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

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.