Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor

Abstract

The Sentinel-3A satellite was launched on 16 February 2016 with the Ocean and Land Colour Instrument (OLCI-A) on-board for the study of ocean color. The accuracy of ocean color parameters depends on the atmospheric correction algorithm (AC). This processing consists of removing the contribution of the atmosphere from the total measured signal by the remote sensor at the top of the atmosphere. Five ACs: the baseline AC, the Case 2 regional coast color neural network AC, its alternative version, the Polymer AC, and the standard NASA AC, are inter-compared over two bio-optical contrasted French coastal waters. The retrieved water-leaving reflectances are compared with in situ ocean color radiometric measurements collected using an ASD FielSpec4 spectrometer. Statistical and spectral analysis were performed to assess the best-performing AC through individual (relative error (RE) at 412 nm ranging between 23.43 and 57.31%; root mean squared error (RMSE) at 412 nm ranging between 0.0077 and 0.0188) and common (RE(412 nm) = 24.15–50.07%; RMSE(412 nm) = 0.0081–0.0132) match-ups. The results suggest that the most efficient schemes are the alternative version of the Case 2 regional coast color neural network AC with RE(412 nm) = 33.52% and RMSE(412 nm) = 0.0101 for the individual and Polymer with RE(412 nm) = 24.15% and RMSE(412 nm) = 0.0081 for the common ACs match-ups. Sensitivity studies were performed to assess the limitations of the AC, and the errors of retrievals showed no trends when compared to the turbidity and CDOM.

Earth observation applications for coastal sustainability: potential and challenges for implementation

“…There are various AC models used over coastal waters (cf. Mograne et al 2019), such as Polymer (Steinmetz et al 2011) and Case 2 Regional Coast Colour (C2RCC) (Doerffer and Schiller 2007), but there is no single model that works well in all cases, so improved understanding of the limitations posed by ineffective AC is required before the OWT approach can be reliably applied at global scales.…”

“…Zhang et al 2018). However, AC models, such as the Polymer and C2RCC have shown great potential in optically complex waters (Mograne et al 2019), such as those found in coastal areas, and it may be the case that different ACs are suited for different regions, coastal water bodies or applications. For example, the image correction for atmospheric effects (iCOR) algorithm was developed for both land and water pixels to reduce discontinuities in reflectance caused by separate application of water-and landspecific AC within one scene (De Keukelaere et al 2018), and could therefore be a suitable option for transitional areas, such as the coastal zone.…”

Correção Atmosférica em Sensoriamento Remoto: Uma Revisão

“…A obtenção da denominada "reflectância de superfície" é importante, porque, ao contrário dos números digitais, este parâmetro traz um significado físico sobre o alvo e pode ser comparado ao longo do tempo e entre sistemas sensores (respeitadas as devidas limitações). O processamento de correção atmosférica tem como objetivo minimizar a influência resultante da interação com os componentes atmosféricos no sinal detectado por um sistema sensor no topo da atmosfera (Mograne et al, 2019). A eficiência deste procedimento é altamente dependente do método de correção atmosférica e das especificações técnicas de cada sensor (Okin e Gu, 2015).…”

Error Budget in the Validation of Radiometric Products Derived from OLCI around the China Sea from Open Ocean to Coastal Waters Compared with MODIS and VIIRS

“…Since OLCI was launched, only a few works have been performed to validate its products [19][20][21][22]. Shen et al (2017) [19] developed a dual band ratio algorithm to calculate the downwelling diffuse attenuation coefficient at 490 nm (Kd(490)) for the waters of Lake Taihu with OLCI data and showed that the new OLCI product has a smoother spatial distribution and finer textural characteristics than does the MODIS product and it contained notably higher-quality data.…”

“…Zibordi et al (2018) [20] summarized a regional assessment of radiometric data products from OLCI with in-situ data from the ocean color component of the Aerosol Robotic Network (AERONET-OC) and the bio-optical mapping of marine properties (BiOMaP) program and revealed that there R rs was systematically underestimated while the aerosol optical thickness was overestimated, explainable by biases in calibration coefficients or poor performance of bright pixel correction. Mograne et al (2019) [21] validated the OLCI water-leaving reflectance products over two contrasted French coastal waters obtained by five different atmospheric correction algorithms (AC), and discovered that the polymer and C2R-CCAltNets algorithms obtain high performances. Gossn et al (2019) [22] developed a new atmospheric correction algorithm (BLR-AC) for turbid waters based on the red, near-infrared (NIR) and 1016 nm bands of OLCI.…”