Ocean Color Measurements Research Articles

Satellite-derived global chlorophyll-a anomaly products

We describe our effort to produce the first-ever near-real-time (NRT) global daily chlorophyll-a (Chl-a) anomaly products that can be used to detect and identify algae blooms from satellite ocean color measurements such as those from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20. Specifically, the production of the two Chl-a anomaly products, one for the Chl-a anomaly in difference and another for the Chl-a anomaly ratio (or in its relative difference) compared to the 61-day Chl-a median value from the previous time period (as a Chl-a reference), has been implemented in the daily global NRT satellite data processing. These two Chl-a anomaly products represent the global ocean and coastal/inland water Chl-a abnormal condition for a given location and can provide more complete characteristics for the daily phytoplankton (or algae) biomass status by comparing to the normal condition. Detailed satellite Chl-a anomaly algorithms, the implementation of the algorithms in the routine satellite data processing, and the global data processing procedure are described and discussed. In addition, we provide several specific examples from the VIIRS-measured global daily Chl-a anomaly products to demonstrate various applications in the detection of algae blooms, including the harmful algal blooms (HABs), in various ocean and coastal/inland waters. Some quantitative seasonal and interannual evaluations and characterizations of Chl-a anomalies over global oceans and several coastal/inland waters are also provided and discussed.

Impact of Temperature on Absorption Coefficient of Pure Seawater in the Blue Wavelengths Inferred from Satellite and In Situ Measurements

There has been a long history of interest on how (if) the absorption coefficient of “pure” fresh water ( a fw λ ) and “pure” seawater ( a sw λ ) changes with temperature ( T ), yet the impact of T reported in the literature differs significantly in the blue domain. Unlike the previous studies based on laboratory measurements, we took an approach based on ~18 years (2002–2020) of MODIS ocean color and temperature measurements in the oligotrophic oceans, along with field measured chlorophyll concentration and phytoplankton absorption coefficient, to examine the relationship between T and the total absorption coefficient ( a λ ) at 412 and 443 nm. We found that the values of a 412 and a 443 in the summer are nearly flat (slightly decreasing) for the observed T range of ~19–27 °C. Since there are no detectable changes of chlorophyll during this period, the results suggest that T has a negligible impact on a sw 412 and a sw 443 in this T range. As a complement, the impact of salinity on a fw λ was also evaluated using three independent determinations of a sw λ and a fw λ , where good agreements were found from these observations.

A New Retrieval of Sun-Induced Chlorophyll Fluorescence in Water from Ocean Colour Measurements Applied on OLCI L-1b and L-2

The retrieval of sun-induced chlorophyll fluorescence is greatly beneficial to studies of marine phytoplankton biomass, physiology, and composition, and is required for user applications and services. Customarily phytoplankton chlorophyll fluorescence is determined from satellite measurements through a fluorescence line-height algorithm using three bands around 680 nm. We propose here a modified retrieval, making use of all available bands in the relevant wavelength range, with the goal to improve the effectiveness of the algorithm in optically complex waters. For the Ocean and Land Colour Instrument (OLCI), we quantify a Fluorescence Peak Height by fitting a Gaussian function and related terms to the top-of-atmosphere reflectance bands between 650 and 750 nm. This algorithm retrieves, what we call Fluorescence Peak Height by fitting a Gaussian function upon other terms to top-of-atmosphere reflectance bands between 650 and 750 nm. This approach is applicable to Level-1 and Level-2 data. We find a good correlation of the retrieved fluorescence product to global in-situ chlorophyll measurements, as well as a consistent relation between chlorophyll concentration and fluorescence from radiative transfer modelling and OLCI/in-situ comparison. Evidence suggests, the algorithm is applicable to complex waters without needing an atmospheric correction and vicarious calibration, and features an inherent correction of small spectral shifts, as required for OLCI measurements.

Atmospheric correction in coastal region using same-day observations of different sun-sensor geometries with a revised POLYMER model.

In this paper, with a revised POLYMER (POLYnomial based approach applied to MERIS data) atmospheric correction model, we present a novel scheme (two-angle atmospheric correction algorithm, termed as TAACA) to remove atmospheric contributions in satellite ocean color measurements for coastal environments, especially when there are absorbing aerosols. TAACA essentially uses the same water properties as a constraint to determine oceanic and atmospheric properties simultaneously using two same-day consecutive satellite images having different sun-sensor geometries. The performance of TAACA is first evaluated with a synthetic dataset, where the retrieved remote-sensing reflectance (Rrs) by TAACA matches very well (the coefficient of determination (R2) ≥ 0.98) with the simulated Rrs for each wavelength, and the unbiased root mean square error (uRMSE) is ∼12.2% for cases of both non-absorbing and strongly absorbing aerosols. When this dataset is handled by POLYMER, for non-absorbing aerosol cases, the R2 and uRMSE values are ∼0.99 and ∼7.5%, respectively, but they are ∼0.92 and ∼39.5% for strongly absorbing aerosols. TAACA is further assessed using co-located VIIRS measurements for waters in Boston Harbor and Massachusetts Bay, and the retrieved Rrs from VIIRS agrees with in situ measurements within ∼27.3% at the visible wavelengths. By contrast, a traditional algorithm resulted in uRMSE as 3890.4% and 58.9% at 410 and 443 nm, respectively, for these measurements. The Rrs products derived from POLYMER also show large deviations from in situ measurements. It is envisioned that more reliable Rrs products in coastal waters could be obtained from satellite ocean color measurements with a scheme like TAACA, especially when there are strongly absorbing aerosols.

Inversion of multiangular polarimetric measurements from the ACEPOL campaign: an application of improving aerosol property and hyperspectral ocean color retrievals

Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the time frame of late 2022 to early 2023, will carry the Ocean Color Instrument (OCI), a hyperspectral scanning radiometer, and two multiangle polarimeters (MAPs), the UMBC Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). One purpose of the PACE MAPs is to better characterize aerosol properties, which can then be used to improve atmospheric correction for the retrieval of ocean color in coastal waters. Though this is theoretically promising, the use of MAP data in the atmospheric correction of colocated hyperspectral ocean color measurements have not yet been well demonstrated. In this work, we performed aerosol retrievals using the MAP measurements from the Research Scanning Polarimeter (RSP) and demonstrate its application to the atmospheric correction of hyperspectral radiometric measurements from SPEX airborne. Both measurements were collected on the same aircraft from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) field campaign in 2017. Two cases over ocean with small aerosol loading (aerosol optical depth ∼0.04) are identified including colocated RSP and SPEX airborne measurements and Aerosol Robotic Network (AERONET) ground-based observations. The aerosol retrievals are performed and compared with two options: one uses reflectance measurement only and the other uses both reflectance and polarization. It is demonstrated that polarization information helps reduce the uncertainties of aerosol microphysical and optical properties. The retrieved aerosol properties are then used to compute the contribution of atmosphere and ocean surface for atmospheric correction over the discrete bands from RSP measurements and the hyperspectral SPEX airborne measurements. The water-leaving signals determined this way are compared with both AERONET and Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color products for performance analysis. The results and lessons learned from this work will provide a basis to fully exploit the information from the unique combination of sensors on PACE for aerosol characterization and ocean ecosystem research.

Antarctic spring ice-edge blooms observed from space by ICESat-2

Polar ocean ecosystems are experiencing rapid environmental change, but measuring the associated phytoplankton responses is challenging using traditional satellite passive ocean color measurements due to signal contamination from clouds and sea ices and to low solar elevation angles. Active satellite lidar measurements allow retrieval of ocean phytoplankton properties under conditions prohibitive to passive ocean color sensors. The ICESat-2 satellite lidar measurements provide two-dimensional distributions of upper ocean phytoplankton properties. The spring phytoplankton blooms extending about 230 km horizontally from dense packs ice near Antarctic marginal ice zones and 15 m vertically below ocean surface are observed from space for the first time. Our findings highlight the advantages of satellite lidar technology for understanding high latitude plankton ecology and biogeochemistry.

Estimation of Hourly Sea Surface Salinity in the East China Sea Using Geostationary Ocean Color Imager Measurements

Sea surface salinity (SSS) is an important tracer for monitoring the Changjiang Diluted Water (CDW) extension into Korean coastal regions; however, observing the SSS distribution in near real time is a difficult task. In this study, SSS detection algorithm was developed based on the ocean color measurements by Geostationary Ocean Color Imager (GOCI) in high spatial and temporal resolution using multilayer perceptron neural network (MPNN). Among the various combinations of input parameters, combinations with three to six bands of GOCI remote sensing reflectance (Rrs), sea surface temperature (SST), longitude, and latitude were most appropriate for estimating the SSS. According to model validations with the Soil Moisture Active Passive (SMAP) and Ieodo Ocean Research Station (I-ORS) SSS measurements, the coefficient of determination (R2) were 0.81 and 0.92 and the root mean square errors (RMSEs) were 1.30 psu and 0.30 psu, respectively. In addition, a sensitivity analysis revealed the importance of SST and the red-wavelength spectral signal for estimating the SSS. Finally, hourly estimated SSS images were used to illustrate the hourly CDW distribution. With the model developed in this study, the near real-time SSS distribution in the East China Sea (ECS) can be monitored using GOCI and SST data.

Evaluation of glint correction approaches for fine-scale ocean color measurements by lightweight hyperspectral imaging spectrometers.

Low-power, lightweight, off-the-shelf imaging spectrometers, deployed on above-water fixed platforms or on low-altitude aerial drones, have significant potential for enabling fine-scale assessment of radiometrically derived water quality properties (WQPs) in oceans, lakes, and reservoirs. In such applications, it is essential that the measured water-leaving spectral radiances be corrected for surface-reflected light, i.e., glint. However, noise and spectral characteristics of these imagers, and environmental sources of fine-scale radiometric variability such as capillary waves, complicate the glint correction problem. Despite having a low signal-to-noise ratio, a representative lightweight imaging spectrometer provided accurate radiometric estimates of chlorophyll concentration-an informative WQP-from glint-corrected hyperspectral radiances in a fixed-platform application in a coastal ocean region. Optimal glint correction was provided by a spectral optimization algorithm, which outperformed both a hardware solution utilizing a polarizer and a subtractive algorithm incorporating the reflectance measured in the near infrared. In the same coastal region, this spectral optimization approach also provided the best glint correction for radiometric estimates of backscatter at 650 nm, a WQP indicative of suspended particle load.

The Plankton, Aerosol, Cloud, Ocean Ecosystem Mission: Status, Science, Advances

AbstractThe Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) mission represents the National Aeronautics and Space Administration’s (NASA) next investment in satellite ocean color and the study of Earth’s ocean–atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth’s changing climate. PACE objectives include extending systematic cloud, aerosol, and ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles, food-web processes, and ecosystem responses to a changing climate, and improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and air and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument is a spectrometer that spans the ultraviolet to shortwave-infrared wavelengths, with a ground sample distance of 1 km at nadir. This payload is complemented by two multiangle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022 to early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean–atmosphere–land system. Here, we present an overview of the PACE mission, including its developmental history, science objectives, instrument payload, observatory characteristics, and data products.

Improving the Accuracy of Single Turnover Active Fluorometry (STAF) for the Estimation of Phytoplankton Primary Productivity (PhytoPP)

Photosystem II (PSII) photochemistry is the ultimate source of reducing power for phytoplankton primary productivity (PhytoPP). Single turnover active chlorophyll fluorometry (STAF) provides a non-intrusive method that has the potential to measure PhytoPP on much wider spatiotemporal scales than is possible with more direct methods such as 14C fixation and O2 evolved through water oxidation. Application of a STAF-derived absorption coefficient for PSII light-harvesting (aLHII) provides a method for estimating PSII photochemical flux on a unit volume basis (JVPII). Within this study, we assess potential errors in the calculation of JVPII arising from sources other than photochemically active PSII complexes (baseline fluorescence) and the package effect. Although our data show that such errors can be significant, we identify fluorescence-based correction procedures that can be used to minimize their impact. For baseline fluorescence, the correction incorporates an assumed consensus PSII photochemical efficiency for dark-adapted material. The error generated by the package effect can be minimized through the ratio of variable fluorescence measured within narrow wavebands centered at 730 nm, where the re-absorption of PSII fluorescence emission is minimal, and at 680 nm, where re-absorption of PSII fluorescence emission is maximal. We conclude that, with incorporation of these corrective steps, STAF can provide a reliable estimate of JVPII and, if used in conjunction with simultaneous satellite measurements of ocean color, could take us significantly closer to achieving the objective of obtaining reliable autonomous estimates of PhytoPP.

Remote radiological assessment in the marine environment: A pilot study based on Cs-137 measurements and satellite observations in the Aegean Sea

A program concept has been developed to utilize sea parameters like sea surface temperature (SST), ocean colour (OC) and sea surface salinity (SSS), in order to explore their potential relations with 137Cs activity concentrations in sea water. These relations are expected to lead to the creation of an innovative tool based on remote sensing data and in real time 137Cs measurements, for the remote radioactivity detection of the Greek marine ecosystem both for routine control and emergency recordings. The presented results are a preliminary effort of the tool’s development. Remote sensing data have been acquired from MIRAS and MODIS instruments on-board ESA-SMOS and NASA-TERRA/AQUA satellites respectively. Satellite data comprise of SST and OC measurements. The ERL’s data of 137Cs activity concentrations (204 measurements) in seawater have been used for the period March 2012 to February 2015. Therefore, a) map analyses in a GIS including interpolation and integration of 83 real time measurements corrected with the effective half live of 7.2 y according to the monthly data of satelites and spatial linear regression have been implemented for the Aegean Sea, b) additional temporal analyses using linear and polynomial regression have been performed for the area of Souda- Crete, for which the most frequent measurements of 137Cs activity concentration in sea water have been measured in ERL. In this study, the first derived results on the correlation between SST measurements with 137Cs activity concentrations are presented, whereas the respective correlation with OC is being under invstigation. Further investigations include multivariate polynomial analyses into the Geographic Information System (GIS) platform with more extensive sampling and satellite data from new systems, whereas comparative correlations of 137Cs with seawater parameters derived by conventional means will be performed.

Sensitivity of inherent optical properties from ocean reflectance inversion models to satellite instrument wavelength suites.

The Earth science community seeks to develop climate data records (CDRs) from satellite measurements of ocean color, a continuous data record which now exceeds twenty years. Space agencies will launch additional instruments in the coming decade that will continue this data record, including the NASA PACE spectrometer. Inherent optical properties (IOPs) quantitatively describe the absorbing and scattering constituents of seawater and can be estimated from satellite-observed spectroradiometric data using semi-analytical algorithms (SAAs). SAAs exploit the contrasting optical signatures of constituent matter at spectral bands observed by satellite sensors. SAA performance, therefore, depends on the spectral resolution of the satellite spectroradiometer. A CDR spanning SeaWiFS, MODIS, OLCI, and PACE, for example, would include IOPs derived using varied wavelength suites if all available wavelengths were considered. Here, we explored differences in derived IOPs that stem simply from the use of (eight) different wavelength suites of input radiometric measurements. Using synthesized data and SeaWiFS Level-3 mission-long composites, we demonstrated equivalent SAA performance for all wavelength suites, but that IOP retrievals vary by several percent across wavelength suites and as a function of water type. The differences equate to roughly ≤ 6, 12, and 7% for adg (443), aph (443), and bbp (443), respectively, for waters with Ca ≤ 1 mg m-3. These values shrink for sensors with similar wavelength suites (e.g., SeaWiFS, MODIS, and MERIS) and rise to substantially larger values for higher Ca waters. Our results also indicate that including 400 nm (in the case of OLCI) influences the derived IOPs, using longer wavelengths (>600 nm) influences the derived IOPs when there is a red signal, and, including additional spectral information shows potential for improved IOP estimation, but not without revisiting SAA parameterizations and execution. While modest in scope, we believe this study contributes to the knowledge base for CDR development. The implication of ignoring such an analysis as CDRs continue to be developed is a prolonged inability to distinguish between algorithmic and environmental contributions to trends and anomalies in the IOP time-series.

Understanding altimetry signals in the Northeastern Ligurian sea using a multi-platform approach

During the Long-Term Glider Mission for Environmental Characterization 2016 sea trial, carried out in the eastern Ligurian Sea (Northwestern Mediterranean Sea), two gliders rated to a maximum depth of 1000 m operated continuously from 3 May 2016–27 June 2016. When possible, glider tracks were synchronized with the contemporaneous footprints of the Jason-2, SARAL/AltiKa and CryoSat-2 altimeters. Temperature and salinity measured by the gliders that were co-localised with the altimeter passages were used to calculate along-track dynamic heights (DH). The latter were then compared with the altimeters’ near real-time absolute dynamic topography (ADT) measurements. ADT and DH values showed very similar, but shifted patterns, suggesting that gliders had sampled the same structures but at different times. Average surface absolute geostrophic velocities at the time of glider transit were used in a novel relocation technique to reposition glider measurements where they would have been at the time of the altimeter passage. The relocation increases the correlation between datasets to a value close to 1 and reduces the RMSE of an order of magnitude, also when the time-difference between measurements was greater than 3 days. The sea level spatial and temporal variability is attributed to the presence of meanders generated by the baroclinic instability of the Liguro- Provençal Current (LPC). These meanders are characterized by a 38 km amplitude and 0.12 m s−1 average propagation speed that are confirmed by the larger scale ocean colour measurements and are in agreement with those previously described in the literature. We found that the presence of LPC meanders in along-track ADT and DH measurements is evidenced by a decrease in sea level when profiles sampled the Modified Atlantic Water, and an increase in sea level when measurements sampled the adjacent waters of Tyrrhenian Sea origin. The relocation technique introduced here is expected to improve future altimetry-glider comparisons, as similar validation experiments were considered possible only when glider and altimetry measurements were near synoptic, and collected up to one day from each other.

Improving ocean color data coverage through machine learning

Oceanic chlorophyll-a concentration (Chl, mg m−3) maps derived from satellite ocean color measurements are the only data source which provides synoptic information of phytoplankton abundance on global scale. However, after excluding data collected under non-optimal observing conditions such as strong sun glint, clouds, thick aerosols, straylight, and large viewing angles, only ~5% of MODIS ocean measurements lead to valid Chl retrievals, regardless of the fact that about 25–30% of the global ocean is cloud free. A recently developed ocean color index (CI) is effective in deriving relative ocean color patterns under most non-optimal observing conditions to improve coverage, but these patterns cannot be interpreted as Chl. In this study, we combine the advantage of the high-quality, low-coverage Chl and lower-quality, higher-coverage CI to improve spatial and temporal coverage of Chl through machine learning, specifically via a random forest based regression ensemble (RFRE) approach. For every MODIS scene, the machine learning requires CI, Rayleigh-corrected reflectance (Rrc (λ = 469, 555, 645 nm), dimensionless), and high-quality low-coverage Chl from the common pixels where they all have valid data to develop an RFRE-based model to convert CI and Rrc (λ) to Chl. The model is then applied to all valid CI pixels of the same scene to derive Chl. This process is repeated for each scene, and the model parameterization is optimized for each scene independently. The approach has been tested for the Yellow Sea and East China Sea (YSECS) where non-optimal observing conditions frequently occur. Validations using extensive field measurements and image-based statistics for 2017 show very promising results, where coverage in the new Chl maps is increased by ~3.5 times without noticeable degradation in quality as compared with the original Chl data products. The improvement in Chl coverage without compromising data quality is not only critical in revealing otherwise unknown bloom patterns, but also important in reducing uncertainties in time-series analysis. Tests of the RFRE approach for several other regions such as the East Caribbean, Arabian Sea, and Gulf of Mexico suggest its general applicability in improving Chl coverage of other regions.

Concentrations of Multiple Phytoplankton Pigments in the Global Oceans Obtained from Satellite Ocean Color Measurements with MERIS

The remote sensing of chlorophyll a concentration from ocean color satellites has been an essential variable quantifying phytoplankton in the past decades, yet estimation of accessory pigments from ocean color remote sensing data has remained largely elusive. In this study, we validated the concentrations of multiple pigments (Cpigs) retrieved from in situ and MEdium Resolution Imaging Spectrometer (MERIS) measured remote sensing reflectance (Rrs(λ)) in the global oceans. A multi-pigment inversion model (MuPI) was used to semi-analytically retrieve Cpigs from Rrs(λ). With a set of globally optimized parameters, the accuracy of the retrievals obtained with MuPI is quite promising. Compared with High-Performance Liquid Chromatography (HPLC) measurements near Bermuda, the concentrations of chlorophyll a, b, c ([Chl-a], [Chl-b], [Chl-c]), photoprotective carotenoids ([PPC]), and photosynthetic carotenoids ([PSC]) can be retrieved from MERIS data with a mean unbiased absolute percentage difference of 38%, 78%, 65%, 36%, and 47%, respectively. The advantage of the MuPI approach is the simultaneous retrievals of [Chl-a] and the accessory pigments [Chl-b], [Chl-c], [PPC], [PSC] from MERIS Rrs(λ) based on a closure between the input and output Rrs(λ) spectra. These results can greatly expand scientific studies of ocean biology and biogeochemistry of the global oceans that are not possible when the only available information is [Chl-a].

Comparison of Aerosol Reflectance Correction Schemes Using Two Near-Infrared Wavelengths for Ocean Color Data Processing

This paper reanalyzes the aerosol reflectance correction schemes employed by major ocean color missions. The utilization of two near-infrared (NIR) bands to estimate aerosol reflectance in visible wavelengths has been widely adopted, for example by SeaWiFS/MODIS/VIIRS (GW1994), OCTS/GLI/SGLI (F1998), MERIS/OLCI (AM1999), and GOCI/GOCI-II (A2016). The F1998, AM1999, and A2016 schemes were developed based on GW1994; however, they are implemented differently in terms of aerosol model selection and weighting factor computation. The F1998 scheme determines the contribution of the most appropriate aerosol models in the aerosol optical thickness domain, whereas the GW1994 scheme focuses on single-scattering reflectance. The AM1999 and A2016 schemes both directly resolve the multiple scattering domain contribution. However, A2016 also considers the spectrally dependent weighting factor, whereas AM1999 calculates the spectrally invariant weighting factor. Additionally, ocean color measurements on a geostationary platform, such as GOCI, require more accurate aerosol correction schemes because the measurements are made over a large range of solar zenith angles which causes diurnal instabilities in the atmospheric correction. Herein, the four correction schemes were tested with simulated top-of-atmosphere radiances generated by radiative transfer simulations for three aerosol models. For comparison, look-up tables and test data were generated using the same radiative transfer simulation code. All schemes showed acceptable accuracy, with less than 10% median error in water reflectance retrieval at 443 nm. Notably, the accuracy of the A2016 scheme was similar among different aerosol models, whereas the other schemes tended to provide better accuracy with coarse aerosol models than the fine aerosol models.

A Color‐Index‐Based Empirical Algorithm for Determining Particulate Organic Carbon Concentration in the Ocean From Satellite Observations

AbstractAn empirical algorithm for estimating particulate organic carbon (POC) concentration in the surface ocean from satellite observations is formulated and validated using in situ POC data and remote‐sensing reflectance (Rrs) data obtained from match‐up satellite ocean color measurements. The algorithm builds upon the band‐difference algorithm concept, which was originally developed for estimating chlorophyll‐a concentration in clear waters. This algorithm utilizes three spectral bands centered approximately at 490, 550, and 670 nm to determine a color index (CIPOC), from which POC can be estimated from satellite measurements. For comparison, the blue‐green band‐ratio algorithm is also formulated using the same data set of in situ POC and satellite‐derived Rrs. Results show that the statistical parameters characterizing the differences between the satellite‐derived POC and matchup in situ POC are similar when the CIPOC and band ratio algorithms are applied to open ocean waters where the values of CIPOC are relatively low. In coastal waters where the values of CIPOC are generally higher, the statistical parameters of algorithm performance are better for the CIPOC algorithm. In addition, because the CIPOC algorithm is less sensitive to errors and noise in the satellite‐derived Rrs, the image quality obtained with this algorithm can be improved for both open‐ocean and coastal waters.

Navigating noise when comparing satellite and acoustic remote sensing data

Using a novel combination of NASA satellite products and acoustic remote sensing measurements, we aim to examine the link between the surface expression and vertical structure of ocean productivity and biomass in the California Current System. Water column sonar data collected by the National Marine Fisheries Service for fisheries management have been archived at the NOAA National Centers for Environmental Information. These data are used to evaluate the variability of the marine biogeography. To identify patterns of acoustic reflectance of marine organisms, the data must first be free of noise and seafloor acoustic returns. However, removing the variety of noise present has proved to be a challenge due to need for manual tuning, a resource prohibitive step, and the size and complexity of the acoustic data. Collaboration with the University of Colorado’s Earth Lab has led to the development of machine learning models to automatically remove noise from this large dataset. An analysis of the fully automated, semi-manual, and machine learning quality control processes will be presented. The cleaned acoustic data are then compared to the interannual variability of the distribution of surface chlorophyll concentration and temperature from satellite ocean color measurements.

CHLOROPHYLL CONCENTRATION DERIVED FROM MICROWAVE REMOTE SENSING MEASUREMENTS USING ARTIFICIAL NEURAL NETWORK ALGORITHM

Ocean color optic remote sensing has been used to estimate various biogeochemical constituents to monitor ocean environmental changes in coastal regions and global open oceans. However, the most problematic issue in utilizing ocean color measurements is cloud coverage. Thus, this study introduces a neural network (NN) algorithm to derive Chlorophyll a (Chl) concentrations, which are unavailable due to the presence of clouds. Because ocean color remote sensing depends on cloud coverage, microwave measurements, including sea-surface temperature, cloud, water vapor, precipitation, and winds, were used as inputs for the NN algorithm. Accordingly, the NN algorithm was designed to predict Chl using five inputs of microwave measurements and geolocation data. The correlation coefficient and root mean square error between the predicted and remotely sensed Chl concentrations were about 0.89 and 0.30 mg/m^3, respectively. Therefore, the developed NN algorithm enabled us to obtain Chl concentration during cloudy days, and even typhoon passages, as demonstrated in this study. However, Chl concentrations along coastal regions could not be predicted based on the inputs of the NN algorithm. Microwave remote sensing could not measure inhomogeneous emissivity for areas that were partially ocean and land, such as near-coastal regions.

Estimation of the Potential Detection of Diatom Assemblages Based on Ocean Color Radiance Anomalies in the North Sea

Over the past years, a large number of new approaches in the domain of ocean-color have been developed, leading to a variety of innovative descriptors for phytoplankton communities. One of these methods, named PHYSAT, currently allows for the qualitative detection of five main phytoplankton groups from ocean-color measurements. Even though PHYSAT products are widely used in various applications and projects, the approach is limited by the fact it identifies only dominant phytoplankton groups. This current limitation is due to the use of biomarker pigment ratios for establishing empirical relationships between \textit{in-situ} information and specific ocean-color radiance anomalies in open ocean waters. However, theoretical explanations of PHYSAT suggests that it could be possible to detect more than dominance cases but move more towards phytoplanktonic assemblage detection. Thus, to evaluate the potential of PHYSAT for the detection of phytoplankton assemblages, we took advantage of the Continuous Plankton Recorder (CPR) survey, collected in both the English Channel and the North Sea. The available CPR dataset contains information on diatom abundance in two large areas of the North Sea for the period 1998-2010. {Using this unique dataset, recurrent diatom assemblages were retrieved based on classification of CPR samples}. Six diatom assemblages were identified \textit{in-situ}, \textcolor{blue}{each having indicators taxa or species}. Once this first step was completed, the \textit{in-situ} analysis was used to empirically associate the diatom assemblages with specific PHYSAT spectral anomalies. This step was facilitated by the use of previous classifications of regional radiance anomalies in terms of shape and amplitude, coupled with phenological tools. Through a matchup exercise, three CPR assemblages were associated \textcolor{blue}{with} specific radiance anomalies. The maps of detection of these specific radiances anomalies are in close agreement with current \textit{in-situ} ecological knowledge.