*3.2. Preprocessing*

Considering the selected imagery used for further analysis, a total of six images needed geometric correction: one from Landsat-1, and all imagery from QuickBird-2 and Geoeye-1, resulting in a RMSE within the two pixel threshold (average RMSE: QuickBird-2: 4.49 m, Geoeye-1:3.61 m, Landsat-1: 16.48 m). For the next step, Rayleigh correction was applied to imagery from Landsat-1–3, QuickBird-2, SPOT 5–7 and Geoeye-1; images acquired by the other sensors (Sentinel-2, RapidEye, PlanetScope and Landsat-5) were provided in atmospherically corrected products. After Rayleigh correction, our results showed that compared to the original values, floating kelp and dark water pixels in the blue band were reduced to zero or just slightly above, in the green band, pixels values decreased by approximately half, and by approximately a third in the red band (Figure 6). This conforms with the shape of kelp spectra and water spectra from in situ hyperspectral measurements of floating kelp and water, in literature [25,44,62].

After geometric and Rayleigh corrections, the different spectral indices were evaluated based on the M-statistics. Among the available spectral indices, the M-statistic results showed different optimal indices for the different satellites (Table 4). For Geoeye-1 and QuickBird-2, a normalized vegetation index with the green band (G-NDVI) instead of the red band had the highest separability (>1.44). Meanwhile, for PlanetScope imagery, a simple ratio combination of the near-infrared and green bands showed the highest separability (>11.34). Lastly, for satellites that included a red-edge band, RapidEye and Worldview, a simple band ratio of red-edge over green (>1.69), and red-edge over yellow (>2.72), was best at separating kelp from water, respectively. The statistically selected indices and bands were used as the input data for the object-oriented classification.

**Table 4.** A summary of the M-statistic of different band indices and ratios for kelp and non-kelp classes observed in the imagery during band selection (R: red, Y: yellow, G: green, B: blue, RE: rededge, NIR: near-infrared, G-NDVI: NDVI with green instead of red, RE-NDVI: NDVI with red-edge instead of NIR, B-NDVI: NDVI with blue instead of red, B-RE-NDVI: NDVI with blue instead of red and red-edge instead of NIR, G-RE-NDVI: NDVI with green instead of red and red-edge instead of NIR). Indices selected for input into classification are bolded.


**Figure 6.** Examples of spectra before and after performing the Rayleigh correction using the (**A**) Rayleigh scattering curve to atmospherically correct imagery. (**B**,**C**) show a QuickBird-2 image before and after correction and (**D**,**E**) show a Worldview-3 image before and after correction. Of note, the magnitude of the digital number and wavelengths varies between sensors.
