**2. Optical fundamentals of water remote sensing**

For the spectral identification of water pixels and the separation from other dark surfaces and shadows it is necessary to understand the influencing factors contributing to the surface reflectance of water bodies and especially to the optical complexity and variability of coastal and inland waters. The spectral reflectance of water (its apparent water colour) is a function of the optically visible water constituents (suspended and dissolved) and the depth of the water body (Effler & Auer, 1987, Bukata *et al.*, 1991, Bukata *et al.*, 1995). The concentration and composition of (i) phytoplankton, (ii) suspended particulate matter (SPM) and (iii) dissolved organic matter loading dominate the optical properties of natural waters. Shallow coastal and inland waters may also contain the spectral signal contribution from the bottom reflectance that significantly differs with the various materials (mainly sands (different colours), muds (different colours), macrophytes (different abundances, groups and compositions), reefs (different structures, different colours).

Smith & Baker (1983) and Pope & Fry (1997) provide absorption spectra of pure water derived from laboratory investigations. The Ocean Optic Protocols (Müller & Fargion, 2002) propose the absorption spectra of Sogandares & Fry (1997) for wavelengths between 340 nm and 380 nm, Pope & Fry (1997) for wavelengths between 380 nm and 700 nm, and Smith & Baker (1983) for wavelengths between 700 nm and 800 nm. Buiteveld et al. (1994) investigated the temperature dependant water absorption properties. Morel (1974) provides spectral values of the pure water volume scattering coefficient at specific temperatures and salinity, and the directional phase function. Gege (2005) used the data from the afore listed publications to construct the WASI absorption spectrum of pure water. This absorption spectrum formed the basis of the knowledge-based algorithm for water identification presented in Section 4.3.1.

these methods have the drawback that they are not fully automated since the analyst has to select a scene-specific threshold (Ji *et al.*, 2009) or training pixels. Moreover there are certain situations where these methods lead to misclassification. For instance, water constituents in turbid water as well as water bottom reflectance and sun glint can raise the reflectance spectrum of surface water even in the NIR spectral range up to a reflectance level which is typical for dark surfaces on land such as dark rocks (e.g., basalt, lava), bituminous roofing materials and in particular shadow regions. Consequently, Carleer & Wolff (2006) amongst others found the land cover classes water and shadow to be highly confused in image classifications. This problem especially occurs in environments where both, a high amount of shadow and water regions can exist, such as urban landscapes, mountainous landscapes or

In this investigation we focus on the development of a new surface water body detection algorithm that can be automatically applied without user knowledge and supplementary data on any hyperspectral image of the visible and near-infrared (VNIR) spectral range. The analysis is strictly focused on the VNIR part of the electromagnetic spectrum due to the growing number of VNIR imaging spectrometers. The developed approach consists of two main steps, the selection of potential water pixels (section 4.1) and the removal of false positives from this mask (sections 4.2 and 4.3). In this context the separation between water bodies and shadowed surfaces is the most challenging task which is implemented by consecutive spectral and spatial processing steps (sections 4.3.1 and 4.3.2) resulting in very

For the spectral identification of water pixels and the separation from other dark surfaces and shadows it is necessary to understand the influencing factors contributing to the surface reflectance of water bodies and especially to the optical complexity and variability of coastal and inland waters. The spectral reflectance of water (its apparent water colour) is a function of the optically visible water constituents (suspended and dissolved) and the depth of the water body (Effler & Auer, 1987, Bukata *et al.*, 1991, Bukata *et al.*, 1995). The concentration and composition of (i) phytoplankton, (ii) suspended particulate matter (SPM) and (iii) dissolved organic matter loading dominate the optical properties of natural waters. Shallow coastal and inland waters may also contain the spectral signal contribution from the bottom reflectance that significantly differs with the various materials (mainly sands (different colours), muds (different colours), macrophytes (different abundances, groups and

Smith & Baker (1983) and Pope & Fry (1997) provide absorption spectra of pure water derived from laboratory investigations. The Ocean Optic Protocols (Müller & Fargion, 2002) propose the absorption spectra of Sogandares & Fry (1997) for wavelengths between 340 nm and 380 nm, Pope & Fry (1997) for wavelengths between 380 nm and 700 nm, and Smith & Baker (1983) for wavelengths between 700 nm and 800 nm. Buiteveld et al. (1994) investigated the temperature dependant water absorption properties. Morel (1974) provides spectral values of the pure water volume scattering coefficient at specific temperatures and salinity, and the directional phase function. Gege (2005) used the data from the afore listed publications to construct the WASI absorption spectrum of pure water. This absorption spectrum formed the basis of the knowledge-based algorithm for water identification presented in Section 4.3.1.

cliffy coasts as well as generally in images with water bodies and cloud shadows.

high detection accuracies.

**2. Optical fundamentals of water remote sensing** 

compositions), reefs (different structures, different colours).

Specular reflection of direct sunlight at the water surface into the sensor should be avoided by choosing a different viewing geometry. Specular reflection of the diffuse incoming sky radiation at the water surface can not be avoided and accounts up to 2 to 4 % of the overall surface reflectance that is measured by a sensor. Thus, most of the incoming radiation penetrates the water. Wavelengths larger than 800 nm are entirely absorbed by a large water column of pure water, so reflectance and transmission are no more significant in those longer wavelengths. As solar and sky radiation transmits into the water, the scattering by suspended particles and the absorption by suspended and dissolved water constituents are the water colouring processes. The wavelength peak of the spectral reflectance from transparent waters lies in the blue wavelength range and in this case energy may be reflected from the bottom up of up to 20 meters deep. If waters are less transparent due to higher concentrations of phytoplankton and sediments, and if the back-reflected signal from the bottom in shallow water bodies reach back to the air/water interface, there is significant reflectance from the water body also at the longer wavelength ranges (green to red) and there is a rise of the water-leaving reflectance even in the NIR wavelength region. In the case of phytoplankton blooming, high sediment loads or shallow waters with a bright bottom reflectance the water leaving signal significantly rises in the NIR and the overall reflectance may reach near 10 to 15 %. Therefore, there is no mono-type of the shape and the magnitude of the spectral water-leaving reflectance (Fig. 1). Inland and coastal waters may exhibit bright, turbid waters due to phytoplankton and sediments or bottom reflectance of their shallow areas, and in these cases simple thresholding techniques are no solution for the extraction and delineation of water bodies.

Fig. 1. Surface reflectance spectra, *RS* (scale 0-1), of different inland waters (Rheinsberg Lake District, Germany) representing different water colours (Reigber, in prep). GWUMM, Grosser Wummsee, highly transparent, oligotrophic (nature reserve, densely forested); ZOOTZ, Zootzensee, mesotroph (rural, forested); ZETHN, Zethner See, turbid, mesotropheutrophic (rural); BRAMI, Braminsee, highly turbid, polytrophic (fish farming, rural)
