Spatiotemporal image-fusion model for enhancing the temporal resolution of Landsat-8 surface reflectance images using MODIS images
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Bibliographic record
Abstract
Our aim was to evaluate a spatiotemporal image-fusion model (STI-FM) for enhancing the temporal resolution (i.e., from 16 to 8 days) of Landsat-8 surface reflectance images by utilizing the moderate-resolution imaging spectroradiometer (MODIS) images, and assess its applicability over a heterogeneous agriculture dominant semiarid region in Jordan. Our proposed model had two major components: (i) establishing relationships between two 8-day MODIS composite images acquired at two different times (i.e., time 1 and time 2); and (ii) generating synthetic Landsat-8 surface reflectance images at time 2 as a function of Landsat-8 images available at time 1 and the relationship constructed in the first component. We evaluated the synthetic images with the actual Landsat-8 images and observed strong relations between them. For example: the coefficient of determination (r2) was in the range: (i) 0.72 to 0.82; (ii) 0.71 to 0.79; and (iii) 0.78 to 0.83; for red, near-infrared (NIR), and shortwave infrared (SWIR2.2 μm) spectral bands, respectively. In addition, root mean square error (RMSE) and absolute average difference (AAD) values were: (i) in between 0.003 and 0.004, and 0.0002, respectively, for red band; (ii) 0.005 and 0.0003, respectively, for NIR band; and (iii) 0.004 and in between 0.0001 and 0.0002, respectively, for SWIR2.2 μm band. The developed method would be useful in understanding the dynamics of environment issues (e.g., agriculture drought and irrigation management), which require both relatively high spatial (i.e., 30 m) and high temporal resolution (i.e., 8 days) images.
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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 it