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

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Presentation on theme: "Geoscience Australia Md Anisul Islam Geoscience Australia Evaluation of IMAPP Cloud Cover Mapping Algorithm for Local application. Australian Government."— Presentation transcript:

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

4 Geoscience Australia Group 1 (IR bands): BT 11, BT 13.9 & BT 6.7 Group 2 (Thermal band differences): Trispectral Test BT 11 - BT 12, BT 11 – BT 3.9 Group 3 (Reflective bands): R 0.66 or R 0.87 & R 0.87 /R 0.66 Group 4 (NIR thin Cirus): R 1.38 Group 5 (IR thin Cirus): BT 3.7 – BT 12 Clear sky conservative approach of Initial cloud mask generation G i=1,5 = min[F i ] & Initial Cloud mask confidence level = (Product of G i=1,5 ) -5 Where, G = Group Three thresholds used in cloud screening Confidence Level (F): > 0.95 & ≤ 0.99 - Uncertain clear ≥ 0.66 & ≤ 0.95 - Uncertain cloud

5 Geoscience Australia 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) of BT 11-3.9 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 (R 0.87 ) applied in Collection 5 algorithm as against spectral tests of Land (R 0.66 & R 0.87 /R 0.66 ) applied in Collection 4 algorithm

6 Geoscience Australia  Threshold values: For BT 13.9 test are 224, 226 and 228 (Kelvin) for Collection 4 algorithm and are 222, 224 and 226 (Kelvin) for Collection 5 algorithm For R 0.87 /R 0.66 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 Geoscience Australia Full swath images acquired from the Orbit covering maximum Land areas of Australia Monthly MODIS image acquired between September 2004 to April 2005 Acquisition dateOrbit no 2 Sept 200425043 20 October 200425742 21 November 200426208 23 December 200426674 9 February 200527373 30 April 200528538 MODIS DATA ASSESSED IN THE PROJECT: MODIS TERRA images capturing high temporal and spatial variation of Land areas of Australia

8 Geoscience Australia  Visual assessment of final Cloud Mask using: 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. Methodology:

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

10 Geoscience Australia 20 October 2004 Confident Clear Probably Clear Uncertain Cloudy

11 Geoscience Australia 21 November 2004 Confident Clear Probably Clear Uncertain Cloudy

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

13 Geoscience Australia 23 December 2004 Confident Clear Probably Clear Uncertain Cloudy

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

15 Geoscience Australia 9 February 2005 Confident Clear Probably Clear Uncertain Cloudy

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

17 Geoscience Australia 2 September 2004 Confident Clear Probably Clear Uncertain Cloudy

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

19 Geoscience Australia Spectral plots of the cloud free samples taken from Multitemporal images. 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)

20 Geoscience Australia 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 BT 11 -BT 3.9 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 R 0.87 test. Modification of threshold values of R 0.87 test and or BT 11 threshold values used in the clear sky restoral test may be modified to improve the results.


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