Establishing the GLOBCARBON cloud detection system over land for the ATSR sensor series

1. Establishing the GLOBCARBON cloud detection system over land for the ATSR sensor series Stephen Plummer (IGBP@ESA)

2. Carbon Data Assimilation To feed in to this Earth observation must deliver long time series estimates of global vegetation behaviour.

3. GLOBCARBON Objectives • develop a service quasi-independent of the original Earth Observation source. • focus on a system to estimate: • Burned area • fAPAR and LAI • Vegetation growth cycle • cover six complete years:1998 to 2003 (now up to 2007) • cover VEGETATION, ATSR-2, ENVISAT (AATSR, MERIS) • be applicable to existing archives and future satellite systems • be available at resolutions of ¼, ½ degree and 10km with statistics • build on the existing research experience

4. Requirements – ATSR Series • Processing of only those pixels not affected by cloud, snow, cloud shadow or atmosphere • This requires processing of approximately 500,000 ATSR-2 scenes and 25,000 AATSR striplines • All processing must be automatic • GLOBCARBON requires the implementation of a effective cloud detection system over land but the existing system was designed for oceans

5. ATSR-2 Cloud system Original RGB (1.6, 0.87, 0.67) ATSR-2 Cloud masked RGB [Remaining cloud has same cloud flag as clear land (1027)]

6. Cloud Mask = 9 tests Implemented on Land = 4 tests Thin cirrus 11/12μm Medium/high level 3.7/12μm (not daytime) Fog/low status 3.7/11μm (not daytime) 11μm spatial coherence 11μm ATSR Cloud Mask RESULT = NEED A NEW CLOUD DETECTION SYSTEM

7. A ‘new’ Cloud Detection System SNOW APOLLO 2003 • GLOBCARBON tight schedule – adopt existing methods • GLOBCARBON high processing throughput – simple methods, low computer cost • Tested CLAVR, APOLLO (2003), GLOBSCAR • APOLLO (2003) chosen with added ‘bells and whistles’ • Added pre-APOLLO snow detection Dynamic Vegetation Test Thermal Gross Cloud Thin Cloud Prob 1 Prob 2 Prob 3 Merge Probs Prob APOLLO Cloud Mask Thermal-SWIR Histogram

8. Snow Detection • Implementation of MODIS method (Hall et al. 2001) • Requirement: GREEN, RED, NIR, SWIR, 11μm • Pre-screening of cloud • Based on Normalised Difference Snow Index: Basic Snow in Forest

9. Thermal Gross Cloud Test • As with ATSR-2 cloud but implemented over land • Requirement: RED, NIR, 11μm, 12μm • NIR/RED used to mask off pixels not cloud (NIR/RED less than 1.6). • Threshold found as 2K less than minimum BT at 11μm • Threshold applied to BT at 12μm • Probability range between threshold and threshold minus 20K RED = CLOUD, GREEN = POSS CLOUD, BLUE = CLEAR

10. Thin Cloud Test • As with ATSR-2 cloud but implemented over land • Requirement: 11μm, 12μm, LUT, SAT ZEN • Threshold from LUT of Thermal Brightness Difference and secant of SAT ZEN • Probability range between threshold ± half min distance between minimum or maximum of TBD for image RED = CLOUD, GREEN = POSS CLOUD, BLUE = CLEAR

11. Dynamic Vegetation Test • Requirement: RED, 11μm • Test 1: BT11 < Threshold of 274.5K (or if desert 290K) and RED > 0.2 • Test 2: RED >0.6 • Probability range = 0.1 ± threshold (RED) and 5K ± threshold (BT11) • Probability Test 1 product of 2 parts • Final Probability maximum of Test 1 and Test 2 RED = CLOUD, GREEN = POSS CLOUD, BLUE = CLEAR

12. APOLLO Final Probability • Clear pixels: the probability is 0 in all three tests • Cloud pixels: the probability of 1 occurs in any of the tests • Suspect pixels: the probability is maximum probability for values between 0 and 1 • Pixels are masked where final probability > 0.5 RED = CLOUD, GREEN = POSS CLOUD, BLUE = CLEAR

13. SWIR Thermal Test • Performed on pixels not flagged by APOLLO • Requirement: SWIR, 11μm • Number APOLLO pixels > 30 Number clear pixels > 2620 Minimum BT11 < 280K. • Thresholds based on histogram maxima • Probability is product of probabilities for BT11 and SWIR • Pixels masked if probability > value already present

14. Results - Amazon

15. Results - Kalahari

16. Results - Siberia

17. Conclusions • ATSR/AATSR Cloud Detection system was developed to serve GLOBCARBON based on APOLLO (2003) and MODIS snow detection • The system proved effective in 3 different biomes and with highly variable cloud when tested on 49 ATSR-2 images. In only one case did was the system not sufficiently effective. • Further testing is required since the examples are limited in time (1 month) and may not represent all cases. • The coefficients used in the system are exactly as used in MODIS snow detection and APOLLO (2003). These may need adjusting for spectral characteristics of the ATSR series. • The snow detection in particular misses far too much snow while also detecting some cloud. • The system has been implemented for processing 500,000 ATSR-2 scenes and 25,000 AATSR orbits.

18. Acknowledgements • Many thanks to: • Walter Heyns at VITO for testing the IDL code and pointing out errors prior to its implementation in the GLOBCARBON processor. • the developers of APOLLO – Karl Kriebel, Gerhard Gesell, Martina Kästner and Herman Mannstein. • the MODIS snow team for providing clear details for the implementation of the algorithm • ESA, especially Olivier Arino, for supporting the GLOBCARBON idea through thick and thin.

19. Failures Cloud dominates the SWIR-BT11 histograms so the determination of the thresholds is not effective