High-quality satellite-based ocean color products derived from Sentinel-2/MSI and Sentinel-3/OLCI provide valuable support and insights in the management and monitoring of coastal ecosystems. The primary ocean color variable is the spectral Remote Sensing Reflectance (RRS), obtained after applying atmospheric correction (AC) on satellite products. AC algorithms, such as C2RCC and ACOLITE/DSF are all well capable of generating RRS products over coastal waters. The question of which approach to choose is important and not obvious, especially considering different water (e.g. turbid, clear or CDOM rich waters) and atmospheric conditions (e.g. sun glint, low sun angles) which can occur in coastal waters. To improve the operational ability to achieve high quality RRS spectra for a maximum number of pixels and yet retain the ability to deal with both unusual water conditions and challenging atmospheric conditions, we present the merged use of two algorithms: C2RCC and ACOLITE/DSF. Combining the two approaches yet required a comprehensive, region independent and pixel-based automatic switching scheme, along with a technique for achieving a seamless transition between the two algorithms. We here used the green-NIR ratio, which offers a clear indication of the saturation of the C2RCC outputs for the most reflective band (i.e., the RRS560), at a level where ACOLITE/DSF typically performs accurately, combined with a weighted transition between the two methods. The approach was applied to both Sentinel-2/MSI and Sentinel-3/OLCI products and validated using autonomous WATERHYPERNET stations located in Oostende (RT1, Belgium) and Venice (AAOT, Italy), showing an improved quality of the RRS products compared to using the ACs independently. The best results are obtained for the merged approach in the bands 443nm to 709nm for both Sentinel-2/MSI (<21% MAPE with a 0.004 RMSD and slopes between 0.93 and 0.98) and Sentinel-3/OLCI (<23% MAPE with a 0.003 RMSD and slopes between 0.91 and 0.98) which have generally the highest reflectance range, and which are generally of interest to retrieve turbidity in low to moderately turbid waters.
K. Ruddick, C. Brockmann, R. Doerffer, Z. Lee, V. Brotas, N. Fomferra, S. Groom, H. Krasemann, V. Martinez-Vicente, C. Sa, R. Santer, S. Sathyendranath, K. Stelzer, S. Pinnock
The MERIS instrument delivers a unique dataset of ocean colour measurements of the coastal zone, at 300m resolution
and with a unique spectral band set. The motivation for the Coastcolour project is to fully exploit the potential of the
MERIS instrument for remote sensing of the coastal zone. The general objective of the project is to develop,
demonstrate, validate and intercompare different processing algorithms for MERIS over a global range of coastal water
types in order to identify best practices. In this paper the Coastcolour project is presented in general and the Regional
Algorithm Round Robin (RARR) exercise is described in detail. The RARR has the objective of determining the best
approach to retrieval of chlorophyll a and other marine products (e.g. Inherent Optical Properties) for each of the
Coastcolour coastal water test sites. Benchmark datasets of reflectances at MERIS bands will be distributed to algorithm
provider participants for testing of both global (Coastcolour and other) algorithms and site-specific local algorithms.
Results from all algorithms will be analysed and compared according to a uniform methodology. Participation of
algorithm providers from outside the Coastcolour consortium is encouraged.
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