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.

https://www.mdpi.com/2072-4292/11/6/668/pdf

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

Spatio-temporal variation of the suspended sediment concentration in the Pearl River Estuary observed by MODIS during 2003–2015

One of the inherent challenges in remote sensing is the effect of atmospheric gases and particles on the light received by the sensor. The effects of atmospheric interference need to be adequately corrected for in order to retrieve quantitative biophysical information about a water body from the remotely sensed signal. Atmospheric correction of remotely sensed data is more challenging for inland waters than for open ocean waters due to a number of factors. The proximity of inland waters to various terrestrial sources of atmospheric pollution results in a more optically heterogeneous atmosphere, which complicates atmospheric modeling; the signal received at the sensor is often contaminated by contribution from the adjacent land, which is particularly problematic in cases of raised topography surrounding the water body and complicates atmospheric correction; non-negligible reflectance of water in the near-infrared region due to high sediment concentrations in inland waters (from terrestrial sources such as soil erosion, surface runoff from rainfall, and agricultural and industrial discharge) makes it difficult to accurately estimate and remove the effect of atmospheric aerosol scattering on the received signal. In spite of these and other challenges, a few algorithms have been developed and used for atmospheric correction of remotely sensed data from inland waters with reasonable success. This chapter contains a discussion of the main challenges in atmospheric correction for inland waters and brief descriptions of a few existing atmospheric correction algorithms that are suitable for inland waters.

Atmospheric Correction for Inland Waters

Satellite-Observed Decreases in Water Turbidity in the Pearl River Estuary: Potential Linkage With Sea-Level Rise

Changes in the distribution of surface sediment in Pearl River Estuary, 1975–2017, largely due to human activity

The algorithms for atmospheric correction (AC) of Moderate Resolution Imaging Spectroradiometer (MODIS) data may yield negative water-leaving radiances in highly turbid coastal waters along the Pearl River Estuary of China. This failure of AC can be attributed to the high reflectance from the suspended sediments in the near-infrared (NIR) bands, which results in an overestimate of the aerosol scattering and hence overcorrection in the visible bands. In this letter, a new shortwave infrared extrapolation (SWIRE) method is presented for Terra-MODIS data. The Rayleigh-corrected reflectances in the SWIR bands (1.24, 1.64, and 2.13 mu m) are used to determine an exponential function with respect to wavelength, which is used to correct the NIR bands (0.748 and 0.869 mu m) for sediment scattering and hence estimate the aerosol scattering reflectances in these bands. Then the Gordon and Wang AC approach is applied. The performance of this new SWIRE algorithm is demonstrated using two examples centred on the Pearl River Estuary, with the results showing that the new approach can improve the AC for these turbid waters.

A new approach for atmospheric correction of MODIS imagery in turbid coastal waters: a case study for the Pearl River Estuary

The advanced geosynchronous radiation imager (AGRI) aboard the Chinese Fengyun-4A (FY-4A) satellite can provide operational hourly sea surface temperature (SST) product. However, the temporal and spatial variation of the errors for this product is still unclear. In this article, FY-4A/AGRI SST is evaluated using the in situ SST from 2019-2021, and a cumulative distribution function matching method is adopted to reduce the errors. Statistical results show that the mean bias and root-mean-square error (RMSE) of FY-4A/AGRI SST are −0.37 °C and 0.98 °C, the median and robust standard deviation (RSD) are −0.30 °C and 0.90 °C. The variations in daily and monthly errors are large and there are no prominent seasonal variations during the period analyzed. There are negative biases exceeding −1.0 °C in low-mid latitude regions and larger positive biases in southern high latitude region. There are dependencies of satellite SST minus in situ SST on satellite zenith angle and on SST itself. After the bias correction, the bias and RMSE are reduced to −0.02 °C and 0.72 °C, and the median and RSD are reduced to 0.00 °C and 0.60 °C. On the time scale, the fluctuation ranges of bias and median are smaller. The difference of satellite SST minus in situ SST can reflect the diurnal variation of SST. The biases are generally within ±0.2 °C in full disk. The error dependencies on satellite zenith angle and SST are also greatly reduced.

https://ieeexplore.ieee.org/ielx7/4609443/4609444/09966802.pdf

Evaluation and Improvement of FY-4A/AGRI Sea Surface Temperature Data

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A new approach for atmospheric correction of MODIS imagery in turbid coastal waters: a case study for the Pearl River Estuary

Evaluation and Improvement of FY-4A/AGRI Sea Surface Temperature Data