1 / 19

DEPARTMENT OF LAND INFORMATION – SATELLITE REMOTE SENSING SERVICES

DEPARTMENT OF LAND INFORMATION – SATELLITE REMOTE SENSING SERVICES. Remote Sensing in Near-Real Time of Atmospheric Water Vapour Using the Moderate Resolution Imaging Spectroradiometer (MODIS) B. K. McAtee. CRCSI AC Workshop 15-18 November 2005.

marnie
Download Presentation

DEPARTMENT OF LAND INFORMATION – SATELLITE REMOTE SENSING SERVICES

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. DEPARTMENT OF LAND INFORMATION – SATELLITE REMOTE SENSING SERVICES Remote Sensing in Near-Real Time of Atmospheric Water Vapour Using the Moderate Resolution Imaging Spectroradiometer (MODIS) B. K. McAtee CRCSI AC Workshop 15-18 November 2005

  2. 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

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

  4. Top-Of-Atmosphere-Reflectance Bottom-Of-Atmosphere-Reflectance What do atmospherically corrected data look like ? MODIS 09/09/2003 01:27UTC 03:04UTC

  5. Flow chart for atmospheric correction algorithm H2O vapour is the primary focus of the current work Taken from MOD09 ATBD Vermote and Vermeulen (1999)

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

  7. 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.

  8. 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

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

  10. Radiosonde Locations

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

  12. Analysis of data rejected by the cloud mask IMAPP Cloud Mask DLI Cloud Mask Choice of cloud mask may limit ‘good’ data by up to 25%

  13. Validation of the MOD05 algorithm No Cloud Mask MOD35 Cloud Mask

  14. Algorithm comparisons

  15. The WVNIR data are a clear improvement over current data sources

  16. 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%

  17. DEPARTMENT OF LAND INFORMATION – SATELLITE REMOTE SENSING SERVICES 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.

  18. Validation of H2O from the BOM LAPS_PT375 model

  19. 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, 5161-5182. Albert et. al. (2005), Remote sensing of atmospheric water vapour using the Moderate Resolution Imaging Spectroradiometer. Journal of Atmospheric and Oceanic Technology, 22, 309-314.

More Related