**4.1 Masking potential water pixels**

Masking of potential water pixels is done by thresholding a spectral mean image of all NIR bands between 860 nm and 900 nm of a sensor. As pointed out before water absorbs most of the incident energy in the NIR spectral region exhibiting a high brightness contrast to the majority of other surfaces. However, since every scene is different a scene-specific threshold has to be found. This is done automatically based on the histogram of the NIR spectral mean image (Fig. 5). After finding the histogram peak of low albedo surfaces (first local

Fig. 5. Histograms (left: full, right: subset) of the NIR spectral mean images of two test sites (top: Helgoland, bottom: Berlin)

On the Use of Airborne Imaging Spectroscopy Data for the

VI\* = modified vegetation index = max(R710, R720) / R680

Reflectance values must be scaled between 0 – 100

R740 = reflectance at wavelength 740 nm

then continues with a spatial analysis.

**4.3 Removal of shadow pixels** 

where

Automatic Detection and Delineation of Surface Water Bodies 11

However, a diagnostic spectral difference between both surfaces can be found in the NIR spectral region where the increasing water absorption causes the reflectance spectra of macrophytes to decrease between 710 – 740 nm as well as 815 – 880 nm. Therefore, pixels of shadowed vegetation can be removed from the low albedo mask using the condition:

VI\* > 1.0 AND (R740 – R710 / 740 – 710 < -0.001 OR R880 – R815 / 880 – 815 < -0.01) (3)

Water and shadow reflectance spectra are on average both very dark. The reflectance level of both decreases with wavelength due to a decreasing proportion of diffuse irradiation (case of shadow) and due to the increasing light absorption (case of water). Additionally, both show a high spectral variability due to different types of shadowed surfaces (case of shadow) and due to varying water constituents and bottom reflection (case of water). However, despite this variation all water reflectance spectra have one thing in common: the pure water itself. Therefore, spectral features of pure water, especially absorption features, can be seen in every reflectance spectrum of water. However, the presence of these spectral features depends on the spectral superimposition of the water constituents and bottom coverage. Section 4.3.1 describes how these aspects can be considered in the development of a knowledge-based classifier for spectrally distinguishing water and shadow. Section 4.3.2

**4.3.1 Spectral analysis for water-shadow-separation based on spectral slopes** 

Fig. 8 shows the absorption spectrum of pure water (logarithmic scale) in comparison with selected surface reflectance spectra of different water bodies of the analyzed datasets. It can be seen that the increasing absorption within specific wavelength intervals (1st, 2nd, 4th and 5th light red bar) results in decreasing reflectance for most of the reflectance spectra. The 3rd light red bar represents a short wavelength interval of stagnating absorption where some water reflectance spectra temporarily rise due to increasing reflectance of water constituents or water bottom before decreasing again. However, these effects are not present within all wavelength intervals of all water reflectance spectra because they can be superimposed by the reflectance of the water constituents and water bottom. In order to find the slope combinations that occur for typical water bodies we analyzed 112.041 surface reflectance spectra from five datasets (two from Helgoland, two from Berlin, one from Potsdam). The selected datasets contain several types of water bodies (rivers, lakes, ponds, North Sea; transparent to productive and turbid waters). A first-degree polynomial was fitted to the spectra within each of the five wavelength intervals using the least squares method. If the algebraic sign of the slope within a wavelength interval met the expectation it was coded to 1 otherwise to 0. This resulted in a five-digit binary vector for each analyzed water reflectance spectrum representing the co-occurrence of slopes within the respective diagnostic wavelength intervals that met the expectation. The 25 possible binary vectors

maximum) and a point near to the second local maximum (red dots in Fig. 5) the histogram between these two points is approximated by a polynomial of degree 5 (magenta dashed lines in Fig. 5). Then, the x value at the local minimum of the polynomial plus a safety margin of 2 is taken as the maximum reflectance threshold to be applied on the NIR spectral mean image. This results in a low albedo mask shown exemplarily for the test site Potsdam in Fig. 6. From this mask the water pixels have to be identified and other low albedo surfaces (mostly shadow) have to be removed.

Fig. 6. Low albedo mask (right-hand) for the test site Potsdam

#### **4.2 Differentiation between macrophytes in water and vegetation under shadow on land**

Reflectance spectra of macrophytes (big emergent, submergent, or floating water plants) are characterized by spectral features of vegetation, such as the chlorophyll absorption features in the blue and red wavelength regions and the red edge in the NIR wavelength region. The light absorbing properties of water result in reflectance spectra exhibiting a comparably low albedo to those of shadowed vegetation on land (Fig. 7). Therefore, shadowed vegetation cannot be removed from the low albedo mask by simply thresholding an NDVI image.

Fig. 7. Reflectance spectra of macrophytes in comparison with a reflectance spectrum of shadowed vegetation on land. The blue bars mark the wavelength of the two ratios used for distinguishing both surface types

However, a diagnostic spectral difference between both surfaces can be found in the NIR spectral region where the increasing water absorption causes the reflectance spectra of macrophytes to decrease between 710 – 740 nm as well as 815 – 880 nm. Therefore, pixels of shadowed vegetation can be removed from the low albedo mask using the condition:

VI\* > 1.0 AND (R740 – R710 / 740 – 710 < -0.001 OR R880 – R815 / 880 – 815 < -0.01) (3)

where

10 Remote Sensing of Planet Earth

maximum) and a point near to the second local maximum (red dots in Fig. 5) the histogram between these two points is approximated by a polynomial of degree 5 (magenta dashed lines in Fig. 5). Then, the x value at the local minimum of the polynomial plus a safety margin of 2 is taken as the maximum reflectance threshold to be applied on the NIR spectral mean image. This results in a low albedo mask shown exemplarily for the test site Potsdam in Fig. 6. From this mask the water pixels have to be identified and other low albedo

**4.2 Differentiation between macrophytes in water and vegetation under shadow on** 

Fig. 7. Reflectance spectra of macrophytes in comparison with a reflectance spectrum of shadowed vegetation on land. The blue bars mark the wavelength of the two ratios used for

Reflectance spectra of macrophytes (big emergent, submergent, or floating water plants) are characterized by spectral features of vegetation, such as the chlorophyll absorption features in the blue and red wavelength regions and the red edge in the NIR wavelength region. The light absorbing properties of water result in reflectance spectra exhibiting a comparably low albedo to those of shadowed vegetation on land (Fig. 7). Therefore, shadowed vegetation cannot be removed from the low albedo mask by simply thresholding an NDVI image.

surfaces (mostly shadow) have to be removed.

**land** 

distinguishing both surface types

Fig. 6. Low albedo mask (right-hand) for the test site Potsdam

VI\* = modified vegetation index = max(R710, R720) / R680 R740 = reflectance at wavelength 740 nm Reflectance values must be scaled between 0 – 100
