An Improved Cloud Masking Method for GOCI Data over Turbid Coastal Waters

Shiming Lu,Peiliang Li,Mingjun He,Yunhe Pan,Wenbin Yin,Shuangyan He,Shuo He

doi:10.3390/rs13142722

Shiming Lu, Peiliang Li + Show 5 more

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https://doi.org/10.3390/rs13142722

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Abstract

Clouds severely hinder the radiative transmission of visible light; thus, correctly masking cloudy and non-cloudy pixels is a preliminary step in processing ocean color remote sensing data. However, cloud masking over turbid waters is prone to misjudgment, leading to loss of non-cloudy pixel data. This research proposes an improved cloud masking method over turbid water to classify cloudy and non-cloudy pixels based on spectral variability of Rayleigh-corrected reflectance acquired by the Geostationary Ocean Color Imager (GOCI). Compared with other existing cloud masking methods, we demonstrated that this improved method can identify the spatial positions and shapes of clouds more realistically, and more accurate pixels of turbid waters were retained. This improved method can be effectively applied in typical turbid coastal waters. It has potential to be used in cloud masking procedures of spaceborne ocean color sensors without short-wave infrared bands.

Highlights

IntroductionSpaceborne sensors observe the Earth from above the top of atmosphere (TOA); the presence of clouds is often inevitable in optical remote sensing images [1]
Data over Turbid Coastal Waters.Spaceborne sensors observe the Earth from above the top of atmosphere (TOA); the presence of clouds is often inevitable in optical remote sensing images [1]
The Geostationary Ocean Color Imager (GOCI) standard atmospheric correction algorithm [41] was developed based on the theoretical basis of the Sea-viewing Wide Fieldof-view Sensor (SeaWiFS) standard atmospheric correction algorithm [42], though partially different in the turbid water near-infrared (NIR) correction method and the aerosol models

Summary

Introduction

Spaceborne sensors observe the Earth from above the top of atmosphere (TOA); the presence of clouds is often inevitable in optical remote sensing images [1]. Effective ocean color and other surface information can only be extracted from cloudless pixels of satellite remote sensing images, as clouds can block the visible light emerging from the ocean to the sensor. The detection and masking of cloud pixels is an essential and important step before further processing in various optical remote sensing applications [2]. The algorithm package called APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) has been used since the late 1980s, and its physics is the backbone of a series of cloud detection schemes for AVHRR (Advanced Very High Resolution Radiometer) heritage sensors

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