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Amin, R; Gould, R; Hou, WL; Lee, Z; Arnone, R (2011). Automated Detection and Removal of Cloud Shadows on HICO Images. OCEAN SENSING AND MONITORING III, 8030, 803004.

Clouds cause a serious problem for optical satellite sensors. Clouds not only conceal the ground, they also cast shadows, which cause either a reduction or total loss of information in an image, by reducing the illumination falling on the shadowed pixels. Ocean color bio-optical inversion algorithms rely on measurements of remote sensing reflectance (R(rs)(lambda)) at each pixel. If shadows are not removed properly across a scene, erroneous R(rs)(lambda) values will be calculated for the shadowed pixels, leading to incorrect retrievals of ocean color products such as chlorophyll. The cloud shadow issue becomes significant especially for high-resolution sensors such as the Hyperspectral Imager for the Coastal Ocean (HICO). On the other hand, the contrast of pixels in and outside a shadow provides opportunities to remove atmospheric contributions for ocean color remote sensing. Although identifying cloud is relatively straightforward using simple brightness thresholds, identifying their shadows especially over water is quite challenging because the brightness of the shadows is very close to the brightness of neighboring sunny regions especially in deep waters. In this study, we present automated procedures for our recently proposed cloud shadow detection technique called the Cloud Shadow Algorithm (CSA) and Lee et al. (2007) cloud and shadow atmospheric correction algorithm. We apply both automated procedures to HICO imagery and show examples of the results.



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