Atmospheric water vapour determination from remotely sensed hyperspectral data.
The accurate estimation of atmospheric water vapour and the subsequent derivation of surface spectral reflectance from hyperspectral VNIR-SWIR remotely sensed data is important for many applications. A number of algorithms have been developed for estimating water vapour content from remotely sensed...
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| Format: | Thesis |
| Language: | English |
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Curtin University
2002
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| Online Access: | http://hdl.handle.net/20.500.11937/701 |
| _version_ | 1848743455268798464 |
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| author | Rodger, Andrew P. |
| author_facet | Rodger, Andrew P. |
| author_sort | Rodger, Andrew P. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The accurate estimation of atmospheric water vapour and the subsequent derivation of surface spectral reflectance from hyperspectral VNIR-SWIR remotely sensed data is important for many applications. A number of algorithms have been developed for estimating water vapour content from remotely sensed hyperspectral data that do not require in-situ measurements. Two algorithms, the Continuum Interpolated Band Ratio (CIBR) and the Atmospheric Precorrected Differential Absorption (APDA) have proven to be highly effective at estimating atmospheric water vapour. Although highly successful, the two methods still exhibit unwanted or spurious results when challenging conditions are encountered. Such conditions include the estimation of atmospheric water vapour over dark targets, when uncorrected atmospheric aerosols are present and over surfaces with complex spectral signatures.A differential absorption method called the Transmittance Slope Ratio (TSR) has been developed that negates these problems. The TSR method is comprised of a weighted mean radiance that is defined between two atmospheric water absorption features which is divided by a reference channel radiance to produce a measurable ratio value. This, is turn, may be related to a reference curve, such that, the TSR value may be expressed as an atmospheric water vapour content. To test the TSR method over real terrains, AVIRIS and HyMap measured hyperspectral radiometric data were used. Three test sites were used in total with each site allowing different aspects of the water vapour estimation to be critically examined. The sites are, Jasper Ridge and Moffett Field in California and Brukunga in South Australia.The TSR method is found to significantly improve estimated atmospheric water vapour over dark targets (with less than 3.5 % error for reflectances as low as 0.5 %), improvement over nonlinear surfaces, and finally, improvement in water vapour estimation when atmospheric aerosol conditions are not well known. In the final case the TSR method is found to estimate atmospheric water vapour with an error of less than 2 % when a 5 km visibility is assumed to be 25 km. The final result is at least an order of magnitude better than the CIBR and APDA methods. |
| first_indexed | 2025-11-14T05:45:50Z |
| format | Thesis |
| id | curtin-20.500.11937-701 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:45:50Z |
| publishDate | 2002 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-7012017-02-20T06:41:21Z Atmospheric water vapour determination from remotely sensed hyperspectral data. Rodger, Andrew P. remotely sensed hyperspectral data atmospheric water vapour determination The accurate estimation of atmospheric water vapour and the subsequent derivation of surface spectral reflectance from hyperspectral VNIR-SWIR remotely sensed data is important for many applications. A number of algorithms have been developed for estimating water vapour content from remotely sensed hyperspectral data that do not require in-situ measurements. Two algorithms, the Continuum Interpolated Band Ratio (CIBR) and the Atmospheric Precorrected Differential Absorption (APDA) have proven to be highly effective at estimating atmospheric water vapour. Although highly successful, the two methods still exhibit unwanted or spurious results when challenging conditions are encountered. Such conditions include the estimation of atmospheric water vapour over dark targets, when uncorrected atmospheric aerosols are present and over surfaces with complex spectral signatures.A differential absorption method called the Transmittance Slope Ratio (TSR) has been developed that negates these problems. The TSR method is comprised of a weighted mean radiance that is defined between two atmospheric water absorption features which is divided by a reference channel radiance to produce a measurable ratio value. This, is turn, may be related to a reference curve, such that, the TSR value may be expressed as an atmospheric water vapour content. To test the TSR method over real terrains, AVIRIS and HyMap measured hyperspectral radiometric data were used. Three test sites were used in total with each site allowing different aspects of the water vapour estimation to be critically examined. The sites are, Jasper Ridge and Moffett Field in California and Brukunga in South Australia.The TSR method is found to significantly improve estimated atmospheric water vapour over dark targets (with less than 3.5 % error for reflectances as low as 0.5 %), improvement over nonlinear surfaces, and finally, improvement in water vapour estimation when atmospheric aerosol conditions are not well known. In the final case the TSR method is found to estimate atmospheric water vapour with an error of less than 2 % when a 5 km visibility is assumed to be 25 km. The final result is at least an order of magnitude better than the CIBR and APDA methods. 2002 Thesis http://hdl.handle.net/20.500.11937/701 en Curtin University fulltext |
| spellingShingle | remotely sensed hyperspectral data atmospheric water vapour determination Rodger, Andrew P. Atmospheric water vapour determination from remotely sensed hyperspectral data. |
| title | Atmospheric water vapour determination from remotely sensed hyperspectral data. |
| title_full | Atmospheric water vapour determination from remotely sensed hyperspectral data. |
| title_fullStr | Atmospheric water vapour determination from remotely sensed hyperspectral data. |
| title_full_unstemmed | Atmospheric water vapour determination from remotely sensed hyperspectral data. |
| title_short | Atmospheric water vapour determination from remotely sensed hyperspectral data. |
| title_sort | atmospheric water vapour determination from remotely sensed hyperspectral data. |
| topic | remotely sensed hyperspectral data atmospheric water vapour determination |
| url | http://hdl.handle.net/20.500.11937/701 |