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Establishing the GLOBCARBON cloud detection system over land for the ATSR sensor series. Stephen Plummer (IGBP@ESA) . Carbon Data Assimilation. To feed in to this Earth observation must deliver long time series estimates of global vegetation behaviour. GLOBCARBON Objectives.

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

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Establishing the globcarbon cloud detection system over land for the atsr sensor series l.jpg

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

Stephen Plummer (IGBP@ESA)


Carbon data assimilation l.jpg

Carbon Data Assimilation

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


Globcarbon objectives l.jpg

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


Requirements atsr series l.jpg

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


Atsr 2 cloud system l.jpg

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)]


Atsr cloud mask l.jpg

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


A new cloud detection system l.jpg

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


Snow detection l.jpg

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


Thermal gross cloud test l.jpg

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


Thin cloud test l.jpg

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


Dynamic vegetation test l.jpg

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


Apollo final probability l.jpg

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


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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


Results amazon l.jpg

Results - Amazon


Results kalahari l.jpg

Results - Kalahari


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Results - Siberia


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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.


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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.


Failures l.jpg

Failures

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


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