69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
"""Pre-selection: NDVI-based cloud/flaw filtering for S2 and S3 data."""
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import json
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from pathlib import Path
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from datetime import datetime
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WINDOW_DAYS = 14
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MIN_WINDOW_SIZE = 3
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THRESHOLDS = {"aggressive": {"threshold": 0.3, "delta": 0.15}, "nonaggressive": {"threshold": 0.2, "delta": 0.25}}
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def detect_clouds(season, site_name, cleaning_strategy="aggressive"):
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"""Filter cloud-covered/flawed S2 and S3 files using NDVI thresholds."""
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output_file = Path(f"data/{site_name}/{season}/clouds_{cleaning_strategy}.json")
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clouds = {"s2": [], "s3": []}
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thresholds = THRESHOLDS[cleaning_strategy]
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for source in ["s2", "s3"]:
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timeseries_file = Path(
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f"data/{site_name}/{season}/raw/ndvi/{source}/timeseries.json"
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)
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if not timeseries_file.exists():
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print(f"[CLOUDS-{source.upper()}] No timeseries.json found")
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continue
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print(f"[CLOUDS-{source.upper()}] Processing {timeseries_file}...")
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with open(timeseries_file) as f:
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timeseries = json.load(f)
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# Flag entries with ndvi: None as outliers (bad/invalid data)
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for e in timeseries:
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if e.get("ndvi") is None:
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clouds[source].append(e["filename"])
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entries = [
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(e, datetime.fromisoformat(e["date"].replace("Z", "+00:00")))
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for e in timeseries
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if e.get("ndvi") is not None
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]
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for entry, entry_date in entries:
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window_ndvi = [
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e["ndvi"]
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for e, d in entries
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if abs((d - entry_date).days) <= WINDOW_DAYS
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]
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if len(window_ndvi) < MIN_WINDOW_SIZE:
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continue
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max_ndvi = max(window_ndvi)
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threshold = max_ndvi - thresholds["delta"]
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if entry["ndvi"] < threshold and entry["ndvi"] < thresholds["threshold"]:
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clouds[source].append(entry["filename"])
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print(
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f"[CLOUDS-{source.upper()}] Found {len(clouds[source])} cloud-covered files"
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)
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output_file.parent.mkdir(parents=True, exist_ok=True)
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with open(output_file, "w") as f:
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json.dump(clouds, f, indent=2)
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print(f"[CLOUDS] Saved: {output_file}")
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# Alias for backward compatibility
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preselect = detect_clouds
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