Snow change detection from polarimetric SAR time-series at X-band (Svalbard, Norway)

2021 ◽  
Author(s):  
Jean-Pierre Dedieu ◽  
Anna Wendleder ◽  
Bastien Cerino ◽  
Julia Boike ◽  
Eric Bernard ◽  
...  
Keyword(s):  
Time Series ◽  
Phase Difference ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
The Arctic ◽  
Matrix Elements ◽  
Polarimetric Sar ◽  
X Band ◽  
Snow Metamorphism

<p>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.</p><p>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.</p><p>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.</p><p>Polarimetric processing is based on the Kennaugh matrix decomposition, copolar phase coherence (CCOH) and copolar phase difference (CPD). The Kennaugh matrix elements K<sub>0</sub>, K<sub>3</sub>, K<sub>4,</sub> and K<sub>7</sub> 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).</p><p>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 R<sup>2</sup> 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.</p><p>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 <sub>VV</sub> - Φ <sub>HH</sub>. 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).</p><p>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.</p>

scholarly journals Sentinel-1 SAR Interferometry for Surface Deformation Monitoring in Low-Land Permafrost Areas

2018 ◽  
Vol 10 (9) ◽  
pp. 1360 ◽  
Author(s):  
Tazio Strozzi ◽  
Sofia Antonova ◽  
Frank Günther ◽  
Eva Mätzler ◽  
Gonçalo Vieira ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Displacement ◽  
Deformation Monitoring ◽  
In Situ Measurements ◽  
River Delta ◽  
Sar Interferometry ◽  
The Arctic ◽  
Lena River ◽  
Lena River Delta

Low-land permafrost areas are subject to intense freeze-thaw cycles and characterized by remarkable surface displacement. We used Sentinel-1 SAR interferometry (InSAR) in order to analyse the summer surface displacement over four spots in the Arctic and Antarctica since 2015. Choosing floodplain or outcrop areas as the reference for the InSAR relative deformation measurements, we found maximum subsidence of about 3 to 10 cm during the thawing season with generally high spatial variability. Sentinel-1 time-series of interferograms with 6–12 day time intervals highlight that subsidence is often occurring rather quickly within roughly one month in early summer. Intercomparison of summer subsidence from Sentinel-1 in 2017 with TerraSAR-X in 2013 over part of the Lena River Delta (Russia) shows a high spatial agreement between both SAR systems. A comparison with in-situ measurements for the summer of 2014 over the Lena River Delta indicates a pronounced downward movement of several centimetres in both cases but does not reveal a spatial correspondence between InSAR and local in-situ measurements. For the reconstruction of longer time-series of deformation, yearly Sentinel-1 interferograms from the end of the summer were considered. However, in order to infer an effective subsidence of the surface through melting of excess ice layers over multi-annual scales with Sentinel-1, a longer observation time period is necessary.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

scholarly journals ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

2018 ◽  
Vol IV-5 ◽  
pp. 223-228 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Remote Sensing ◽  
Water Resource Management ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Physical Relationship ◽  
Sensing Data ◽  
Surface Snow ◽  
Sentinel 2

<p><strong>Abstract.</strong> Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.</p>

scholarly journals Numerical simulation of extreme snow melt observed at the SIGMA-A site, northwest Greenland, during summer 2012

2015 ◽  
Vol 9 (1) ◽  
pp. 495-539
Author(s):  
M. Niwano ◽  
T. Aoki ◽  
S. Matoba ◽  
S. Yamaguchi ◽  
T. Tanikawa ◽  
...  
Keyword(s):  
Heat Flux ◽  
Grain Size ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
In Situ Measurements ◽  
The Arctic ◽  
Snow Surface ◽  
Near Surface ◽  
Surface Snow

Abstract. The surface energy balance (SEB) from 30 June to 14 July 2012 at site SIGMA (Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic)-A, (78°03' N, 67°38' W; 1490 m a.s.l.) on the northwest Greenland Ice Sheet (GrIS) was investigated by using in situ atmospheric and snow measurements, as well as numerical modeling with a one-dimensional, multi-layered, physical snowpack model called SMAP (Snow Metamorphism and Albedo Process). At SIGMA-A, remarkable near-surface snowmelt and continuous heavy rainfall (accumulated precipitation between 10 and 14 July was estimated to be 100 mm) were observed after 10 July 2012. Application of the SMAP model to the GrIS snowpack was evaluated based on the snow temperature profile, snow surface temperature, surface snow grain size, and shortwave albedo, all of which the model simulated reasonably well. However, comparison of the SMAP-calculated surface snow grain size with in situ measurements during the period when surface hoar with small grain size was observed on-site revealed that it was necessary to input air temperature, relative humidity, and wind speed data from two heights to simulate the latent heat flux into the snow surface and subsequent surface hoar formation. The calculated latent heat flux was always directed away from the surface if data from only one height were input to the SMAP model, even if the value for roughness length of momentum was perturbed between the possible maximum and minimum values in numerical sensitivity tests. This result highlights the need to use two-level atmospheric profiles to obtain realistic latent heat flux. Using such profiles, we calculated the SEB at SIGMA-A from 30 June to 14 July 2012. Radiation-related fluxes were obtained from in situ measurements, whereas other fluxes were calculated with the SMAP model. By examining the components of the SEB, we determined that low-level clouds accompanied by a significant temperature increase played an important role in the melt event observed at SIGMA-A. These conditions induced a remarkable surface heating via cloud radiative forcing in the polar region.

Landslide dynamics inferred from in-situ measurements and time series of terrestrial imagery: the Cliets rockslide (Savoie, French Alps)

2020 ◽  
Author(s):  
Mathilde Desrues ◽  
Jean-Philippe Malet ◽  
Ombeline Brenguier ◽  
Aurore Carrier ◽  
Lionel Lorier
Keyword(s):  
Time Series ◽  
Mechanical Behavior ◽  
Optical Sensors ◽  
In Situ Measurements ◽  
Displacement Fields ◽  
Total Station ◽  
French Alps ◽  
Geophysical Surveys ◽  
Landslide Dynamics

&lt;p&gt;Several geodetic methods can be combined to better understand landslide dynamics and behavior. The obtained deformation/displacement fields can be analyzed to inverse the geometry of the moving mass and the mechanical behavior of the slope (kinematic regime, rheological properties of the media), and sometimes anticipate the time of failure. Among them, dense in-situ measurements (total station measurements, extensometer data and GNSS surveys) allow reaching accuracy close to the centimeter. These techniques can be combined to dense time series of passive terrestrial imagery in order to obtain distributed information. Actually, more and more passive optical sensors are used to provide both qualitative information (detection of surface change) and quantitative information using either a single camera (quantification of displacement by correlation techniques) or stereo-views (creation of Digital Surface Models, DSM).&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;In this study, we analyze a unique dataset of the Cliets rockslide event that occurred on 9 February 2019. The pre-failure and failure stages were documented using the above mentioned methods. The performance of the methods are evaluated in terms of their possible contribution to a monitoring survey.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;span&gt;The Cliets landslide is located in the French Alps (Savoie) and is affecting the high traffic road of Gorges de l&amp;#8217;Arly. Located upstream of a tunnel, the unstable slope was instrumented by the SAGE Society during the crisis in the period July&amp;#8211;February 2019. About 8000 m&lt;/span&gt;&lt;sup&gt;&lt;span&gt;3&lt;/span&gt;&lt;/sup&gt;&lt;span&gt; collapsed closing the tunnel access for one year. Topographic measurements of a series of 41 benchmarks by automated total station were used to determined the time of rupture and the landslide mechanical behavior (tertiary creep vs stable regime). Additionally, a fixed CANON EOS 2000D with a lens with a focal length of 24 mm, was installed in front of the landslide. Images were acquired hourly and the time series was processed using the TSM processing toolbox (Desrues et al., 2019). Displacement fields were generated over time and compared to the topographic measurements. Photogrammetric surveys were carried out to generate several DSMs before and after the crisis. It allowed to estimate the volume of the collapsed masses. Finally, geophysical surveys were included in the study to determine the thickness of the potential unstable layer. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;The results allow highlighting (1) different kind of behaviors which are identified and explained by a simple physical models, (2) the volumes of the displaced masses, and (3) the absence of a direct relation of the failure with the meterological forcing factors.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;span&gt;&lt;strong&gt;Acknowledgments&lt;/strong&gt;&lt;/span&gt;&lt;span&gt;: These works are part of a CIFRE / ANRT agreement between IPGS/CNRS UMR7516 and the SAGE Society.&lt;/span&gt;&lt;/p&gt;

Remote and in situ measurements of aerosol concentration in the Arctic troposphere from the Yak-42D “Roshydromet” research aircraft

2016 ◽  
Vol 41 (5) ◽  
pp. 365-372
Author(s):  
A. S. Kuz’michev ◽  
T. I. Babukhina ◽  
A. V. Gan’shin ◽  
A. N. Luk’yanov ◽  
R. M. Markov ◽  
...  
Keyword(s):  
In Situ Measurements ◽  
Aerosol Concentration ◽  
The Arctic ◽  
Research Aircraft

In situ measurements of BrO in the Arctic stratosphere

1990 ◽  
Vol 17 (4) ◽  
pp. 513-516 ◽  
Author(s):  
D. W. Toohey ◽  
J. G. Anderson ◽  
W. H. Brune ◽  
K. R. Chan
Keyword(s):  
In Situ Measurements ◽  
The Arctic

scholarly journals In situ measurements of mesospheric turbulence during spring transition of the Arctic mesosphere

2002 ◽  
Vol 29 (10) ◽  
pp. 115-1-115-4 ◽  
Author(s):  
Arno Müllemann ◽  
Markus Rapp ◽  
Franz-Josef Lübken ◽  
Peter Hoffmann
Keyword(s):  
In Situ Measurements ◽  
The Arctic ◽  
Spring Transition

Towards the removal of model bias from ESA CCI SM by using an L-band scaling reference

2021 ◽  
Author(s):  
Rémi Madelon ◽  
Nemesio Rodriguez-Fernandez ◽  
Robin Van Der Shalie ◽  
Yann Kerr ◽  
Tracy Scalon ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Climate Models ◽  
In Situ Measurements ◽  
Time Data ◽  
Data Set ◽  
Spatial Coverage ◽  
L Band ◽  
Better Than

&lt;p&gt;Merging data from different instruments is required to construct long time data records of soil moisture (SM). This is the goal of projects such as the ESA Climate Change Initiative (CCI) for SM (Gruber et al., 2019), which uses both active and passive microwave sensors. Currently, the GLDAS v2.1 model is used as reference to re-scale active and passive time series by matching their Cumulative Density Function (CDF) to that of the model. Removing the dependency on models is important, in particular for data assimilation applications into hydrological or climate models, and it has been proposed (Van der Schalie et al., 2018) to use L-band data from one of the two instruments specifically designed to measure SM, ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, as reference to re-scale other time series.&lt;br&gt;To investigate this approach, AMSR-2 SM time series obtained from C1-, C2- and X-band observations using LPRM (Land Parameter Retrieval Model) were re-scaled by CDF-matching (Brocca et al., 2011) using different SMAP and SMOS official (SMAP L2 V005, SMOS L3 V300, SMOS NRT V100&amp;V200) and research (SMOS IC V103) SM products as well as the SMAP and SMOS LPRM v6 SM data used by the ESA CCI. The time series re-scaled using L-band remote sensing data were compared to those re-scaled using GLDAS and were evaluated against in situ measurements at several hundred sites retrieved from the International Soil Moisture Network (Dorigo et al., 2011). The results were analyzed as a function of the land cover class and the Koppen-Geiger climate classification.&lt;br&gt;Overall, AMSR-2 time series re-scaled using SMAP L2, SMAP LPRM and SMOS IC data sets as reference gave the best correlations with respect to in situ measurements, similar to those obtained by the time series re-scaled using GLDAS and slightly better than those of the original AMSR-2 time series. These results imply that different SMAP and SMOS products could actually be used to replace GLDAS as reference for the re-scaling of other sensors time series within the ESA CCI. However, one must bear in mind that this study is limited to the re-scaling of AMSR-2 data at a few hundred sites.&lt;br&gt;For a more detailed assessment of the L-band data set to be used for a global re-scaling, it is necessary to investigate other effects such as the spatial coverage or the time series length. SMAP spatial coverage is better than that of SMOS in regions affected by radio frequency interference. In contrast, the length of SMAP time series can be too short to capture the long term SM variability for climate applications in some regions. The CDF of SMOS time series computed from the date of SMAP launch is significantly different to those of the full length SMOS time series in some regions of the Globe. Possible ways of using a coherent SMAP/SMOS L-band data set will be discussed.&lt;/p&gt;

Vertical distribution of aerosol particles over the Russian Arctic derived from in-situ aircraft measurements: the September 2020 campaign

2021 ◽  
Author(s):  
Mikhail Yu. Arshinov ◽  
Boris Belan ◽  
Denis Davydov ◽  
Artem Kozlov ◽  
Alexandr Fofonov
Keyword(s):  
Vertical Distribution ◽  
Lower Troposphere ◽  
In Situ Measurements ◽  
The Arctic ◽  
Future Climate Change ◽  
Russian Arctic ◽  
Size Distributions ◽  
Cloudy Weather ◽  
Wide Range

&lt;p&gt;The Arctic is warming much faster than other regions of the globe. In 2020, temperature anomalies in the Russian Arctic reached unprecedented high levels. The atmospheric composition in this key region still remains insufficiently studied that makes difficult predicting future climate change.&lt;/p&gt;&lt;p&gt;In September 2020, an extensive aircraft campaign was conducted to document the tropospheric composition over the Russian Arctic. The Optik Tu-134 research aircraft was equipped with instruments to carry out in-situ measurements of trace gases and aerosols, as well as with a lidar for profiling of aerosol backscatter. The aircraft flew over a vast area from Arkhangelsk to Anadyr. Six measurement flights with changing altitudes from 0.2 to 9.0 m were conducted over the waters of the Barents, Kara, Laptev, East Siberian, Chukchi, and Bering Seas. The weather was unusually warm for this period of the year, surface air temperatures were above 0&amp;#176;C through the campaign.&lt;/p&gt;&lt;p&gt;Here, we present the results of in-situ measurements of the vertical distribution of aerosol number concentrations in a wide range of sizes. A modified diffusional particle sizer (DPS) consisted of the Novosibirsk-type eight-stage screen diffusion battery connected to the TSI condensation particle counter Model 3756 was used to determine the number size distribution of particles between 0.003 mm and 0.2 mm (20 size bins). Distribution of particles in the size range from 0.25 &amp;#181;m to 32 &amp;#181;m (31 size bins) was measured by means of the Grimm aerosol spectrometer Model 1.109.&lt;/p&gt;&lt;p&gt;The flights over Barents and Kara Seas were predominantly performed under clear sky or partly cloudy weather conditions. Number size distributions were wide representing particles of almost all aerosol fractions. When flying in the upper troposphere with a constant altitude over these seas, some cases of enhanced concentrations of nucleation and Aitken mode particles comparable to ones in the lower troposphere were recorded, suggesting in situ new particle formation was likely to be taking place via gas-to-particle conversion aloft.&lt;/p&gt;&lt;p&gt;East of the Kara Sea, flights were conducted under mostly cloudy conditions resulting in a lower median aerosol number concentration and narrower size distributions.&lt;/p&gt;&lt;p&gt;This work was supported by the Russian Foundation for Basic Research (Grant No. 19-05-50024).&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document