On the computation of phase inconsistencies of Sentinel-1 interferograms over snow-covered areas

2020 ◽  
Author(s):  
Maria Gritsevich ◽  
Giovanni Nico ◽  
Vasco Conde ◽  
Pedro Mateus ◽  
Joao Catalao
Keyword(s):  
Remote Sensing ◽  
Snow Water Equivalent ◽  
Temporal Variations ◽  
Temporal Changes ◽  
Sar Interferometry ◽  
Snow Melt ◽  
Scattering Mechanism ◽  
Snow Layer ◽  
Interferometric Phase ◽  
Snow Water

<p>We have recently investigated the use of SAR interferometry for the mapping of Snow Water Equivalent (SWE) temporal variations using Sentinel-1 data [1]. Maps of temporal changes of SWE, measured with a sub-centimetre accuracy and updated every six days have been obtained over a study area in Finland. This methodology relies on the shift in the interferometric phase caused by the refraction of the microwave signal penetrating the snow layer. In this work, we investigate phase inconsistencies [2] of a sets of three interferograms obtained from three Sentinel-1 images acquired along the same orbit at different acquisition times to study the snow melt. We find that while phase inconsistencies are not expected to be present in case of examining surfaces covered with frozen snow, the scattering mechanism of microwave in the snow layer during the melting phase affects both the interferometric phase and coherence.</p><p> </p><p>This work was supported, in part, by the Academy of Finland project no. 325806.</p><p> </p><p>References:</p><p>[1] V. Conde, G. Nico, P. Mateus, J. Catalão, A. Kontu, M. Gritsevich, On the estimation of temporal changes of snow water equivalent by spaceborne SAR interferometry: a new application for the Sentinel-1 mission, J. Hydrol. Hydromech., 67, 2019, 1, 93–100. DOI: 10.2478/johh-2018-0003</p><p>[2] F. De Zan, M. Zonno, P. López-Dekker, Phase inconsistencies and multiple scattering in SAR interferometry, IEEE Transactions on Geoscience and Remote Sensing, 53(12), 6608-6616, 2015</p>

scholarly journals On The Estimation of Temporal Changes of Snow Water Equivalent by Spaceborne Sar Interferometry: A New Application for the Sentinel-1 Mission

2019 ◽  
Vol 67 (1) ◽  
pp. 93-100 ◽  
Author(s):  
Vasco Conde ◽  
Giovanni Nico ◽  
Pedro Mateus ◽  
João Catalão ◽  
Anna Kontu ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Snow Water Equivalent ◽  
Temporal Variations ◽  
Sar Interferometry ◽  
Synthetic Aperture ◽  
Snow Layer ◽  
Illumination Condition ◽  
Interferometric Phase ◽  
Snow Water

Abstract In this work we present a methodology for the mapping of Snow Water Equivalent (SWE) temporal variations based on the Synthetic Aperture Radar (SAR) Interferometry technique and Sentinel-1 data. The shift in the interferometric phase caused by the refraction of the microwave signal penetrating the snow layer is isolated and exploited to generate maps of temporal variation of SWE from coherent SAR interferograms. The main advantage of the proposed methodology with respect to those based on the inversion of microwave SAR backscattering models is its simplicity and the reduced number of required in-situ SWE measurements. The maps, updated up to every 6 days, can attain a spatial resolution up to 20 m with sub-centimetre ΔSWE measurement accuracy in any weather and sun illumination condition. We present results obtained using the proposed methodology over a study area in Finland. These results are compared with in-situ measurements of ΔSWE, showing a reasonable match with a mean accuracy of about 6 mm.

scholarly journals Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

2019 ◽  
Vol 13 (11) ◽  
pp. 3045-3059 ◽  
Author(s):  
Nick Rutter ◽  
Melody J. Sandells ◽  
Chris Derksen ◽  
Joshua King ◽  
Peter Toose ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Layer Thickness ◽  
Snow Water Equivalent ◽  
Estimation Accuracy ◽  
Snow Layer ◽  
Data Set ◽  
Snow Water ◽  
Retrieval Errors ◽  
Snow Microstructure ◽  
The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

&lt;p&gt;Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake &amp;#214;veruman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km&lt;sup&gt;2&lt;/sup&gt; grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.&lt;/p&gt;

Nutrient load simulations at Lake Puruvesi, Finland: extreme case event in 2012

2020 ◽  
Author(s):  
Natalia Korhonen ◽  
Sirkka Tattari ◽  
Antti Leinonen ◽  
Markus Huttunen ◽  
Leena Finér ◽  
...  
Keyword(s):  
Land Use ◽  
Nutrient Loading ◽  
Snow Water Equivalent ◽  
Urban Land Use ◽  
Nutrient Load ◽  
Total Precipitation ◽  
Snow Melt ◽  
Weather Station ◽  
Model Simulations ◽  
Snow Water

&lt;p&gt;In the Open-Air Laboratory (OAL)-Finland, Lake Puruvesi, the main land-use is forested areas, with minor areas in agriculture, and urban land-use. Activities related to these land-uses together with infrequently occurring high runoff peaks due to heavy rain events or rapid snowmelt cause nutrient (phosphorus, nitrogen) and sediment load risks and thus threaten recreation, fishing (professional and recreational) and biodiversity of the area. Various Nature- Based Solutions (NBS) are planned to reduce nutrient loading for the Puruvesi area. Modelling will be used to estimate the impact of NBSs on nutrient loading. It is important to increase understanding of the impacts of the extreme weather events on the amount of nutrient concentration in the water.&lt;/p&gt;&lt;p&gt;According to model simulations the nutrient load increases during the years with high precipitation. However, the total annual precipitation alone explain only partly the variations in the nutrient loads. The nutrient load depends also on the timing of the precipitation and the moisture condition and nutrient content of soil before the precipitation or snow melting event. Typically in Finland, the high nutrient load peaks take place during spring snow melt or after the autumn precipitation. Heavy precipitation during summer may as well induce a peak in nutrient concentrations.&lt;/p&gt;&lt;p&gt;Here we focus on the impacts of an extreme spring snow melt event in year 2012. In the Puruvesi region the winter 2012 was wetter than average with snow depths reaching more than 50 cm in March and lasting until mid-April. During the permanent snow cover period (31.12.2011-23.4.2012) the total precipitation was 150 mm at the weather station in the Lake Puruvesi catchment area. The snow water equivalent, i.e., the amount of water contained within the snow, is not measured in Lake Puruvesi. However, the Finnish Environment Institute produces estimates of snow water equivalents over Finland with the Watershed simulation and forecasting system (VEMALA). According to modelling the snow water equivalent was about 120 mm in mid-April in Savonlinna located about 10 km west from the Punkaharju weather station. The whole snow pack melted during 13 days (11.4.2012-23.4.2012) from 50 cm to 0 cm as the daily mean temperatures rose permanently above 0 &amp;#176;C. During the snow melt period the total precipitation was about 30 mm. The VEMALA model simulations show a peak of 90 &amp;#181;g/l in phosphorus concentrations during the snow melt in the end of April 2012. As a comparison, the drier than average year, 1993, with less snow (max depth 30 cm and slower melting) lead to a lower phosphorus concentration peak of 60 &amp;#181;g/l. Furthermore, the total phosphorus load in 2012 was 2.5 times higher than the load in 1993. This review demonstrates that, in extreme years, the number or effectiveness of NBS measures must be significantly increased to achieve the required reduction in nutrient leaching compared to normal or drier years.&lt;/p&gt;&lt;p&gt;The work is carried out as co-operation between OPERANDUM EU and Freshabit Life IP -projects.&lt;/p&gt;

scholarly journals SNOW-WATER EQUIVALENT MEASUREMENT USING NATURAL GAMMA EMISSION

1973 ◽  
Vol 4 (1) ◽  
pp. 1-16 ◽  
Author(s):  
R. L. GRASTY
Keyword(s):  
Gamma Ray ◽  
Snow Water Equivalent ◽  
High Sensitivity ◽  
Test Strip ◽  
Gamma Emission ◽  
Absorption Coefficients ◽  
Snow Layer ◽  
Gamma Ray Spectrometer ◽  
Natural Gamma ◽  
Snow Water

The natural gamma radiation emitted by potassium, uranium and thorium is attenuated by snow. This attenuation depends on the water-equivalent of the snow layer. Air absorption coefficients were determined by flying at different altitudes over a uniform test strip and used to calculate the absorption coefficients for water. Results using the Geological Survey of Canada high sensitivity airborne gamma-ray spectrometer showed that snow water-equivalents up to 18 cm could be measured to an accuracy of 2 cm over suitable terrain. The importance of temperature and soil moisture corrections are discussed, together with statistical, instrumental and navigational errors.

scholarly journals Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

2018 ◽  
Vol 19 (11) ◽  
pp. 1777-1791 ◽  
Author(s):  
Nicholas Dawson ◽  
Patrick Broxton ◽  
Xubin Zeng
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Snow Cover Extent ◽  
Snow Water ◽  
The Mean

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.

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