Cloud Detection Method Based on Spectral Area Ratios in MODIS Data
Bibliographic record
Abstract
A variety of clouds are present in almost all moderate-resolution imaging spectroradiometer (MODIS) images. To extract accurate information from MODIS data, a key preprocessing step is to detect the cloudy pixels. This article proposes a new algorithm to distinguish between cloudy and cloud-free pixels in MODIS images. This algorithm is based on the differences in the spectral areas between clouds and other surface features. It uses as many as 24 of the 36 MODIS spectral bands to obtain the integrated spectral information. The method has been illustrated by an example of 76 MODIS images recorded from 2011 to 2013. The results show that the algorithm is capable of correctly identifying most of the cloud-contaminated pixels except for some thin cloud pixels. We compared the new method with the MODIS Cloud Mask algorithm and found that the new algorithm performs better than the MODIS MOD35 Cloud Mask in some situation, such as coastal area, sun glint, and data with invalid values.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".