Comparison of Above-Water Seabird and TriOS Radiometers along an Atlantic Meridional Transect

The Fiducial Reference Measurements for Satellite Ocean Color (FRM4SOC) project has carried out a range of activities to evaluate and improve the state-of-the-art in ocean color radiometry. This paper described the results from a ship-based intercomparison conducted on the Atlantic Meridional Transect 27 from 23rd September to 5th November 2017. Two different radiometric systems, TriOS-Radiation Measurement Sensor with Enhanced Spectral resolution (RAMSES) and Seabird-Hyperspectral Surface Acquisition System (HyperSAS), were compared and operated side-by-side over a wide range of Atlantic provinces and environmental conditions. Both systems were calibrated for traceability to SI (Système international) units at the same optical laboratory under uniform conditions before and after the field campaign. The in situ results and their accompanying uncertainties were evaluated using the same data handling protocols. The field data revealed variability in the responsivity between TRiOS and Seabird sensors, which is dependent on the ambient environmental and illumination conditions. The straylight effects for individual sensors were mostly within ±3%. A near infra-red (NIR) similarity correction changed the water-leaving reflectance (ρw) and water-leaving radiance (Lw) spectra significantly, bringing also a convergence in outliers. For improving the estimates of in situ uncertainty, it is recommended that additional characterization of radiometers and environmental ancillary measurements are undertaken. In general, the comparison of radiometric systems showed agreement within the evaluated uncertainty limits. Consistency of in situ results with the available Sentinel-3A Ocean and Land Color Instrument (OLCI) data in the range from (400…560) nm was also satisfactory (-8% < Mean Percentage Difference (MPD) < 15%) and showed good agreement in terms of the shape of the spectra and absolute values

Consistency between Satellite Ocean Colour Products under High Coloured Dissolved Organic Matter Absorption in the Baltic Sea

Ocean colour (OC) remote sensing is an important tool for monitoring phytoplankton in the global ocean. In optically complex waters such as the Baltic Sea, relatively efficient light absorption by substances other than phytoplankton increases product uncertainty. Sentinel-3 OLCI-A, Suomi-NPP VIIRS and MODIS-Aqua OC radiometric products were assessed using Baltic Sea in situ remote sensing reflectance

An ensemble neural network atmospheric correction for Sentinel-3 OLCI over coastal waters providing inherent model uncertainty estimation and sensor noise propagation

Accurate atmospheric correction (AC) is a prerequisite for quantitative ocean colour remote sensing and remains a challenge in particular over coastal waters. Commonly AC algorithms are validated by establishing a mean retrieval error from match-up analysis, which compares the satellite-derived surface reflectance with concurrent ground radiometric observations. Pixel-based reflectance uncertainties however, are rarely provided by AC algorithms and those for the operational Ocean and Land Colour Instrument (OLCI) marine reflectance product are not yet recommended for use. AC retrieval errors and uncertainties directly determine the quality with which ocean colour products can be estimated from the marine surface reflectance. Increasingly there is also the need for reflectance uncertainty products to be used as data assimilation inputs into biogeochemical models. This paper describes the development of a new coastal AC algorithm for Sentinel-3 OLCI that provides pixel-based estimation of the inherent model inversion uncertainty and sensor noise propagation. The algorithm is a full-spectral model-based inversion of radiative transfer (RT) simulations in a coupled atmosphere–ocean system using an ensemble of artificial neural networks (ANN) that were initialized differently during the training process, but composed of the same network architecture. The algorithm has been validated against in-situ radiometric observations across a wide range of optical water types, and has been compared with the latest EUMETSAT operational Level 2 processor IPF-OL-2 v7.01. In this analysis we found that the ensemble ANN showed improved performance over the operational Level 2 processor with a band-averaged (412–708 nm) mean absolute percentage error (MAPE) of 16% compared to 37% and a four-times lower band-averaged bias of -0.00045 sr-1. In the ensemble inversion process we account for three uncertainty components: (1) the total model variance that describes the variance of the data from the different ANNs, (2) the prediction variance of the mean, which is based on calculations of the RT simulations and (3) the instrument noise variance of the mean by propagating the OLCI spectral signal-to-noise ratios (SNR). To study algorithm performance and to quantify the contribution of the different uncertainty components to the total uncertainty, we applied the algorithm to an optically complex full resolution (FR) test scene covering coastal waters of the Great Barrier Reef, Australia. The uncertainties associated with the instrument noise variance were found to be two orders of magnitude lower than the uncertainty components of the prediction and total model variances. The overall largest uncertainty component in our uncertainty framework is attributed to the total model inversion error from averaging the responses of the slightly different adapted networks in the ensemble. The algorithm is made publicly available as a Python/C plugin for the Sentinel Application Platform (SNAP)

Wind speed and mesoscale features drive net autotrophy in the South Atlantic Ocean

This is the final version. Available on open access from Elsevier via the DOI in this recordA comprehensive in situ dataset of chlorophyll a (Chl a; N = 18,001), net primary production (NPP; N = 165) and net community production (NCP; N = 95), were used to evaluate the performance of Moderate Resolution Imaging Spectroradiometer on Aqua (MODIS-A) algorithms for these parameters, in the South Atlantic Ocean, to facilitate the accurate generation of satellite NCP time series. For Chl a, five algorithms were tested using MODIS-A data, and OC3-CI performed best, which was subsequently used to compute NPP. Of three NPP algorithms tested, a Wavelength Resolved Model (WRM) was the most accurate, and was therefore used to estimate NCP with an empirical relationship between NCP with NPP and sea surface temperature (SST). A perturbation analysis was deployed to quantify the range of uncertainties introduced in satellite NCP from input parameters. The largest reductions in the uncertainty of satellite NCP came from MODIS-A derived NPP using the WRM (40%) and MODIS-A Chl a using OC3-CI (22%). The most accurate NCP algorithm, was used to generate a 16 year time series (2002 to 2018) from MODIS-A to assess climate and environmental drivers of NCP across the South Atlantic basin. Positive correlations between wind speed anomalies and NCP anomalies were observed in the central South Atlantic Gyre (SATL), and the Benguela Upwelling (BENG), indicating that autotrophic conditions may be fuelled by local wind-induced nutrient inputs to the mixed layer. Sea Level Height Anomalies (SLHA), used as an indicator of mesoscale eddies, were negatively correlated with NCP anomalies offshore of the BENG upwelling fronts into the SATL, suggesting autotrophic conditions are driven by mesoscale features. The Agulhas bank and Brazil-Malvinas confluence regions also had a strong negative correlation between SLHA and NCP anomalies, similarly indicating that NCP is forced by mesoscale eddy generation in this region. Positive correlations between SST anomalies and the Multivariate ENSO Index (MEI) in the SATL, indicated the influence of El Niño events on the South Atlantic Ocean, however the plankton community response was less clear.Natural Environment Research Council (NERC)European Space Agency (ESA)P&D ANP/BRASOILOceanographic Institute of the University of São Paulo (IOUSP

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

The accuracy of remote-sensing reflectance (Rrs) estimated from ocean color imagery through the atmospheric correction step is essential in conducting quantitative estimates of the inherent optical properties and biogeochemical parameters of seawater. Therefore, finding the main source of error is the first step toward improving the accuracy of Rrs. However, the classic validation exercises provide only the total error of the retrieved Rrs. They do not reveal the error sources. Moreover, how to effectively improve this satellite algorithm remains unknown. To better understand and improve various aspects of the satellite atmospheric correction algorithm, the error budget in the validation is required. Here, to find the primary error source from the OLCI Rrs, we evaluated the OLCI Rrs product with in-situ data around the China Sea from open ocean to coastal waters and compared them with the MODIS-AQUA and VIIRS products. The results show that the performances of OLCI are comparable to those MODIS-AQUA. The average percentage difference (APD) in Rrs is lowest at 490 nm (18%), and highest at 754 nm (79%). A more detailed analysis reveals that open ocean and coastal waters show opposite results: compared to coastal waters the satellite Rrs in open seas are higher than the in-situ measured values. An error budget for the three satellite-derived Rrs products is presented, showing that the primary error source in the China Sea was the aerosol estimation and the error on the Rayleigh-corrected radiance for OLCI, as well as for MODIS and VIIRS. This work suggests that to improve the accuracy of Sentinel-3A in the coastal waters of China, the accuracy of aerosol estimation in atmospheric correction must be improved

Performance of Ocean Colour Chlorophyll a algorithms for Sentinel-3 OLCI, MODIS-Aqua and Suomi-VIIRS in open-ocean waters of the Atlantic

This is the final version. Available on open access from Elsevier via the DOI in this recordThe proxy for phytoplankton biomass, Chlorophyll a (Chl a), is an important variable to assess the health and state of the oceans which are under increasing anthropogenic pressures. Prior to the operational use of satellite ocean-colour Chl a to monitor the oceans, rigorous assessments of algorithm performance are necessary to select the most suitable products. Due to their inaccessibility, the oligotrophic open-ocean gyres are under-sampled and therefore under-represented in global in situ data sets. The Atlantic Meridional Transect (AMT) campaigns fill the sampling gap in Atlantic oligotrophic waters. In-water underway spectrophotometric data were collected on three AMT field campaigns in 2016, 2017 and 2018 to assess the performance of Sentinel-3A (S3-A) and Sentinel-3B (S3-B) Ocean and Land Colour Instrument (OLCI) products. Three Chl a algorithms for OLCI were compared: Processing baseline (pb) 2, which uses the ocean colour 4 band ratio algorithm (OC4Me); pb 3 (OL_L2M.003.00) which uses OC4Me and a colour index (CI); and POLYMER v4.8 which models atmosphere and water reflectance and retrieves Chl a as a part of its spectral matching inversion. The POLYMER Chl a for S-3A OLCI performed best. The S-3A OLCI pb 2 tended to under-estimate Chl a especially at low concentrations, while the updated OL_L2M.003.00 provided significant improvements at low concentrations. OLCI data were also compared to MODIS-Aqua (R2018 processing) and Suomi-NPP VIIRS standard products. MODIS-Aqua exhibited good performance similar to OLCI POLYMER whereas Suomi-NPP VIIRS exhibited a slight under-estimate at higher Chl a values. The reasons for the differences were that S-3A OLCI pb 2 Rrs were over-estimated at blue bands which caused the under-estimate in Chl a. There were also some artefacts in the Rrs spectral shape of VIIRS which caused Chl a to be under-estimated at values >0.1 mg m-3. In addition, using in situ Rrs to compute Chl a with OC4Me we found a bias of 25% for these waters, related to the implementation of the OC4ME algorithm for S-3A OLCI. By comparison, the updated OLCI processor OL_L2M.003.00 significantly improved the Chl a retrievals at lower concentrations corresponding to the AMT measurements. S-3A and S-3B OLCI Chl a products were also compared during the Sentinel-3 mission tandem phase (the period when S-3A and S-3B were flying 30 sec apart along the same orbit). Both S-3A and S-3B OLCI pb 2 under-estimated Chl a especially at low values and the trend was greater for S-3A compared to S-3B. The performance of OLCI was improved by using either OL_L2M.003.00 or POLYMER Chl a. Analysis of coincident satellite images for S-3A OLCI, MODIS-Aqua and VIIRS as composites and over large areas illustrated that OLCI POLYMER gave the highest Chl a concentrations and percentage (%) coverage over the north and south Atlantic gyres, and OLCI pb 2 produced the lowest Chl a and % coverage.European Space Agency (ESA)Natural Environment Research Council (NERC)National Centre for Earth Observation (NCEO

Uncertainties in Retrieval of Remote Sensing Reflectance from Ocean Color Satellite Observations

Ocean Color radiometry uses remote sensing to interpret ocean dynamics by retrieving remote sensing reflectance () from satellite imagery at different scales and over different time periods. spectrum characterizes the ocean color that we observe, and from which we can discern concentrations of chlorophyll, organic and inorganic particles, and carbon fluxes in the ocean and atmosphere. is derived from the total radiance at the top of the atmosphere (TOA). However, it only represents up to ten percent of the total signal. Hence, the retrieval of from the total radiance at TOA involves the application of atmospheric correction (AC) algorithms, which include accurate modeling of Rayleigh and aerosol scattering, glint, and water variability. Each of these components yields uncertainties in the retrieved value of , especially in the blue bands. It is important to understand the main sources of uncertainties in , as uncertainties propagate into the retrieval of water parameters, which in turn inform climate models. In this study, a model was developed that quantifies the uncertainties of the main components in the current AC algorithm and used to analyze holistically the influence of these components on the uncertainties spatially and temporally in different water types taking advantage of the spectral differences between the components. The uncertainties were determined by comparing satellite and in situ data, with the in situ data obtained from the AErosol RObotic NETwork - Ocean Color (AERONET-OC) around the Northern Hemisphere and the Marine Optical BuoY (MOBY), Lanai, Hawaii. The satellite sensor data are from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the S-NPP platform, the Ocean and Land Colour Instruments (OLCI) on Sentinel 3A and 3B, and the Operational Land Imager (OLI) on Landsat 8. Results showed that the Rayleigh component (molecular scattering and surface effects) is the main source of uncertainties for all water types, followed by water variability, which is more influential in coastal areas. The contributions of other components, including aerosol scattering, are usually smaller. In addition, wind speed ranges can influence results, especially in coastal regions. Across spatial scales, water variability played a dominant role in uncertainty and increased proportionally to the ground sampling distance

Carbon from Space: determining the biological controls on the ocean sink of CO2 from satellites, in the Atlantic and Southern Ocean

Increasing anthropogenic carbon dioxide (CO2) emissions to the atmosphere have partially been absorbed by the global oceans. The role which the plankton community contributes to this net CO2 sink, and how it may change under climate change has been identified as a key issue to address within the United Nations decade of ocean science (2021-2030) Integrated Ocean Carbon Research (IOC-R) programme. This thesis sets out to explore how the net community production (NCP; the balance between photosynthesis and respiration) of the plankton community contributes to the variability in air-sea CO2 flux in the South Atlantic Ocean. In Chapter 2, NCP is shown to be accurately and precisely estimated from satellite measurements with respect to in situ observations. For this, weighted statistics are used to account for satellite, in situ and model uncertainties. The accuracy of satellite NCP could be improved by up to 40% by reducing uncertainties in net primary production (NPP). In Chapter 3, these satellite NCP observations were then used within a feed forward neural network scheme (SA-FNN) to extrapolate partial pressure of CO2 in seawater (pCO2 (sw)) over space and time, which is a key component to estimating the CO2 flux. NCP improved the accuracy and precision of pCO2 (sw) fields compared to using chlorophyll a (Chl a); the primary pigment in phytoplankton which is often used as a proxy for the biological CO2 drawdown. Compared to in situ observations, the seasonal variability in pCO2 (sw) was improved using the SA-FNN in key areas such as the Amazon River plume and Benguela upwelling, which make large regional contributions to the air-sea CO2 flux in the South Atlantic Ocean. In Chapter 4, these complete pCO2 (sw) fields were used with a timeseries decomposition method to determine the drivers of air-sea CO2 flux over seasonal, interannual and multi-year timescales. NCP was shown to correlate with the variability in CO2 flux on a seasonal basis. At interannual and mutli-year timescales, NCP became a more important contributor to variability in CO2 flux. This has not been previously analysed for this region. Mesoscale eddies in the global ocean can modify the biological, physical, and chemical properties and therefore may modify the CO2 flux. In Chapter 5, the cumulative CO2 flux of 67 long lived eddies (lifetimes > 1 year) was estimated using Lagrangian tracking with satellite observations. The eddies could enhance the CO2 flux into the South Atlantic Ocean by up to 0.08 %, through eddy modification of biological and physical properties. Collectively this research has shown that the plankton community plays a more significant role in modulating the air-sea CO2 flux in the South Atlantic Ocean, which has significant implications for the global ocean