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Daytime Cloud Shadow Detection With MODIS

Study focused on using clear-sky reflectance maps to detect cloud shadows during daytime from MODIS images in West Africa, aiming to filter out shadow pixels without clouds. Research compared measured reflectances to clear-sky composites, filtering water and cloud pixels and setting a percentage threshold for shadow detection. Results included visual examples from Eastern and Western Africa, highlighting the challenge of spatial resolution and suggesting improvements for accurate shadow detection. Proposed enhancements include refining the land-water mask and adjusting the threshold criteria. There was an attempt to set the threshold using histograms and identify a possible natural threshold value for more precise shadow identification.

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Daytime Cloud Shadow Detection With MODIS

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  1. Daytime Cloud Shadow Detection With MODIS Denis Grljusic Philipps University Marburg, Germany Kathy Strabala, Liam Gumley CIMSS Paul Menzel NOAA / NESDIS Bryan Baum NASA Langley Research Center

  2. Goal: To use clear-sky reflectance maps to help filter clear-sky pixels that contain cloud shadows Note: Not trying to detect cloud shadows on clouds Approach: Comparison of measured to clear-sky weekly composite reflectances at 1.6 mm Data required: - MOD021km and MOD03 - MOD35 - Cloud mask - clear-sky weekly composite (25 km resolution, 8 bands, includes 1.6 mm)

  3. Approach • From Level1B data: • filter out water pixels ( land-water mask in MOD03 ) • filter out cloud pixels ( cloud mask MOD35 ) • Clear-Sky Weekly Composite: • creating subset of global 1.6 mm-daytime-reflectance composite map • Algorithm: • compare reflectance of clear-sky image and level1B image • set threshold as percentage of clear-sky value (e.g. 80%) • pixels with values lower than the threshold are flagged as shadow pixels

  4. MODIS-RGB-Composite of Eastern Africa (29 June2002, 07:45 UTC) 1.6-mm reflectance with water pixels filtered out of image

  5. Study area: West Africa Water pixels filtered using 1 km land-water database Clear-Sky Weekly Composite (25 km resolution) MODIS-RGB-Composite of Western Africa (28 June2002, 11:50 UTC)

  6. Location of example areas

  7. RGB-composite of area 1 • Mauritania • water clouds over desert • surface has a very high reflectance • little if any vegetation

  8. 0.65 mm-Reflectance 1.6 mm-Reflectance Surface brighter than clouds Clouds brighter than surface

  9. 0.65 mm-Reflectance Shadow detection Shadows are red

  10. 0.65 mm-Reflectance Shadow detection (combined with cloud mask) Shadows adjacent to clouds

  11. Location of example areas

  12. RGB-composite of area 2 • Mauritania - Senegal • desert-like area • crossed by Senegal river • mainly ice clouds

  13. 1.6 mm-Reflectance RGB - Composite • shadows on eastern edge • Senegal river not well detected • by land-water mask

  14. Shadow detection

  15. Shadow detection (combined with cloud mask) not detected shadows are often already detected as cloud

  16. 1.6 mm-Reflectance overview high diversity of soil types in the north (diverse reflectance)

  17. 1.6 mm-Reflectance overview including detected “cloud shadows” darker parts detected as shadows

  18. 1.6 mm-Reflectance overview including falsely detected cloud shadows and cloud mask Cloud mask indicates that shadows are falsely detected (possibly because of coarse resolution of clear-sky reflectance map)

  19. Spatial resolution problem 1 km - resolution MOD021km (1.6 mm) 25 km - resolution Clear-sky map

  20. Land-water mask Senegal river

  21. Conclusion • Initial attempt to detect cloud shadows by comparing • images with clear-sky composites is encouraging • Suggested improvements • shadows should be next to clouds • improve spatial resolution of clear-sky reflectance map • can we find a higher resolution land/water mask? • might improve detection of nondetected cloud shadows by checking nearest-neighbor pixels and relaxing threshold criteria

  22. Problems: • spatial resolution of clear-sky map • setting threshold • land-water mask • cloud mask Suggested improvements • shadows should be next to clouds • finding missing cloud shadows by pixel walking

  23. Additional

  24. Attempt to set the threshold by using histograms Question: Is there any “natural” threshold ?

  25. Histogram-based threshold Decreasing number of pixels Number of pixels Ratio of measured reflectance to clear-sky reflectance

  26. Histogram - 1.05 threshold Number of pixels Decreasing number of pixels ratio actual value to clear-sky value

  27. Preliminary indications • seems that threshold could be set by use of histograms • in this example it could be set higher than 0.8 • but... the share of false shadows might be higher • would help to have a clear-sky map with higher spatial resolution

  28. Additional Areas

  29. Location of example areas

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