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Abstract
This paper introduces a discussion about the need for atmospheric corrections by comparing data-driven sea surface salinity (SSS) derived from Top- and Bottom-of-Atmosphere imagery. Atmospheric corrections are used to remove the effect of the atmosphere in reflectances acquired by satellite sensors. The Sentinel-2 Level-2A product provides atmospherically corrected Bottom-of-Atmosphere (BOA) imagery, derived from Level-1C Top-of-Atmosphere (TOA) tiles using the Sen2Cor processor. SSS at high resolution in coastal areas (100m) is derived from multispectral signatures using artificial neural networks. These obtain relationships between satellite band information and in situ SSS data. Four scenarios with different input variables are tested for both TOA and BOA imagery, for interpolation (previous information on all platforms is available in the training dataset) and extrapolation (certain platforms are isolated and the network does not have any previous information on these) problems. Results show that TOA always outperforms BOA in terms of higher coefficient of determination (R2), lower mean absolute error (MAE) and lower most common error (μe). The best TOA results are R2=0.99, MAE=0.4PSU and μe=0.2PSU. Moreover, the evaluation of the neural network in all the pixels of Sentinel-2 tiles shows that BOA results are accurate only far away from the coast, while TOA data provides useful information on nearshore mixing patterns, estuarine processes and is able to estimate freshwater salinity values. This suggests that land adjacency corrections could be a relevant source of error. Sun glint corrections appear to be another source of error. TOA imagery is more accurate than BOA imagery when using machine learning algorithms and big data, as there is a clear loss of information in the atmospheric correction process that affects the multispectral–in situ relationships. Finally, the time and computational resources gained by avoiding atmospheric corrections can make the use of TOA imagery interesting in future studies, such as the estimation of chlorophyll or coloured dissolved organic matter.
Highlights
The Sentinel-2 mission is a constellation of two multispectral polar-orbiting satellites placed in the same sun-synchronous orbit, phased 180◦ to each other at a mean altitude of 786 km [1]
Narrowing the width of the Sentinel-2 spectral bands limits the influence of atmospheric constituents, as it was observed that the original LANDSAT NIR band was highly contaminated by water vapour [2]
This paper explores the differences in data-driven methods using L1C and L2A Sentinel-2 imagery to estimate sea surface salinity (SSS) in coastal areas
Summary
The Sentinel-2 mission is a constellation of two multispectral polar-orbiting satellites placed in the same sun-synchronous orbit, phased 180◦ to each other at a mean altitude of 786 km [1]. The main mission objectives are the systematic global acquisition of high-resolution, multispectral images linked to a high revisit frequency; the continuity of multispectral imagery provided by the SPOT series of satellites and the LANDSAT Thematic Mapper instrument; and the provision of observation data for the generation of operational products, such as land cover maps, land change detection maps and geophysical variables [2]. The Sentinel-2 multispectral instrument (MSI) collects rows of data across the orbital swath and uses the forward motion of the spacecraft along the path of the orbit to acquire new rows. The bands cover optical (red, green and blue), NIR, SWIR, red-edge, aerosols, water vapour and cirrus clouds. Narrowing the width of the Sentinel-2 spectral bands limits the influence of atmospheric constituents, as it was observed that the original LANDSAT NIR (near-infrared) band was highly contaminated by water vapour [2]. Precise aerosol correction of acquired data is enabled by the inclusion of a spectral band in the blue domain at 443 nm (Band 1) [2]
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