River ice and water velocities using the Planet optical cubesatconstellation
Bibliographic record
Abstract
Abstract. The PlanetScope constellation consists of ~ 150 optical cubesats that are evenly distributed like strings of pearls in two orbital planes and scan the Earth's land surface once per day with ~ 3 m spatial image resolution. Subsequent cubesats in each of the orbital planes image the Earth surface with a nominal time lapse of ~ 90 s between each other, which produces near-simultaneous pairs of scenes over the across-track overlaps of the cubesat swaths. We exploit this short time lapse between subsequent Planet cubesat images to track river ice floes on Northern rivers as indicators of water surface velocities. The method is demonstrated for a 60 km long reach of the Amur River in Siberia, and a 200 km long reach of the Yukon River, Alaska. The accuracy of the estimated horizontal surface velocities is on the order of ±0.01 m s−1. The application of our approach is complicated by cloud cover and low sun angles at high latitudes during the periods where rivers typically carry ice floes, and by the fact that the near-simultaneous swath overlaps by design do not cover the complete Earth surface. Still, the approach enables direct remote sensing of river surface velocities over many cold-region rivers and several times per year – much more frequent and over much larger areas than feasible so far, if at all. We find that freeze-up conditions seem in general to offer ice floes that are more suitable for tracking, and over longer time periods, compared to typical ice break-up conditions. The coverage of river velocities obtained could be particularly useful in combination with satellite measurements of river area, and river surface height and slope.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".