Assessing cloud mask fusion strategy

User’s approach uses GEO satellite cloud mask at 2km scale for coarse screening and ECOSTRESS at 70m for refinement. We need to consider potential problems, like GEO’s false negatives or scale mismatches. I’ll suggest methods like creating probabilities, morphological changes for thin clouds, and resampling steps, weighing pros and cons. I’ll also propose a 3-case logic to make decisions clear in different situations.

Considering union vs intersection for cloud mask fusion

The user proposes using GEO clear for overriding ECOSTRESS cloudy to reduce false positives, but that risks missing small clouds. GEO might misclassify over cold surfaces, though it likely uses multi-spectral data. The idea is to use ECOSTRESS for edge refinement only within GEO cloud pixels, yielding a more conservative approach while maintaining key features like low-temp surfaces.

Resampling and addressing potential errors in cloud mapping

When resampling from 2km to 70m, we need to handle cases where GEO says "clear" but small clouds exist. A "probably clear" threshold could help, along with probabilistic Bayesian fusion—treating GEO as prior and refining with ECOSTRESS data. I'll also account for discrepancies like parallax, timing differences, and cloud misalignment.

Evaluating cloud detection with comparisons and morphological operations

I’d recommend testing the performance by comparing against higher-resolution data like MODIS and Landsat, or even manual labels. For cloud refinement, consider dilation for GEO cloud areas to account for coarse pixels. For GEO clear, use ECOSTRESS only if cloud confidence is high and avoid low-temp regions to prevent misclassification. Adjust thresholds for better accuracy, possibly incorporating NDVI for additional context. ECOSTRESS' thermal bands provide useful data, but might be confused by cold surfaces.

Thought for 30s

Refining evaluation of user's 3-step plan

The user’s 3-step plan is coherent, but refinement is needed. For step 1, dilating by one pixel might be too small or large, depending on scale (e.g., 2 km). The goal is to ensure no cloud areas are missed, but treating other areas as clear could still overlook distant clouds. Step 2 allows override with high confidence from ECOSTRESS, which makes sense for increasing reliability.

Refining cloud mask evaluation

The user’s approach seems solid for handling small clouds outside the GEO mask — especially when ECOSTRESS is confidently identifying clouds. They avoid false positives by not letting ECOSTRESS 2 override outside the buffer zone. In Step 3, using ECOSTRESS 2 or 3 within the buffer makes sense. I think the key could be treating the original GEO cloud region similarly to the buffer for consistency.

Incorporating elevation and emissivity considerations

I’m considering differentiating areas where cloud is surely present, possibly ignoring ECOSTRESS in those regions. Also, the ECOSTRESS guide suggests using elevation to refine the mask — treating confidence 2+3 as cloud below 2 km and 3 exclusively above. I’ll suggest incorporating the guide’s elevation and emissivity thresholds for more accuracy, especially for cold surfaces or high mountains.

Structured suggestions for algorithm refinement

1. Confirm interpretations of confidence values (0 = clear, 1 = probably clear, 2 = probably cloudy, 3 = clear), and reference the Cloud_final definition with the elevation threshold.

2. Evaluate the steps:

   • For GEO dilation, consider adjusting the radius based on factors like wind or distance.
   • Step 2's ECOSTRESS override seems consistent, but keep in mind that thin clouds flagged as 2 might be missed.
   • For the buffer zone, apply ECOSTRESS 2/3 as cloud, consistent with Cloud_final for low elevations.

Decision logic for cloud masking

1. Follow the product recommendation for high elevation or snow/ice: only treat eco_conf=3 as cloud.
2. Suggest pseudocode:
   • Build GEO mask at ECOSTRESS grid and dilate by n pixels.
   • Outside dilated area: clear unless eco_conf=3 (consider elevation).
   • Inside dilated area: eco_conf ≥ 2 = cloud, else clear.
3. Elevation condition: above 2000m, treat eco_conf=3 as cloud even in buffer.
4. Optional: compute morphological closing to fill holes.
5. Consider tri-state output for eco_conf=2.
6. Incorporate guidelines for cloud tolerance: zero tolerance = confident clear, some tolerance = confident cloudy.

Thought for 57s