Abstract
Satellite-based ocean color sensors have provided an unprecedentedly large amount of information on ocean, coastal and inland waters at varied spatial and temporal scales. However, observations are often adversely affected by cloud coverage and other poor weather conditions, like sun glint, and this influences the accuracy associated with long-term monitoring of water quality parameters. This study uses long-term (2013–2017) and high-frequency (eight observations per day) datasets from the Geostationary Ocean Color Imager (GOCI), the first geostationary ocean color satellite sensor, to quantify the cloud coverage over China’s seas, the resultant interrupted observations in remote sensing, and their impacts on the retrieval of total suspended sediments (TSS). The monthly mean cloud coverage for the East China Sea (ECS), Bohai Sea (BS) and Yellow Sea (YS) were 62.6%, 67.3% and 69.9%, respectively. Uncertainties regarding the long-term retrieved TSS were affected by a combination of the effects of cloud coverage and TSS variations. The effects of the cloud coverage dominated at the monthly scale, with the mean normalized bias (Pbias) at 14.1% (±2.6%), 7.6% (±2.3%) and 12.2% (±4.3%) for TSS of the ECS, BS and YS, respectively. Cloud coverage-interfering observations with the Terra/Aqua MODIS systems were also estimated, with monthly Pbias ranging from 6.5% (±7.4%) to 20% (±13.1%) for TSS products, and resulted in a smaller data range and lower maximum to minimum ratio compared to the eight GOCI observations. Furthermore, with approximately 16.7% monthly variations being missed during the periods, significant “missing trends” effects were revealed in monthly TSS variations from Terra/Aqua MODIS. For the entire region and the Bohai Sea, the most appropriate timeframe for sampling ranges from 12:30 to 15:30, while this timeframe was narrowed to from 13:30 to 15:30 for observations in the East China Sea and the Yellow Sea. This research project evaluated the effects of cloud coverage and times for sampling on the remote sensing monitoring of ocean color constituents, which would suggest the most appropriate timeframe for ocean color sensor scans, as well as in situ data collection, and can provide design specification guidance for future satellite sensor systems.
Highlights
Effective monitoring of water quality in coastal and open seas, including variations of phytoplankton levels, suspended sediments and colored dissolved organic matter, calls for uninterrupted sampling over appropriate time scales
The extremely high cloud coverage percentage (CCP) for the China seas indicates that large portions of the observations from remote-sensing satellites are blinded by clouds
It is imperative to understand the effects of the cloud coverage on the remote sensing of total suspended sediments (TSS) in these highly dynamic waters, and to assess the uncertainties in the observations from data from the Terra/Aqua MODI satellite, the most commonly used satellite sensors
Summary
Effective monitoring of water quality in coastal and open seas, including variations of phytoplankton levels, suspended sediments and colored dissolved organic matter, calls for uninterrupted sampling over appropriate time scales. The benefits of remote sensing include its wider temporal and spatial range (coverage and scale) for water resource monitoring, and relatively low costs compared to the conventional field sampling approach [1,2]. Ocean color remote sensing unprecedentedly provides data for water quality properties at various spectral, temporal and spatial scales [9,10,11]. Applications of both in-situ and remote sensing methods are often limited by either low temporal or spatial resolutions, for regions that experience diurnal or semi-diurnal variations [12,13]. The most widely used Terra/Aqua MODIS satellites, designed to perform two observations a day, are prone to disruption from cloud coverage and other poor weather conditions, as well as sun glints [16,17,18]
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