DEPARTMENT OF LAND INFORMATION – SATELLITE REMOTE SENSING SERVICES CRCSI AC Workshop 15-18...
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DEPARTMENT OF LAND INFORMATION – SATELLITE REMOTE SENSING SERVICES
CRCSI AC Workshop 15-18 November 2005
Remote Sensing in Near-Real Time of Atmospheric
Water Vapour Using the Moderate Resolution Imaging
Spectroradiometer (MODIS)
B. K. McAtee
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• This work is part of CRCSI Project 4.1, Automatic Near Real-Time Thematic Mapping Based on MODIS.
• The aim of Project 4.1 as a whole is :
“To better utilise the spectral information from MODIS”
• This requires (1) atmospheric correction of remotely sensed data(2) operational processes in Near-Real Time (NRT)(3) optimal choice of available ancillary data
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cloudmasking
BRDFdetermination
atmosphericcorrection
vegetationparameter
atmosphericparameters
changedetection
land coverclassification
MODIS DataAn example :
The operational processing sequence at DLI
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MODIS
09/09/2003
01:27UTC
03:04UTC
Top-Of-Atmosphere-Reflectance
Bottom-Of-Atmosphere-Reflectance
What do atmospherically corrected data look like ?
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Taken from MOD09 ATBD Vermote and Vermeulen (1999)
H2O vapour is the primaryfocus of the current work
Flow chart for atmospheric correction algorithm
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The objective of this work is to define the optimum source of H2O vapour data for input to the NRT atmospheric correction process.
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• Two algorithms for NRT H2O vapour estimation from MODIS wereevaluated, here termed -
1) The WVNIR algorithm (Albert et al. (2005))2) The Sobrino algorithm (Sobrino et. al. (2003))
• The two algorithms employ a technique based on Near Infrared (NIR)data:
• Briefly,the ratio between the radiance measured in an NIR H2Oabsorption region and a second band outside theabsorption region may be related to the concentration of water vapour in the atmosphere
• MODIS has bands at 905 (Band 17), 936 (Band 18) and 940 nm (Band 19) within the NIR absorption and a band at 858 nm (Band 2) outside the region.
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2L
LR ii
2iiiiii RcRbaw
ii
iwfW
19
17
NIR radiance ratios along the 2-way optical pathare determined from MODIS
The ratios are related to atmospheric water vapourvia radiative transfer modeling
The water vapour estimate is obtained by a sensitivity-weighted average
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The algorithms producea water vapour map over WA at 1km resolution.
Precipitable W
ater (kgm
-2)
MODIS Terra 02:08 UTC17/12/2004
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Radiosonde Locations
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Validation of MODIS H2O algorithms
Sobrino et al.
WVNIR
IMA
PP
Clo
ud
M
ask
DL
I Clo
ud
M
ask
No
Clo
ud
M
ask
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Analysis of data rejected by the cloud mask
Choice of cloud mask may limit ‘good’ data by up to 25%
DLI Cloud MaskIMAPP Cloud Mask
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Validation of the MOD05 algorithm
MOD35 Cloud MaskNo Cloud Mask
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Algorithm comparisons
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The WVNIR data are a clear improvement over current data sources
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Error in MODIS Surface Reflectance at Nadir
-8-7-6-5-4-3-2-101234
-2 -1.5 -1 -0.5 0 0.5 1 1.5
dH2O (gcm^-2)
dR
ef
(%)
Band 1 Band 2 Band 3 Band 4 Band 5
Band 6 Band 7
Error in MODIS Surface Reflectance at 50 deg
-8-7-6-5-4-3-2-101234
-2 -1.5 -1 -0.5 0 0.5 1 1.5
dH2O (gcm^-2)
dR
ef
(%)
Band 1 Band 2 Band 3 Band 4 Band 5
Band 6 Band 7
Impact of uncertainty in H2O Ancillary data
Results @ nadir Band +/- 1 +/- 0.6 1 0.3% 0.7% 2 1.3% 0.8% 3 4.8% 3.1% 4 5 0.4% 0.2% 6 0.08% 0.1% 7 4.0% 3.0%
Results at 50 deg Band +/- 1 +/- 0.6 1 5% 3% 2 4% 2.5% 3 6.5% 4% 4 8% 5% 5 5.2% 3.2% 6 0.25% 0.15% 7 4.5% 3.5%
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DEPARTMENT OF LAND INFORMATION – SATELLITE REMOTE SENSING SERVICESDEPARTMENT OF LAND INFORMATION – SATELLITE REMOTE SENSING SERVICES
Conclusions
• The WVNIR algorithm with the regionally tuned DLI cloudmask optimises the accuracy of the H2O ancillary data necessaryfor the atmospheric correction of MODIS data in NRT.
• The WVNIR data exhibited an RMS error of 28% about a negligible bias with the DLI cloud mask applied. This is aresult comparable to other studies.
• Importantly, the regionally tuned DLI cloud mask limits the numberof ‘false positives’ returned thereby maximising the numberof NRT data available to downstream processes.
• The WVNIR data represent a significant improvement to the accuracy of the H2O data sources currently used.
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Validation of H2O from the BOM LAPS_PT375 model
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References
Vermote & Vermuelen (1999), Atmospheric correction algorithm:spectral reflectances (MOD09). Algorithm Theoretical BasisDocument Version 4.0. Department of Geography, University of Maryland.
Sobrino, El Kharraz & Li (2003), Surface temperature and watervapour retrieval from MODIS data. International Journal ofRemote Sensing, 24, 5161-5182.
Albert et. al. (2005), Remote sensing of atmospheric water vapourusing the Moderate Resolution Imaging Spectroradiometer.Journal of Atmospheric and Oceanic Technology, 22,309-314.