1 / 20

Md Anisul Islam Geoscience Australia

Australian Government Geoscience Australia. Evaluation of IMAPP Cloud Cover Mapping Algorithm for Local application. Md Anisul Islam Geoscience Australia. Objective of the study:

dacey-buck
Télécharger la présentation

Md Anisul Islam Geoscience Australia

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Australian Government Geoscience Australia Evaluation of IMAPP Cloud Cover Mapping Algorithm for Local application. Md Anisul Islam Geoscience Australia

  2. Objective of the study: Evaluation of IMAPP Cloud Cover Mapping algorithm (Collection 4 & 5) for local application to utilise the cloud mask for generating higher level Land application products.

  3. Image Pixel Ancillary data Ecosystem file Land/water map DEM Daily snow/ice SST Global data assimilation system (GDAS1) Labelling the pixels to Surface types Water Land Desert Snow/Ice Clear Sky Restoral tests Sunglint region Shallow water Desert & Land 10 Spectral Cloud tests Group 1 Group 2 Group 3 Group 4 Group 5 Cloudy Pixels Initial Cloud mask obtained from Clear sky conservative approach Clear Pixels Final Cloud mask Schematic diagram of IMAPP MODIS Cloud Cover Algorithm

  4. Group 1 (IR bands): BT11, BT13.9 & BT6.7 Group 2 (Thermal band differences): Trispectral Test BT11- BT12, BT11 – BT3.9 Group 3 (Reflective bands): R0.66 or R0.87 & R0.87/R0.66 Group 4 (NIR thin Cirus): R1.38 Group 5 (IR thin Cirus): BT3.7 – BT12 Three thresholds used in cloud screening Confidence Level (F): > 0.95 & ≤ 0.99 - Uncertain clear ≥ 0.66 & ≤ 0.95 - Uncertain cloud Clear sky conservative approach of Initial cloud mask generation Gi=1,5 = min[Fi] & Initial Cloud mask confidence level = (Product of Gi=1,5)-5 Where, G = Group

  5. Difference between Collection 4 and Collection 5 Cloud Mask Algorithms • Surface type Labelling scheme: • Pixels belonging to Ecosystems: Savanna (Woods), Hot & Mild Grasses and • Shrubs, Woody Savana are labelled as surface type Land in Collection 4 • algorithm, are labelled as Desert for Australian Continent (latitude 11.0 - 40.0 (S) • & longitude 110.0 -155.0 (E)) in Collection 5 algorithm. • Major Effects on the above pixels in Collection 5 algorithm are : • Subject to different threshold values(for Desert: -20, -18, -16) ofBT11-3.9 • spectral test in Collection 5 algorithm as against threshold values of Land • (-14, -12, -10) in Collection 4 algorithm • Subject to spectral test of Desert (R0.87) applied in Collection 5 algorithm as • against spectral tests of Land (R0.66 & R0.87/R0.66 ) applied in Collection 4 • algorithm

  6. Threshold values: • For BT13.9 test are 224, 226 and 228 (Kelvin) for Collection 4 algorithm and are 222,224 and 226 (Kelvin) for Collection 5 algorithm • For R0.87/R0.66 test are 0.55, 0.40 & 0.30 for Water in Collection 4 algorithm and are 0.65, 0.55,& 0.45 in Collection 5 algorithm • Additional ancillary data used in Collection 5 Cloud Mask Algorithm: • NOAA optimum Interpolation (OI) Sea Surface Temperature (SST) V2 product at 1 degree resolution – helps to improve the cloud mask over ocean at night. • Global assimilation system (GDAS1) for retrievals of temperature and moisture profile – helps to improve the cloud mask over many areas of the land.

  7. MODIS DATA ASSESSED IN THE PROJECT: MODIS TERRA images capturing high temporal and spatial variation of Land areas of Australia Full swath images acquired from the Orbit covering maximum Land areas of Australia Monthly MODIS image acquired between September 2004 to April 2005

  8. Methodology: • Visual assessment of final Cloud Maskusing: RGB of visual bands • Gray scale image bands used in the spectral test • Spectral Analysis of data from sample sites in areas where there are errors as determined by visual assessment • Convertion of image band Digital numbers (DN) to the units of the thresh values: • Convertion of reflective image band DN to reflectance unit & Thermal image band DN to Kelvin • Extraction of sample data from the images • Scatterplots of the bands used in spectral tests having errors versus sample site attributes to determine the amount of errors.

  9. 20 October 2004 Confident Clear Probably Clear Uncertain Cloudy Relective bands 1 4 3 Inverse BT11 – BT3.9 Collection 4 Cloud Mask Collection 5 Cloud Mask

  10. 20 October 2004 Confident Clear Probably Clear Uncertain Cloudy

  11. 21 November 2004 Confident Clear Probably Clear Uncertain Cloudy

  12. 21 November 2004 Relective band 26 (R1.38 Thin Cirus test) Relective bands 1 4 3 Confident Clear Probably Clear Uncertain Cloudy Inverse BT11 – BT3.9

  13. 23 December 2004 Confident Clear Probably Clear Uncertain Cloudy

  14. 23 December 2004 Relective bands 1 4 3 Relective band 26 (R1.38 Thin Cirus test) Confident Clear Probably Clear Uncertain Cloudy Inverse BT11 – BT3.9

  15. 9 February 2005 Confident Clear Probably Clear Uncertain Cloudy

  16. 9 February 2005 Relective bands 1 4 3 Relective band 26 (R1.38 Thin Cirus test) Confident Clear Probably Clear Uncertain Cloudy Inverse BT11 – BT3.9

  17. 2 September 2004 Confident Clear Probably Clear Uncertain Cloudy

  18. 2 September 2004 Relective bands 1 4 3 Relective band 26 (R1.38 Thin Cirus test) Confident Clear Probably Clear Uncertain Cloudy Inverse BT11 – BT3.9

  19. THCCLD - threshold corresponding to High Confident Cloudy pixels (α) TV - threshold value for pass or fail (β) THCCLR - threshold corresponding to High Confident Clear pixels (γ) Spectral plots of the cloud freesamples taken fromMultitemporal images.

  20. Conclusions and Recommendations • Collection 5 cloud mask significantly reduces misclassification of the clear pixels as cloudy pixels relative to Collection 4 Cloud mask. However, collection 5 cloud mask may detect patchy Low cloud as uncertain cloud and uncertain clear pixels (October 04). • Collection 5 cloud mask may be highly sensitive in detecting High thin clouds (November 04, December 04), which may be better evaluated using additional data. • Collection 5 cloud mask may have small errors of misclassifying clear pixels as cloud in the surface type Land (February 05), which may be attributed to BT11-BT3.9 test. Modification of the threshold values of this test may further improve the cloud mask. • Clear pixels of bright desert and salt lake may be misclassified as uncertain clear pixels and cloudy pixels (Sep 04 & April 05), which may be attributed to Reflectance R0.87 test. Modification of threshold values of R0.87 test and or BT11 threshold values used in the clear sky restoral test may be modified to improve the results.

More Related