River Basin, Northwest China

ABSTRACTThe reliability of seasonal snow cover information is constrained by limitation of in situ observations and uncertainties in remote sensing data and model simulations in alpine region, thus posing important challenges to understanding the climate system and water resource management in alpine region. Here, the assimilation of daily cloud‐free Moderate Resolution Imaging Spectroradiometer (MODIS) normalised difference snow index (NDSI) product into an intermediate complexity snow mass and energy balance model—Flexible Snow Model (version FSM2_MO)—was implemented. The aim is to improve the model simulations of seasonal snow cover (snow‐covered extent; SCE, snow depth; SD, snow water equivalent; SWE, and snowmelt runoff; SMR) in the alpine region (a case of the upper‐middle reaches of the Heihe River basin, Northwest China). The results indicate comprehensive improvement in the simulation of SCE, SD, and SMR in the study area through data assimilation, with the ability to significantly reduce prior biases of the FSM2_MO. Based on the independent daily cloud‐free Advanced Very High Resolution Radiometer (AVHRR) SCE product, the updated SCE simulation (i.e., data assimilation) showed a reduction in mean absolute error (MAE) from 10.46% to 7.16%, root mean square error (RMSE) from 16.14% to 12.26%, and an increase in Pearson's correlation coefficient (CC) from 0.18 to 0.67 compared with the open loop simulation (i.e., without assimilation). The evaluation results of SD observation data showed that data assimilation improved SD simulation compared with the open loop run (OL). And utilising the monthly discharge observations at the Yingluoxia hydrological station, data assimilation slightly improved the SMR simulation. The updated SMR simulation achieved a CC of 0.91, Nash‐Sutcliffe efficiency coefficient (NSE) of 0.73, and Kling‐Gupta efficiency coefficient (KGE) of 0.76. Moreover, the Landsat 8‐derived snow cover map and Sentinel‐1‐derived SD also indicated that the updated simulation effectively filled in the missing snow cover and removed the superfluous snow cover predicted by the OL simulation in terms of spatial distribution.

Reconstructing MODIS normalized difference snow index product on Greenland ice sheet using spatiotemporal extreme gradient boosting model

The spatiotemporally continuous data of normalized difference snow index (NDSI) are key to understanding the mechanisms of snow occurrence and development as well as the patterns of snow distribution changes. However, the presence of clouds, particularly prevalent in polar regions such as the Greenland Ice Sheet (GrIS), introduces a significant number of missing pixels in the MODIS NDSI daily data. To address this issue, this study proposes the utilization of a spatiotemporal extreme gradient boosting (STXGBoost) model generate a comprehensive NDSI dataset. In the proposed model, various input variables are carefully selected, encompassing terrain features, geometry-related parameters, and surface property variables. Moreover, the model incorporates spatiotemporal variation information, enhancing its capacity for reconstructing the NDSI dataset. Verification results demonstrate the efficacy of the STXGBoost model, with a coefficient of determination of 0.962, root mean square error of 0.030, mean absolute error of 0.011, and negligible bias (0.0001). Furthermore, simulation comparisons involving missing data and cross-validation with Landsat NDSI data illustrate the model’s capability to accurately reconstruct the spatial distribution of NDSI data. Notably, the proposed model surpasses the performance of traditional machine learning models, showcasing superior NDSI predictive capabilities. This study highlights the potential of leveraging auxiliary data to reconstruct NDSI in GrIS, with implications for broader applications in other regions. The findings offer valuable insights for the reconstruction of NDSI remote sensing data, contributing to the further understanding of spatiotemporal dynamics in snow-covered regions.

NDWI as a method for studying natural and technogenic aufeis along the Lena Highway (Southern Yakutia)

The research conducted has yielded, for the first time, a quantitative evaluation of the NDWI (Normalized Difference Water Index) for the purpose of identifying areas of natural and technogenic aufeis within Southern Yakutia. In contrast to the widely used NDSI (Normalized Difference Snow Index) in aufeis investigations, the NDWI offers the advantage of delineating aufeis contours without being influenced by the presence or absence of snow cover. Calculations pertaining to the aufeis areas in the “Gorbyllakh” and “Durai” sections of the Federal Highway “Lena” during the winters of 2020–2021 and 2021–2022 indicate a notable increase in these areas, attributed to heightened precipitation levels anticipated in the subsequent summer period. However, limitations of this approach include the interference of cloud cover in satellite imagery, which can obscure the study area, as well as the potential for ambiguous interpretations of shaded regions within the aufeis meadow, where the NDWI may suggest the presence of surface water.

HIGH-RESOLUTION SATELLITE ESTIMATION OF SNOW COVER FOR FLOOD ANALYSIS IN EAST KAZAKHSTAN REGION

The increasing frequency of extreme weather events linked to climate change has made flood forecasting an important issue, particularly in mountainous regions where snowmelt is a major driver of seasonal flooding. This study explores the application of snow cover estimation techniques to assess snowmelt dynamics and their potential impact on flood risks in the Ulba and Uba basins in East Kazakhstan. To achieve this, high-resolution multispectral satellite imagery from the Sentinel-2 Surface Reflectance dataset is used, focusing on images collected between March and October for the years 2021 to 2024. The images are processed in Google Earth engine platform with strict filtering based on spatial intersection with the basins and cloud cover pixels percentage, ensuring high-quality data for snow cover analysis. The study utilizes multiple remote sensing indices for snow cover estimation. The normalized difference snow index is calculated using the green and shortwave infrared bands to detect snow-covered pixels. Fractional snow-covered area is derived from the NDSI using the 'FRA6T' relationship, offering a more nuanced estimate of snow distribution across the basins. Additionally, a near-infrared to shortwave infrared ratio threshold is employed to minimize confusion between snow and water, improving the detection of snow cover, particularly in regions near water bodies or during melt periods. The resulting snow cover maps and fSCA estimates provide a detailed picture of snow distribution and melt dynamics, contributing to the assessment of snowmelt’s role in flood risk development. The obtained insights can assist in refining flood forecasting models, improving early warning systems, and supporting informed water resource management in vulnerable regions.

Spatio-Temporal Monitoring and Risk Mapping of Glacial Lake Outburst Flood in Hunza Valley, Pakistan

Glacial lake outburst flood (GLOF) disasters are serious and potentially increase huge risks to livelihoods and infrastructure in the mountain regions of the world. The northern highland regions of Pakistan are home to some of the biggest alpine glaciers. In this investigation, the Hunza Valley of Pakistan has undergone remote sensing-based risk assessment for Glacial Lake Outburst Floods. Borith and Passu Lakes were chosen to identify flood risk in the downstream areas. For such, Landsat images were used from 1990-2020. Different spectral indices such as the Normalized Difference Snow Index (NDSI), Normalized Difference Glacier Index (NDGI) and Normalized Difference Water Index (NDWI) were applied to evaluate snow cover changes. Furthermore, these Lakes were digitized to evaluate and check the total increase in Lake Areas. Also, built-up areas close to lakes were digitized to identify total risk. The Land Surface Temperature (LST), NDSI, NDGI, NDWI, Digital Elevation Model (DEM) and Slope variables were given weights using the Analytic Hierarchy Process (AHP) method. In the analysis of flood risk mapping, maximum weight was assigned to Land Surface Temperature, and minimum weight was assigned to the slope. The result revealed that the settlements located in the Ghulkin, Gulmit, Husseini, Passu, Zarabad, and Khorramabad are at moderate risk while settlements located near Hunza River such as Karimabad, Khanna Abad, and Aliabad are at high risk. The outcome also showed that Borith Lake's area expanded, going from 0.059 km2 in 1985 to 0.074 km2 in 2020 and Passu Lake's area also grew, going from 0.074 km2 in 2005 to 0.077 km2 in 2020. In last, Buffer analysis was performed to identify areas that are likely to be affected by the flood. The result of the study can help carry out a downstream risk assessment and better preparedness for future flood hazards.

Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series

ABSTRACT Observation of glacier surface characteristics through remotely sensed time-series data is essential for understanding glacier seasonality, mass balance, and long-term trends. Yet, the reliability of these observations depends significantly on the quality of the time-series data. This study presents a meticulous preprocessing scheme to improve the quality of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Snow Index (NDSI) time-series data for glacier monitoring. We propose a three-step algorithm specifically crafted to overcome the challenges associated with cloud contamination reduction, outlier removal and data gap handling. This innovative approach iteratively compares the median values of automatically adjusted asymmetrical moving windows to achieve convergence, removing outliers using minimal window size to keep the temporal resolution as high as possible. The methodology’s effectiveness is demonstrated through its application to two glaciers from the United States Geological Survey (USGS) Benchmark Project, showcasing significant improvements in the quality of smoothed MODIS NDSI time series. These results affirm the efficacy of the proposed scheme in rendering a more reliable evaluation of glacier surface condition and seasonal fluctuations. Consequently, this study contributes significant methodological advancements to the fields of remote sensing and glaciology, enhancing the accuracy of glacier monitoring techniques.

Spatial–Temporal Variations and Driving Factors of the Albedo of the Qilian Mountains from 2001 to 2022

Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI.

Universal Snow Avalanche Modeling Index Based on SAFI–Flow-R Approach in Poorly-Gauged Regions

Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the collection of data in the field and their processing in geographic information systems and remote sensing. In the period 2023–2024, avalanches were mapped in the field, and later, avalanches as points in geographic information systems (GIS) were overlapped with the dominant natural conditions in the study area. The second step involves determining the main criteria (snow cover, terrain slope, and land use) and evaluating the values to obtain the Snow Avalanche Formation Index (SAFI). Thresholds obtained through field research and the formation of avalanche inventory were used to develop the SAFI index. The index is applied with the aim of identifying locations susceptible to avalanche formation (source areas). The values used for the calculation include Normalized Difference Snow Index (NDSI > 0.6), terrain slope (20–60°) and land use (pastures, meadows). The third step presents the analysis of SAFI locations with meteorological conditions (winter precipitation and winter air temperature). The fourth step is the modeling of the propagation (simulation) of other parts of the snow avalanche in the Flow-R software 2.0. The results show that 282.9 km2 of the study area (Šar Mountains, Serbia) is susceptible to snow avalanches, with the thickness of the potentially triggered layer being 50 cm. With a 5 m thick snowpack, 299.9 km2 would be susceptible. The validation using the ROC-AUC method confirms a very high predictive power (0.94). The SAFI–Flow-R approach offers snow avalanche modeling for which no avalanche inventory is available, representing an advance for all mountain areas where historical data do not exist. The results of the study can be used for land use planning, zoning vulnerable areas, and adopting adequate environmental protection measures.

Assessing Machine Learning and Statistical Methods for Rock Glacier‐Based Permafrost Distribution in Northern Kargil Region

ABSTRACTEstimating permafrost distribution in high‐mountain areas is challenging. In these situations, rock glaciers, provide valuable insights into permafrost distribution and are often used as proxies for identifying permafrost occurrence. Integrating various climatological and topographical conditioning factors with rock glaciers enables inferring the distribution of permafrost in these environments. This study utilized three machine learning models such as random forest (RF), support vector (SVM), and artificial neural network (ANN), and one statistical model, namely, the frequency ratio (FR), to assess the permafrost probability over the northern Kargil region of Indian Himalayas. Among 198 rock glaciers identified through high‐resolution images from Google Earth, 70% are used as training dataset, rest 30% as testing dataset. The study considered eight factors: slope, aspect, elevation, curvature, mean annual land surface temperature (MA‐LST), mean annual normalized difference snow index (MA‐NDSI), mean annual normalized difference water index (MA‐NDWI), and lithology for mapping. Furthermore, the SHapley Additive exPlanations (SHAP) test assessed the variable importance for model performance. The results revealed that the RF model performs best for permafrost probability mapping, followed by the SVM, FR, and ANN models. The study also found that 11% of the total geographic area has a high and very high probability of permafrost occurrence.

Combined Use of Multiple Cloud‐Free Snow Cover Products in China and Its High‐Mountain Region: Implications From Snow Cover Identification to Snow Phenology Detection

AbstractAccurate snow phenology detection, including snow cover days (SCD), snow start date (SSD), and snow end date (SED), is increasingly important for understanding mountain hydrology such as snow heterogeneity and snowmelt seasonality. Multiple cloud‐free daily snow cover products have recently been developed in China, employing diverse retrieval algorithms and cloud‐gap‐filling methods, resulting in varying accuracy levels. However, comprehensive analysis of differences among products and their impact on snow phenology detection is lacking. This study systematically evaluates eight state‐of‐the‐art snow cover products in China, focusing on the challenging Tibetan Plateau (TP). We introduce a novel metric, the consistency‐weighted correlation coefficient (CWR), customized for SSD and SED detection, and propose product‐combining schemes like “ensemble voting” and “sensor preference” to enhance reliability. Our findings highlight the prime influence of retrieval algorithms under clear‐sky conditions on accuracy, surpassing the importance of cloud‐gap‐filling methods. Specifically, a product optimizing normalized difference snow index thresholds for diverse landcover types consistently outperforms others in detecting all three snow phenology parameters, with correlation coefficients for SCD of 0.82 and 0.69, and CWR values for SSD of 0.54 and 0.40, and for SED of 0.53 and 0.37 in both China and the TP, respectively. Moreover, our proposed scheme combining three high‐accuracy products significantly enhances snow cover identification and SCD detection, especially when the best‐performing product alone faces substantial uncertainty. These findings provide immediate, crucial implications for optimizing the use of multiple cloud‐free products to enhance snow phenology detection, ultimately advancing the applicability of derived snow parameters in mountain hydrology research.

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.

Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia

Accurate snow cover data are critical for understanding the Earth’s climate system, and exploring hydrological processes and regional water resource management over High Mountain Asia (HMA). However, satellite-based remote sensing observations of snow cover have inevitable data gaps originating from cloud cover, sensor, orbital limitations and other factors. Here an effective cloud-gap-filled (CGF) method was developed to fully fill the data gaps in Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference snow index (NDSI) product. The CGF method combines the respective strengths of the cubic spline interpolation method and the spatio-temporal weighted method for generating the CGF Terra-Aqua MODIS NDSI product over HMA from 2000 to 2021. Based on the validation results of in situ snow-depth observations, the CGF NDSI product achieves a high range overall accuracy (OA) of 93.54–98.08%, a low range underestimation error (MU) of 0.15–3.49% and an acceptable range overestimation error (MO) of 0.84–5.77%. Based on the validation results of high-resolution Landsat images, this product achieves the OA of 88.52–92.40%, the omission error (OE) of 1.42–10.28% and the commission error (CE) of 5.97–17.58%. The CGF MODIS NDSI product can provide scientific support for eco-environment sustainable management in the high mountain region.

Glacial Cover Assessment from 1993 to 2021 Using Normalized Difference Snow Index with Landsat Imagery in the Colorado Front Range, USA

Glacial Cover Assessment from 1993 to 2021 Using Normalized Difference Snow Index with Landsat Imagery in the Colorado Front Range, USA

TESTING OPTICAL SPECTRAL INDICES FOR ASSESSMENT OF SURFACE CHANGES DUE TO PERMAFROST MELTING ON LIVINGSTON ISLAND, ANTARCTICA

The present study aims to examine the potential of optical satellite data and spectral indices to assess surface changes induced by permafrost melting. Surface changes related to permafrost melting on Livingston Island, Antarctica were examined using optical satellite data from Sentinel-2 sensors of the European Space Agency (ESA). The study area coincides with previous field studies by electrical resistivity tomographic profiles made to establish and visualize the presence of permafrost. Utilizing the advantages of remote sensing methods and calculation of optical indices, it was tracked whether and to what extent there was a surface change and melting of the permafrost in the study area. The observation period encompasses the astral summer seasons from 2016 to 2023. The results show that the combination of different optical indices gives a better understanding of changes in the terrain. The combined use of the Normalized Difference Glacier Index (NDGI), Normalized Difference Snow Index (NDSI), Normalized Difference Snow and Ice Index (NDSII), Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and Moisture Stress Index (MSI) indicates for a pronounced trend of melting of the active layer of the permafrost periglacial area of research in March 2016 and 2017, and from January to mid of March 2023.

A method for robust estimation of snow seasonality metrics from Landsat and Sentinel-2 time series data over Atlas Mountains scale using Google Earth Engine

Seasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to the great spatial and temporal heterogeneity of the snowpack and the inaccessibility of high mountainous areas, gapless satellite remote sensing presents an unprecedented opportunity to monitor snow cover effectively and affordably on a fine scale, from different aspects, and with regular revisit time. This study derives the snow seasonality metrics (first day of snowfall, last day of snow melt; and snow cover duration) over a large semi-arid region in Morocco’s Atlas Mountains. We calculate these metrics by combining over 10,000 images from Landsat 8 and Sentinel 2 satellites for four hydrological years (2016–2021) to create a harmonized product with a time interval of about 3 days using the Google Earth Engine (GEE) platform. This dense and large time series facilitates a gap-filling method to minimize and overcome the effect of cloud cover, and its assessment shows a positive correlation between the masked pixels and the interpolated ones. These methods allowed us to realize a map of the snow cover area and extract a homogeneous Normalized Difference Snow Index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence. The results showed that derived snow cover metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be noted. Overall, the snow duration ranges between November and April depending on the characteristics of each hydrological year. The retrieved Landsat 8 and Sentinel 2 snow dates had a high level of agreement with in-situ data observations with almost a day-and-a-half delay with an overall accuracy equal to 0.96. The analysis of snow cover dynamics via GEE has offered the ability to calculate the first day of snowfall, last day of snowmelt, and snow cover duration annually at a pixel level, providing the user with the ability to track the seasonal and interannual variability in the timing of snowmelt toward a better understanding of how the hydrological cycles of higher latitude and mountainous regions are responding to climate change.

Snow accumulation, albedo and melt patterns following road construction on permafrost, Inuvik–Tuktoyaktuk Highway, Canada

Abstract. Roads constructed on permafrost can have a significant impact on the surrounding environment, potentially inducing permafrost degradation. These impacts arise from factors such as snow accumulation near the road, which affects the soil's thermal and hydrological regime, and road dust that decreases the snow's albedo, altering the timing of snowmelt. However, our current understanding of the magnitude and the spatial extent of these effects is limited. In this study we addressed this gap by using remote sensing techniques to assess the spatial effect of the Inuvik to Tuktoyaktuk Highway (ITH) in Northwest Territories, Canada, on snow accumulation, snow albedo and snowmelt patterns. With a new, high resolution snow depth raster from airborne laser scanning, we quantified the snow accumulation at road segments in the Trail Valley Creek area using digital elevation model differencing. We found increased snow accumulation up to 36 m from the road center. The magnitude of this snow accumulation was influenced by the prevailing wind direction and the embankment height. Furthermore, by analyzing 43 Sentinel-2 satellite images between February and May 2020, we observed reduced snow albedo values within 500 m of the road, resulting in a 12-days-earlier onset of snowmelt within 100 m from the road. We examined snowmelt patterns before, during and after the road construction using the normalized difference snow index from Landsat-7 and Landsat-8 imagery. Our analysis revealed that the road affected the snowmelt pattern up to 600 m from the road, even in areas which appeared undisturbed. In summary, our study improves our understanding of the spatial impact of gravel roads on permafrost due to enhanced snow accumulation, reduced snow albedo and earlier snowmelt. Our study underscores the important contribution that remote sensing can provide to improve our understanding of the effects of infrastructure development on permafrost environments.

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.

The superiority of the Adjusted Normalized Difference Snow Index (ANDSI) for mapping glaciers using Sentinel-2 multispectral satellite imagery

ABSTRACT Accurate monitoring of glaciers’ extents and their dynamics is essential for improving our understanding of the impacts of climate and environmental changes in cold regions. The satellite-based Normalized Difference Snow Index (NDSI) has been widely used for mapping snow cover and glaciers around the globe. However, mapping glaciers in snow-covered areas using existing indices remains a challenging task due to their incapabilities in separating snow, glaciers, and water. This study aimed to evaluate a new satellite-based index and apply machine learning algorithms to improve the accuracy of mapping glaciers. A new index based on satellite data from Sentinel-2 was tested, which we call the Adjusted Normalized Difference Snow Index (ANDSI). ANDSI (besides NDSI) was used with five different machine learning algorithms, namely Artificial Neural Network, C5.0 Decision Tree Algorithm, Naive Bayes classifier, Support Vector Machine, and Extreme Gradient Boosting, to map glaciers, and their performance was evaluated against ground reference data. Four glacierized regions in different countries (Canada, China, Sweden, and Switzerland-Italy) were selected as study sites to evaluate the performance of the proposed ANDSI. Results showed that the proposed ANDSI outperformed the original NDSI, and the C5.0 classifier showed the best overall accuracy and Kappa among the selected five machine learning classifiers in the majority of cases. The original NDSI yielded results with an average overall accuracy of (around) 91% and the proposed ANDSI with (around) 95% for glacier mapping across all models and study regions. This study demonstrates that the proposed ANDSI serves as a superior and improved method for accurately mapping glaciers in cold regions.

Historical and projected evolutions of glaciers in response to climate change in High Mountain Asia

Glacier changes are regarded as the conspicuous icon of climate change in High Mountain Asia (HMA) alpine environments. Multi-temporal glacier coverage is essential for mass balance estimations and understanding glacial changes in response to climate variability. However, consistent multi-temporal glacier area datasets across the HMA are limited due to challenges posed by seasonal snow and cloud cover in remote sensing satellite images. In this study, a new method is proposed to estimate glacier and nonseasonal snow (GNS) areas by combining the normalized difference snow index (NDSI) and bright temperature bands from Landsat remote sensing images in 1990s, 2000s, 2010s and 2020s. Meanwhile, the integrated ice dynamic Open Global Glacier Model (OGGM) is applied to simulate nine typical glaciers and project their changes using 15 general circulation models (GCMs) under four shared socioeconomic pathways (SSPs). The results showed that total GNS areas in HMA were estimated at 71.26 × 103 km2 in the 1990s, and decreased to 53.17 × 103 km2 in the 2020s. Massive proportions of GNS were mainly distributed in the southeast and northwest region. Tipping points of GNS indicating a transition from mass balance gain to loss during four periods were detected in the Upper Tarim basin, Inner Tibetan Plateau, Upper Yangtze river basin, Upper Brahmaputra river basin, and Upper Salween river basin. The reduction of total GNS area was attributed to significantly rising temperature and decreasing solid precipitation. The projections indicate that glacier shrinkages will be the dominant trend in the future, and most typical glaciers will experience a mass loss of 60% under SSP245 (SSP2-RCP4.5) and over 90% under SSP585 (SSP5-RCP8.5) by the end of the century. The response of glaciers to climate change is also found to be influenced by factors including local topography and elevation ranges. The findings of this study provides a better understanding of glacial dynamics in response to climate change and highlights the importance of making water resources management strategies to mitigate the influence of glacier retreat.

Enhanced snow cover mapping using object-based classification and normalized difference snow index (NDSI)

Enhanced snow cover mapping using object-based classification and normalized difference snow index (NDSI)