10:15 AM - 10:30 AM
[ACG42-05] Performance Validation and Characterization of IOPs Estimation Algorithms by Water Mass Classification Based on Optical Properties
Keywords:Ocean Color Remote Sensing, Inherent Optical Properties, Algorithms Validation, Water Mass classification
In ocean color remote sensing, inherent optical properties (IOPs), which describe the light absorption and scattering properties of in-water materials, are estimated from the remote sensing reflectance (Rrs) just above the sea surface detected by satellites via an IOPs estimation algorithm. Since IOPs are used to estimate biogeochemical indicators such as primary production and dissolved carbon stocks in the oceans, they are expected to be used to monitor changes in the marine environment due to climate change (Werdell et al., 2018).
Various IOPs estimation algorithms have been developed and their performance has been validated for the global ocean. However, previous studies related to the development and validation of IOPs estimation algorithms have mainly focused on the open ocean, and there is a lack of knowledge on the applicability of the algorithms to high turbidity water bodies such as coastal areas and lakes. In general, IOPs estimation algorithms assume the spectral shape of IOPs empirically, which may limit their applicability to coastal areas and lakes with complex optical properties. Therefore, it is important to classify and select an IOPs estimation method suitable for the target water mass.
In this study, we validated the performance of several IOPs estimation algorithms with different methods using a global dataset covering a wide range of optical properties (Valente et al., 2019) and a Japanese-coastal dataset with high turbidity optical properties. Water masses were statistically classified based on their apparent optical properties (AOPs) and IOPs, and the applicability of various IOPs estimation algorithms to these classification results was discussed.
Various IOPs estimation algorithms have been developed and their performance has been validated for the global ocean. However, previous studies related to the development and validation of IOPs estimation algorithms have mainly focused on the open ocean, and there is a lack of knowledge on the applicability of the algorithms to high turbidity water bodies such as coastal areas and lakes. In general, IOPs estimation algorithms assume the spectral shape of IOPs empirically, which may limit their applicability to coastal areas and lakes with complex optical properties. Therefore, it is important to classify and select an IOPs estimation method suitable for the target water mass.
In this study, we validated the performance of several IOPs estimation algorithms with different methods using a global dataset covering a wide range of optical properties (Valente et al., 2019) and a Japanese-coastal dataset with high turbidity optical properties. Water masses were statistically classified based on their apparent optical properties (AOPs) and IOPs, and the applicability of various IOPs estimation algorithms to these classification results was discussed.