Using temporal variability to improve spatial mapping with application to satellite data
Bibliographic record
Abstract
Abstract The National Aeronautics and Space Administration (NASA) has a remote‐sensing program with a large array of satellites whose mission is earth‐system science. To carry out this mission, NASA produces data at various levels; level‐2 data have been calibrated to the satellite's footprint at high temporal resolution, although there is often a lot of missing data. Level‐3 data are produced on a regular latitude—longitude grid over the whole globe at a coarser spatial and temporal resolution (such as a day, a month, or a repeat‐cycle of the satellite), and there are still missing data. This article demonstrates that spatio‐temporal statistical models can be made operational and provide a way to estimate level‐3 values over the whole grid and attach to each value a measure of its uncertainty. Specifically, a hierarchical statistical model is presented that includes a spatio‐temporal random effects (STRE) model as a dynamical component and a temporally independent spatial component for the fine‐scale variation. Optimal spatio‐temporal predictions and their mean squared prediction errors are derived in terms of a fixed‐dimensional Kalman filter. The predictions provide estimates of missing values and filter out unwanted noise. The resulting fixed‐rank filter is scalable, in that it can handle very large data sets. Its functionality relies on estimation of the model's parameters, which is presented in detail. It is demonstrated how both past and current remote‐sensing observations on aerosol optical depth (AOD) can be combined, yielding an optimal statistical predictor of AOD on the log scale along with its prediction standard error. The Canadian Journal of Statistics 38: 271–289; 2010 © 2010 Statistical Society of Canada
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".