Global Adaptability Assessment of Ten Common Topographic Correction Models for Landsat 8 OLI Images
Bibliographic record
Abstract
Sloping terrain distorts the sun-target-sensor geometry, resulting in biases of the optical reflectance measured by remote sensors relative to flat situations. Performing topographic correction (TC) is, therefore, deemed mandatory to foster the full exploitation of satellite images worldwide to support various applications in mountainous regions. Various TC models have already been proposed and developed, while most of them were previously evaluated at local or regional scales using a few images with various evaluation criteria. Therefore, a systematic and comprehensive assessment has yet to be done on these TC models in the global mountainous regions. In the present study, 10523 Landsat 8 OLI images filtered by land cover types and seasons sampled in the global mountains are corrected by ten popular TC models (SE, b correction, VECA, CC, SCS, DS, SCS+C, PLC, Minnaert, and Minnaert+SCS) with a unified evaluation criterion on the Google Earth Engine platform. The outcomes are that: (1) global TC effects on Landsat 8 OLI images generally increase with sun zenith angles and latitudes; (2) six models (SE, b correction, CC, VECA, Minnaert, and Minnaert+SCS) show good adaptability among the ten models for the global mountainous placing a disregard to land cover types and seasons; (3) considering permanent snow and ice, needle-leaved forests in winter, and null values might appear in b correction, SE is deemed to be with the most global adaptability. This study pioneers an evaluation of fashionable TC models concerning mountainous regions worldwide and will be useful for applying TC to Landsat images for the benefit of making global TC products in the future and a fair inter-comparison of OLI surface reflectance measured in various mountainous areas of the globe.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".