TerraSAR-X in support of the Canadian dual Ku-Band SAR mission
Bibliographic record
Abstract
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.194 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".