Skip to main content

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

Buy Article:

$63.00 plus tax (Refund Policy)

Abstract:

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.

Document Type: Research Article

DOI: https://doi.org/10.1080/01431160701442062

Affiliations: 1: Institute for Space Sciences, Freie Universitaet Berlin, 12165 Berlin, Germany,Department of Civil Engineering, Sharif University of Technology, Tehran, Iran 2: Institute for Space Sciences, Freie Universitaet Berlin, 12165 Berlin, Germany 3: Informus GmbH, 13355 Berlin, Germany 4: Inland Waters Aquaculture Institute, Bandar Anzali, Iran 5: Department of Computer Engineering, Sharif University of Technology, Tehran, Iran 6: Department of Civil Engineering, Sharif University of Technology, Tehran, Iran 7: Department of Electrical Engineering, Amir Kabir University of Technology, Tehran, Iran

Publication date: 2007-01-01

More about this publication?
  • Access Key
  • Free ContentFree content
  • Partial Free ContentPartial Free content
  • New ContentNew content
  • Open Access ContentOpen access content
  • Partial Open Access ContentPartial Open access content
  • Subscribed ContentSubscribed content
  • Partial Subscribed ContentPartial Subscribed content
  • Free Trial ContentFree trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more