DEPARTMENT OF LAND INFORMATION – SATELLITE REMOTE SENSING SERVICES CRCSI AC Workshop 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

cloud masking BRDF determination atmospheric correction vegetation parameter atmospheric parameters change detection land cover classification MODIS Data An example : The operational processing sequence at DLI

MODIS 09/09/ :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 primary focus 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 were evaluated, 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 H2O absorption region and a second band outside the absorption 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.

NIR radiance ratios along the 2-way optical path are determined from MODIS The ratios are related to atmospheric water vapour via radiative transfer modeling The water vapour estimate is obtained by a sensitivity- weighted average

The algorithms produce a water vapour map over WA at 1km resolution. Precipitable Water (kgm -2 ) MODIS Terra 02:08 UTC 17/12/2004

Radiosonde Locations

Validation of MODIS H2O algorithms Sobrino et al. WVNIR IMAPP Cloud MaskDLI Cloud Mask No Cloud Mask

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

Impact of uncertainty in H 2 O Ancillary data nadir Band +/- 1 +/ % 0.7% 2 1.3% 0.8% 3 4.8% 3.1% % 0.2% % 0.1% 7 4.0% 3.0% Results at 50 deg Band +/- 1 +/ % 3% 2 4% 2.5% 3 6.5% 4% 4 8% 5% 5 5.2% 3.2% % 0.15% 7 4.5% 3.5%

DEPARTMENT 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 necessary for 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 a result comparable to other studies. Importantly, the regionally tuned DLI cloud mask limits the number of false positives returned thereby maximising the number of 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 Basis Document Version 4.0. Department of Geography, University of Maryland. Sobrino, El Kharraz & Li (2003), Surface temperature and water vapour retrieval from MODIS data. International Journal of Remote Sensing, 24, Albert et. al. (2005), Remote sensing of atmospheric water vapour using the Moderate Resolution Imaging Spectroradiometer. Journal of Atmospheric and Oceanic Technology, 22,