Snow microstructure on sea ice: Importance for remote sensing applications

European Geosciences Union (EGU) General Assembly, 19-30 Apr 2021.-- 2 pagesSnow plays a key role in interpreting satellite remote sensing data from both active and passive sensors in the high Arctic and therefore impacts retrieved sea ice variables from these systems ( e.g., sea ice extent, thickness and age). Because there is high spatial and temporal variability in snow properties, this porous layer adds uncertainty to the interpretation of signals from spaceborne optical sensors, microwave radiometers, and radars (scatterometers, SAR, altimeters). We therefore need to improve our understanding of physical snow properties, including the snow specific surface area, snow wetness and the stratigraphy of the snowpack on different ages of sea ice in the high Arctic. The MOSAiC expedition provided a unique opportunity to deploy equivalent remote sensing sensors in-situ on the sea ice similar to those mounted on satellite platforms. To aid in the interpretation of the in situ remote sensing data collected, we used a micro computed tomography (micro-CT) device. This instrument was installed on board the Polarstern and was used to evaluate geometric and physical snow properties of in-situ snow samples. This allowed us to relate the snow samples directly to the data from the remote sensing instruments, with the goal of improving interpretation of satellite retrievals. Our data covers the full annual evolution of the snow cover properties on multiple ice types and ice topographies including level first-year (FYI), level multi-year ice (MYI) and ridges. First analysis of the data reveals possible uncertainties in the retrieved remote sensing data products related to previously unknown seasonal processes in the snowpack. For example, the refrozen porous summer ice surface, known as surface scattering layer, caused the formation of a hard layer at the multiyear ice/snow interface in the winter months, leading to significant differences in the snow stratigraphy and remote sensing signals from first-year ice, which has not experienced summer melt, and multiyear ice. Furthermore, liquid water dominates the extreme coarsening of snow grains in the summer months and in winter the temporally large temperature gradients caused strong metamorphism, leading to brine inclusions in the snowpack and large depth hoar structures, all this significantly influences the signal response of remote sensing instrumentsPeer reviewe

Passive L-Band Remote Sensing Applications Over Cryospheric Regions

The Earth’s climate and its evolution are heavily influenced by the state of cryosphere snow cover and its subnivean ground through their determinative role in the exchange of water, heat, and greenhouse gases between land and the atmosphere. Previous research has shown that L-band radiometry can be used for the estimation of snow mass density rho_S and ground permittivity epsilon_G. In this thesis an unprecedented approach for the estimation of snow liquid water content (wetness) and the beginning of snow melt, using L-band radiometry and the L-band Specific Microwave Emission Model of Layered Snowpack (LS–MEMLS), is proposed. Two snow wetness retrieval data products are computed using tower-based radiometry over snow-covered natural ground and areas with a metal grid placed on the ground beneath the accumulating snow. It is experimentally demonstrated that the metal grid isolates snowpack’s own emission from the emission of the underlying ground. This allows considering the snow wetness retrievals derived from brightness temperatures measured over this artificially prepared area as references for comparison with snow wetness retrievals from brightness temperatures over natural ground. Furthermore, the disruptive effects of “geophysical noise” on (rho_S, epsilon_G) retrievals are investigated for dry snow conditions. Results indicate robust performance of the two-parameter retrieval approach against spatial variabilities of snow cover and subnivean soil. This is a promising base for the application of this two-parameter retrieval approach with coarse resolution satellite data. Further synthetic and experimental sensitivity analyses of the melting effects, in form of snow wetness and ground permittivity heterogeneities, are conducted which quantify their disruptive effects on the (rho_S, epsilon_G) retrievals. Results indicate reliable retrievals during snow free and cold winter periods. With the beginning of the early spring period, retrievals’ accuracy decrease and increased time-correlation is recognized between retrievals rho_S and epsilon_G. Retrieval quality flags are raised based on this type of retrievals’ correlation which is very useful for the application with space-borne radiometry data. Other achievements of this thesis work include the establishment of the Davos-Laret Remote Sensing Field Laboratory and significant improvement of radiometry data–measured with ELBARA-II radiometer–calibration and quality assessment. The latter includes a new more-accurate Radio Frequency Interference (RFI) filtering approach based on Gaussian curve fitting and time-dependent correction of transmission losses taking into account the transmission line ageing effects, for instance

Effect of Solar Wind Fluctuations on Geomagnetic Activity

The term space weather refers to conditions on the Sun and in the solar wind, the magnetosphere, ionosphere and thermosphere that can influence the performance and reliability of space-borne and ground-based technological systems and pose danger on human life or health. The time scales of interest in space weather processes are determined by the intrinsic nature of the processes themselves and the lead time that the predictions can be given. In order to predict an event, some indications of it must be observed in advance. Due to the distance between the Sun and the Earth, the solar wind and energetic particles' speed etc, our capability in predicting the solar wind properties impacting the Earth is poor. We are currently limited to at best warnings 80 hours in advance and predictions at maximum 1 hour before the event starts. This thesis studies the relationship between the solar wind ULF power fluctuations and the magnetospheric activity using the known solar wind - magnetosphere coupling functions and the activity indices. The magnetospheric activity index AL has been presented as a function of different solar wind driving parameters and their level of fluctuations to deduce the significance of the fluctuations in generating magnetospheric activity. In order to examine the effects of specific fluctuation frequencies, the power at different frequencies was integrated using wavelet analysis with the Morlet mother wavelet. Our results show some points about the solar wind-magneotsphere coupling: 1)They approve the difference between the northward and southward IMF in driving the magnetospheric activity; 2)They demonstrate that the ULF IMF fluctuations can noticeably increase the activity level such that the higher the fluctuation power, the stronger the activity level; 3)The results provide further evidence to the previous findings indicating that under certain circumstances, northward IMF can also trigger activity; 4)Our results indicate that fluctuations in the solar wind speed can also have a small enhancing effect on the AL index; however, this effect is not isolated to the ULF range

Snow Wetness Retrieved from L-Band Radiometry

The present study demonstrates the successful use of the high sensitivity of L-band brightness temperatures to snow liquid water in the retrieval of snow liquid water from multi-angular L-band brightness temperatures. The emission model employed was developed from parts of the “microwave emission model of layered snowpacks” (MEMLS), coupled with components adopted from the “L-band microwave emission of the biosphere” (L-MEB) model. Two types of snow liquid water retrievals were performed based on L-band brightness temperatures measured over (i) areas with a metal reflector placed on the ground (“reflector area”— T B , R ), and (ii) natural snow-covered ground (“natural area”— T B , N ). The reliable representation of temporal variations of snow liquid water is demonstrated for both types of the aforementioned quasi-simultaneous retrievals. This is verified by the fact that both types of snow liquid water retrievals indicate a dry snowpack throughout the “cold winter period” with frozen ground and air temperatures well below freezing, and synchronously respond to snowpack moisture variations during the “early spring period”. The robust and reliable performance of snow liquid water retrieved from T B , R , together with their level of detail, suggest the use of these retrievals as “references” to assess the meaningfulness of the snow liquid water retrievals based on T B , N . It is noteworthy that the latter retrievals are achieved in a two-step retrieval procedure using exclusively L-band brightness temperatures, without the need for in-situ measurements, such as ground permittivity ε G and snow mass-density ρ S . The latter two are estimated in the first retrieval-step employing the well-established two-parameter ( ρ S , ε G ) retrieval scheme designed for dry snow conditions and explored in the companion paper that is included in this special issue in terms of its sensitivity with respect to disturbative melting effects. The two-step retrieval approach proposed and investigated here, opens up the possibility of using airborne or spaceborne L-band radiometry to estimate ( ρ S , ε G ) and additionally snow liquid water as a new passive L-band data product

“Tau-Omega”- and Two-Stream Emission Models Used for Passive L-Band Retrievals: Application to Close-Range Measurements over a Forest

Microwave Emission Models (EM) are used in retrieval algorithms to estimate geophysical state parameters such as soil Water Content ( W C ) and vegetation optical depth ( τ ), from brightness temperatures T B p , θ measured at nadir angles θ at Horizontal and Vertical polarizations p = { H , V } . An EM adequate for implementation in a retrieval algorithm must capture the responses of T B p , θ to the retrieval parameters, and the EM parameters must be experimentally accessible and representative of the measurement footprint. The objective of this study is to explore the benefits of the multiple-scattering Two-Stream (2S) EM over the “Tau-Omega” (TO) EM considered as the “reference” to retrieve W C and τ from L-band T B p , θ . For sparse and low-scattering vegetation T B , E M p , θ simulated with E M = { TO ,   2 S } converge. This is not the case for dense and strongly scattering vegetation. Two-Parameter (2P) retrievals 2 P R C = ( W C R C , τ R C ) are computed from elevation scans T B p , θ j = T B , TO p , θ j synthesized with TO EM and from T B p , θ j measured from a tower within a deciduous forest. Retrieval Configurations ( R C ) employ either E M = TO or E M = 2 S and assume fixed scattering albedos. W C R C achieved with the 2S RC is marginally lower ( ~ 1   m 3 m − 3 ) than if achieved with the “reference” TO RC, while τ R C is reduced considerably when using 2S EM instead of TO EM. Our study outlines a number of advantages of the 2S EM over the TO EM currently implemented in the operational SMOS and SMAP retrieval algorithms

Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis

The L-band radiometry data and in-situ ground and snow measurements performed during the 2016/2017 winter campaign at the Davos-Laret remote sensing field laboratory are presented and discussed. An improved version of the procedure for the computation of L-band brightness temperatures from ELBARA radiometer raw data is introduced. This procedure includes a thorough explanation of the calibration and filtering including a refined radio frequency interference (RFI) mitigation approach. This new mitigation approach not only performs better than conventional “normality” tests (kurtosis and skewness) but also allows for the quantification of measurement uncertainty introduced by non-thermal noise contributions. The brightness temperatures of natural snow covered areas and areas with a reflector beneath the snow are simulated for varying amounts of snow liquid water content distributed across the snow profile. Both measured and simulated brightness temperatures emanating from natural snow covered areas and areas with a reflector beneath the snow reveal noticeable sensitivity with respect to snow liquid water. This indicates the possibility of estimating snow liquid water using L-band radiometry. It is also shown that distinct daily increases in brightness temperatures measured over the areas with the reflector placed on the ground indicate the onset of the snow melting season, also known as “early-spring snow”

Optical IP Camera images (VIS_INFRALAN_02) at the remote sensing site on the ice floe during MOSAiC expedition 2020

The Optical IP Camera was attached to the remote sensing hut during the MOSAiC expedition, saving 4K images in JPEG format in 5 minute intervals. It was installed during MOSAiC leg 2 and leg 3, 10 February to 17 April 2020. The camera is equipped with high brightness NIR LED spotlights, turning on automatically in low light situations. During its operation time, the camera had several remote sensing instruments in the field of view and can be used as a supportive dataset for events at the remote sensing site

Hyperspectral camera raw data (SPECIM_IQ_01) at the remote sensing site on the ice floe during MOSAiC expedition 2019/2020

The Hyperspectral camera Specim IQ measures relative reflectances for 512x512 pixels in 204 bands in a wavelength range from 400 nm to 1000 nm. Within the field of view, there was a white reference placed so that the reflectances can be compared to other datasets. During the MOSAiC expedition, the camera was installed at the remote sensing site on the ice. It was looking at the snow and ice, taking an image every 30 minutes for periods in April 2020 and July 2020. Each data record is a folder (compressed as one file) readable by the Specim IQ software, which is downloadable free of charge from the manufacturer (Specim) after registration. This dataset is from an experimental setup to explore the use of spatially resolved hyperspectral imaging of Arctic sea ice