What is the value added of 4 bands within the submetric remote sensing of tropical coastscape? QuickBird-2 vs WorldView-2

The seamless tropical coastscape was investigated in comparing remotely-sensed by-products derived from the spaceborne 8 band WorldView-2 (WV-2) and the 4 band QuickBird-2-like (QB-2-like) sensors. The value added of the 4 bands allowed both land and water surveys to be significantly improved. The WV-2-derived NDVI means were more correlated (R
²
=0.96) with in situ "greenness" than those of QB-2-like (R
²
=0.89), and the WV-2-derived NDVI standard deviations were systematically inferior to those of QB-2-like. The cross-validation of the bathymetric products indicated a better performance of WV-2 (R
²
=0.76; maximum depth, 9.4 m) than QB-2-like (R
²
=0.74; maximum depth, 8.9 m). Results of classification showed that land cover was best mapped by WV-2 with the Neural Network algorithm (8 classes, kappa=0.92), and seafloor cover (resulting from an underwater radiative transfer model) was best classified by WV-2 trained with Support Vector Machine (6 classes, kappa=0.76). These improvements were then discussed based upon their spectral properties.