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Application of the Active Learning Method for the estimation of geophysical variables in the Caspian Sea from satellite ocean colour observations

Shahraiyni, T ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1080/01431160701442062
  3. Publisher: Taylor and Francis Ltd , 2007
  4. Abstract:
  5. Remotely sensed data inherently contain noise. The development of inverse modelling methods with a low sensitivity to noise is in demand for the estimation of geophysical variables from remotely sensed data. The Active Learning Method (ALM) is well known to have a low sensitivity to noise. For the first time, ALM was utilized for the inversion of radiative transfer calculations with the aim of estimating chlorophyll a (Chl a), coloured dissolved organic matter (CDOM), and suspended particulate matter (SPM) in the Caspian Sea using MERIS (MEdium Resolution Imaging Spectrometer) data. ALM training is straightforward and fast. The ALM inversion models revealed the most relevant variables and showed a short processing time in operational applications for the estimation of geophysical variables. The mean absolute percentage errors of Chl a, SPM, and CDOM estimation using ALM inversion models were 44, 70, and 73%, respectively. According to the ALM results, it can be introduced as a new method for inverse modelling of ocean colour observations
  6. Keywords:
  7. Chlorophyll ; Geophysics ; Inverse problems ; Oceanography ; Radiative transfer ; Active Learning Method ; Caspian Sea ; Coloured dissolved organic matter ; Medium Resolution Imaging Spectrometer (MERIS) ; Ocean colour observations ; Remote sensing ; Chlorophyll a ; Dissolved organic matter ; Estimation method ; MERIS ; Modeling ; Noise ; Ocean color ; Radiative transfer ; Satellite data ; Suspended particulate matter ; Eurasia
  8. Source: International Journal of Remote Sensing ; Volume 28, Issue 20 , 2007 , Pages 4677-4683 ; 01431161 (ISSN)
  9. URL: https://www.tandfonline.com/doi/abs/10.1080/01431160701442062