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

• 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

cloudmasking

BRDFdetermination

atmosphericcorrection

vegetationparameter

atmosphericparameters

changedetection

land coverclassification

MODIS DataAn example :

The operational processing sequence at DLI

MODIS

09/09/2003

01:27UTC

03:04UTC

Top-Of-Atmosphere-Reflectance

Bottom-Of-Atmosphere-Reflectance

What do atmospherically corrected data look like ?

Taken from MOD09 ATBD Vermote and Vermeulen (1999)

H2O vapour is the primaryfocus of the current work

Flow chart for atmospheric correction algorithm

The objective of this work is to define the optimum source of H2O vapour data for input to the NRT atmospheric correction process.

• 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.

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

The algorithms producea water vapour map over WA at 1km resolution.

Precipitable W

ater (kgm

-2)

MODIS Terra 02:08 UTC17/12/2004

Radiosonde Locations

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

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

Validation of the MOD05 algorithm

MOD35 Cloud MaskNo Cloud Mask

Algorithm comparisons

The WVNIR data are a clear improvement over current data sources

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%

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.

Validation of H2O from the BOM LAPS_PT375 model

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.