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. 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 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 for the ATSR sensor series

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


Globcarbon objectives l.jpg
GLOBCARBON Objectives for the ATSR sensor series

  • 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 for the ATSR sensor 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 for the ATSR sensor series

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 for the ATSR sensor series

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 for the ATSR sensor series

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 for the ATSR sensor series

  • 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 for the ATSR sensor series

  • 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 for the ATSR sensor series

  • 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 for the ATSR sensor series

  • 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 for the ATSR sensor series

  • 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


Swir thermal test l.jpg
SWIR Thermal Test for the ATSR sensor series

  • 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 for the ATSR sensor series


Results kalahari l.jpg
Results - Kalahari for the ATSR sensor series


Results siberia l.jpg
Results - Siberia for the ATSR sensor series


Conclusions l.jpg
Conclusions for the ATSR sensor series

  • 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.


Acknowledgements l.jpg
Acknowledgements for the ATSR sensor series

  • 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 for the ATSR sensor series

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