Remote sensing of seasonal snow



Snow constitutes a freshwater resource for over a billion of people world-wide. High percentage of this snow mainly come from seasonal snow located in mid-latitude regions. The current warming situation alerts that these snow water storages are in high risk to be dramatically reduced, affecting not only water supply but also ecosystems in these areas. Remote sensing has been the main technique used to monitor the snow properties across mid-large extensions for decades. The recent advances are focused on the study of snow dynamics at higher spatio-temporal scales (i.e., small-scale snow-topography interactions, diurnal variation of snow).

This session will focus on remote sensing studies dealing with techniques and data from different technologies, such as time-lapse imagery, laser scanners, radar, optical photography, thermal and hyperspectral technologies, or other new applications, with the aim of quantifying and better understanding snow characteristics (i.e., snow grain size, snow depth, albedo, pollution load, snow specific area and snow density), snow related processes (snowfall, melting, evaporation and sublimation), snow dynamics, snow hydrological impacts and snow environmental effects.

Co-organized by CR2
Convener: Rafael PimentelECSECS | Co-conveners: Claudia Notarnicola, Alexander Kokhanovsky
vPICO presentations
| Thu, 29 Apr, 13:30–14:15 (CEST)

Session assets

Session materials

vPICO presentations: Thu, 29 Apr

Chairperson: Rafael Pimentel
Kamil Mroz, Mario Montopoli, Giulia Panegrossi, Luca Baldini, Alessandro Battaglia, and Pierre Kirstetter

In this talk, surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s Core Observatory sensors and the CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product over the continental United States. The analysis spans a period between Nov. 2014 and Sept. 2020 and covers the following products: the Dual-Frequency Precipitation Radar product (2A.GPM.DPR) and its single frequency counterparts (2A.GPM.Ka, 2A.GPM.Ku); GPM Combined Radar Radiometer Algorithm (2B.GPM.DPRGMI.CORRA); the CloudSat Snow Profile product (2C-SNOW-PROFILE) and two passive microwave retrievals i.e. the Goddard PROFiling algorithm (2A.GPM.GMI.GPROF) and the Snow retrievaL ALgorithm fOr gMi (SLALOM). 

The 2C-SNOW product has the highest Heidke Skill Score (HSS=75%) for detecting snowfall among all the analysed products. SLALOM ranks the second (60%) while the Ka-band products falls at the end of the spectrum, with the HSS of 10% only. Low detection capabilities of the DPR products are a result of its low sensitivity. All the GPM retrievals underestimate not only the snow occurances but also snowfall volumes. Underestimation by a factor of two is present for all the GPM products compared to MRMS data. Large discrepancies (RMSE of 0.7 to 1.5 mm/h) between space-borne and ground-based snowfall rate estimates can be attributed to the complexity of ice scattering properties and differences in the algorithms' assumptions.

How to cite: Mroz, K., Montopoli, M., Panegrossi, G., Baldini, L., Battaglia, A., and Kirstetter, P.: Cross-validation of microwave snowfall products over the continental United States, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9971, https://doi.org/10.5194/egusphere-egu21-9971, 2021.

Nathan Letheule, Flora Weissgerber, Céline Monteil, and Alexandre Girard

Snow dynamics is a key hydrological process in alpine catchments. The snow accumulation formed during the winter feeds the dams during melting and so the snow quantification is important for dams managing. Snow data obtained from optical images (MODIS product) can be used to improve the simulation of the water flow using an hydrological model (MORDOR-TS, Le Lay, 2018). However, when there are clouds, this data cannot give any information.

To overcome this difficulty, this study presents an additional snow detection method using Synthetic Aperture Radar (SAR) data. The SAR images analysed come from Sentinel-1 (C-band) acquired in IW mode with a resolution of 5m by 20m. These images are obtained under two different polarizations (VV and VH). Before analyzing the SAR images, several pre-treatments such as despeckling, radiometric calibration, coregistration and layover detection are carried out.

The study is conducted around two snow gauges located at high altitude (2275m and 2685m) in the Guil catchment during an accumulation-melting cycle (September 2018-June 2019).

Two types of snow detection methods are used. The first one is a wet snow detection method (Nagler et al., 2016) that compares the analyzed image with a reference image. It allows to determine in a binary format if there is snow or not. The second one is a dry snow detection method (Lievens et al., 2019) which performs a comparison between the two polarizations of the analyzed image and determines a proportional snow depth.

The results were compared to the snow gauges data. Both methods appear to be complementary. Moreover, the time series obtained with snow dry detection method follows the tendency of snow gauges data during cold periods. Spatially over an area of 1600m by 1000m, the complementarity of the two methods can be seen once again. Despite this complementarity, a little presence of misdetection are observed at the resolution of the S1-images. However, when averaged to the resolution of MODIS data (500m by 500m), the detection results are consistent with the ground truth data. 

In the end, this study shows that we can efficiently detect snow with SAR images thanks to two complementary methods. Thus, SAR images add information about the snow cover up to the point of even estimating the snow depth with higher resolution than optical images. 

Le Lay M., Rouhier L., Garavaglia F., Hendrickx F., Monteil C., Le Moine N., and Ribstein P. (2018) Use of snow data in a hydrological distributed model: different approaches for improving model realism, EGU General Assembly 2018, Vienna, Austria.

Lievens, M. D., Marshall, H.-P., Reichle, R. H., Brucker, L., Brangers, I.,de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W., Jonas, T., Kim, E. J., Koch, I.,Marty, C., Saloranta, T., Schöber, J., and Lannoy, G. J. D. (2019). Snow depth variability inthe Northern Hemisphere mountains observed from space.nature communications.

Nagler, H. R., Ripper, E., Bippus, G., and Hetzenecker, M. (2016). Advan-cements for Snowmelt Monitoring by Means of Sentinel-1 SAR.remote sensing.

How to cite: Letheule, N., Weissgerber, F., Monteil, C., and Girard, A.: Analysis of the contribution of radar satellite images for the snow cover estimation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7773, https://doi.org/10.5194/egusphere-egu21-7773, 2021.

Jean-Pierre Dedieu, Anna Wendleder, Bastien Cerino, Julia Boike, Eric Bernard, Jean-Charles Gallet, and Hans-Werner Jacobi

Due to recent climate change conditions, i.e. increasing temperatures and changing precipitation patterns, arctic snow cover dynamics exhibit strong changes in terms of extent and duration. Arctic amplification processes and impacts are well documented expected to strengthen in coming decades. In this context, innovative observation methods are helpful for a better comprehension of the spatial variability of snow properties relevant for climate research and hydrological applications.

Microwave remote sensing provides exceptional spatial and temporal performance in terms of all-weather application and target penetration. Time-series of Synthetic Active Radar images (SAR) are becoming more accessible at different frequencies and polarimetry has demonstrated a significant advantage for detecting changes in different media. Concerning arctic snow monitoring, SAR sensors can offer continuous time-series during the polar night and with cloud cover, providing a consequent advantage in regard of optical sensors.

The aim of this study is dedicated to the spatial/temporal variability of snow in the Ny-Ålesund area on the Br∅gger peninsula, Svalbard (N 78°55’ / E 11° 55’). The TerraSAR-X satellite (DLR, Germany) operated at X-band (3.1 cm, 9.6 GHz) with dual co-pol mode (HH/VV) at 5-m spatial resolution, and with high incidence angles (36° to 39°) poviding a better snow penetration and reducing topographic constraints. A dataset of 92 images (ascending and descending) is available since 2017, together with a high resolution DEM (NPI 5-m) and consistent in-situ measurements of meteorological data and snow profiles including glaciers sites.

Polarimetric processing is based on the Kennaugh matrix decomposition, copolar phase coherence (CCOH) and copolar phase difference (CPD). The Kennaugh matrix elements K0, K3, K4, and K7 are, respectively, the total intensity, phase ratio, intensity ratio, and shift between HH and VV phase center. Their interpretation allows analysing the structure of the snowpack linked to the near real time of in-situ measurements (snow profiles).

The X-band signal is strongly influenced by the snow stratigraphy: internal ice layers reduce or block the penetration of the signal into the snow pack. The best R2 correlation performances between estimated and measured snow heights are ranging from 0.50 to 0.70 for dry snow conditions. Therefore, the use of the X-band for regular snow height estimations remains limited under these conditions.

Conversely, this study shows the benefit of TerraSAR-X thanks to the Kennaugh matrix elements analysis. A focus is set on the Copolar Phase Difference (CPD, Leinss 2016) between VV and HH polarization: Φ CPD = Φ VV - Φ HH. Our results indicate that the CPD values are related to the snow metamorphism: positive values correspond to dry snow (horizontal structures), negative values indicate recrystallization processes (vertical structures).

Backscattering evolution in time offer a good proxy for meteorological events detection, impacting on snow metamorphism. Fresh snowfalls or melting processes can then be retrieved at the regional scale and linked to air temperature or precipitation measurements at local scale. Polarimetric SAR time series is therefore of interest to complement satellite-based precipitation measurements in the Arctic.

How to cite: Dedieu, J.-P., Wendleder, A., Cerino, B., Boike, J., Bernard, E., Gallet, J.-C., and Jacobi, H.-W.: Snow change detection from polarimetric SAR time-series at X-band (Svalbard, Norway), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-149, https://doi.org/10.5194/egusphere-egu21-149, 2021.

Fatemeh Zakeri and Gregoire Mariethoz

Snow cover maps are critical for hydrological studies as well as climate change impacts assessment. Remote sensing plays a vital role in providing snow cover information. However, acquisition limitations such as clouds, shadows, or revisiting time limit accessing daily complete snow cover maps obtained from remote sensing. This study explores the generation of synthetic daily Landsat time-series data focusing on snow cover using available Landsat data and climate data for 2020 in the Western Swiss Alps (Switzerland). 
Landsat surface reflectance is predicted using all available Landsat imagery from 1984 to2020 and ERA5 reanalysis precipitation and air temperature daily data in this study. For a given day where there is no Landsat data, the proposed procedure computes a similarity metric to find a set of days having a similar climatic pattern and for which satellite data is available. These best match images constitute possible snow cover scenarios on the target day and can be used as stochastic input to impact models. 
Visual comparison and quantitative assessment are used to evaluate the accuracy of the generated images. In both accuracy assessments, some real Landsat data are omitted from the searching data set, and synthetic images are compared visually with real Landsat images. In the quantitative evaluation, the RSME between the real and artificial images is computed in a cross-validation fashion. Both accuracy procedures demonstrate that the combination of Landsat and climate data can predict Landsat's daily reflectance focusing on snow cover.

How to cite: Zakeri, F. and Mariethoz, G.: Producing Daily Landsat Snow Cover Time-Series Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-881, https://doi.org/10.5194/egusphere-egu21-881, 2021.

Arnab Muhuri, Simon Gascoin, Lucas Menzel, Tihomir S. Kostadinov, Adrian A. Harpold, Alba Sanmiguel-Vallelado, and Juan I. López-Moreno

In cold regions of the world with significant forest cover, a notable volume of precipitated snow resides under the forest cover. In such regions, snow is an abundant and valuable natural resource and assessing the winter extent of snow precipitation is particularly important for forecasting hydroelectric power potential, managing forests for maximizing the spring snowmelt yield, and monitoring animal habitats.

Forest presents challenging scenarios by obscuring much of the underlying snow over the forest floor from the view of the imaging spaceborne sensors. Moreover, due to the prevalence of mixed pixels, particularly in the forested landscapes, merely binarizing pixels into snow/snow-free can introduce errors while integrating the snow-covered area (SCA) information for hydro-climatological modeling. Therefore, the fractional snow-covered area (fSCA), which is a finer representation of the binary SCA and defines the snow-covered fraction of the pixel area, is a more reliable indicator. 

The recently launched High Resolution Snow & Ice (HRSI) monitoring service by Copernicus allows exploitation of the high-resolution Sentinel-2 data by facilitating free distribution of NDSI-based operational snow cover maps. It also offers the feasibility to estimate the fractional snow cover (FSC) without the requirement of any end-member spectra. In this investigation, we assessed the performance of the NDSI-based operational snow cover area (SCA) monitoring algorithm and the associated FSC with respect to factors influencing the algorithm's performance. The investigation focused over test sites located in the northern Sierra Nevada mountain range in California, US and the central Spanish Pyrenees. The analyses indicated that terrestrial characteristics like tree cover density (TCD) and meteorological factors like incoming solar irradiance impacts the performance of the optical satellite-based snow cover monitoring algorithms. A strong dependence of the algorithm's performance on TCD (negatively correlated) and solar irradiance (positively correlated) was observed.

How to cite: Muhuri, A., Gascoin, S., Menzel, L., Kostadinov, T. S., Harpold, A. A., Sanmiguel-Vallelado, A., and López-Moreno, J. I.: Factors Impacting Performance of the NDSI-Based Operational Snow Cover Monitoring Algorithm in Forested Landscapes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5139, https://doi.org/10.5194/egusphere-egu21-5139, 2021.

Florentin Hofmeister, Leonardo F. Arias-Rodriguez, Marco Borga, Valentina Premier, Carlo Marin, Claudia Notarnicola, Markus Disse, and Gabriele Chiogna

Modeling the runoff generation of high elevation Alpine catchments requires fundamental knowledge of the snow storage and the spatial distribution of snow cover. Since in-situ snow observations are often very scarce and represent only a point information, spatial snow information from satellite data is used since decades. However, the accuracy of snow cover maps through remote sensing products depends strongly on the cloudiness. In order to generate a spatial and temporal highly resolved dataset of snow cover maps, we applied the pixel identification processor (IdePix available in SNAP v7.0) to retrieve diverse cloud layers from Sentinel-2 Level-1C products. This makes it possible to use also high-clouded images for the snow detection, which increases significantly the data availability for the later performed snow model calibration. Cloudy areas, for which snow detection by the NDSI calculation is not possible, are set to no data. Sentinel-2 images that do not have cloud information require an extra correction based on the assumption that the snow cover has a pronounced elevation gradient. The entire NDSI dataset is subdivided into 200 m elevation zones and statistically analyzed. Thereby, the cloud-influenced images clearly stand out as outliers in the elevation zones >3000 m. If an elevation zone is detected as an outlier, the corresponding elevation zone is set to no data as well. After the comprehensive cloud detection, a pixel wise comparison with in-situ snow depth observation of four different sites allows us a first validation of the snow detection quality. In a second step, the generated snow maps are compared with the snow and cloud detection algorithm developed by Eurac Research. The final snow cover maps are used together with the in-situ snow depth observations to calibrate two different snowmelt approaches of the hydrological model WaSiM - the T-index and the energy balance-based approach (including gravitational snow redistribution) - over a mountainous basin in the Eastern Italian Alps.

How to cite: Hofmeister, F., Arias-Rodriguez, L. F., Borga, M., Premier, V., Marin, C., Notarnicola, C., Disse, M., and Chiogna, G.: Generation of a high-resolution snow cover dataset from Sentinel-2 images for snow model calibration, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16016, https://doi.org/10.5194/egusphere-egu21-16016, 2021.

Roberto Salzano, Christian Lanconelli, Giulio Esposito, Marco Giusto, Mauro Montagnoli, and Rosamaria Salvatori

Polar areas are the most sensitive targets of the climate change and the continuous monitoring of the cryosphere represents a critical issue. The satellite remote sensing can fill this gap but further integration between remotely-sensed multi-spectral images and field data is crucial to validate retrieval algorithms and climatological models. The optical behaviour of snow, at different wavelengths, provides significant information about the micro-physical characteristics of the surface and this allow to discriminate different snow/ice covers. The aim of this work is to present an approach based on combining unmanned observations on spectral albedo and on the analysis of time-lapse images of sky and ground conditions in an Arctic test-site (Svalbard, Norway). Terrestrial photography can provide, in fact, important information about the cloud cover and support the discrimination between white-sky or clear-sky illuminating conditions. Similarly, time-lapse cameras can provide a detailed description of the snow cover, estimating the fractional snow cover area. The spectral albedo was obtained by a narrow band device that was compared to a full-range commercial system and to remotely sensed data acquired during the 2015 spring/summer period at the Amundsen - Nobile Climate Change Tower (Ny Ålesund). The results confirmed the possibility to have continuous observations of the snow surface (microphisical) characteristics and highlighted the opportunity to monitor the spectral variations of snowed surfaces during the melting period. It was possible, therefore, to estimate spectral indexes, such as NDSI and SWIR albedo, and to found interesting links between both features and air/ground temperatures, wind-speed and precipitations. Different melting phases were detected and different processes were associated with the observed spectral variations.

How to cite: Salzano, R., Lanconelli, C., Esposito, G., Giusto, M., Montagnoli, M., and Salvatori, R.: The optical behaviour of snow during a melting season at Ny Ålesund (Svalbard, Norway), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14667, https://doi.org/10.5194/egusphere-egu21-14667, 2021.

Henna-Reetta Hannula, Roberta Pirazzini, and Petri Räisänen

Snow metamorphism is a continuous process affecting the snow albedo on time scales ranging from minutes to days, depending on the weather regime. To understand the complex interactions between snow microstructure, snow surface roughness, and surface albedo, these properties need to be observed at sufficiently high temporal resolution. For this reason, a new device, the SVC-FMI spectro-albedometer, was designed by Spectra Vista Corporation (SVC, USA) in collaboration with the Finnish Meteorological Institute (FMI, Finland) to continuously measure the surface spectral albedo while withstanding the cold and harsh weather conditions typical of polar regions. It consists of a SVC HR-1024i high resolution field portable spectroradiometer, with 3-10nm spectral resolution in the range 350-2500 nm, connected with an optical tube to two integrating spheres, one facing upward and the other facing downward, which collect the irradiance received from the sky and reflected from the surface, respectively. The whole system is enclosed in a weatherproof case which also partially provides thermal stabilization, with ventilated glass domes on the aperture of the integrating spheres, and it is installed in a fixed supporting structure that enables the control of the horizontal alignment of the integrating spheres.

SVC and later FMI calibrated the instrument and characterized the thermal drift of the instrument’s sensitivity and the deviation from the ideal cosine response. In spring 2019 and 2020 the instrument was installed in Sodankylä (northern Finland) over a flat wetland area where about 80-110 cm of snow had accumulated during the winter. The measurement campaigns were carried out in the framework of the Academy of Finland project SnowAPP (“Modelling of the Snow microphysical-radiative interaction and its APPlications”) with funding also from the H2020 EU project INTAROS (“Integrated Arctic Observation System”). The aperture of the downfacing integrating sphere was at 2 m from the snow surface, i.e. high enough to minimize shadows and light obstructions caused by the supporting structure, but low enough to enable easy installation and access to the instrument. Here we illustrate a selection of the collected data, showing all the steps of the data processing, which include the corrections to compensate the temperature drift, the deviation from the ideal cosine response, and shadows and light obstructions. The most complex correction and the one with the largest impact on the data is the deviation from the ideal cosine response. It involves radiative transfer modelling and the measurement (or modelling) of fraction of direct incoming irradiance and snow bidirectional reflectance distribution function. We discuss the different impact of these corrections in case of overcast and clear sky conditions.

Simultaneous measurements of snow properties as well as passive and active microwave signals were carried out, thus these spectral albedo data are very relevant for snow process studies and for the validation of snow products derived from satellite optical sensors.

How to cite: Hannula, H.-R., Pirazzini, R., and Räisänen, P.: Ground-based observations of snow spectral albedo with an autonomous device, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11188, https://doi.org/10.5194/egusphere-egu21-11188, 2021.