## Abstract

Remote sensing data over a water body are related to the physical and biological properties of water constituents through inherent optical properties (IOPs). These IOPs characterize the absorption and scattering of the water column and are used as proxies to water quality variables. The scientific procedure to derive IOPs from ship/space borne remote sensing data can be divided into three steps: i- forward modeling, relates the radiometric data to the IOPs of the water column; ii- parametrization, defines the minimal set of IOPs whose values completely characterize the observed radiance; iii- inversion, derives the values of IOPs, and hence water quality variables, from radiometric data.

Reliable methods for uncertainty quantification of earth observation (EO) products of IOPs are important for sensor and algorithm validation, assessment, and operational monitoring. High accuracy in both observations and algorithms may reduce considerable ranges of errors. EO derived IOPs, however, have an inherent stochastic component. This is due to the dynamic nature of aquatic biogeophysical quantities, intrinsic fluctuations, model approximations, correction schemes, and inversion methods. Due to stochasticity of the measurements, as well as model approximations and inversion ambiguity, the retrieved IOPs are not the only possible set that caused the observed spectrum (Sydor et al., 2004). Instead, many other IOPs sets may be derived. Each of these sets has an unknown probability of being the derived product. The probability distribution of the estimated IOPs provides, therefore, all the necessary information about the variability and uncertainties of derived IOPs.

Reliable methods for uncertainty quantification of earth observation (EO) products of IOPs are important for sensor and algorithm validation, assessment, and operational monitoring. High accuracy in both observations and algorithms may reduce considerable ranges of errors. EO derived IOPs, however, have an inherent stochastic component. This is due to the dynamic nature of aquatic biogeophysical quantities, intrinsic fluctuations, model approximations, correction schemes, and inversion methods. Due to stochasticity of the measurements, as well as model approximations and inversion ambiguity, the retrieved IOPs are not the only possible set that caused the observed spectrum (Sydor et al., 2004). Instead, many other IOPs sets may be derived. Each of these sets has an unknown probability of being the derived product. The probability distribution of the estimated IOPs provides, therefore, all the necessary information about the variability and uncertainties of derived IOPs.

Original language | English |
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Title of host publication | Earth observation |

Editors | R.B. Rustamov, S.E. Salahova |

Place of Publication | Rijeka |

Publisher | InTech |

Pages | 229-254 |

ISBN (Print) | 978-953-307-973-8 |

DOIs | |

Publication status | Published - 2012 |