Abstract

ABSTRACT Coastal and lagoon water colour products are often greatly contaminated by clouds and accompanying shadows. Adjacency effects due to the strong reflection from contiguous cloud pixels can introduce further uncertainty into the water-leaving radiance retrieval process. This study aims to propose a robust cloud-index dynamic-threshold (CIDT) algorithm based on the spectral and spatial characteristics of the clouds and evaluate its performance in comparison with results obtained from the existing algorithms using multispectral and hyperspectral images from the lagoon and coastal water zones. Detection of cloud contamination from these images is performed before applying atmospheric correction in order to prevent significant loss of valuable data and ensure the quality of higher-level products. The CIDT starts with data pre-processing (in order to avoid misclassification across the clouds and non-cloud regions) by excluding signatures other than the cloud and non-cloud (water and land) pixels. Based on the spectral and spatial variability characteristic of clouds, a new cloud index is then introduced that uses the top-of-atmosphere reflectance in conjugation with the dynamic threshold values to detect the cloud pixels. For quantitative and qualitative analyses, CIDT is tested on a number of scenes provided by Landsat 8 Operational Land Imager (OLI) and Hyperspectral Imager for the Coastal Ocean (HICO) sensors over the coastal and lagoon zones. According to the comparison of the algorithm results with the reference cloud flags, CIDT shows a significant improvement in identifying clouds of varying opacity, achieves a higher cloud detection accuracy, and reduces the number of the previously misclassified pixels by other algorithms in different coastal and lagoon water regions. The CIDT is extensively tested in mid-latitude and tropical areas and CIDT can be applied to both multispectral and hyperspectral images over the lagoon and coastal zones without the need for ancillary information.

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