Using the MicroASAR on the NASA SIERRA UAS in the Characterization of Arctic Sea Ice Experiment

Zaugg, Evan C.; Long, D.G.; Edwards, M. C.; Fladeland, Matthew; Kolyer, R.; Crocker, Ian; Maslanik, James A; Herzfeld, U. C.; Wallin, Bruce

doi:10.1109/radar.2010.5494611

Cited by 21 publications

(12 citation statements)

References 15 publications

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“…Fig. 7 is an example of the imagery created using data collected with the microASAR over the Arctic ocean during the characterization of sea ice experiment (CASIE) in 2009 [5], [6]. The ice edges and ice flows are easy to discriminate.…”

Section: Resultsmentioning

confidence: 99%

Digital receiver design for an offset IF LFM-CW SAR

Stringham¹,

Long²,

Wicks

et al. 2011

2011 IEEE RadarCon (RADAR)

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Abstract-This paper describes the digital receiver for the microASAR, a small, powerful, LFM-CW SAR. The digital receiver uses a high-speed ADC providing three key benefits; namely: 1) the de-chirped signal can be at an arbitrary intermediate frequency (IF), enabling better RF filtering; 2) quantization noise can be reduced via digital filtering; and 3) the flexibility to enable the SAR to operate in both de-chirped and pulsed modes. This paper also outlines the FPGA design used for most applications.

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Section: Resultsmentioning

confidence: 99%

Digital receiver design for an offset IF LFM-CW SAR

Stringham¹,

Long²,

Wicks

et al. 2011

2011 IEEE RadarCon (RADAR)

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“…(1) Since parameter p2 and other p2-type parameters quantify relative significance of surface structures independent of dimensions, these parameters facilitate a comparison between high-resolution observations and observations of lower resolution. Examples include airborne microSAR data collected from unmanned and manned aircraft (Crocker et al [23], Zaugg et al [105]) and satellite SAR data of any resolution, as well as radar and laser altimeter data; (2) Mapping of roughness types of sea-ice areas (and land ice areas, using similar methods) from SAR data may yield the surface information that is needed to study the influence of ice roughness on the backscatter and return signal of radar and laser altimeter data, such as those from ICESat (GLAS) Zwally et al [106], Schutz et al [107], CRYOSat-2 R.K. [108], Wingham et al [109], Drinkwater et al [110] and ICESat-2 (launch 2017). Information on surface roughness at several scales, as can be derived from SAR-data classification, may be relevant for understanding satellite altimeter data.…”

Section: Applicationsmentioning

confidence: 99%

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

et al. 2016

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Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line.

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“…The CASIE experiment was conducted over Fram Strait in July/August 2009 from a base in Ny-Alesund, Svalbard, using the NASA SIERRA unmanned aircraft. CASIE data consist of up to 1000 km long tracks per flight, which extend from Svalbard about three quarters of the way to the eastern coast of Greenland, with altimeter data recorded every 7-10 cm over sea ice (Crocker et al, 2011;Zaugg et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Sea ice deformation in Fram Strait — Comparison of CICE simulations with analysis and classification of airborne remote-sensing data

Herzfeld

Hunke

McDonald

et al. 2015

Cold Regions Science and Technology

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Complex surface topography is a characteristic especially of older sea ice, and an observed loss of older or multiyear sea ice in the Arctic indicates an imminent transition of the Arctic ice cover from perennial to seasonal. Prediction of this transition in the Arctic system is one of today's "big science" questions. The objective of this paper is to compare model output and data analysis, toward addressing a key problem in sea-ice modeling, the correct representation of ridges and other spatial features that result from deformation. Morphologically complex, ridged ice exists in Fram Strait. High-resolution airborne remote sensing data, including image data and altimeter data showing ridging, collected from unmanned aircraft over Fram Strait during the Characterization of Arctic Sea Ice Experiment (CASIE) in 2009, are analyzed using geostatistical classification. This approach results in parameters that capture deformation characteristics and facilitates comparison to model results. Ridging and other forms of deformation are implemented in the Los Alamos sea-ice model CICE. The main parameters that are compared are freeboard as a proxy of ice thickness and percentages of level versus ridged ice from modeling and laser altimeter data analysis. Results from freeboard analysis indicate that except for the elevation class 0.1 m-0.2 m, models and observations match very well. For concentration of deformed ice, results from CICE using the standard parameter configuration are within 20% of deformed ice area concentration compared to results from altimeter data analysis. Variation of several physical parameters indicates the sensitivity of model results to ridging parameters and provides results that are within 7% of data analysis results in each grid cell, with the parameter that yields the best match depending on geographic location and morphologic province. In general our approach demonstrates an avenue for parameterization on both the data analysis side and the modeling side that allows a direct comparison of results from sea-ice models and data analysis and hence an evaluation of numerical sea-ice models.

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Using the MicroASAR on the NASA SIERRA UAS in the Characterization of Arctic Sea Ice Experiment

Abstract: Abstract-

Cited by 21 publications

References 15 publications

Digital receiver design for an offset IF LFM-CW SAR

Digital receiver design for an offset IF LFM-CW SAR

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

Sea ice deformation in Fram Strait — Comparison of CICE simulations with analysis and classification of airborne remote-sensing data

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