Radiometric validation of atmospheric correction for MERIS in the Baltic Sea based on continuous observations from ships and AERONET-OC

Abstract

The Baltic Sea is a semi-enclosed sea that is optically dominated by coloured dissolved organic material (CDOM) and has relatively low sun elevation which makes accurate ocean colour remote sensing challenging in these waters. The high absorption, low scattering properties of the Baltic Sea are representative of other optically similar water bodies including the Arctic Ocean, Yellow Sea, Black Sea, coastal regions adjacent to the CDOM-rich estuaries such as the Amazon, and highly absorbing lakes where radiometric validation is essential in order to develop accurate remote sensing algorithms. Previous studies in this region mainly focused on the validation and improvement of standard Chlorophyll-a (Chl a) and attenuation coefficient (k d ) ocean colour products. The primary input to derive these is the water-leaving radiance (L w ) or remote sensing reflectance (R rs ) and it is therefore fundamental to obtain the most accurate L w or R rs before deriving higher level products. To this end, the retrieval accuracy of R rs from Medium Resolution Imaging Spectrometer (MERIS) imagery using six atmospheric correction processors was assessed through above-water measurements at two sites of the Aerosol Robotic Network for Ocean Colour (AERONET-OC; 363 measurements) and a shipborne autonomous platform from which the highest number of measurements were obtained (4986 measurements). The six processors tested were the CoastColour processor (CC), the Case 2 Regional processor for lakes (C2R-Lakes), the Case 2 Regional CoastColour processor (C2R-CC), the FUB/WeW water processor (FUB), the MERIS ground segment processor (MEGS) and POLYMER. All processors except for CC had small average absolute percentage differences (ψ) in the wavelength range from 490 nm to 709 nm (ψ < 40%), while other bands had larger differences with ψ > 60%. Compared to in situ values, the R rs (709)/R rs (665) band ratio had ψ < 30% for all processors. The most accurate R rs in the 490 to 709 nm domain was obtained from POLYMER with ψ < 30% and coefficients of determination (R 2 ) > 0.6. Using a score system based on all statistical tests, POLYMER scored highest, while C2R-CC, C2R-Lakes and FUB had lower scores. This study represents the largest data base of in situ R rs , the most comprehensive analysis of AC models for highly absorbing waters and for MERIS, conducted to date. The results have implications for the new generation of Copernicus Sentinel ocean colour satellites.

Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

“…This is the reason reflectance derived from different AC processors must be analyzed and validated. These validation exercises are usually done over coastal waters and only in a few cases it is done over inland waters in the recent literature [3,[12][13][14][15]19].…”

“…But in general Polymer shows a good performance. Qin et al [14] evaluated Polymer in the Baltic Sea using MERIS. The authors obtained R 2 values between 0.6 to 0.81 in the visible bands.…”

“…The Global Lakes Sentinel Services (GLASS), funded by the European Commission (EC), researched, applied and compared several commercial and open source AC algorithms over European inland waters (Finland, Netherlands, Estonia, Italy and Sweden) [13]. Other initiatives [14] have analyzed several AC processors like C2RCC, ACOLITE, Polymer and the "standard" Sentinel 2 Correction (Sen2Cor) in the Baltic Sea. In Latin America, Souza Martins et al [15] applied and assessed the performance of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV), ACOLITE and Sen2Cor AC processors to S2-MSI over Amazon floodplain lakes.…”

Retrieval and mapping of chlorophyll-a concentration from Sentinel-2 images in an urban river in the semiarid region of Brazil

“…The R² metric represents the linear consistency between the observations and the proportion of the variation explained by the linear regression, the RMSE measures the precision of the combinations and the absolute difference, which is sensitive to the extreme values, and the Bias determines the underestimation or overestimation of the calculated data, compared to field-measured data (Qin et al, 2017;Ansper and Alikas, 2019). A high R² value indicates a high degree of correlation between in situ observations and Sentinel-2A, while a low RMSE value indicates that Sentinel-2A observations resemble well with in situ observations, and a value close to zero for Bias suggests that there is no systematic underestimation or overestimation of Sentinel-2 and in situ data (Qin et al, 2017). Table 2 shows the descriptive statistics of in situ chlorophyll-a (Chla-is) punctual concentration levels during the sampling period.…”

Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface

“…gsfc.nasa.gov), with low uncertainty (0.1-0.2) and high temporal resolution (15 min), by measuring spectral solar irradiance and sky radiance from a Sun photometer (Holben et al, 1998). AERONET measurements have been used to validate and correct the bias of satellite AOD retrievals (Almazroui, 2019;Che et al, 2018;Qin et al, 2017), due to its consistent international standardization of instruments, rigorous calibration and processing, globally distributed sites, and convenient data access. The AOD observations are divided Earth and Space Science into three types: Level 1.0 (unscreened), Level 1.5 (cloud screened), and Level 2.0 (cloud screened and quality assured) (Holben et al, 2001;Smirnov et al, 2000).…”