remote-sensing/TROUBLESHOOT_S2_LOADING.md

📋 Quick Troubleshooting: Sentinel-2 Loading

Before You Run

✅ Verify dask cluster is running:

# Cell 2 output should show:
# Scheduler: 127.0.0.1:8786 (or gateway address)
# Workers: 4 (or your configured number)

✅ Verify S3 access is configured:

# Cell 2 should complete without errors
# If you see authentication errors, check credentials

Running Notebook 01

Step-by-step execution:

Cell 1: Introduction (markdown, no action)

Cell 2: Initialize Dask + Datacube

• Wait for cluster to initialize (10-30 seconds)
• Should show worker status

Cell 3: Set coordinates

• Automatic, takes <1 second

Cell 4: Diagnostic Check ⭐ RUN THIS FIRST

• Purpose: Verify datacube can find scenes without loading
• Expected output:

  Available S2 products:
    name           description
  s2_l2a    Sentinel-2 L2A Data
  
  📊 Metadata check for Jan 2023:
     Found 28 scenes
     First scene: 2023-01-15 10:30:45
     Bounds: BoundingBox(...)
     CRS: EPSG:32648
  

• If fails: S3 connection issue - check credentials in Cell 2

Cell 5: Load Sentinel-2 Data (THE FIXED CELL)

• Expected duration: 5-15 minutes (depending on workers)
• Watch for: Monthly progress bars

  [01/13] 2022-09-01 → 2022-10-01  ✓ 32 scenes
  [02/13] 2022-10-01 → 2022-11-01  ✓ 28 scenes
  ...
  

• Expected final output:

  ✅ Success! Shape: {'time': 396, 'y': ~10000, 'x': ~10000}
     Memory: 15-20 GB
  

Common Issues & Fixes

❌ "Still getting huge dimensions error"

Symptom:

Error: shape (396, 563539, 992108)

Cause: Datacube function is still loading full tiles

Fixes (in order):

1. Run Cell 4 diagnostic → Check actual bounds returned
2. Verify load_s2l2a_with_offset() in new_import_ODC.py includes spatial subsetting
3. Add manual clipping:

   # After line: monthly_data = load_s2l2a_with_offset(...)
   # Add this:
   if monthly_data.sizes['y'] > 15000:
       print(f"⚠️  Clipping oversized data: {monthly_data.dims}")
       monthly_data = monthly_data.sel(
           x=slice(longtitude_range[0], longtitude_range[1]),
           y=slice(latitude_range[0], latitude_range[1]),
       )
   

❌ "Cell 4 says 0 scenes found"

Cause: S3 data may not exist for your region/dates

Fixes:

1. Check if S3 bucket/path is correct
2. Try different time range (e.g., "2023-01-01" to "2023-12-31")
3. Verify coordinates are in correct order: (longitude_min, longitude_max), (latitude_min, latitude_max)

❌ "Dask workers running out of memory"

Symptom:

MemoryError during ...
Killed process (out of memory)

Quick fix:

1. Reduce chunk size in Cell 5:

   'dask_chunks': {'x': 256, 'y': 256, 'time': 1}  # Smaller chunks
   

2. Or reduce number of workers in Cell 2:

   cluster, client = notebook_utils.initialize_dask(
       use_gateway=True, 
       workers=(1, 5)  # Reduce from (1, 10)
   )
   

3. Or load fewer months at once - split Cell 5 manually

❌ "One month loaded but then it fails"

Cause: One S3 object is corrupted/missing

Expected behavior:

• Code continues to next month (has try-except)
• Check output logs for which month failed
• You can manually skip it by removing from date_ranges list

Is this okay? ✅ Yes! If 12/13 months load, you have 380+ scenes (good dataset)

⚠️ "Still taking too long / worker still slow"

Cause: Network latency from S3, or insufficient workers

Options:

1. Increase workers: Cell 2 workers=(1, 15) (if hardware allows)
2. Enable rechunking: Add to Cell 5:

   monthly_data = monthly_data.rechunk({'x': 'auto', 'y': 'auto'})
   

3. Check Dask dashboard: Ask instructor for URL (port 8787)

Success Criteria ✅

After Cell 5 completes, you should have:

1. Variable data exists and is not None
2. Dimensions match AOI:

   time: 396 (or close - some months may have 0 scenes)
   y:    ~10000 pixels (±10%)
   x:    ~10000 pixels (±10%)
   

3. No memory errors (or only 1-2 skipped months)
4. Dask workers still healthy (can continue to next cells)

Next: Cells 6-10

Once Cell 5 succeeds, remaining cells should work automatically:

• Cell 6: Cloud masking
• Cell 7: NDVI calculation
• Cell 8: Fill NaN values
• Cell 9: Monthly aggregation
• Cell 10: Sentinel-1 loading

These cells don't involve loading new data, just processing the data variable.

---

Need help? Check:

1. /home/x79/CSIROBoeingPhase5-Vietnam/MEMORY_FIX_EXPLAINED.md (detailed explanation)
2. Dask dashboard if available
3. Datacube documentation: dc.list_products()