In this work, we analyze the relationship between albedo and temperature using albedo and Land Surface Temperature (LST) MODIS collection 6 (C6) and in situ data at Livingston Island, Maritime Antarctica. It is known that the relationship between temperature and albedo could have an important impact on global climate models, especially in places where permafrost distribution is complex, as in the South Shetland Islands (SSI) archipelago. Our results show that LST is not well correlated with albedo, which is consistent with the fact that air temperature (Ta) and surface temperature (Ts) do not separately explain the albedo drop, as previous work in the study area has shown. The best agreement was obtained between Aqua and Terra LST and in situ albedo, while the comparison between albedo MODIS and LST yields the worst results, which could be due to the difference in pixel size of MODIS albedo and LST products (500 m and 1000 m, respectively). However, for Ta versus albedo for all data, the decreasing slope of the fit suggests that higher temperatures are associated with lower snow albedo values. This reaffirms the idea that in polar areas, due to their characteristics, the decrease in snow albedo depends not only or mainly on temperature, but also on multiple factors such as the evolution of snow grain size and precipitation rates, among others. 

How to cite: Corbea-Pérez, A., Recondo, C., and Calleja, J. F.: Correlation of Land Surface Temperature and air temperature with albedo in Maritime Antarctica using MODIS and in situ data., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11557, https://doi.org/10.5194/egusphere-egu24-11557, 2024.

Remote sensing technologies provide continuous and detailed observations of various land surface parameters, including snow cover, vegetation, land surface temperature, soil moisture, and evapotranspiration, offering invaluable information at various scales and contexts. One of the major uses is the precise mapping and monitoring of seasonal snow cover dynamics, which are essential for water management and global water balance modeling. Since an intelligent ecosystem based on accurate snow cover estimation requires a collection of high-resolution satellite images, both temporally and spatially, to capture snow dynamics, particularly in semi-arid areas where snowfall is extremely variable. These requirements can be difficult to achieve based on a single sensor, mainly due to the trade-offs between the temporal, spectral, and spatial resolutions of the available satellites. In addition, atmospheric conditions and cloud contamination can increase the number of missing satellite observations. However, there is a promising solution to these limitations. Exploiting the complementary capabilities of the new-generation multispectral sensors aboard Landsat-8 (L8) and Sentinel-2 (S2), with spatial resolutions ranging from 10 to 30 meters, offers an unprecedented opportunity to significantly advance the accuracy of snow cover mapping. Hence, this study aims to investigate the effectiveness of the combined use of optical sensors through deep learning-based spatiotemporal image fusion to capture snow dynamics and produce detailed and dense Normalized Snow Difference Index (NDSI) time series in a semi-arid context. Three distinct deep learning models, namely Very Deep Super Resolution (VDSR), Super Resolution Unet (SR-Unet), and Residual Convolutional Neural Network (RCNN), were evaluated and compared to fuse L8 and S2 data. The findings indicate that all three approaches can provide accurate estimates for a coarse-resolution image at a given fusion date, although there are notable disparities in prediction quality between the different approaches. Specifically, R-squared values were measured at 0.94, 0.92, and 0.96 for RCNN, SR-Unet, and VDSR, respectively, with corresponding root mean square error (RMSE) values of 0.09, 0.11, and 0.08. Our results suggest that the VDSR model is particularly effective in producing high-resolution merged snow time series and can compensate for the absence of ground snow cover data.

How to cite: Bousbaa, M., Boudhar, A., Kinnard, C., Elyoussfi, H., Elbouanani, N., Htitiou, A., Bargam, B., Nifa, K., and Chehbouni, A.: Towards a Deep Learning-based Spatio-temporal Fusion Approach for Accurately Improving Snow Cover Mapping: A Case Study in the Moroccan Atlas Mountains with Performance Evaluation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13030, https://doi.org/10.5194/egusphere-egu24-13030, 2024.

The lack of knowledge about the temporal variability, or snow cover phenology and its spatial variation poses enormous challenges to water resource managers who mostly rely on a few weather stations with limited spatial coverage which prevents them from having a complete understanding of snow changes as a whole. Meanwhile, the free availability, wide-coverage, frequent updating, and long-term time horizon make data from programs such as Landsat and Sentinel-2 a valuable data source for reliable snow data information at an unprecedented spatial scale.

In this context, this research aims to derive the snow phenology parameters (first day of snowfall, last day of snow melt; and snow duration) over Morocco’s Atlas Mountains 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 Google Earth Engine platform. The time series produced allowed us to create detailed maps of snow cover 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 seasonality snow metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be observed. The experimental results demonstrate that the proposed workflow can accurately derive snow seasonality timing with almost a day and a half delay than the in-situ observed dates and with an overall accuracy equal to 0.96.

  We expect these results to benefit various applications such as hydrological modeling, natural hazards, and regional climate change studies.

How to cite: El jabiri, Y., Boudhar, A., Htitiou, A., Sproles, E., Bousbaa, M., Bouamri, H., and Chehbouni, A.: Towards operational mapping and estimation of snow cover phenology parameters in the Atlas Mountains, Morocco, using multi-sensor satellite data and Google Earth Engine. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11620, https://doi.org/10.5194/egusphere-egu24-11620, 2024.

In this study, we attempted to classify snowfall patterns using multiple dual-polarization radars and quantitatively review the amount of snowfall observed from radar using a ground observation network (snow depth). In order to more quantitatively compare the difference between radar reflectivity and precipitation (snow) intensity compared to ground observed snow depth, comparison was made on an hourly basis, taking into account the Korea Meteorological Administration's snow observation data provision period (1 hour). Radar observation data were compared with precipitation intensity based on cumulative reflectivity, differential reflectivity, and specific differential phase difference. Compared to radar reflectivity, there were various delays ranging from 2 to 7 hours from the time the precipitation intensity accumulated with the snow depth. In addition, the difference between the time of increase in snow cover is judged to be an error generated by the wind, and it is necessary to expand the range of radar pixels as well as the blinding factor to take into account the influence of wind.

Acknowledgments

This research was supported by a grant(2022-MOIS61-003(RS-2022-ND634022)) of Development Risk Prediction Technology of Storm and Flood for Climate Change based on Artificial Intelligence funded by Ministry of Interior and Safety(MOIS, Korea).

How to cite: Kang, N., Yoon, J., and Hwang, S.: Snow-depth Spatial Distribution Analysis Technology linked to Ground Observation Network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14040, https://doi.org/10.5194/egusphere-egu24-14040, 2024.

Despite the critical importance of understanding trends in snow depth and mass for making informed decisions about water resources and adaptation to climate change, these properties are challenging to quantify, especially in remote, mountainous areas with complex topography.  The increasing availability of frequent, high resolution synthetic aperture radar (SAR) observations from active microwave satellites has provided the opportunity to provide high-resolution estimates of mountain snow depth at large spatial and frequent temporal scales. As a result, novel approaches have been developed for SAR-based snow depth retrievals utilizing C-band microwave imagery. These SAR-based methods are not without their own set of limitations and are challenged by shallow snowpacks, high vegetation cover, and wet snow conditions. Here, we seek to overcome these existing challenges by developing a machine learning approach to estimate snow depth over the European Alps using Sentinel-1 imagery, an optical satellite-based snow cover product, and static information such as elevation, slope, aspect, topographical position index and forest cover fraction. We demonstrate that our machine learning approach can more accurately estimate snow depth than existing methods at independent in-situ test sites throughout the Alps and has especially improved performance in deep snow and wet snow conditions. Using feature importance scores, we also investigate when and where the Sentinel-1 data provides the most benefit for snow depth estimation. Our approach optimizes the use of Sentinel-1 imagery by learning when these observations are effective for retrieving snow depth, while relying on other topographical information when Sentinel-1 observations are not suitable.

How to cite: Dunmire, D., Lievens, H., Brangers, I., Boeykens, L., and De Lannoy, G.: A novel machine learning approach for estimating snow depth in the European Alps from Sentinel-1 imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11677, https://doi.org/10.5194/egusphere-egu24-11677, 2024.

Monitoring snowmelt dynamics in mountainous catchments  is essential for comprehending downstream water release, streamflow response and consequently, for a better management of water resources at the catchment scale.  In a consolidated snowpack when external inputs become positive, the snow turns into wet snow and the melting phase begins. This change modifies the dielectric constant of the snowpack which can be detected remotely using information in the microwave region of the electromagnetic spectrum. Sentinel-1 (S-1) synthetic-aperture radar (SAR) has emerged as a widely utilized technique for this purpose due to its frequent acquisitions and all-weather capability. 

This study seeks to, first, explore the capabilities of C-band S-1 SAR imagery, which has been demonstrated in previous studies in other regions such as the Alps, in capturing multi-seasonal snowmelt dynamics and, second to linked these wet-dynamics to changes in streamflow response over Mediterranean mountain areas . The study was carried out at two scales: plot and catchment: At the plot scale, the Refugio Poqueira experimental site, which is located at 2500 m a.s.l. was chosen. At the catchment scale, the headwaters of the Poqueria River, which is a  snow-driven catchment  located in the southern face of Sierra Nevada, was selected. Four hydrological years with high hydroclimatic variability, from 2016-2017 to 2019-2020, were used in the study to capture the heterogeneity of the area. 

The general change detection approach for identifying wet snow was adapted for these regions, utilizing the average S-1 SAR image from the preceding summer as  reference imagery and employing a threshold of −3.00 dB for discriminating wet snow. This adaptation was validated using Landsat images as a reference dataset, yielding a general accuracy of 0.79. The local scale analysis demonstrates that S-1 SAR imagery was  able to capture four types of melting cycles including the well-known main melting event during the spring season. The other three melting cycles are linked to the Mediterranean mountains climate and can occur throughout the hydrological year. When applied at the catchment scale, distributed melting-runoff onset maps were developed to enhance understanding of the spatiotemporal evolution of melting dynamics. Finally, a linear correlation between melting dynamics and streamflow was established for prolonged melting cycles, with a determination coefficient (R2) ranging from 0.62 to 0.83 and an average delay of approximately 21 days between melting onset and streamflow peak.

Acknowledgments: This work has been funded by the project PID2021-12323SNB-I00, HYPOMED—“Incorporating hydrological uncertainty and risk analysis to the operation of hydropower facilities in Mediterranean mountain watersheds”.

How to cite: Torralbo Muñoz, P., Pimentel Leiva, R., Polo Gómez, M. J., and Notarnicola, C.: Using Multitemporal Sentinel-1 imagery for wet snow dynamics characterization in Mediterranean mountain catchments: a case study in Sierra Nevada, Spain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11034, https://doi.org/10.5194/egusphere-egu24-11034, 2024.

Over the past decade, heavy snow has caused the third-largest amount of disaster damage in South Korea, following typhoons and heavy rain. To prevent damage from heavy snow effectively, it is necessary to forecast weather conditions. The Korea Meteorological Administration uses the Local Data Assimilation and Prediction System (LDAPS) to forecast hydrometeorological factors. However, the performance of LDAPS snow depth data is inferior to that of other models and requires correction. In this study, a cumulative distribution function (CDF) matching was used to correct LDAPS snow depth data. The CDF matching was carried out by utilizing ERA5-Land snow depth data to generate snow depth forecasting data for 12, 24, and 36-hour intervals. The forecasting data for snow depth is expected to generate snow disaster risk prediction data that can help reduce disaster losses on the Korean Peninsula.

How to cite: Jeon, H., Lee, S., and Choi, M.: A new snow depth forecast data using cumulative distribution function matching in South Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15736, https://doi.org/10.5194/egusphere-egu24-15736, 2024.