## 1 Introduction  
The term, \"remote sensing\" encompasses a series of well established procedures for bathymetric surveys of the seabed [1,2], but these procedures are not without limitations in terms of both the sensors and those imposed by the environment [3]. Remote sensing for bathymetry can be subdivided into active and passive techniques. Active remote sensing for bathymetry is generally represented by ship borne multi-beam echo sounding (MBES) sensor arrays [4]. MBES surveys produce accurate depth measurements along transects on the seabed but this method is constrained by high operating costs and an inability to survey in very shallow waters, marine protected areas or endangered habitats such as coral reefs [5]. Additionally, ship-borne surveys are time consuming and swath widths can be very narrow in shallow waters. Although single beam surveys are less costly than their multi-beam alternative, their low sampling density requires advanced interpolation (or prediction) techniques [6] to estimate seabed topography, and, therefore, are generally not used for high accuracy surveys but rather commissioned by local interest groups as an interim measure. A second non-imaging method known as satellite altimetry can be used to measure the geoidal height and marine gravity field which, in turn, can be used to determine the water depth from the linear relationship between the gravity anomaly and square of the depth [7]. This method is only suitable for deep sea, for example, surveying large sea mounts [8]. Other active methods such as airborne bathymetric LiDAR (Light Detection and Ranging) have achieved a spatial resolution of 4 m (horizontal) and 20 cm in vertical accuracy at depths of over 30 m. However, LiDAR is not ideal for all water types, as experience gained in Irish waters from the INFOMAR [9] research program demonstrates. These tests resulted in very poor seabed detection along the east coast of Ireland and limited penetration in the west coast up to 15 m. Satellite LiDAR (e.g., ICESat) has been employed by [PERSON] [10] in conjunction with imagery from Landsat 5 and 7 to ascertain the water levels of inland lakes but this type of LiDAR platform is not designed for high-resolution, high-accuracy bathymetric surveys. Although [11] has employed spatial video to monitor wave patterns to calculate near shore bathymetry, passive remote sensing for bathymetry is predominantly performed using multispectral satellite imagery. Multispectral imagery is preferable because electromagnetic radiation at different wavelengths can penetrate the water column to different depths and the land-water interface can be clearly defined. Bathymetry is calculated using analytical [12,13] or empirical methods [14,15], based on the statistical relationships between image pixel values and ground truth depth measurements and is applicable in shallow coastal waters. Accuracy is high using these methods for depths of water penetration not exceeding 20 m but becomes less accurate in deeper waters [16]. Turbidity is an issue for imagery derived bathymetry in all water types, as [17] have demonstrated in their study of high sediment rivers in tidal inlets [18], and summarized by [PERSON] [16]. Hyperspectral satellite data can provide an important insight into the water column properties when deriving satellite bathymetry (e.g., turbidity,oceanographic) [19] due to the greater number and narrower bands, however, the spatial resolution is generally much larger than with multispectral (1 or 2 orders of magnitude). Optical multi-spectral satellite-derived bathymetry (SDB) that implements analytical or empirical methods, based on the statistical relationships between image pixel values and field measured water depth measurements, apply the general physical principle that sea water transmittances at near-visible wavelengths are functions of a general optical equation depending on the intrinsic optical properties of sea water.