Temporal dynamics of sand dune bidirectional reflectance characteristics for absolute radiometric calibration of optical remote sensing data
Bibliographic record
Abstract
The use of Pseudo Invariant Calibration Sites (PICS) for establishing the radiometric trending of optical remote sensing systems has a long history of successful implementation. Past studies have shown that the PICS method is useful for evaluating the trend of sensors over time or cross-calibration of sensors but was not considered until recently for deriving absolute calibration. Current interest in using this approach to establish absolute radiometric calibration stems from recent research that indicates that with empirically derived models of the surface properties and careful atmospheric characterisation Top of Atmosphere (TOA) reflectance values can be predicted and used for absolute sensor radiometric calibration. Critical to the continued development of this approach is the accurate characterization of the Bidirectional Reflectance Distribution Function (BRDF) of PICS sites. This paper presents BRDF data collected by a high-performance portable goniometer system in order to develop a temporal BRDF model for the Algodones Dunes in California. The results demonstrated that the BRDF of a reasonably simple sand surface was complex with changes in anisotropy taking place in response to changing solar zenith angles. The nature of these complex interactions would present challenges to future model development.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".