Remote Sensing Systems for Ocean: A Review (Part 1: Passive Systems)
Bibliographic record
Abstract
Reliable, accurate, and timely information about oceans is important for many applications, including water resource management, hydrological cycle monitoring, environmental studies, agricultural and ecosystem health applications, economy, and the overall health of the environment. In this regard, remote sensing (RS) systems offer exceptional advantages for mapping and monitoring various oceanographic parameters with acceptable temporal and spatial resolutions over the oceans and coastal areas. So far, different methods have been developed to study oceans using various RS systems. This urges the necessity of having review studies that comprehensively discuss various RS systems, including passive and active sensors, and their advantages and limitations for ocean applications. In this article, the goal is to review most RS systems and approaches that have been worked on marine applications. This review paper is divided into two parts. Part 1 is dedicated to the passive RS systems for ocean studies. As such, four primary passive systems, including optical, thermal infrared radiometers, microwave radiometers, and Global Navigation Satellite Systems, are comprehensively discussed. Additionally, this article summarizes the main passive RS sensors and satellites, which have been utilized for different oceanographic applications. Finally, various oceanographic parameters, which can be retrieved from the data acquired by passive RS systems, along with the corresponding methods, are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".