Robustness Comparison of Spatiotemporal Fusion Models With High Spatial Resolution Satellite Images
Bibliographic record
Abstract
The construction of high spatial and temporal image time series through spatiotemporal image fusion is vital for environmental monitoring and time-series analysis. However, most existing spatiotemporal image fusion models were originally developed for mid- and coarse spatial resolution satellite images, limiting their applicability to high spatial resolution images. To address this limitation, it is critical to identify models that are robust across different fusion strategies when applied to high spatial resolution images. This study evaluates the robustness of three representative weight function-based models, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal Data Fusion (FSDAF), and Fit-FC, using PlanetScope and Sentinel-2 images from agricultural sites with varying degrees of spatial heterogeneity. The analysis focuses on three key factors related to input data characteristics that influence fusion performance: differences in image acquisition times, differences in spatial resolution ratio, and the impact of radiometric normalization. A relative robustness index (RRI), defined as the coefficient of variation of multi-band prediction results across experimental scenarios, is introduced to facilitate the quantitative comparison of model robustness. Experimental results show that model robustness depends on both landscape heterogeneity and input data characteristics. Fit-FC was the most robust among the three models, particularly in heterogeneous landscapes. These findings provide practical insights for selecting robust models for spatiotemporal fusion in real-world applications using multi-sensor high spatial resolution images.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".