Snow Mapping Research Articles

Spatio‐temporal wet snow dynamics from model simulations and remote sensing: A case study from the Rofental, Austria

AbstractThe formation and concentration of liquid water (LW) in the snowpack constitute key processes linking snow and runoff. Hence, the LW content of the snowpack represents a crucial target variable to investigate for snowmelt‐induced runoff predictions. In this study, we capture the wet snow dynamics at higher than hectometre resolution in the alpine headwater catchment Rofental, Tyrol, Austria (98.1 km2) by means of distributed model simulations and remote sensing data for the 5 year period 10/2017–09/2022. The model simulations are conducted using the intermediate complexity open‐source snow‐hydrological model openAMUNDSEN. Simulation results are compared to wet snow maps (WSM) derived from Sentinel‐1 data. Our investigations indicate that distributed snow models of intermediate complexity, such as openAMUNDSEN and satellite‐based wet snow data are well capable of capturing the wet snow dynamics in high spatial and temporal resolutions. The areal extents of wet snow as well as the upward movement of the wet snow line to higher elevation with progressing snowmelt are captured well by both approaches. In order to evaluate the snow simulations, we use fractional snow cover (FSC) data based on Sentinel‐2, which proved to provide valuable small‐scale snow and snow redistribution patterns in alpine catchments. The comparison of model simulations with FSC maps with more than 50% of the non‐glaciated area being cloud‐free (i.e. 364 images) results in an accuracy of 0.91. This study represents a further step towards a serviceable operational snow‐hydrological monitoring and modelling framework for mountain regions including wet snow dynamics in high spatial and temporal resolutions.

From satellite image to glacier scenery: A novel GlacierPix2Pix model for glacier visualization

Glacier visualization is critical for studying climate change as glaciers respond rapidly to global warming. It serves as an indicator of environmental changes and provides insights into the magnitude of climate change. Traditional on-site photography faces challenges such as safety hazards, high costs, and tight time constraints. Existing terrain generation models have limitations such as not being capable of using real satellite imagery to control generated terrain and achieving photorealistic image quality. To address these issues, this study introduces GlacierPix2Pix, a novel deep learning model that generates glacier scenery from satellite images. This study uses the Landscape HQ dataset containing 90K terrain images and the USGS Glacier Benchmark dataset containing Digital Elevation Models of 5 different glaciers over many years. This study curates a dataset of over 9K glacier images that can be used in related studies from the LHQ dataset. Glacier contours are extracted from the DEM images and glacier images. Based on the StyleGAN architecture, GlacierPix2Pix comprises a terrain generator, discriminator, and style encoder, and it supports both 2D and 3D methods. The model achieves a Frchet Inception Distance (FID) of 31.90, which surpasses the performance of other similar datadriven methods. It can successfully produce photorealistic glacier images aligned with satellite-derived contours. This research contributes to a better understanding of the urgent issues surrounding climate change, especially its effect on glaciers. Potential applications of this study include demonstrating glacier recession, helping with mapping snow cover and mass balance, and allowing detailed changes in remote locations to be observed.

Evaluating MODIS cloud-free snow cover datasets using massive spatial benchmark data in the Tibetan Plateau

Accurate snow cover data is crucial for understanding climate change, managing water resources, and calibrating models. The MODIS (Moderate-resolution Imaging Spectroradiometer) and its cloud-free snow cover datasets are widely used, but they have not been systematically evaluated due to different benchmark data and evaluation parameters. Conventional methods using station observations as a ground truth suffer from underrepresentation and mismatches in temporal and spatial scales. This study established a scale-matched spatial benchmark dataset, compiling from 18,433 Landsat series and 11,172 Sentinel-2 images over two decades, totaling ∼1.86 billion samples and ∼320 million snow samples. We evaluated seven MODIS cloud-free snow cover datasets for seasons, elevation zones, land covers and subregions using this benchmark data. For the clear-sky part, NIEER_MODIS_SCE (MODIS snow cover extent product over China) performs best due to its use of optimal NDSI thresholds suitable for each land use type. This highlights the importance of regional customization in snow mapping algorithms, and it can be further improved in spring, forests and zone 1 by combining it with M*D10A1GL06. For the cloud removed part, one-step integrated spatiotemporal cloud removal datasets perform better than any other approach does. The second-best dataset is obtained from a simple but effective single temporal cloud removal method using nearby time information. For the whole dataset, the best NIEER_MODIS_SCE has an overall accuracy of 0.82 and snow retrieval accuracy of 84.56 %. It performs excellently in most settings but weakest in forests thus requiring more efficient strategies. This research provides new perspectives and methods for objectively assessing MODIS snow cover products and other relevant datasets. These methods can be readily extended to other regions and adapted to future satellite missions. And such findings may guide the selection of more valid snow cover data and the developing of even better snow detecting strategies.

An accurate snow cover product for the Moroccan Atlas Mountains: Optimization of the MODIS NDSI index threshold and development of snow fraction estimation models

In semi-arid Mediterranean areas, a significant proportion of the population living downstream depends on water resources from snowmelt and precipitation as their main source of water. Consequently, snow-covered mountain regions can be considered as a vital water tower, providing a steady supply of water, and contributing significantly to streamflow and groundwater recharge. Given the scarcity of ground-based hydroclimatic measurements, remote sensing could be an effective technique for mapping and monitoring snow cover. This study evaluates the last version of MODIS (version 6, called V6) snow cover product, optimizing the NDSI threshold for accurate snow cover mapping and developing models for local fractional snow cover estimation in the southern Mediterranean region, particularly in the Moroccan Atlas Mountains. For this purpose, 448 Sentinel-2 (S2) scenes from six different regions across the Atlas Range were used to adjust the NDSI threshold and to develop FSC estimation models. In addition, a total of 8419 MOD10A1 images from March 2000 to June 2023, and 7561 MYD10A1 images from September 2002 to June 2023, were processed to improve cloud filtering and to develop a highly accurate daily snow cover product suitable for the Moroccan Atlas Mountains. The cloud correction approach significantly reduced the number of cloud-covered pixels, from 25.7% to 0.4% after filtering. Two schemes for selecting the MODIS NDSI threshold were tested: (1) the global reference of 0.4 and (2) the locally optimal threshold of 0.2. The average snow cover estimation errors using the optimal and global NDSI thresholds for Terra are an average overestimation of 0.34% and a significant underestimation of 6.13%, respectively. For Aqua, the corresponding errors are an overestimation of 1.4% and an underestimation of 6.8%. Thus, the optimal NDSI threshold of 0.2 could be more appropriate than the threshold of 0.4 for use in the southern Mediterranean region. The new FSC estimation models developed showed satisfactory performance with significant correlation coefficients (mean of 0.85 for Terra and 0.83 for Aqua), and with low RMSE and MAE values (mean of 0.17 and 0.12 for Terra and mean of 0.19 and 0.14 for Aqua) when comparing FSC derived from high-resolution S2 data with predicted FSC from MODIS NDSI. The daily snow cover product developed was compared with the high-resolution snow maps obtained from S2 satellite imagery in different regions of the Moroccan Atlas. On average, the product showed a mean correlation coefficient of 0.96, a mean absolute error of 0.22%, and a mean reasonable negative bias of −0.17%. This research concludes that the enhanced daily snow cover product could improve the understanding of spatiotemporal dynamics of snow extent and, therefore, contribute to quantifying the snowmelt contribution to the water budget through modeling approaches in the southern Mediterranean region.

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.

Improving Mountain Snowpack Estimation Using Machine Learning With Sentinel‐1, the Airborne Snow Observatory, and University of Arizona Snowpack Data

AbstractAccurate mapping of snow amount in the mountains is critical as mountain snowpacks are water supply for millions of people. Satellite remote sensing has been largely unable to reliably detect the amount of snowpack in these areas. Recently, C‐band Synthetic Aperture Radar (SAR) data from the Sentinel‐1 (S1) satellites have shown potential for measuring snow depth in the mountains. However, their spatiotemporal coverage is incomplete, and their evaluation with robust, aerial snow depth data is limited. Here, we evaluate two S1 snowpack datasets with some of the best available gridded snowpack data over the Colorado Rockies and Sierra Nevada mountains in the western US: the Airborne Snow Observatory (ASO) and the University of Arizona (UA) snowpack datasets. Compared to ASO and UA data, the S1 data are biased high when snow is shallow, and biased low when snow is deep (particularly later in spring when there is wet snow), though these biases are reduced for deep snow areas when wet snow pixels are removed. We then apply corrections based on machine learning that account for physiographic characteristics to improve the accuracy of the S1 data. Furthermore, we fill gaps in the S1 data by using snow persistence, but also account for potential snow accumulation and ablation, to generate temporally complete snow depth maps over mountainous areas. Corrected and gap‐filled S1 snow depth mapping could be especially important for snow monitoring in remote mountain areas where other techniques for snow mapping do not work or are logistically infeasible or cost‐prohibitive.

Synthesizing long-term satellite imagery consistent with climate data: Application to daily snow cover

High-resolution daily snow cover estimation is challenging due to irregular satellite data availability. In this study, we create synthetic 30 m snow cover images for dates with no satellite data. It is based on the relationship between meteorological predictors and available clear sky 30 m Landsat/Sentinel-2 snow cover images. Our approach relies on the fact that while snow cover can vary in a matter of days, its patterns may repeat from year to year if meteorological characteristics are similar. In this context, we apply a K-nearest neighbor algorithm with a similarity metric tailored to estimating snow cover.The approach is tested with different data availability scenarios to generate twenty years of daily synthetic snow cover images for two regions of interest in Switzerland. The results are assessed based on a leave-one-out analysis, comparisons with PlanetScope, MODIS, and Copernicus High Resolution Snow & Ice Fractional Snow Cover On Ground (HRSI-FSCOG) images, comparisons with data collected at ground stations, and a comparison with a simple snow accumulation and melt model.The performance of our mapping approach suggests that it is possible to use recurrent snow patterns and climate reanalysis to generate missing data that are virtually and statistically indistinguishable from real observations. The results also indicate that low-resolution climate data, such as in the ERA5-Land reanalysis dataset, provide sufficient performance in the generated synthetic snow maps, opening possibilities for applications in areas with limited in situ snow or climatic data. Our approach could be used to create historical snow cover images for Pre-Satellite periods, or even for future periods by using climate projections.

Satellite mapping of red snow on North American glaciers.

Red snow caused by blooms of microalgae darkens the surface of summer snowfields, increasing snowmelt. To assess the contribution of red snow to supraglacial snowmelt in northwestern North America, we systematically mapped the 2019-2022 distribution of blooms by applying supervised classification to 6158 satellite images. Blooms occurred on 5% of the total glaciated area, heavily affecting many glaciers in years of prolonged snow cover duration. Individual glaciers had up to 65% of their surface area affected by bloom in one melt season, which we estimate caused as much as 3 cm of snow meltwater equivalent averaged across the glacier surface. These results demonstrate appreciable snowmelt caused by red snow albedo over vast areas of North American glaciers.

Mapping snow depth distribution from 1980 to 2020 on the tibetan plateau using multi-source remote sensing data and downscaling techniques

Although passive microwave remote sensing has been widely used for snow depth (SD) retrieval, its coarse spatial resolution has led to large uncertainties in various applications, particularly in mountainous regions. Therefore, downscaling passive microwave data is an effective way to improve the accuracy of regional SD monitoring. In this study, we developed a SD downscaling algorithm based on multisource remote sensing data, generating a long-time series of daily 500 m resolution SD data from 1980 to 2020 over the Tibetan Plateau (TP). The downscaling SD product improved the spatial resolution and significantly reduced the overestimation of the SD for the TP. Based on this product, the spatial pattern and the spatiotemporal trend of the SD were analyzed by the Theil-Sen median method and the Mann–Kendall test. SD on the TP showed clear spatial heterogeneity by vertical zonal distribution, bimodal distribution features changing with longitude, and a thick SD distribution pattern at 30-32°N. The annual average SD, maximum SD, and accumulation of SD on the TP from 1980 to 2020 were 1.02 cm, 5.91 cm, and 39.45 cm, respectively, with trends of −0.06 cm/10a, −0.44 cm/10a, and 1.80 cm/10a (P > 0.05), respectively. SD on the TP increases with elevation above 5.5 km in mountainous areas and decreases in areas below 5.5 km.

Snow Cover Detection Using Multi-Temporal Remotely Sensed Images of Fengyun-4A in Qinghai-Tibetan Plateau

Differentiating between snow and clouds presents a formidable challenge in the context of mapping snow cover over the Qinghai–Tibetan Plateau (QTP). The frequent presence of cloudy conditions severely complicates the discrimination of snow cover from satellite imagery. To accurately monitor the spatiotemporal evolution of snow cover, it is imperative to address these challenges and enhance the segmentation schemes employed for snow cover assessment. In this study, we devised a pixel-wise classification algorithm based on Support Vector Machine (SVM) called the 3-D Orientation Gradient algorithm (3-D OG), which captures the variations of the gradient direction of snow and clouds in spatiotemporal dimensions based on geostationary satellite “Fengyun-4A” (FY-4A) multi-spectral and multi-temporal optical imagery. This algorithm assumes that the speed and direction of clouds and snow are different in the process of movement leading to their discrepancy of gradient characteristics in time and space. Therefore, in this algorithm, the gradient of the images in the spatiotemporal dimensions is calculated first, and then the movement angle and trend are obtained based on that. Finally, the feature space is composed of the multi-spectral image, gradient image, and movement feature maps, which are used as the input of the SVM. Our results demonstrate that the proposed algorithm can identify snow and clouds more accurately during snowfall by utilizing the FY-4A’s high temporal resolution image. Weather station data, which was collected during snowstorms in the QTP, were used for evaluating the accuracy of our algorithm. It is demonstrated that the overall accuracy of snow cover segmentation by using the 3-D OG algorithm is improved by at least 12% and 10% as compared to snow products of Fengyun-2 and MODIS, respectively. Overall, the proposed algorithm has overcome the axial swing errors existing in Geostationary satellites and is successfully applied to cloud and snow segmentation in QTP. Furthermore, our study underscores that the visible and near-infrared bands of Fengyun-4A can be used for near real-time snow cover monitoring with high performance using the 3-D OG algorithm.

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.

INFLUENCE OF LINEAR MIXING ON CONTAMINATED SNOW SPECTRAL SIGNATURES

Abstract. Experiments using an SVC spectroradiometer ranging from 0.35 to 2.5 µm are being conducted in the field as part of the current research in order to gain a better understanding of how contamination affects spectral characteristics. Utilizing a spectroradiometer (0.35–2.5 µm), studies were carried out in the field to determine the linear mixing of snow pollutants (such as coal, ash, wood, and soil) with snow in terms of concentration of contaminants in order to simulate and comprehend the spectrum response of real-world scenarios. Present studies contribute to mapping snow and contaminated snow pixels in remote sensing RS data based on of linear mixing, and to identifying and discriminating between different types of snow contaminants in respect of linear mixing manner, via appropriate wavelength selections for future studies.

Combining Daily Sensor Observations and Spatial LiDAR Data for Mapping Snow Water Equivalent in a Sub‐Alpine Forest

AbstractSnow interacts with its environment in many ways and thus has a highly heterogeneous spatial and temporal variability. Therefore, modeling snow variability is difficult, especially in forested environments. To increase the understanding of the spatio‐temporal variability of snow and to validate snow models, reliable observation data at similar spatial and temporal scales is needed. For these purposes, airborne LiDAR surveys or time series derived from ground‐based snow sensors are commonly used. However, these are limited either to one point in space or in time. A new, extensive data set of daily snow variability in a sub‐alpine forest in the Alptal valley, Switzerland is presented. The core data set consists of a dense sensor network, repeated high‐resolution LiDAR data acquired using a fixed‐wing UAV, and manual snow depth and snow density measurements. Using machine learning algorithms, four distinct spatial clusters of similar snow depth dynamics are determined. By combining these spatial clusters with the observed snow depth time series, daily high‐resolution maps of snow depth and snow water equivalent (SWE) are derived. These products are the first to our knowledge that provide spatio‐temporally continuous snow depth and SWE based almost exclusively on field data. The presented workflow is transferrable to different regions, climates and scales. Moreover, the results allow future field campaigns to find representative sensor locations and target their LiDAR surveys to derive similar continuous products with less involved effort.

Multi-criteria evaluation for parameter uncertainty assessment and ensemble runoff forecasting in a snow-dominated basin

Abstract The increase in global temperatures undesirably affects the ever-growing world population and reveals the significance of hydrology science. Hydrological models might estimate spatial and temporal variability in hydrological components at the basin scale, which is critical for efficient water resource management. Satellite data sets with enhanced snow mapping with high spatial and temporal resolutions have been developed. The potential of these satellite data sets is evaluated in this study for multi-criteria evaluation of a conceptual hydrological model to improve model performance and reduce uncertainty. The upstream part of the transboundary Coruh River is selected for this study because snowmelt contributes a significant portion of the streamflow feeding major reservoirs during the spring and early summer months. The region’s snow cover dynamic has been analyzed using a combination of two satellite products. Hydrologic modeling is performed using the HBV model for the 2003–2015 water years (01 Oct–30 Sep). The Monte Carlo method is used for multi-criteria optimization exploiting satellite snow cover data besides runoff data. The sensitivity and uncertainty analysis on the model parameters indicate that multi-criteria calibration effectively reduces the uncertainty of the parameters and increases the model performance. Moreover, ensemble runoff forecasts are generated with several best model parameters using 1-day and 2-day lead time numerical weather prediction data for the snowmelt period (March–June) of the 2015 water year. The results indicate that the use of multiple remote sensing products in combination better represents the snow-covered area for the region. Additionally, including these data sets into hydrological models enhances the representation of hydrological components while reducing runoff prediction uncertainty.

Mapping snow depth on Canadian sub-arctic lakes using ground-penetrating radar

Abstract. Ice thickness across lake ice is mainly influenced by the presence of snow and its distribution, which affects the rate of lake ice growth. The distribution of snow depth over lake ice varies due to wind redistribution and snowpack metamorphism, affecting the variability of lake ice thickness. Accurate and consistent snow depth data on lake ice are sparse and challenging to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models to improve the understanding of how the varying distribution of snow depth influences lake ice formation and growth. This study was conducted using ground-penetrating radar (GPR) acquisitions with ∼9 cm sampling resolution along transects totalling ∼44 km to map snow depth over four Canadian sub-arctic freshwater lakes. The lake snow depth derived from GPR two-way travel time (TWT) resulted in an average relative error of under 10 % when compared to 2430 in situ snow depth observations for the early and late winter season. The snow depth derived from GPR TWTs for the early winter season was estimated with a root mean square error (RMSE) of 1.6 cm and a mean bias error of 0.01 cm, while the accuracy for the late winter season on a deeper snowpack was estimated with a RMSE of 2.9 cm and a mean bias error of 0.4 cm. The GPR-derived snow depths were interpolated to create 1 m spatial resolution snow depth maps. The findings showed improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information. The results suggest that GPR acquisitions can be used to derive lake snow depth, providing a viable alternative to manual snow depth monitoring methods. The findings can lead to an improved understanding of snow and lake ice interactions, which is essential for northern communities' safety and wellbeing and the scientific modelling community.

Evaluating MODIS snow products using an extensive wildlife camera network

Snow covers a maximum of 47 million km2 of Earth's northern hemisphere each winter and is an important component of the planet's energy balance, hydrology cycles, and ecosystems. Monitoring regional and global snow cover has increased in urgency in recent years due to warming temperatures and declines in snow cover extent. Optical satellite instruments provide large-scale observations of snow cover, but cloud cover and dense forest canopy can reduce accuracy in mapping snow cover. Remote camera networks deployed for wildlife monitoring operate below cloud cover and in forests, representing a virtually untapped source of snow cover observations to supplement satellite observations. Using images from 1181 wildlife cameras deployed by the Norwegian Institute for Nature Research (NINA), we compared snow cover extracted from camera images to Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products during winter months of 2018–2020. Ordinal snow classifications (scale = 0–4) from cameras were closely related to normalized difference snow index (NDSI) values from the MODIS Terra Snow Cover Daily L3 Global 500 m (MOD10A1) Collection 6 product (R2 = 0.70). Tree canopy cover, the normalized difference vegetation index (NDVI), and image color mode influenced agreement between camera images and MOD10A1 NDSI values. For MOD10A1F, MOD10A1's corresponding cloud-gap filled product, agreement with cloud-gap filled values decreased from 78.5% to 56.4% in the first three days of cloudy periods and stabilized thereafter. Using our camera data as validation, we derived a threshold to create daily binary maps of snow cover from the MOD10A1 product. The threshold corresponding to snow presence was an NDSI value of 40.50, which closely matched a previously defined global binary threshold of 40 using the MOD10A2 8-day product. These analyses demonstrate the utility of camera trap networks for validation of snow cover products from satellite remote sensing, as well as their potential to identify sources of inaccuracy.

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.

Constraining Mountain Streamflow Constituents by Integrating Citizen Scientist Acquired Geochemical Samples and Sentinel‐1 SAR Wet Snow Time‐Series for the Shimshal Catchment in the Karakoram Mountains of Pakistan

AbstractUpper Indus Basin (UIB) streamflow originates largely from glacier and snow melt in the upstream Himalaya, Karakoram, and Hindu Kush mountain ranges and is extremely vulnerable because of its projected climate changes, dense populations, and hydropolitical tensions. Accurate knowledge of streamflow constituents is required for resilient water resources management; this is precluded by a paucity of measurement as well as climatological and topographic complexity. Here we integrate citizen scientist acquired geochemical samples, collected from October 2018 through September 2019 in the Shimshal watershed of the Karakoram Mountains of Pakistan, with Sentinel‐1 (S1) synthetic aperture radar (SAR)‐derived wet snow maps, to better understand streamflow constituents for the high altitude and heavily glaciated catchment. We use Bayesian end‐member mixture analysis to separate river flows into baseflow and meltwater constituents, using fixed and time‐variant melt end‐member values. We compare river hydrograph separation results with S1 wet snow time series maps for the same timeframe. We then utilize S1 imagery to inform end‐member mixture analysis to separate meltwaters into snow and glacier melt. For the Shimshal catchment, we find that about 85% of annual river flows are derived from snow and glacier melt; 45% of annual flows are derived from snow melt and 40% glacier melt. Engaged and committed citizen scientists enabled geochemical sample collection and analysis on a significant temporal and spatial scale. In the future, co‐produced knowledge that both implements local expertise and that is also planned and utilized by diverse stakeholders may increase climatological awareness and resilience in the UIB.

Evaluation of passive microwave dry snow detection algorithms and application to SWE retrieval during seasonal snow accumulation

The microwave emission of snow has distinctive, frequency-dependent characteristics that enable the use of passive microwave remote sensing for the detection of (dry) snow alongside the study of further snow properties. Passive microwave approaches for the retrieval of snow water equivalent (SWE) often implement dry snow detection as one of the main processing steps. Reliable dry snow detection is thus crucial; however, common algorithms tend to underestimate snow cover extent. This study conducts a long-term assessment of six dry snow detection algorithms as part of ongoing GlobSnow SWE product development. We focus on terrestrial snow on the Northern Hemisphere, using exhaustive in situ snow depth measurements and spatially complete snow maps by the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS). In addition to using conventional daily snow masks, cumulative snow masks are investigated as means to counteract underestimation and the results emphasise their potential for this purpose. Best performances are achieved by the cumulative versions of the algorithm of the EUMETSAT H SAF H11 product and of the decision tree by Grody and Basist (1996), which agree with in situ data by approximately 81% and 84% whilst differing from IMS maps by only about 15% and 11%, respectively. Their validation for hemispheric SWE retrieval in the GlobSnow framework, using in situ snow course SWE observations, shows a noticeable improvement of the current statistics especially during shallow snow conditions in autumn—reducing the bias by up to 2.2 mm and the RMSE by up to 2.0 mm. This highlights the impact that the algorithm choice for dry snow detection has on the quality of SWE retrieval and therefore on long-term climate data records.

Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets

Abstract. Snow cover mapping algorithms utilizing multispectral satellite data at various spatial resolutions are available, each treating subpixel variation differently. Past evaluations of snow mapping accuracy typically relied on satellite data collected at a higher spatial resolution than the data in question. However, these optical data cannot characterize snow cover mapping performance under forest canopies or at the meter scale. Here, we use 3 m spatial resolution snow depth maps collected on 116 d by an aerial laser scanner to validate band ratio and spectral-mixture snow cover mapping algorithms. Such a comprehensive evaluation of sub-canopy snow mapping performance has not been undertaken previously. The following standard (produced operationally by an agency) products are evaluated: NASA gap-filled Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A1F, NASA gap-filled Visible Infrared Imaging Radiometer Suite (VIIRS) VNP10A1F, and United States Geological Survey (USGS) Landsat 8 Level-3 Fractional Snow Covered Area. Two spectral-unmixing approaches are also evaluated: Snow-Covered Area and Grain Size (SCAG) and Snow Property Inversion from Remote Sensing (SPIReS), both of which are gap-filled MODIS products and are also run on Landsat 8. We assess subpixel snow mapping performance while considering the fractional snow-covered area (fSCA), canopy cover, sensor zenith angle, and other variables within six global seasonal snow classes. Metrics are calculated at the pixel and basin scales, including the root-mean-square error (RMSE), bias, and F statistic (a detection measure). The newer MOD10A1F Version 61 and VNP10A1F Version 1 product biases (− 7.1 %, −9.5 %) improve significantly when linear equations developed for older products are applied (2.8 %, −2.7 %) to convert band ratios to fSCA. The F statistics are unchanged (94.4 %, 93.1 %) and the VNP10A1F RMSE improves (18.6 % to 15.7 %), while the MOD10A1F RMSE worsens (12.7 % to 13.7 %). Consistent with previous studies, spectral-unmixing approaches (SCAG, SPIReS) show lower biases (−0.1 %, −0.1 %) and RMSE (12.1 %, 12.0 %), with higher F statistics (95.6 %, 96.1 %) relative to the band ratio approaches for MODIS. Landsat 8 products are all spectral-mixture methods with low biases (−0.4 % to 0.3 %), low RMSE (11.4 % to 15.8 %), and high F statistics (97.3 % to 99.1 %). Spectral-unmixing methods can improve snow cover mapping at the global scale.