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Record W48834712

Exploring snow information content of interferometric SAR Data

2008· article· en· W48834712 on OpenAlexaboutno aff
Ali Esmaeily Gazkohani

Bibliographic record

VenueKnowledge UdeS (Institutional Deposit of the University of Sherbrooke) · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsRemote sensingSnowContent (measure theory)Environmental scienceComputer scienceGeologyGeographyMeteorologyMathematics
DOInot available

Abstract

fetched live from OpenAlex

The objective of this research is to explore the information content of repeat-pass cross-track Interferometric SAR (InSAR) with regard to snow, in particular Snow Water Equivalent (SWE) and snow depth. The study is an outgrowth of earlier snow cover modeling and radar interferometry experiments at Schefferville, Quebec, Canada and elsewhere which has shown that for reasons of loss of coherence repeat-pass InSAR is not useful for the purpose of snow cover mapping, even when used in differential InSAR mode. Repeat-pass cross-track InSAR would overcome this problem. As at radar wavelengths dry snow is transparent, the main reflection is at the snow/ground interface. The high refractive index of ice creates a phase delay which is linearly related to the water equivalent of the snow pack. When wet, the snow surface is the main reflector, and this enables measurement of snow depth. Algorithms are elaborated accordingly. Field experiments were conducted at two sites and employ two different types of digital elevation models (DEM) produced by means of cross track InSAR. One was from the Shuttle Radar Topography Mission digital elevation model (SRTM DEM), flown in February 2000. It was compared to the photogrammetrically produced Canadian Digital Elevation Model (CDEM) to examine snow-related effects at a site near Schefferville, where snow conditions are well known from half a century of snow and permafrost research. The second type of DEM was produced by means of airborne cross track InSAR (TOPSAR). Several missions were flown for this purpose in both summer and winter conditions during NASA's Cold Land Processes Experiment (CLPX) in Colorado, USA. Differences between these DEM's were compared to snow conditions that were well documented during the CLPX field campaigns. The results are not straightforward. As a result of automated correction routines employed in both SRTM and AIRSAR DEM extraction, the snow cover signal is contaminated. Fitting InSAR DEM's to known topography distorts the snow information, just as the snow cover distorts the topographic information. The analysis is therefore mostly qualitative, focusing on particular terrain situations. At Schefferville, where the SRTM was adjusted to known lake levels, the expected dry-snow signal is seen near such lakes. Mine pits and waste dumps not included in the CDEM are depicted and there is also a strong signal related to the spatial variations in SWE produced by wind redistribution of snow near lakes and on the alpine tundra. In Colorado, cross-sections across ploughed roads support the hypothesis that in dry snow the SWE is measurable by differential InSAR. They also support the hypothesis that snow depth may be measured when the snow cover is wet. Difference maps were also extracted for a 1 km2 Intensive Study Area (ISA) for which intensive ground truth was available. Initial comparison between estimated and observed snow properties yielded low correlations which improved after stratification of the data set.In conclusion, the study shows that snow-related signals are measurable. For operational applications satellite-borne cross-track InSAR would be necessary. The processing needs to be snow-specific with appropriate filtering routines to account for influences by terrain factors other than snow.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.129
GPT teacher head0.208
Teacher spread0.079 · 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 designObservational
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

Citations1
Published2008
Admission routes1
Has abstractyes

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