scholarly journals Potential of X-band polarimetric SAR co-polar phase difference for Arctic snow depth estimation

2021 ◽  
Author(s):  
Joëlle Voglimacci-Stephanopoli ◽  
Anna Wendleder ◽  
Hugues Lantuit ◽  
Alexandre Langlois ◽  
Samuel Stettner ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Phase Difference ◽  
Temporal Variability ◽  
Snow Depth ◽  
High Spatial Resolution ◽  
Incidence Angle ◽  
Ground Vegetation ◽  
Spatial And Temporal Variability ◽  
Polar Phase ◽  
Topographic Wetness Index

Abstract. Changes in snowpack associated with climatic warming has drastic impacts on surface energy balance in the cryosphere. Yet, traditional monitoring techniques, such as punctual measurements in the field, do not cover the full snowpack spatial and temporal variability, which hampers efforts to upscale measurements to the global scale. This variability is one of the primary constraints in model development. In terms of spatial resolution, active microwaves (synthetic aperture radar—SAR) can address the issue and outperform methods based on passive microwaves. Thus, high spatial resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow. The overall objective of this study is to evaluate the potential of the TerraSAR-X (TSX) SAR sensor and the wave co-polar phase difference (CPD) method for characterizing snow cover at high spatial resolution. Consequently, we first (1) quantified the spatio-temporal variability of the geophysical properties of the snowpack in an Arctic catchment, we then (2) studied the links between snow properties and CPD, considering ground vegetation. Snow depth (SD) could be extracted using the CPD when certain conditions are met. A high incidence angle (> 30°) with a high Topographic Wetness Index (TWI) (> 7.0) showed correlation between SD and CPD (R-squared up to 0.72). Further, future work should address a threshold of sensitivity to TWI and incidence angle to map snow depth in such environments and assess the potential of using interpolation tools to fill in gaps in SD information on drier vegetation types.

scholarly journals Spatial and temporal variability of snow depth and SWE in a small mountain catchment

2010 ◽  
Vol 4 (1) ◽  
pp. 1-30 ◽  
Author(s):  
T. Grünewald ◽  
M. Schirmer ◽  
R. Mott ◽  
M. Lehning
Keyword(s):  
Temporal Variability ◽  
Snow Depth ◽  
Energy Transport ◽  
Snow Water Equivalent ◽  
Laser Scanner ◽  
Ablation Rate ◽  
Spatial And Temporal Variability ◽  
M Cell ◽  
Snow Water ◽  
Spatio Temporal

Abstract. The spatio-temporal variability of the mountain snow cover determines the avalanche danger, snow water storage, permafrost distribution and the local distribution of fauna and flora. Using a new type of terrestrial laser scanner (TLS), which is particularly suited for measurements of snow covered surfaces, snow depth, snow water equivalent (SWE) and melt rates have been monitored in a high alpine catchment during an ablation period. This allowed for the first time to get a high resolution (2.5 m cell size) picture of spatial variability and its temporal development. A very high variability in which maximum snow depths between 0–9 m at the end of the accumulation season was found. This variability decreased during the ablation phase, although the dominant snow deposition features remained intact. The spatial patterns of calculated SWE were found to be similar to snow depth. Average daily melt rate was between 15 mm/d at the beginning of the ablation period and 30 mm/d at the end. The spatial variation of melt rates increased during the ablation rate and could not be explained in a simple manner by geographical or meteorological parameters, which suggests significant lateral energy fluxes contributing to observed melt. It could be qualitatively shown that the effect of the lateral energy transport must increase as the fraction of snow free surfaces increases during the ablation period.

scholarly journals Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment

2010 ◽  
Vol 4 (2) ◽  
pp. 215-225 ◽  
Author(s):  
T. Grünewald ◽  
M. Schirmer ◽  
R. Mott ◽  
M. Lehning
Keyword(s):  
Snow Cover ◽  
Temporal Variability ◽  
Snow Depth ◽  
Energy Transport ◽  
Snow Water Equivalent ◽  
Laser Scanner ◽  
Ablation Rate ◽  
Spatial And Temporal Variability ◽  
M Cell ◽  
Snow Water

Abstract. The spatio-temporal variability of the mountain snow cover determines the avalanche danger, snow water storage, permafrost distribution and the local distribution of fauna and flora. Using a new type of terrestrial laser scanner, which is particularly suited for measurements of snow covered surfaces, snow depth was monitored in a high alpine catchment during an ablation period. From these measurements snow water equivalents and ablation rates were calculated. This allowed us for the first time to obtain a high resolution (2.5 m cell size) picture of spatial variability of the snow cover and its temporal development. A very high variability of the snow cover with snow depths between 0–9 m at the end of the accumulation season was observed. This variability decreased during the ablation phase, while the dominant snow deposition features remained intact. The average daily ablation rate was between 15 mm/d snow water equivalent at the beginning of the ablation period and 30 mm/d at the end. The spatial variation of ablation rates increased during the ablation season and could not be explained in a simple manner by geographical or meteorological parameters, which suggests significant lateral energy fluxes contributing to observed melt. It is qualitatively shown that the effect of the lateral energy transport must increase as the fraction of snow free surfaces increases during the ablation period.

scholarly journals A Global Dynamic Long-Term Inundation Extent Dataset at High Spatial Resolution Derived through Downscaling of Satellite Observations

2017 ◽  
Vol 18 (5) ◽  
pp. 1305-1325 ◽  
Author(s):  
Filipe Aires ◽  
Léo Miolane ◽  
Catherine Prigent ◽  
Binh Pham ◽  
Etienne Fluet-Chouinard ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Google Earth ◽  
Satellite Observations ◽  
Smooth Transition ◽  
Spatial And Temporal Variability ◽  
Global Dynamic ◽  
Inundation Extent ◽  
Smoothing Procedure

Abstract A new procedure is introduced to downscale low-spatial-resolution inundation extents from Global Inundation Extent from Multi-Satellites (GIEMS) to a 3-arc-s (90 m) dataset (known as GIEMS-D3). The methodology is based on topography and hydrography information from the HydroSHEDS database. A new floodability index is introduced and an innovative smoothing procedure is developed to ensure a smooth transition, in the high-resolution maps, between the low-resolution boxes from GIEMS. Topography information is pertinent for natural hydrology environments controlled by elevation but is more limited in human-modified basins. However, the proposed downscaling approach is compatible with forthcoming fusion of other, more pertinent satellite information in these difficult regions. The resulting GIEMS-D3 database is the only high-spatial-resolution inundation database available globally at a monthly time scale over the 1993–2007 period. GIEMS-D3 is assessed by analyzing its spatial and temporal variability and evaluated by comparisons to other independent satellite observations from visible (Google Earth and Landsat), infrared (MODIS), and active microwave (synthetic aperture radar).

Backward snow depth reconstruction at high spatial resolution based on time-lapse photography

2016 ◽  
Vol 30 (17) ◽  
pp. 2976-2990 ◽  
Author(s):  
Jesús Revuelto ◽  
Tobias Jonas ◽  
Juan-Ignacio López-Moreno
Keyword(s):  
Spatial Resolution ◽  
Snow Depth ◽  
High Spatial Resolution ◽  
Time Lapse ◽  
Depth Reconstruction ◽  
Time Lapse Photography

scholarly journals Spatial–Temporal Variability of Snow Cover and Depth in the Qinghai–Tibetan Plateau

2017 ◽  
Vol 30 (4) ◽  
pp. 1521-1533 ◽  
Author(s):  
Wenfang Xu ◽  
Lijuan Ma ◽  
Minna Ma ◽  
Haicheng Zhang ◽  
Wenping Yuan
Keyword(s):  
Climate Change ◽  
Tibetan Plateau ◽  
Snow Cover ◽  
Temporal Variability ◽  
Snow Depth ◽  
Initial Increase ◽  
Spatial And Temporal Variability ◽  
Important Challenge ◽  
In Situ Observations

Abstract Changes in snow cover over the Qinghai–Tibetan Plateau have attracted much attention in recent years owing to climate change. Because of the limitations of in situ observations, only a few studies have analyzed the dynamics of snow cover. Using observations from 103 meteorological stations across the Qinghai–Tibetan Plateau, this study investigated the spatial and temporal variability of snow depth and the number of snow-cover days. The results show a very weak negative trend for the snow depth and the number of snow-cover days in spring and winter from 1961 to 2010, but two different trends were found: an initial increase followed by a decrease. In summer and autumn, snow depth and the number of snow-cover days show a significant decreasing trend for most sites. The duration of snow cover exhibits a significant decreasing trend (−3.5 ± 1.2 days decade−1), which was jointly controlled by a later snow starting time (1.6 ± 0.8 days decade−1) and an earlier snow ending time (−1.9 ± 0.8 days decade−1) consistent with a response to climate change. This study highlights the competing effects of rising temperatures and changing precipitation, which remain an important challenge in understanding and interpreting the observed changes in snow depth and the number of snow-cover days for the Qinghai–Tibetan Plateau.

scholarly journals Quantitative Evaluation of the Soil Signal Effect on the Correlation between Sentinel-1 Cross Ratio and Snow Depth

2021 ◽  
Vol 13 (22) ◽  
pp. 4691
Author(s):  
Tianwen Feng ◽  
Xiaohua Hao ◽  
Jian Wang ◽  
Hongyi Li ◽  
Juan Zhang
Keyword(s):  
Spatial Resolution ◽  
Snow Depth ◽  
High Spatial Resolution ◽  
Soil Surface ◽  
Mean Value ◽  
Snow Accumulation ◽  
Cross Ratio ◽  
Retrieval Algorithm ◽  
Observation Data ◽  
The Impact

High-resolution Synthetic Aperture Radar (SAR), as an efficient Earth observation technology, can be used as a complementary means of observation for snow depth (SD) and can address the spatial heterogeneity of mountain snow. However, there is still uncertainty in the SD retrieval algorithm based on SAR data, due to soil surface scattering. The aim of this study is to quantify the impact of soil signals on the SD retrieval method based on the cross-ratio (CR) of high-spatial resolution SAR images. Utilizing ascending Sentinel-1 observation data during the period from November 2016 to March 2020 and a CR method based on VH- and VV-polarization, we quantitatively analyzed the CR variability characteristics of rock and soil areas within typical thick snow study areas in the Northern Hemisphere from temporal and spatial perspectives. The correlation analysis demonstrated that the CR signal in rock areas at a daily timescale shows a strong correlation (mean value > 0.60) with snow depth. Furthermore, the soil areas are more influenced by freeze-thaw cycles, such that the monthly CR changes showed no or negative trend during the snow accumulation period. This study highlights the complexity of the physical mechanisms of snow scattering during winter processes and the influencing factors that cause uncertainty in the SD retrieval, which help to promote the development of high-spatial resolution C-band data for snow characterization applications.

Towards a new ground snow-load map for structural design in Germany

2021 ◽  
Author(s):  
Fabiana Castino ◽  
Bodo Wichura
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
High Spatial Resolution ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Snow Load ◽  
Characteristic Values ◽  
Snow Loads

<p>The current European standard for snow loads on structures relies on characteristic values (i.e., snow loads with an annual probability of exceedance of 0.02 and referred to as the 50-year mean return levels) derived for Germany in 2005 using about 350 snow water-equivalent (SWE) time series from ground stations operated by the German National Weather Service (DWD) [<em>DIN EN 1991-1-3/NA:2019-04</em>, 2019]. Here we present a methodology for generating a new ground snow-loads map for Germany, which aims at improving the relative coarse spatial resolution and reducing uncertainties and inconsistencies at national borders of the actual standard. Our methodology is based on (1) high-quality and homogeneous snow-cover time series, including both daily snow-depth (from about 6000 stations in Germany and in neighbouring countries) and three-weekly water-equivalent observation (from about 10<sup>3</sup> German stations) over the period from 1950 to 2020, (2) an integrated model combining an empirical regression model for snow bulk density and the semi-empirical multi-level ΔSNOW model for generating accurate daily SWE values from 6000 snow-depth time series [<em>Castino et al.</em>, 2022], (3) the spatial interpolation of both daily snow-depth and modelled-SWE time series using a universal-kriging method to generate high spatial-resolution (~1km<sup>2</sup>) rasterised daily snow loads over the period from 1950 to 2020, and (4) the extreme value analysis of the rasterized daily snow loads for estimating the characteristic values at high spatial resolution for the entire German territory. The uncertainties of the obtained characteristic snow-load values will be estimated using a leave-one-out cross validation based on a selection of observed-SWE time series representative of the diversity of the regional snow climatology in Germany. Finally, the characteristic values of the snow-load map generated with this methodology will be compared with the current German standard.   </p> <p> </p> <p><strong>References</strong></p> <p>Castino, F., H. Schellander, B. Wichura, and M. Winkler (2022), SWE modelling: comparison between different approaches applied to Germany, abstract submitted to D-A-CH MeteorologieTagung - 21-25.03.2022, Leipzig.</p> <p>DIN EN 1991-1-3/NA:2019-04 (2019), Nationaler Anhang - National festgelegte Parameter - Eurocode 1: Einwirkungen auf Tragwerke - Teil 1-3: Allgemeine Einwirkungen - Schneelasten, edited, p. 22, Deutsches Institut für Normung e.V., Beuth-Verlag, Berlin.</p>

Investigating the ability of multiple reanalysis datasets to simulate snow depth variability over mainland China from 1981 to 2018

2021 ◽  
pp. 1-48
Author(s):  
Hongbo Zhang ◽  
Fan Zhang ◽  
Tao Che ◽  
Wei Yan ◽  
Ming Ye
Keyword(s):  
Temporal Variability ◽  
Snow Depth ◽  
Mainland China ◽  
High Accuracy ◽  
Spatial And Temporal Variability ◽  
The Tibetan Plateau ◽  
Combined Effects ◽  
Driving Mechanisms ◽  
Depth Increase ◽  
Precipitation And Temperature

AbstractThough the use of reanalysis datasets to analyze snow changes is increasingly popular, the snow depth variability in China simulated by multiple reanalysis datasets has not been well evaluated. Also, the extent of regional snow depth variability and its driving mechanisms are still unknown. In this study, monthly snow depth observations from 325 stations during the period of 1981–2018 were taken to evaluate the ability of five reanalysis datasets (JRA55, MERRA2, GLDAS2, ERA5, and ERA5L) to simulate the spatial and temporal variability of snow depth in China. The evaluation results indicate that MERRA2 has the lowest root-mean-square deviation of snow depth and a high spatial correlation coefficient with observations. This may be partly related to the high accuracy of precipitation and temperature in MERRA2. Also, the 31 combinations of the five reanalysis datasets do not yield better accuracy in snow depth than MERRA2 alone. This is because the other four datasets have larger uncertainty. Based on MERRA2, four hotspot regions with significant snow depth changes from 1981–2018 were identified, including the central Xinjiang (XJ-C), the southern part of the Northeastern Plain and Mountain (NPM-S), and the southwestern (TP-SW) and southeastern (TP-SE) of the Tibetan Plateau. Snow depth changes mostly occurred in spring in TP-SW and winter in XJ-C, NPM-S, and TP-SE. The snow depth increase in XJ-C, NPM-S, and TP-SW is mainly caused by increased seasonal precipitation, while the snow depth decrease in TP-SE is attributed to the combined effects of decreased precipitation and warming temperature in winter.

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