Application of the Support Vector Regression Method for Turbidity Assessment with MODIS on a Shallow Coral Reef Lagoon (Voh-Koné-Pouembout, New Caledonia)

Abstract

Particle transport by erosion from ultramafic lands in pristine tropical lagoons is a crucial problem, especially for the benthic and pelagic biodiversity associated with coral reefs. Satellite imagery is useful for assessing particle transport from land to sea. However, in the oligotrophic and shallow waters of tropical lagoons, the bottom reflection of downwelling light usually hampers the use of classical optical algorithms. In order to address this issue, a Support Vector Regression (SVR) model was developed and tested. The proposed application concerns the lagoon of New Caledonia—the second longest continuous coral reef in the world—which is frequently exposed to river plumes from ultramafic watersheds. The SVR model is based on a large training sample of in-situ turbidity values representative of the annual variability in the Voh-Koné-Pouembout lagoon (Western Coast of New Caledonia) during the 2014–2015 period and on coincident satellite reflectance values from MODerate Resolution Imaging Spectroradiometer (MODIS). It was trained with reflectance and two other explanatory parameters—bathymetry and bottom colour. This approach significantly improved the model’s capacity for retrieving the in-situ turbidity range from MODIS images, as compared with algorithms dedicated to deep oligotrophic or turbid waters, which were shown to be inadequate. This SVR model is applicable to the whole shallow lagoon waters from the Western Coast of New Caledonia and it is now ready to be tested over other oligotrophic shallow lagoon waters worldwide.

Keywords

• turbidity
• remote-sensing
• MODerate Resolution Imaging Spectroradiometer (MODIS)
• Support Vector Regression (SVR)
• oligotrophic lagoon
• bathymetry
• reflectance
• seabed colour
• coral reef
• New Caledonia