scholarly journals Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion

2021 ◽  
Vol 2 ◽  
Author(s):  
Grant E. Gunn ◽  
Benjamin M. Jones ◽  
Rodrigo C. Rangel
Keyword(s):  
High Resolution ◽  
Correlation Length ◽  
Snow Depth ◽  
Depth Map ◽  
The Arctic ◽  
The West ◽  
Lake Ice ◽  
Snowpack Properties ◽  
Northern Alaska

The presence and thickness of snow overlying lake ice affects both the timing of melt and ice-free conditions, can contribute to overall ice thickness through its insulative capacity, and fosters the development of variable ice types. The use of UAVs to retrieve snow depths with high spatial resolution is necessary for the next generation of ultra-fine hydrological models, as the direct contribution of water from snow on lake ice is unknown. Such information is critical to the understanding of the physical processes of snow redistribution and capture in catchments on small lakes in the Arctic, which has been historically estimated from its relationship to terrestrial snowpack properties. In this study, we use a quad-copter UAV and SfM principles to retrieve and map snow depth at the winter maximum at high resolution over a the freshwater West Twin Lake on the Arctic Coastal Plain of northern Alaska. The accuracy of the snow depth retrievals is assessed using in-situ observations (n = 1,044), applying corrections to account for the freeboard of floating ice. The average snow depth from in-situ observations was used calculate a correction factor based on the freeboard of the ice to retrieve snow depth from UAV acquisitions (RMSE = 0.06 and 0.07 m for two transects on the lake. The retrieved snow depth map exhibits drift structures that have height deviations with a root mean square (RMS) of 0.08 m (correlation length = 13.8 m) for a transect on the west side of the lake, and an RMS of 0.07 m (correlation length = 18.7 m) on the east. Snow drifts present on the lake also correspond to previous investigations regarding the variability of snow on lakes, with a periodicity (separation) of 20 and 16 m for the west and east side of the lake, respectively. This study represents the first retrieval of snow depth on a frozen lake surface from a UAV using photogrammetry, and promotes the potential for high-resolution snow depth retrieval on small ponds and lakes that comprise a significant portion of landcover in Arctic environments.

scholarly journals Evidence for long-lived polar vortex air in the mid-latitude summer stratosphere from in situ laser diode CH<sub>4</sub> and H<sub>2</sub>O measurements

2005 ◽  
Vol 5 (6) ◽  
pp. 1467-1472 ◽  
Author(s):  
G. Durry ◽  
A. Hauchecorne
Keyword(s):  
High Resolution ◽  
Laser Diode ◽  
Polar Vortex ◽  
The Arctic ◽  
Concentration Profiles ◽  
Spatial Structures ◽  
Laser Spectrometer ◽  
Southern France ◽  
Diode Laser Spectrometer

Abstract. A balloon borne diode laser spectrometer was launched in southern France in June 2000 to yield in situ stratospheric CH4 and H2O measurements. In the altitude region ranging from 20km to 25km, striking large spatial structures were observed in the vertical concentration profiles of both species. We suggest these patterns are due to the presence of long-lived remnants of the wintertime polar vortex in the mid-latitude summer stratosphere. To support this interpretation, a high resolution advection model for potential vorticity is used to investigate the evolution of the Arctic vortex after its breakdown phase in spring 2000.

scholarly journals Low Cost and Compact FMCW 24 GHz Radar Applications for Snowpack and Ice Thickness Measurements

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3909
Author(s):  
Patrick Pomerleau ◽  
Alain Royer ◽  
Alexandre Langlois ◽  
Patrick Cliche ◽  
Bruno Courtemanche ◽  
...  
Keyword(s):  
Snow Depth ◽  
Continuous Wave ◽  
Snow Water Equivalent ◽  
Low Cost ◽  
Ice Thickness ◽  
Lake Ice ◽  
Wide Range ◽  
Thickness Measurements ◽  
24 Ghz

Monitoring the evolution of snow on the ground and lake ice—two of the most important components of the changing northern environment—is essential. In this paper, we describe a lightweight, compact and autonomous 24 GHz frequency-modulated continuous-wave (FMCW) radar system for freshwater ice thickness and snow mass (snow water equivalent, SWE) measurements. Although FMCW radars have a long-established history, the novelty of this research lies in that we take advantage the availability of a new generation of low cost and low power requirement units that facilitates the monitoring of snow and ice at remote locations. Test performance (accuracy and limitations) is presented for five different applications, all using an automatic operating mode with improved signal processing: (1) In situ lake ice thickness measurements giving 2 cm accuracy up to ≈1 m ice thickness and a radar resolution of 4 cm; (2) remotely piloted aircraft-based lake ice thickness from low-altitude flight at 5 m; (3) in situ dry SWE measurements based on known snow depth, giving 13% accuracy (RMSE 20%) over boreal forest, subarctic taiga and Arctic tundra, with a measurement capability of up to 3 m in snowpack thickness; (4) continuous monitoring of surface snow density under particular Antarctic conditions; (5) continuous SWE monitoring through the winter with a synchronized and collocated snow depth sensor (ultrasonic or LiDAR sensor), giving 13.5% bias and 25 mm root mean square difference (RMSD) (10%) for dry snow. The need for detection processing for wet snow, which strongly absorbs radar signals, is discussed. An appendix provides 24 GHz simulated effective refractive index and penetration depth as a function of a wide range of density, temperature and wetness for ice and snow.

scholarly journals Tuning and assessment of the HYCOM-NORWECOM V2.1 modeling system

2014 ◽  
Vol 7 (6) ◽  
pp. 8399-8432 ◽  
Author(s):  
A. Samuelsen ◽  
C. Hansen ◽  
H. Wehde
Keyword(s):  
High Resolution ◽  
Arctic Ocean ◽  
Basic Research ◽  
Grazing Rate ◽  
The Arctic ◽  
Atlantic Sector ◽  
In Situ Data ◽  
Resolution Model ◽  
High Resolution Model

Abstract. The HYCOM-NORWECOM modeling system is used both for basic research and as a part of the forecasting system for the Arctic Marine Forecasting Centre through the MyOcean project. Here we present a revised version of this model. The present model, as well as the sensitivity simulations leading up to this version, has been compared to a dataset of in-situ measurements of nutrient and chlorophyll from the Norwegian Sea and the Atlantic sector of the Arctic Ocean. The revisions having most impact included adding diatoms to the diet of micro-zooplankton, increasing micro-zooplankton grazing rate and decreased silicate-to-nitrate ratio in diatoms. Model runs are performed both with a coarse- (~50 km) and higher-resolution (~15 km) model configuration, both covering the North Atlantic and Arctic Ocean. While the new model formulation improves the results in both the coarse- and high-resolution model, the nutrient bias is smaller in the high-resolution model, probably as a result of the better resolution of the main processes and with that improved circulation. The final revised version delivers satisfactory results for all three nutrients as well as improved result for chlorophyll in terms of the annual cycle amplitude. However, for chlorophyll the correlation with in-situ data remains relatively low. Besides the large uncertainties associated with observational data this is possibly caused by the fact that constant C / N and Chl / N ratios are implemented in the model.

scholarly journals Advances in altimetric snow depth estimates using bi-frequency SARAL/CryoSat-2 Ka/Ku measurements

2021 ◽  
Author(s):  
Florent Garnier ◽  
Sara Fleury ◽  
Gilles Garric ◽  
Jérôme Bouffard ◽  
Michel Tsamados ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ocean Modeling ◽  
The Arctic ◽  
Validation Data ◽  
Band Frequency ◽  
In Situ Data ◽  
Wide Range ◽  
Assimilation System

Abstract. Although snow depth on sea ice is a key parameter for Sea Ice Thickness (SIT), there currently does not exist reliable estimations. In Arctic, nearly all SIT products use a snow depth climatology (the Warren-99 modified climatology, W99m) constructed from in-situ data obtained prior to the first significant impacts of climate change. In Antarctica, the lack of information on snow depth remains a major obstacle in the development of reliable SIT products. In this study, we present the latest version of the Altimetric Snow Depth (ASD) product computed over both hemispheres from the difference of the radar penetration into the snow pack between the CryoSat-2 Ku-band and the SARAL Ka-band frequency radars. The ASD solution is compared against a wide range of snow depth products including model data (Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) or its equivalent in Antarctica the Global Ice-Ocean Modeling and Assimilation System (GIOMAS), the MERCATOR model and NASA's Eulerian Snow On Sea Ice Model (NESOSIM, only in Arctic)), the Advanced Microwave Scanning Radiometer 2 (AMSR-2) passive radiometer data, and the Dual-altimeter Snow Thickness (DuST) Ka-Ku product (only in Arctic). It is validated in the Arctic against in-situ and airborne validation data. These comparisons demonstrate that ASD provide a consistent snow depth solution, with space and time patterns comparable with those of the alternative Ka-Ku DuST product, but with a mean bias of about 6.5 cm. We also demonstrate that ASD is consistent with the validation data. Comparisons with Operation Ice Bridge's (OIB) airborne snow radar in Arctic during the period of 2014–2018 show a correlation of 0.66 and a RMSE of about 6 cm. Furthermore, a first-guess monthly climatology has been constructed in Arctic from the ASD product, which shows a good agreement with OIB during 2009–2012. This climatology is shown to provide a better solution than the W99m climatology when compared with validation data. Finally, we have characterised the SIT uncertainty due to the snow depth from an ensemble of SIT solutions computed for the Arctic by using the different snow depth products previously used in the comparison with the ASD product. During the period of 2013–2019, we found a spatially averaged SIT mean standard deviation of 20 cm. Deviations between SIT estimations due to different snow depths can reach up to 77 cm. Using the ASD data instead of W99m to estimate SIT over this time period leads to a reduction of the average SIT of about 30 cm.

scholarly journals Aerosol size distributions during the Atmospheric Tomography Mission (ATom): methods, uncertainties, and data products

2019 ◽  
Vol 12 (6) ◽  
pp. 3081-3099 ◽  
Author(s):  
Charles A. Brock ◽  
Christina Williamson ◽  
Agnieszka Kupc ◽  
Karl D. Froyd ◽  
Frank Erdesz ◽  
...  
Keyword(s):  
High Resolution ◽  
Size Distribution ◽  
Atmospheric Composition ◽  
The Arctic ◽  
Distribution Data ◽  
Size Distributions ◽  
Aerosol Composition ◽  
Aerosol Mass ◽  
Data Products

Abstract. From 2016 to 2018 a DC-8 aircraft operated by the US National Aeronautics and Space Administration (NASA) made four series of flights, profiling the atmosphere from 180 m to ∼12 km above sea level (km a.s.l.) from the Arctic to the Antarctic over both the Pacific and Atlantic oceans. This program, the Atmospheric Tomography Mission (ATom), sought to sample the troposphere in a representative manner, making measurements of atmospheric composition in each season. This paper describes the aerosol microphysical measurements and derived quantities obtained during this mission. Dry size distributions from 2.7 nm to 4.8 µm in diameter were measured in situ at 1 Hz using a battery of instruments: 10 condensation particle counters with different nucleation diameters, two ultra-high-sensitivity aerosol size spectrometers (UHSASs), one of which measured particles surviving heating to 300 ∘C, and a laser aerosol spectrometer (LAS). The dry aerosol measurements were complemented by size distribution measurements from 0.5 to 930 µm diameter at near-ambient conditions using a cloud, aerosol, and precipitation spectrometer (CAPS) mounted under the wing of the DC-8. Dry aerosol number, surface area, and volume, and optical scattering and asymmetry parameters at several wavelengths from the near-UV to the near-IR ranges were calculated from the measured dry size distributions (2.7 nm to 4.8 µm). Dry aerosol mass was estimated by combining the size distribution data with particle density estimated from independent measurements of aerosol composition with a high-resolution aerosol mass spectrometer and a single-particle soot photometer. We describe the instrumentation and fully document the aircraft inlet and flow distribution system, the derivation of uncertainties, and the calculation of data products from combined size distributions. Comparisons between the instruments and direct measurements of some aerosol properties confirm that in-flight performance was consistent with calibrations and within stated uncertainties for the two deployments analyzed. The unique ATom dataset contains accurate, precise, high-resolution in situ measurements of dry aerosol size distributions, and integral parameters, and estimates and measurements of optical properties, for particles < 4.8 µm in diameter that can be used to evaluate aerosol abundance and processes in global models.

Using CloudSat snowfall rate observations to constrain and characterize the uncertainties of Arctic snow-on-sea-ice

2020 ◽  
Author(s):  
Alex Cabaj ◽  
Paul Kushner ◽  
Alek Petty ◽  
Stephen Howell ◽  
Christopher Fletcher
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
The Arctic Ocean ◽  
Ice Model

&lt;p&gt;&lt;span&gt;Snow on Arctic sea ice plays multiple&amp;#8212;and sometimes contrasting&amp;#8212;roles in several feedbacks between sea ice and the global climate &lt;/span&gt;&lt;span&gt;system.&lt;/span&gt;&lt;span&gt; For example, the presence of snow on sea ice may mitigate sea ice melt by&lt;/span&gt;&lt;span&gt; increasing the sea ice albedo &lt;/span&gt;&lt;span&gt;and enhancing the ice-albedo feedback. Conversely, snow can&lt;/span&gt;&lt;span&gt; in&lt;/span&gt;&lt;span&gt;hibit sea ice growth by insulating the ice from the atmosphere during the &lt;/span&gt;&lt;span&gt;sea ice &lt;/span&gt;&lt;span&gt;growth season. &lt;/span&gt;&lt;span&gt;In addition to its contribution to sea ice feedbacks, snow on sea ice also poses a challenge for sea ice observations. &lt;/span&gt;&lt;span&gt;In particular, &lt;/span&gt;&lt;span&gt;snow &lt;/span&gt;&lt;span&gt;contributes to uncertaint&lt;/span&gt;&lt;span&gt;ies&lt;/span&gt;&lt;span&gt; in retrievals of sea ice thickness from satellite altimetry &lt;/span&gt;&lt;span&gt;measurements, &lt;/span&gt;&lt;span&gt;such as those from ICESat-2&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;Snow-on-sea-ice models can&lt;/span&gt;&lt;span&gt; produce basin-wide snow depth estimates, but these models require snowfall input from reanalysis products. In-situ snowfall measurements are a&lt;/span&gt;&lt;span&gt;bsent&lt;/span&gt;&lt;span&gt; over most of the Arctic Ocean, so it can be difficult to determine which reanalysis &lt;/span&gt;&lt;span&gt;snowfall&lt;/span&gt;&lt;span&gt; product is b&lt;/span&gt;&lt;span&gt;est&lt;/span&gt;&lt;span&gt; suited to be used as&lt;/span&gt;&lt;span&gt; input for a snow-on-sea-ice model.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In the absence of in-situ snowfall rate measurements, &lt;/span&gt;&lt;span&gt;measurements from &lt;/span&gt;&lt;span&gt;satellite instruments can be used to quantify snowfall over the Arctic Ocean&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;The CloudSat satellite, which is equipped with a 94 GHz Cloud Profiling Radar instrument, measures vertical radar reflectivity profiles from which snowfall rate&lt;/span&gt;&lt;span&gt;s&lt;/span&gt;&lt;span&gt; can be retrieved. &lt;/span&gt; &lt;span&gt;T&lt;/span&gt;&lt;span&gt;his instrument&lt;/span&gt;&lt;span&gt; provides the most extensive high-latitude snowfall rate observation dataset currently available. &lt;/span&gt;&lt;span&gt;CloudSat&amp;#8217;s near-polar orbit enables it to make measurements at latitudes up to 82&amp;#176;N, with a 16-day repeat cycle, &lt;/span&gt;&lt;span&gt;over the time period from 2006-2016.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;We present a calibration of reanalysis snowfall to CloudSat observations over the Arctic Ocean, which we then apply to reanalysis snowfall input for the NASA Eulerian Snow On Sea Ice Model (NESOSIM). This calibration reduces the spread in snow depths produced by NESOSIM w&lt;/span&gt;&lt;span&gt;hen&lt;/span&gt;&lt;span&gt; different reanalysis inputs &lt;/span&gt;&lt;span&gt;are used&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;In light of this calibration, we revise the NESOSIM parametrizations of wind-driven snow processes, and we characterize the uncertainties in NESOSIM-generated snow depths resulting from uncertainties in snowfall input. &lt;/span&gt;&lt;span&gt;We then extend this analysis further to estimate the resulting uncertainties in sea ice thickness retrieved from ICESat-2 when snow depth estimates from NESOSIM are used as input for the retrieval.&lt;/span&gt;&lt;/p&gt;

scholarly journals Application of Low-Cost UASs and Digital Photogrammetry for High-Resolution Snow Depth Mapping in the Arctic

2017 ◽  
Vol 9 (11) ◽  
pp. 1144 ◽  
Author(s):  
Emiliano Cimoli ◽  
Marco Marcer ◽  
Baptiste Vandecrux ◽  
Carl E. Bøggild ◽  
Guy Williams ◽  
...  
Keyword(s):  
High Resolution ◽  
Snow Depth ◽  
Low Cost ◽  
The Arctic ◽  
Digital Photogrammetry

scholarly journals Remote sensing of lake water volumes on the Arctic Coastal Plain of Northern Alaska

2020 ◽  
Author(s):  
Claire E. Simpson ◽  
Christopher D. Arp ◽  
Yongwei Sheng ◽  
Mark L. Carroll ◽  
Benjamin M. Jones ◽  
...  
Keyword(s):  
Lake Water ◽  
Coastal Plain ◽  
Water Storage ◽  
Regional Model ◽  
The Arctic ◽  
Lake Depth ◽  
Arctic Coastal Plain ◽  
Water Volumes ◽  
Northern Alaska

Abstract. The Pleistocene Sand Sea on the Arctic Coastal Plain (ACP) of northern Alaska is underlain by an ancient sand dune field, a geological feature that affects regional lake characteristics. Many of these lakes, which cover approximately 20 % of the Pleistocene Sand Sea, are relatively deep (up to 25 m). In addition to the natural importance of ACP Sand Sea lakes for water storage, energy balance, and ecological habitat, the need for winter water for industrial development and exploration activities makes lakes in this region a valuable resource. However, ACP Sand Sea lakes have received little prior study. Here, we use in situ bathymetric data to test 12 model variants for predicting Sand Sea lake depth based on analysis of Landast-8 Operational Land Imager (OLI) images. Lake depth gradients were measured at 17 lakes in mid-summer 2017 using a HumminBird 798ci HD SI Combo automatic sonar system (Simpson and Arp, 2018). The field measured data points were compared to Red-Green-Blue (RGB) bands of a Landsat-8 OLI image acquired on 8 August 2016 to select and calibrate the most accurate spectral-depth model for each study lake and estimate bathymetry (Simpson, 2019). Exponential functions using a simple band ratio (with bands selected based on lake turbidity and bed substrate) yielded the most successful model variants. For each lake, the most accurate model explained 81.8 % of the variation in depth, on average. Modeled lake bathymetries were integrated with remotely sensed lake surface area to quantify lake water storage volumes, which ranged from 1.056 × 10−3 km3 to 57.416 × 10−3 km3. Due to variation in depth maxima, substrate, and turbidity between lakes, a regional model is currently infeasible, rendering necessary the acquisition of additional in situ data with which to develop a regional model solution. Estimating lake water volumes using remote sensing will facilitate better management of expanding development activities and serve as a baseline by which to evaluate future responses to ongoing and rapid climate change in the Arctic. All sonar depth data and modeled lake bathymetry rasters can be freely accessed at https://doi.org/10.18739/A2SN01440 (Simpson and Arp, 2018) and https://doi.org/10.18739/A2TQ5RD83 (Simpson, 2019), respectively.

scholarly journals Theia Snow collection: high resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

2018 ◽  
Author(s):  
Simon Gascoin ◽  
Manuel Grizonnet ◽  
Marine Bouchet ◽  
Germain Salgues ◽  
Olivier Hagolle
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Snow Depth ◽  
Western Europe ◽  
Landsat 8 ◽  
Mountain Regions ◽  
Snow Cover Area ◽  
Level 2 ◽  
Sentinel 2

Abstract. The Theia Snow collection routinely provides high resolution maps of the snow cover area from Sentinel-2 and Landsat-8 observations. The collection covers selected areas worldwide including the main mountain regions in Western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of the Snow collection contains four classes: snow, no-snow, cloud and no-data. We present the algorithm to generate the snow products and provide an evaluation of their accuracy using in situ snow depth measurements, higher resolution snow maps, and visual control. The results suggest that the snow is accurately detected in the Theia snow collection, and that the snow detection is more accurate than the sen2cor outputs (ESA level 2 product). An issue that should be addressed in a future release is the occurrence of false snow detection in some large clouds. The snow maps are currently produced and freely distributed in average 5 days after the image acquisition as raster and vector files via the Theia portal (http://doi.org/10.24400/329360/F7Q52MNK).

scholarly journals Mapping snow depth in open alpine terrain from stereo satellite imagery

2016 ◽  
Author(s):  
R. Marti ◽  
S. Gascoin ◽  
E. Berthier ◽  
M. de Pinel ◽  
T. Houet ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Snow Depth ◽  
Depth Map ◽  
Unmanned Aircraft ◽  
Mountainous Areas ◽  
Ground Control Points ◽  
Accuracy And Precision ◽  
Digital Elevation ◽  
Probe Measurements

Abstract. To date, there is no direct approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) stereo satellites to this purpose. Two triplets of 70 cm-resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km2) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points, to generate raw photogrammetric clouds and to convert them into high-resolution Digital Elevation Models (DEMs) at 1-m, 2-m, and 4-m resolutions. The DEMs difference (dDEM) were computed after 3D-coregistration, including a correction of a −0.48 m vertical bias. The bias-corrected dDEMs maps were compared to 451 snow probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is −0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m-Pléiades dDEM to a 2 m-dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km2). The UAV-derived snow depth map exhibit the same patterns as the Pléiades-derived snow map. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas without any field data.

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