Abstract
Currently, observations from low-Earth orbit (LEO) ocean color sensors represent one of the most used tools to study surface optical and biogeochemical properties of the ocean. LEO observations are available at daily temporal resolution, and are often combined into weekly, monthly, seasonal, and annual averages in order to obtain sufficient spatial coverage. Indeed, daily satellite maps of the main oceanic variables (e.g., surface phytoplankton chlorophyll-a) generally have many data gaps, mainly due to clouds, which can be filled using either Optimal Interpolation or the Empirical Orthogonal Functions approach. Such interpolations, however, may introduce large uncertainties in the final product. Here, our goal is to quantify the potential benefits of having high-temporal resolution observations from a geostationary (GEO) ocean color sensor to reduce interpolation errors in the reconstructed hourly and daily chlorophyll-a products. To this aim, we used modeled chlorophyll-a fields from the Copernicus Marine Environment Monitoring Service’s (CMEMS) Baltic Monitoring and Forecasting Centre (BAL MFC) and satellite cloud observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor (on board the geostationary satellite METEOSAT). The sampling of a GEO was thus simulated by combining the hourly chlorophyll fields and clouds masks, then hourly and daily chlorophyll-a products were generated after interpolation from neighboring valid data using the Multi-Channel Singular Spectral Analysis (M-SSA). Two cases are discussed: (i) A reconstruction based on the typical sampling of a LEO and, (ii) a simulation of a GEO sampling with hourly observations. The results show that the root mean square and interpolation bias errors are significantly reduced using hourly observations.
Highlights
Accurate knowledge of biogeochemical parameters is extremely important for many marine environmental applications
The approach developed here uses hourly Chl fields generated by numerical simulations and actual cloud distributions derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) data
The main goal of the study was to quantify the benefits of having high-temporal resolution observations from space in order to reduce errors in the reconstructed surface hourly and daily Chl fields in the Baltic Sea
Summary
Accurate knowledge of biogeochemical parameters is extremely important for many marine environmental applications. A variety of methods allow the in situ determination of Chl with optical sensors deployed on autonomous platforms such as Biogeochemical (BGC)-Argo floats [3,4] and fixed moorings (e.g., BOUSSOLE) [5,6], or through laboratory analysis after water sample collection during oceanographic cruises (e.g., High-Performance Liquid Chromatography, HPLC, analysis) [7] None of these approaches ensure global and synoptic coverage, and often the duration of measurements is limited in time. Missing data in daily Chl can be reconstructed with statistical methods such as optimal interpolation or Empirical Orthogonal Functions (EOFs) [8] It has been demonstrated, either in coastal or open ocean waters, how the diel variability of bio-optical properties is a fundamental time scale to be evaluated and studied, in the context of ocean productivity [9,10,11,12,13,14,15,16,17,18]. With its six visible bands centered at the wavelengths of 412, 443, 490, 555, 660, and 680 nm and two near-infrared (NIR) bands at wavelengths of 745 and 865 nm, GOCI can monitor the marine environment and provide a variety of ocean optical, biological, and biogeochemical property products for an area of about 2500 × 2500 km around the Korean Peninsula [26,27,28,29,30,31]
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