Establishing a Remote Sensing Science Center in Cyprus: First Year of Activities of ATHENA Project

2016 ◽  
pp. 275-282 ◽  
Author(s):  
Diofantos Hadjimitsis ◽  
Athos Agapiou ◽  
Vasiliki Lysandrou ◽  
Kyriacos Themistocleous ◽  
Branka Cuca ◽  
...  
Keyword(s):  
Remote Sensing ◽  
First Year ◽  
Science Center

scholarly journals Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice

2020 ◽  
Vol 14 (2) ◽  
pp. 751-767
Author(s):  
Shiming Xu ◽  
Lu Zhou ◽  
Bin Wang
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Snow Depth ◽  
Spatial Scales ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
First Year ◽  
Noise Levels ◽  
Wide Range

Abstract. Satellite and airborne remote sensing provide complementary capabilities for the observation of the sea ice cover. However, due to the differences in footprint sizes and noise levels of the measurement techniques, as well as sea ice's variability across scales, it is challenging to carry out inter-comparison or consistently study these observations. In this study we focus on the remote sensing of sea ice thickness parameters and carry out the following: (1) the analysis of variability and its statistical scaling for typical parameters and (2) the consistency study between airborne and satellite measurements. By using collocating data between Operation IceBridge and CryoSat-2 (CS-2) in the Arctic, we show that consistency exists between the variability in radar freeboard estimations, although CryoSat-2 has higher noise levels. Specifically, we notice that the noise levels vary among different CryoSat-2 products, and for the European Space Agency (ESA) CryoSat-2 freeboard product the noise levels are at about 14 and 20 cm for first-year ice (FYI) and multi-year ice (MYI), respectively. On the other hand, for Operation IceBridge and NASA's Ice, Cloud, and land Elevation Satellite (ICESat), it is shown that the variability in snow (or total) freeboard is quantitatively comparable despite more than a 5-year time difference between the two datasets. Furthermore, by using Operation IceBridge data, we also find widespread negative covariance between ice freeboard and snow depth, which only manifests on small spatial scales (40 m for first-year ice and about 80 to 120 m for multi-year ice). This statistical relationship highlights that the snow cover reduces the overall topography of the ice cover. Besides this, there is prevalent positive covariability between snow depth and snow freeboard across a wide range of spatial scales. The variability and consistency analysis calls for more process-oriented observations and modeling activities to elucidate key processes governing snow–ice interaction and sea ice variability on various spatial scales. The statistical results can also be utilized in improving both radar and laser altimetry as well as the validation of sea ice and snow prognostic models.

Sea ice characteristics during the Weddell Sea expedition explored by geophysical and remote sensing methods

2020 ◽  
Author(s):  
Wolfgang Rack ◽  
Frazer Christie ◽  
Evelyn Dowdeswell ◽  
Julian Dowdeswell ◽  
Paul Wachter ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Extended Period ◽  
Weddell Sea ◽  
Altimeter Data ◽  
First Year ◽  
Limited Information ◽  
Radar Images ◽  
Ice Floes ◽  
Remote Sensing Methods

<p>The 2019 Weddell Sea expedition provided a unique opportunity for geophysical and glaciological sea ice measurements in one of the least accessible regions of the Southern Ocean. Although the extent and area of sea ice is well known based on satellite measurements, the limited information on thickness does still hinder the calculation of trends trends in volume and mass. Sea ice thickness is therefore one of the missing key variables in the global cryosphere mass balance, and difficult logistics are a challenge for near synchronous satellite validation measurements. Another key variable in this context is snow on sea ice, as knowledge of snow is required to convert satellite-derived freeboard to thickness.</p><p>We measured the sea-ice morphology by a combination of on ice and remote sensing methods: near-synchronous temporal and spatial measurements from a drone equipped with a radar sensor and camera, manually-derived on-ice surveys and samples such as snow pits, snow-depth transects and drill holes, and a AUV with upward-looking multibeam sonars. We also deployed ice-drifter buoys on several ice floes which we used to provide floe drift over an extended period of time.</p><p>In this contribution we present the results of our observations in conjunction with a close sequence of high resolution satellite radar images (TerraSAR-X, Sentinel-1) and altimeter data (ICESat-2 and CryoSat-2) to characterise the sea ice conditions in the western Weddell Sea. We found a mixture of fragments of deformed first-year and multi-year sea-ice which was consolidated in larger ice floes. A thick snow cover frequently depressed the ice cover of the thinner first year ice below sea level. Satellite data allow to extend our findings in time to a larger area and to improve our information on sea ice over a larger region.</p>

scholarly journals Snow microstructure on sea ice: Importance for remote sensing applications

2021 ◽  
Author(s):  
Amy R. Macfarlane ◽  
Stefanie Arndt ◽  
Ruzica Dadic ◽  
Carolina Gabarró ◽  
Bonnie Light ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Remote Sensing Data ◽  
High Arctic ◽  
First Year ◽  
Sensing Data ◽  
Snow Properties ◽  
Multiyear Ice ◽  
Snow Samples

<p>Snow 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.</p><p>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.</p><p>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 instruments.</p>

scholarly journals Correction: Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization. Remote Sensing 2018, 10, 594

2018 ◽  
Vol 10 (10) ◽  
pp. 1616
Author(s):  
Mohammed Dabboor ◽  
Benoit Montpetit ◽  
Stephen Howell
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
First Year ◽  
Spearman Correlation ◽  
A Minor ◽  
Multiyear Ice

In Figure 5 of [1], we detected a minor mistake in the visualization of the Spearman correlation related to the color bar.[...]

scholarly journals Variability Scaling and Consistency of Airborne and Satellite Altimetry Measurements of Arctic Sea Ice

2019 ◽  
Author(s):  
Shiming Xu ◽  
Lu Zhou ◽  
Bin Wang
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Snow Depth ◽  
Spatial Scales ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
First Year ◽  
Noise Levels ◽  
Wide Range

Abstract. Satellite and airborne remote sensing provide complementary capabilities for the observation of the sea ice cover. However, due to the differences in footprint sizes and noise levels of the measurement techniques, as well as sea ice's variability across scales, it is challenging to carry out inter-comparison or consistency study of these observations. In this study we focus on the remote sensing of sea ice thickness parameters, and carry out: (1) the analysis of variability and its statistical scaling for typical parameters, and (2) the consistency study between airborne and satellite measurements. By using collocating data between Operation IceBridge and CryoSat-2 in the Arctic, we show that there exists consistency between the variability of radar freeboard estimations, although CryoSat-2 has higher noise levels. Specifically, we notice that the noise levels vary among different CryoSat-2 products, and for ESA CryoSat-2 freeboard product the noise levels are at about 14 and 20 cm for first-year and multiyear ice, respectively. On the other hand, for Operation IceBridge and ICESat, it is shown that the variability of snow (or total) freeboard is quantitatively comparable, despite over 5 years' the time difference between the two datasets. Furthermore, by using Operation IceBridge data, we also find wide-spread negative covariance between ice freeboard and snow depth, which only manifest at small spatial scales (40 m for first-year ice and about 80 to 120 m for MYI). This statistical relationship highlights that the snow cover reduces the overall topography of the ice cover. Besides, there is prevalent positive covariability between snow depth and snow freeboard across a wide range of spatial scales. The variability and consistency analysis calls for more process-oriented observations and modeling activities to elucidate key processes governing snow-ice interaction and sea ice variability on various spatial scales. The statistical results can also be utilized in improving both radar and laser altimetry, as well as the validation of sea ice and snow prognostic models.

scholarly journals Differing Fine-Scale Responses of Vegetation and Bare Soil to Moisture Variation in a Pinyon-Juniper Woodland Underlie Landscape-Scale Responses Observed from Remote Sensing

2021 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
D. A. Devitt ◽  
B. Bird ◽  
L. Fenstermaker ◽  
M. D. Petrie
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Bare Soil ◽  
First Year ◽  
Shrub Species ◽  
Moisture Variation ◽  
Significant Difference ◽  
Dry Down ◽  
Pinyon Pine ◽  
Second Year

Pinyon juniper woodlands in the American southwest face an uncertain ecological future with regard to climate altered precipitation. Although satellite remote sensing will be relied upon to assess the overall health of these plant communities more fine scaled information is needed to elucidate the mechanisms shaping the broader scaled regional assessments. We conducted a study to assess the NDVI response at the plant canopy level (insitu sensors placed over the canopies) of three tree and one shrub species to changes in precipitation, reference evapotranspiration and soil volumetric water content. Landsat data was used to compare stand integrated and satellite NDVI values. We also provided supplemental water in the amount of 10.85 cm over the study period to additional trees and shrubs which also had insitu NDVI sensors placed over their canopies. NDVI at the canopy level separated statistically by species and when contrasted with bare soil (p<0.001). Spring early summer dry down events were inversely related to increasing ETref-precipitation with a steeper dry down slope in the first year associated with no rainfall occurring in May and June. All three-tree species did not show any significant difference in canopy NDVI based on supplemental water, however the shrub species did reveal a significant response to water (p<0.001). Although all of the three-tree species revealed a one-month period in which they responded to precipitation in July of the first year after 11.2 cm of precipitation, no immediate (day of or next day) response was observed to precipitation or supplemental water events. Snowberry was unique in its NDVI response during the spring green up period in the second year revealing a highly linear shift over a 40-day period with a clear separation between treatments (p<0.001) with those plants receiving supplemental water having a higher more positive slope. Landsat NDVI values revealed an inverse sinusoidal relationship with ETref-precipitation (R2=0.59 p=0.012). Landsat values (0.19+/- 0.01) were found to have no significant difference with bare soil NDVI (0.17+/- 0.01) but were significantly different from all four tree and shrub species. Integrated NDVI based on sensor weighted % cover estimates (0.37+/-0.03) were nearly double Landsat values (0.19+/-0.01). Both NDVI values of pinyon pine and Utah juniper were found to be linear correlated with Landsat NDVI in the second Year (R2>0.75, p<0.001). Multiple regression analysis revealed that 95% of the variation in Landsat NDVI in the second year could be accounted for based on bare soil NDVI and pinyon pine NDVI (p<0.001). et al., NDVI interspace (bare soil) of pinyon juniper woodlands dominated the nature of the Landsat curve. Our results demonstrate the value of ground sensors to help fill the gap between what can be inferred at the forest canopy level and what is occurring at the plant level.

Passive microwave remote sensing of seasonal snow-covered sea ice

2007 ◽  
Vol 31 (6) ◽  
pp. 539-573 ◽  
Author(s):  
Alexandre Langlois ◽  
David G. Barber
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Microwave Emission ◽  
The Arctic ◽  
First Year ◽  
Climate Variability And Change ◽  
Current State ◽  
Thermodynamic Processes

The Arctic is thought to be an area where we can expect to see the first and strongest signs of global-scale climate variability and change. We have already begun to see a reduction in: (1) the aerial extent of sea ice at about 3% per decade and (2) ice thickness at about 40%. At the current rate of reduction we can expect a seasonally ice-free Arctic by midway through this century given the current changes in thermodynamic processes controlling sea-ice freeze-up and decay. Many of the factors governing the thermodynamic processes of sea ice are strongly tied to the presence and geophysical state of snow on sea ice, yet snow on sea ice remains poorly studied. In this review, we provide a summary of the current state of knowledge pertaining to the geophysical, thermodynamic and dielectric properties of snow on sea ice. We first give a detailed description of snow thermophysical properties such as thermal conductivity, diffusivity and specific heat and how snow geophysical/electrical properties and the seasonal surface energy balance affect them. We also review the different microwave emission and scattering mechanisms associated with snow-covered first-year sea ice. Finally, we discuss the annual evolution of the Arctic system through snow thermodynamic (heat/mass transfer, metamorphism) and aeolian processes, with linkages to microwave remote sensing that have yet to be defined from an annual perspective in the Arctic.

scholarly journals Complex for reception and real time processing of remote sensing data

2019 ◽  
Vol 75 ◽  
pp. 03003 ◽  
Author(s):  
Oleg Yakubailik ◽  
Victor Romas'ko ◽  
Evgeny Pavlichenko
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Real Time ◽  
Remote Sensing Data ◽  
Satellite Monitoring ◽  
Area Network ◽  
Science Center ◽  
Real Time Processing ◽  
Time Processing ◽  
Sensing Data

The basic problems and trends in the development of modern systems for the reception, storage and real-time processing of satellite data are considered. Abrupt increase in the capability of satellite systems, significant increase in the amount of satellite information and its availability, the development of data processing and presentation technologies, and the use of web technologies are discussed. Data sources of modern remote sensing systems of the Earth and the features of their practical use are considered. It is concluded that the most effective way to obtain real-time information from meteorological satellites are satellite stations that receive data in the X-band at a frequency of 8 GHz. The performance characteristics and capabilities of the equipment of the new satellite receiving complex at Krasnoyarsk Science Center are given. Use of up-to-date computer equipment (high-performance servers and storage systems, local area network with a bandwidth of 10 Gbit/s) and logical separation into the stages of data conversion (data reception, primary and thematic processing) provide the construction of a modern scalable data-processing system for remote sensing data. The paper presents the results of the work on creation of specialized software for information and analytical systems for real-time satellite monitoring.

Use of EDS mapping to characterize thermochemical stability of fibers in intermetallic matrix composites

1992 ◽  
Vol 50 (1) ◽  
pp. 160-161
Author(s):  
John R. Porter
Keyword(s):  
Spatial Information ◽  
Beam Current ◽  
Fiber Types ◽  
Ceramic Fibers ◽  
Dye Transfer ◽  
Science Center ◽  
Commercial Development ◽  
Nih Image ◽  
Intermetallic Matrix Composites ◽  
Thermochemical Stability

New ceramic fibers, currently in various stages of commercial development, have been consolidated in intermetallic matrices such as γ-TiAl and FeAl. Fiber types include SiC, TiB2 and polycrystalline and single crystal Al2O3. This work required the development of techniques to characterize the thermochemical stability of these fibers in different matrices.SEM/EDS elemental mapping was used for this work. To obtain qualitative compositional/spatial information, the best realistically achievable counting statistics were required. We established that 128 × 128 maps, acquired with a 20 KeV accelerating voltage, 3 sec. live time per pixel (total mapping time, 18 h) and with beam current adjusted to give 30% dead time, provided adequate image quality at a magnification of 800X. The maps were acquired, with backgrounds subtracted, using a Noran TN 5500 EDS system. The images and maps were transferred to a Macintosh and converted into TIFF files using either TIFF Maker, or TNtolMAGE, a Microsoft QuickBASIC program developed at the Science Center. From TIFF files, images and maps were opened in either NIH Image or Adobe Photoshop for processing and analysis and printed from Microsoft Powerpoint on a Kodak XL7700 dye transfer image printer.

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