cloud screening and snow detection with meris
TRANSCRIPT
MERIS AATSR MEETING, ESRIN, 23.9.2008
Cloud screening and snow detection
with MERIS
Rene Preusker, Jürgen Fischer, Carsten Brockmann, Marco Zühlke, Uwe krämer, Anja Hünerbein
MERIS AATSR MEETING, ESRIN, 23.9.2008
Prelude
The following algorithm has been compiled in the frame of the ALBEDOMAP project (the generation of global 16 day spectral albedo maps; see presentation of J. Fischer). It is therefore limited to land surfaces!
MERIS AATSR MEETING, ESRIN, 23.9.2008
Outline
• Objectives• Algorithm
• Likelihood of cloudiness• blue band screening• cloud edge processing• snow restoration• analysis of short term variability• analysis of long term variability
• Summary
MERIS AATSR MEETING, ESRIN, 23.9.2008
Objective, or: Why did we need a new cloud detection?
There is a standard cloud detection in MEGS. It is using two prominent features of clouds:
1. clouds are white2. clouds are bright
from satellite.
This is by far not enough since:1. it misses many thin clouds 2. it treats often snow as clouds (same for sun-glint)3. it misses partly cloudy pixel
In 16 days averages (with 1-6 overpaths) only one missed cloud “destroys” any kind of average
MERIS AATSR MEETING, ESRIN, 23.9.2008
Example for insufficient MERIS L2 cloud detection
RGB MERIS L2
MERIS AATSR MEETING, ESRIN, 23.9.2008
Example for insufficient MERIS L2 cloud detection
RGB MERIS L2
MERIS AATSR MEETING, ESRIN, 23.9.2008
Algorithm: Likelihood of cloudiness
• Clouds are white • Clouds are bright• Clouds are higher than ground
Direct utilization of the O2 absorption band measured by MERIS at 760nm
MERIS AATSR MEETING, ESRIN, 23.9.2008
• radiance ratio between an absorbing and a windowchannel depends on photon path length
• photon path length is mainly determined by air-mass above the cloud = cloud top pressure
Oxygen-A band differential absorption
small ratio == no/low cloud
ratio close to 1 == high (opaque) cloud
MERIS AATSR MEETING, ESRIN, 23.9.2008
Sketch of Implementation
Cls are brightCls are white Cls are high
Refl 1
Refl 2
Refl 3
Refl 4
Refl 5
Refl 6
Refl 9
Refl 10
Refl 13
O2A
Srf.-Press
Wvl 11
Cloudiness Probability Estimator
(based on huge amount of RTM
simulations, ANN )
Likelihood of clouds
MERIS AATSR MEETING, ESRIN, 23.9.2008
Intermediate
Although the cloud probability processor is working much better than the standard L2 processing:
1. many, especially thin/broken clouds slipthrough
2. snow had often a high cloud probability
--> Need for some additional spectral filter to find partially cloudy pixel and a snow restoration
MERIS AATSR MEETING, ESRIN, 23.9.2008
Blue band thresholding
• Physical Background:• surfaces are very dark in the blue (412 nm)
exceptions are:• Clouds• Snow• Sunglint (not applicable) • very high aerosol loadings (difficult
atmospheric correction --> unwanted) • Implementation:
Simple threshold of 0.2 in (Rayleigh corrected) ρ
MERIS AATSR MEETING, ESRIN, 23.9.2008
Snow restoration
• Why wanted: Cloud probability and blue band threshold often falsely identify snow as cloud
• Physical Background:• snow shows absorption in SWIR, that other
surfaces don't show. Exceptions:• water, but this is very dark in NIR• few desert sites, but these are very dark in UV• some huge tropical clouds (climatology!!)
• This absorption is not very pronounced for the MERIS channels, but (thanks to MERIS's excellentradiometric resolution) sufficient!
MERIS AATSR MEETING, ESRIN, 23.9.2008
MERIS norm. diff. snow index:
mndsi := (13-14)/(13+14)
If (mndsi > 0.02) and not dark and not (sub)tropics
then SNOW
Snow restoration example
MERIS AATSR MEETING, ESRIN, 23.9.2008
Cloud edge processing
• Why wanted: • Cloud shadow can not be adequately
atmospherically corrected• partially cloudy pixel are more often close to cloudy
pixel• removal of adjacency effects (brightening) due to
clouds• Implementation:
• 4x4 neighborhood around clouds is automatically excluded
• cloud shadow is geometrically calculated from MERIS ctp and sun geometry
MERIS AATSR MEETING, ESRIN, 23.9.2008
Post processing
Although we squeezed out the spectral potential of MERIS we still missed many, especially thin/broken clouds!
--> Need to extend the feature vector by a temporal dimension. (This is of course only possible when producing some kind of L3)
MERIS AATSR MEETING, ESRIN, 23.9.2008
Analysis of 16 day (short term) temporal variability in the blue band
• Physical background:• Most land surfaces are very dark in UV. Exception: snow• --> small cloud contaminations are first seen in the UV
• Implementation (empirical):• If variability in 16 day bin exceeds 12.5% then each
ρ > (16_day_mean + 2 stdv) is excluded• If variability in 16 day bin exceeds 25% then each
ρ > (16_day_mean + 1 stdv) is excluded
MERIS AATSR MEETING, ESRIN, 23.9.2008
Analysis of the (long term) temporal variability in the blue band
• This test is not an test on ρ ! Instead it finds L3 means/albedos which are still cloud contaminated.
• Why wanted: 16 day (short term) variability is in some cases not applicable, in particular when the weather is not changing during that time period. Low 16 day variability because of “constant” partly cloud cover false negative
• Implementation: A L3 bin is assumed to be cloud contaminated if the white sky albedo at 412nm exceeds the annual median by 50%. (Plus some extra logic to avoid false positive by snow)
MERIS AATSR MEETING, ESRIN, 23.9.2008
MERIS AATSR MEETING, ESRIN, 23.9.2008
MERIS AATSR MEETING, ESRIN, 23.9.2008
Summary1. MERIS is NOT a god instrument to detect clouds
(missing SWIR an TIR channels) even though all standard MERIS algorithms need a preceding cloud screening or cloud detection. (OLCI will benefit from SLSTR!!!)
2. We developed an highly improved cloud detection using all spectral features some temporal informationalmost no textual information
3. The investigation of MERIS L3 16 day albedo (which is very sensitive) indicates high quality of cloud detection (however a comparison with thermal sensors and ground based measurements may look different)