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Record W7062291920

TerraSAR-X in support of the Canadian dual Ku-Band SAR mission

2023· other· en· W7062291920 on OpenAlexaboutno aff

Bibliographic record

Venueelib (German Aerospace Center) · 2023
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsSnowRadarSynthetic aperture radarEarth observationSatelliteConstellationAncillary data
DOInot available

Abstract

fetched live from OpenAlex

Environment and Climate Change Canada (ECCC) and the Canadian Space Agency (CSA) continue to advance a new satellite Ku-band radar mission focused on providing moderate resolution (500 m) information on seasonal snow mass. Like many regions of the northern hemisphere, estimates of the amount of water stored as seasonal snow are highly uncertain across Canada. To address this gap, a technical concept capable of providing dual-polarization (VV/VH), moderate resolution (500 m), wide swath (~250 km), and high duty cycle (~25% SAR-on time) Ku-band radar measurements at two frequencies (13.5; 17.25 GHz) is under development. An intensive field campaign, Trail Valley Creek experiment (TVCEx), conducted in winter of 2018-19 near Inuvik, NWT, Canada, was conducted. Another field campaign, CryoSAR 2022-23, was conducted in Powassan, Ontario, Canada. Both campaigns acquired airborne Ku-band (13.5 GHz) SAR data to support the science readiness activities of the future Canadian mission. A major challenge of effectively retrieving snow mass information from Ku-Band SAR data is to be able to decouple the contribution of the snow from the underlying background to the backscattered signal. To achieve this, C-Band RADARSAT-2 (TVCEx) and Radarsat Constellation Mission (CryoSAR 2022-23) and X-Band TerraSAR-X data were acquired. Since these radar bands are less sensitive to snow mass of shallow snowpacks, data acquired at both frequencies are used in a forward modeling approach, using the Snow Microwave Radiative Transfer (SMRT) model, to retrieve the effective soil parameters such as soil permittivity and texture. Current results from the TVCEx show that the retrieved soil texture parameters at the lower frequencies are very similar to the measured values using airborne LiDAR. This shows that using a multi-frequency approach is greatly beneficial in retrieving snow mass at higher frequencies. Preliminary results from the CryoSAR2022-23 campaign shows that the Ku-Band data is very sensitive to the snow mass for agricultural landscapes of southern Canada. With as little as 15 cm of snow, the backscattered signature from the soil texture is lost at Ku-Band but it is still visible at C- and X-Band throughout the winter season in dry snow conditions. Data acquired during these two field campaigns strongly show that TSX data is very valuable to retrieve snow mass properties at higher frequencies such as proposed in the future Canadian satellite mission.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.197
Threshold uncertainty score0.396

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.274
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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