Nimbus-7 Coastal Zone Color Scanner: System Description and Initial Imagery

A New Algorithm for Simultaneous Retrieval of Aerosols and Marine Parameters

We present an algorithm for simultaneous retrieval of aerosol and marine parameters in coastal waters. The algorithm is based on a radiative transfer forward model for a coupled atmosphere-ocean system, which is used to train a radial basis function neural network (RBF-NN) to obtain a fast and accurate method to compute radiances at the top of the atmosphere (TOA) for given aerosol and marine input parameters. The inverse modelling algorithm employs multidimensional unconstrained non-linear optimization to retrieve three marine parameters (concentrations of chlorophyll and mineral particles, as well as absorption by coloured dissolved organic matter (CDOM)), and two aerosol parameters (aerosol fine-mode fraction and aerosol volume fraction). We validated the retrieval algorithm using synthetic data and found it, for both low and high sun, to predict each of the five parameters accurately, both with and without white noise added to the top of the atmosphere (TOA) radiances. When varying the solar zenith angle (SZA) and retraining the RBF-NN without noise added to the TOA radiance, we found the algorithm to predict the CDOM absorption, chlorophyll concentration, mineral concentration, aerosol fine-mode fraction, and aerosol volume fraction with correlation coefficients greater than 0.72, 0.73, 0.93, 0.67, and 0.87, respectively, for 45∘≤ SZA ≤ 75∘. By adding white Gaussian noise to the TOA radiances with varying values of the signal-to-noise-ratio (SNR), we found the retrieval algorithm to predict CDOM absorption, chlorophyll concentration, mineral concentration, aerosol fine-mode fraction, and aerosol volume fraction well with correlation coefficients greater than 0.77, 0.75, 0.91, 0.81, and 0.86, respectively, for high sun and SNR ≥ 95.

Evolution of Ocean Color Atmospheric Correction: 1970–2005

Retrieval of water properties from satellite-borne imagers viewing oceans and coastal areas in the visible region of the spectrum requires removing the effect of the atmosphere, which contributes approximately 80–90% of the measured radiance over the open ocean in the blue spectral region. The Gordon and Wang algorithm originally developed for SeaWiFS (and used with other NASA sensors, e.g., MODIS) forms the basis for many atmospheric removal (correction) procedures. It was developed for application to imagery obtained over the open ocean (Case 1 waters), where the aerosol is usually non-absorbing, and is used operationally to process global data from SeaWiFS, MODIS and VIIRS. Here, I trace the evolution of this algorithm from early NASA aircraft experiments through the CZCS, OCTS, SeaWiFs, MERIS, and finally the MODIS sensors. Strategies to extend the algorithm to situations where the aerosol is strongly absorbing are examined. Its application to sensors with additional and unique capabilities is sketched. Problems associated with atmospheric correction in coastal waters are described.

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.

Water Quality Properties Derived from VIIRS Measurements in the Great Lakes

Refined empirical algorithms for chlorophyll-a (Chl-a) concentration, using the maximum ratio of normalized water-leaving radiance nLw(λ) at the blue and green bands, and Secchi depth (SD) from nLw(λ) at 551 nm, nLw(551), are proposed for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite in the Great Lakes. We demonstrated that water quality properties and phytoplankton production can be successfully monitored and assessed using the new regional Chl-a and SD algorithms, with reasonably accurate estimates of Chl-a and SD from the VIIRS-SNPP ocean color data in the Great Lakes. VIIRS-derived Chl-a and SD products using the proposed algorithms provide the temporal and spatial variabilities in the Great Lakes. Overall, Chl-a concentrations are generally low in lakes Michigan and Huron, while Chl-a data are highest in Lake Erie. The seasonal pattern shows that overall low Chl-a concentrations appear in winter and high values in June to September in the lakes. The distribution of SD in the Great Lakes is spatially and temporally different from that of Chl-a. The SD data are generally lower in summer and higher in winter in most of the Great Lakes. However, the highest SD in Lake Erie appears in summer, and lower values in winter. Significantly high values in Chl-a, and lower values in SD, in the nearshore regions, such as Thunder Bay, Saginaw Bay, and Whitefish Bay, can be related to the very shallow bathymetry and freshwater inputs from the land. The time series of VIIRS-derived Chl-a and SD data provide strong interannual variability in most of the Great Lakes.

Eutrophication Monitoring for Lake Pamvotis, Greece, Using Sentinel-2 Data

The use of remote sensing to monitor inland waters and their current state is of high importance, as fresh waters are the habitat of many species of flora and fauna, and are also important for anthropogenic activities. Water quality can be monitored by many parameters, including dissolved suspended matter, phytoplankton, turbidity, and dissolved organic matter, while the concentration of chlorophyll-a (chl-a) is a representative indicator for detecting phytoplankton and monitoring water quality. The detection of phytoplankton in water layers, through chl-a indicators, is an effective method for displaying eutrophication. Numerous scientific publications and studies have shown that remote sensing data and techniques are capable of monitoring the temporal and spatial distribution and variation of this phenomenon. This study aimed to investigate the eutrophication in Pamvotis Lake, in Ioannina, Greece with the application of chl-a detection algorithms, by using Sentinel-2 satellite imagery data for the time period of 2016–2018. The maximum chlorophyll index (MCI) and maximum peak-height (MPH) algorithms have been applied to top of atmosphere (TOA) reflectance data, to detect chl-a and monitor the trophic range of the water body. Both algorithms were correlated and resulted in Pearson’s r values up to 0.95. Finally, the chl-a concentration was estimated by applying an empirical equation that correlates the MPH and chl-a concentration developed within previous studies. Those results were further analyzed and interpreted with spatial statistical methods, to understand the spatial distribution pattern of the eutrophication in our study area. Our results demonstrated that Pamvotis Lake is a eutrophic lake, and the highest chl-a concentration was located in the east and south-east of the lake during the study period. Sentinel-2 data can be a useful tool for lake managers, in order to estimate the spatial distribution of the chl-a concentration and identify areas prone to eutrophication, as well as the coastal zones that may influence the lake through water canals.

RadCalNet: A Radiometric Calibration Network for Earth Observing Imagers Operating in the Visible to Shortwave Infrared Spectral Range

Vicarious calibration approaches using in situ measurements saw first use in the early 1980s and have since improved to keep pace with the evolution of the radiometric requirements of the sensors that are being calibrated. The advantage of in situ measurements for vicarious calibration is that they can be carried out with traceable and quantifiable accuracy, making them ideal for interconsistency studies of on-orbit sensors. The recent development of automated sites to collect the in situ data has led to an increase in the available number of datasets for sensor calibration. The current work describes the Radiometric Calibration Network (RadCalNet) that is an effort to provide automated surface and atmosphere in situ data as part of a network including multiple sites for the purpose of optical imager radiometric calibration in the visible to shortwave infrared spectral range. The key goals of RadCalNet are to standardize protocols for collecting data, process to top-of-atmosphere reflectance, and provide uncertainty budgets for automated sites traceable to the international system of units. RadCalNet is the result of efforts by the RadCalNet Working Group under the umbrella of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) and the Infrared Visible Optical Sensors (IVOS). Four radiometric calibration instrumented sites located in the USA, France, China, and Namibia are presented here that were used as initial sites for prototyping and demonstrating RadCalNet. All four sites rely on collection of data for assessing the surface reflectance as well as atmospheric data over that site. The data are converted to top-of-atmosphere reflectance within RadCalNet and provided through a web portal to allow users to either radiometrically calibrate or verify the calibration of their sensors of interest. Top-of-atmosphere reflectance data with associated uncertainties are available at 10 nm intervals over the 400 nm to 1000 nm spectral range at 30 min intervals for a nadir-viewing geometry. An example is shown demonstrating how top-of-atmosphere data from RadCalNet can be used to determine the interconsistency between two sensors.

Satellite Ocean Colour: Current Status and Future Perspective

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and interannual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.

VIIRS-Derived Water Turbidity in the Great Lakes

Satellite ocean color products from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) since 2012 and in situ water turbidity measurements from the U.S. Environmental Protection Agency’s Great Lakes Environmental Database System are used to develop a water turbidity algorithm for satellite ocean color applications in the Great Lakes for water quality monitoring and assessments. Results show that the proposed regional algorithm can provide reasonably accurate estimations of water turbidity from satellite observations in the Great Lakes. Therefore, VIIRS-derived water turbidity data are used to investigate spatial and temporal variations in water turbidity for the entirety of the Great Lakes. Water turbidity values are overall the highest in Lake Erie, moderate in Lake Ontario, and relatively low in lakes Superior, Michigan, and Huron. Significantly high values in water turbidity appear in the nearshore regions, particularly in Thunder Bay (Lake Superior), Green Bay (Lake Michigan), and Saginaw Bay (Lake Huron). Seasonal patterns of water turbidity are generally similar in lakes Superior, Michigan, Huron, and Ontario, showing relatively high values in the spring and autumn months and lows in the winter season, while the seasonal pattern in Lake Erie is apparently different from the other lakes, with the highest value in the winter season and the lowest in the summer season. A strong interannual variability in water turbidity is shown in the time series of the VIIRS-derived water turbidity data for most of the lakes.

Sensor performance requirements for atmospheric correction of satellite ocean color remote sensing

We analyze the effects of the sensor signal-to-noise ratio (SNR) requirements for atmospheric correction of satellite ocean color remote sensing using the near-infrared (NIR) and shortwave infrared (SWIR) bands. Using the Gaussian noise model for the sensor noise distribution in the NIR and SWIR bands, some extensive simulations have been carried out to evaluate and assess the effects of sensor NIR and SWIR SNR values on the retrieved normalized water-leaving reflectance spectra ρwN(λ), which are used to derive all ocean or inland water biological and biogeochemical property data. The standard atmospheric correction algorithm for global oceans and inland waters using the two NIR bands, i.e., Gordon and Wang (1994) [Appl. Opt.33, 443 (1994)Appl. Opt.46, 1535 (2007)], is assumed in the evaluation. Specifically, the minimum and goal SNR requirements for the NIR and SWIR bands for atmospheric correction are estimated. The minimum SNR values are those with which sufficiently accurate ρwN(λ) can be derived, while the goal SNR requirements are those with which the atmospheric correction algorithms reach to their corresponding inherent limitations (or inherent errors), i.e., no gains can be achieved with further increase of SNR values in the NIR and SWIR bands. Evaluation results show that the minimum SNR requirement for the two NIR bands is ~200-300, while the minimum SNR requirement for the three SWIR bands is ~100. For the goal SNR requirements, the recommendations are SNR's of ~600 and ~200 for the two NIR bands and three SWIR bands, respectively.

Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI chlorophyll-a data

In this work, trend estimates are used as indicators to compare the multi-annual variability of different satellite chlorophyll-a (Chla) data and to assess the fitness-for-purpose of multi-mission Chla products as climate data records (CDR). Under the assumption that single-mission products are free from spurious temporal artifacts and can be used as benchmark time series, multi-mission CDRs should reproduce the main trend patterns observed by single-mission series when computed over their respective periods. This study introduces and applies quantitative metrics to compare trend distributions from different data records. First, contingency matrices compare the trend diagnostics associated with two satellite products when expressed in binary categories such as existence, significance and signs of trends. Contingency matrices can be further summarized by metrics such as Cohen's κ index that rates the overall agreement between the two distributions of diagnostics. A more quantitative measure of the discrepancies between trends is provided by the distributions of differences between trend slopes. Thirdly, maps of the level of significance P of a t-test quantifying the degree to which two trend estimates differ provide a statistical, spatially-resolved, evaluation. The proposed methodology is applied to the multi-mission Ocean Colour-Climate Change Initiative (OC-CCI) Chla data. The agreement between trend distributions associated with OC-CCI data and single-mission products usually appears as good as when single-mission products are compared. As the period of analysis is extended beyond 2012 to 2015, the level of agreement tends to be degraded, which might be at least partly due to the aging of the MODIS sensor on-board Aqua. On the other hand, the trends displayed by the OC-CCI series over the short period 2012-2015 are very consistent with those observed with VIIRS. These results overall suggest that the OC-CCI Chla data can be used for multi-annual time series analysis (including trend detection), but with some caution required if recent years are included, particularly in the central tropical Pacific. The study also recalls the challenges associated with creating a multi-mission ocean color data record suitable for climate research.

Environmental structuring of marine plankton phenology

Seasonal cycles of primary production (phenology) critically influence biogeochemical cycles, ecosystem structure and climate. In the oceans, primary production is dominated by microbial phytoplankton that drift with currents, and show rapid turnover and chaotic dynamics, factors that have hindered understanding of their phenology. We used all available observations of upper-ocean phytoplankton concentration (1995-2015) to describe global patterns of phytoplankton phenology, the environmental factors that structure them, and their relationships to terrestrial patterns. Phytoplankton phenologies varied strongly by latitude and productivity regime: those in high-production regimes were governed by insolation, whereas those in low-production regimes were constrained by vertical mixing. In eight of ten ocean regions, our findings contradict the hypothesis that phytoplankton phenologies are coherent at basin scales. Lastly, the spatial organization of phenological patterns in the oceans was broadly similar to those on land, suggesting an overarching effect of insolation on the phenology of primary producers globally.

Rayleigh radiance computations for satellite remote sensing: accounting for the effect of sensor spectral response function

To understand and assess the effect of the sensor spectral response function (SRF) on the accuracy of the top of the atmosphere (TOA) Rayleigh-scattering radiance computation, new TOA Rayleigh radiance lookup tables (LUTs) over global oceans and inland waters have been generated. The new Rayleigh LUTs include spectral coverage of 335-2555 nm, all possible solar-sensor geometries, and surface wind speeds of 0-30 m/s. Using the new Rayleigh LUTs, the sensor SRF effect on the accuracy of the TOA Rayleigh radiance computation has been evaluated for spectral bands of the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) satellite and the Joint Polar Satellite System (JPSS)-1, showing some important uncertainties for VIIRS-SNPP particularly for large solar- and/or sensor-zenith angles as well as for large Rayleigh optical thicknesses (i.e., short wavelengths) and bands with broad spectral bandwidths. To accurately account for the sensor SRF effect, a new correction algorithm has been developed for VIIRS spectral bands, which improves the TOA Rayleigh radiance accuracy to ~0.01% even for the large solar-zenith angles of 70°-80°, compared with the error of ~0.7% without applying the correction for the VIIRS-SNPP 410 nm band. The same methodology that accounts for the sensor SRF effect on the Rayleigh radiance computation can be used for other satellite sensors. In addition, with the new Rayleigh LUTs, the effect of surface atmospheric pressure variation on the TOA Rayleigh radiance computation can be calculated precisely, and no specific atmospheric pressure correction algorithm is needed. There are some other important applications and advantages to using the new Rayleigh LUTs for satellite remote sensing, including an efficient and accurate TOA Rayleigh radiance computation for hyperspectral satellite remote sensing, detector-based TOA Rayleigh radiance computation, Rayleigh radiance calculations for high altitude lakes, and the same Rayleigh LUTs are applicable for all satellite sensors over the global ocean and inland waters. The new Rayleigh LUTs have been implemented in the VIIRS-SNPP ocean color data processing for routine production of global ocean color and inland water products.

Out-of-band effects of satellite ocean color sensors

We analyze the sensor out-of-band (OOB) effects for satellite ocean color sensors of the sea-viewing wild field-of-view sensor (SeaWiFS), the moderate resolution imaging spectroradiometer (MODIS), and the visible infrared imaging radiometer suite (VIIRS) for phytoplankton-dominated open oceans and turbid coastal and inland waters, following the approach of Wang et al. [Appl. Opt.40, 343 (2001)APOPAI0003-693510.1364/AO.40.000343]. The applicability of the open ocean water reflectance model of Morel and Maritorena [J. Geophys. Res.106, 7163 (2001)JGREA20148-022710.1029/2000JC000319] (MM01) for the sensor OOB effects is analyzed for oligotrophic waters in Hawaii. The MM01 model predicted OOB contributions for oligotrophic waters are consistent with the result from in situ measurements. The OOB effects cause an apparent shift in sensor band center wavelengths in radiometric response, which depends on the sensor spectral response function and the target radiance being measured. Effective band center wavelength is introduced and calculated for three satellite sensors and for various water types. Using the effective band center wavelengths, satellite and in situ measured water optical property data can be more meaningfully and accurately compared. It is found that, for oligotrophic waters, the OOB effect is significant for the SeaWiFS 555 nm band (and somewhat 510 nm band), MODIS 412 nm band, and VIIRS 551 nm band. VIIRS and SeaWiFS have similar sensor OOB performance. For coastal and inland waters, however, the OOB effect is generally not significant for all three sensors, even though some small OOB effects do exist. This study highlights the importance of understanding the sensor OOB effect and the necessity of a complete prelaunch sensor characterization on the quality of ocean color products. Furthermore, it shows that hyperspectral in situ optics measurements are preferred for the purpose of accurately validating satellite-measured normalized water-leaving radiance spectra data.

A new simple concept for ocean colour remote sensing using parallel polarisation radiance

Ocean colour remote sensing has supported research on subjects ranging from marine ecosystems to climate change for almost 35 years. However, as the framework for ocean colour remote sensing is based on the radiation intensity at the top-of-atmosphere (TOA), the polarisation of the radiation, which contains additional information on atmospheric and water optical properties, has largely been neglected. In this study, we propose a new simple concept to ocean colour remote sensing that uses parallel polarisation radiance (PPR) instead of the traditional radiation intensity. We use vector radiative transfer simulation and polarimetric satellite sensing data to demonstrate that using PPR has two significant advantages in that it effectively diminishes the sun glint contamination and enhances the ocean colour signal at the TOA. This concept may open new doors for ocean colour remote sensing. We suggest that the next generation of ocean colour sensors should measure PPR to enhance observational capability.

Identification of pixels with stray light and cloud shadow contaminations in the satellite ocean color data processing

A new flag/masking scheme has been developed for identifying stray light and cloud shadow pixels that significantly impact the quality of satellite-derived ocean color products. Various case studies have been carried out to evaluate the performance of the new cloud contamination flag/masking scheme on ocean color products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). These include direct visual assessments, detailed quantitative case studies, objective statistic analyses, and global image examinations and comparisons. The National Oceanic and Atmospheric Administration (NOAA) Multisensor Level-1 to Level-2 (NOAA-MSL12) ocean color data processing system has been used in the study. The new stray light and cloud shadow identification method has been shown to outperform the current stray light flag in both valid data coverage and data quality of satellite-derived ocean color products. In addition, some cloud-related flags from the official VIIRS-SNPP data processing software, i.e., the Interface Data Processing System (IDPS), have been assessed. Although the data quality with the IDPS flags is comparable to that of the new flag implemented in the NOAA-MSL12 ocean color data processing system, the valid data coverage from the IDPS is significantly less than that from the NOAA-MSL12 using the new stray light and cloud shadow flag method. Thus, the IDPS flag/masking algorithms need to be refined and modified to reduce the pixel loss, e.g., the proposed new cloud contamination flag/masking can be implemented in IDPS VIIRS ocean color data processing.

Evaluation of the aerosol models for SeaWiFS and MODIS by AERONET data over open oceans

The operational atmospheric correction algorithm for Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) uses the predefined aerosol models to retrieve aerosol optical properties, and their accuracy depends on how well the aerosol models can represent the real aerosol optical properties. In this paper, we developed a method to evaluate the aerosol models (combined with the model selection methodology) by simulating the aerosol retrieval using the Aerosol Robotic Network (AERONET) data. Our method can evaluate the ability of aerosol models themselves, independent of the sensor performance. Two types of aerosol models for SeaWiFS and MODIS operational atmospheric correction algorithms are evaluated over global open oceans, namely the GW1994 models and Ahmad2010 models. The results show that GW1994 models significantly overestimate the aerosol optical thicknesses and underestimate the Ångström exponent, which is caused by the underestimation of the scattering phase function. However, Ahmad2010 models can significantly reduce the overestimation of the aerosol optical thickness and the underestimation of the Ångström exponent as a whole, but this improvement depends on the backscattering angle. Ahmad2010 models have a significant improvement in the retrieval of the aerosol optical thickness at a backscattering angle less than 140°. For a backscattering angle larger than 140°, GW1994 models are better at retrieving the aerosol optical thickness than the Ahmad2010 models.

Satellite color observations of the phytoplankton distribution in the eastern equatorial pacific during the 1982-1983. El nino

Dramatic changes in the patterns of satellite-derived pigment concentrations around the Galápagos Islands during February and March 1983 are associated with unusual oceanographic conditions observed during the 1982-1983 El Niño. The redistribution of food resources might have contributed to the reproductive failure of seabirds and marine mammals on these islands during this El Niño.

Oceanic primary production: estimation by remote sensing at local and regional scales

Satellites provide the only avenue by which marine primary production can be studied at ocean-basin scales. With maps of chlorophyll distribution derived from remotely sensed data on ocean color as input, deduction of a suitable algorithm for primary production is a problem in applied plant physiology. An algorithm is proposed that combines a spectral and angular model of submarine light with a model of the spectral response of algal photosynthesis. To apply the algorithm at large horizontal scale, a dynamic biogeography is needed for the physiological rate parameters and the biological structure of the water column. Fieldwork to obtain this type of data should be undertaken so that the use of satellite data in modern biological oceanography may be optimized.

Comparison of SeaWiFS measurements of the Moon with the U.S. Geological Survey lunar model

The Sea-Viewing Wide-Field-of-View Sensor (SeaWiFS) has made monthly observations of the Moon since 1997. Using 66 monthly measurements, the SeaWiFS calibration team has developed a correction for the instrument's on-orbit response changes. Concurrently, a lunar irradiance model has been developed by the U.S. Geological Survey (USGS) from extensive Earth-based observations of the Moon. The lunar irradiances measured by SeaWiFS are compared with the USGS model. The comparison shows essentially identical response histories for SeaWiFS, with differences from the model of less than 0.05% per thousand days in the long-term trends. From the SeaWiFS experience we have learned that it is important to view the entire lunar image at a constant phase angle from measurement to measurement and to understand, as best as possible, the size of each lunar image. However, a constant phase angle is not required for using the USGS model. With a long-term satellite lunar data set it is possible to determine instrument changes at a quality level approximating that from the USGS lunar model. However, early in a mission, when the dependence on factors such as phase and libration cannot be adequately determined from satellite measurements alone, the USGS model is critical to an understanding of trends in instruments that use the Moon for calibration. This is the case for SeaWiFS.

SIMBAD: a field radiometer for satellite ocean-color validation

A hand-held radiometer, called SIMBAD, has been designed and built specifically for evaluating satellite-derived ocean color. It provides information on the basic ocean-color variables, namely aerosol optical thickness and marine reflectance, in five spectral bands centered at 443, 490, 560, 670, and 870 nm. Aerosol optical thickness is obtained by viewing the Sun disk and measuring the direct atmospheric transmittance. Marine reflectance is obtained by viewing the ocean surface and measuring the upwelling radiance through a vertical polarizer in a geometry that minimizes glitter and reflected sky radiation, i.e., at 45 degrees from nadir (near the Brewster angle) and at 135 degrees in azimuth from the Sun's principal plane. Relative inaccuracy on marine reflectance, established theoretically, is approximately 6% at 443 and 490 nm, 8% at 560 nm, and 23% at 670 nm for case 1 waters containing 0.1 mg m(-3) of chlorophyll a. Measurements by SIMBAD and other instruments during the Second Aerosol Characterization Experiment, the Aerosols-99 Experiment, and the California Cooperative Oceanic Fisheries Investigations cruises agree within uncertainties. The radiometer is compact, light, and easy to operate at sea. The measurement protocol is simple, allowing en route measurements from ships of opportunity (research vessels and merchant ships) traveling the world's oceans.

Accurate and self-consistent ocean color algorithm: simultaneous retrieval of aerosol optical properties and chlorophyll concentrations

A new algorithm has been developed for simultaneous retrieval of aerosol optical properties and chlorophyll concentrations in case I waters. This algorithm is based on an improved complete model for the inherent optical properties and accurate simulations of the radiative transfer process in the coupled atmosphere-ocean system. It has been tested against synthetic radiances generated for the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) channels and has been shown to be robust and accurate. A unique feature of this algorithm is that it uses the measured radiances in both near-IR and visible channels to find that combination of chlorophyll concentration and aerosol optical properties that minimizes the error across the spectrum. Thus the error in the retrieved quantities can be quantified.

Pitfalls in atmospheric correction of ocean color imagery: how should aerosol optical properties be computed?: Comment

The Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) experience suggests that in most situations the aerosol models presently in use for atmospheric correction of ocean color imagery are sufficient for this task. It has been shown [Appl. Opt. 41, 412 (2002)] that the top-of-atmosphere reflectances computed for more realistic aerosol models differ from those computed for presently used models but have not shown that they will yield a better atmospheric correction, e.g., through direct application to ocean color imagery. Thus they provide no evidence that the presently used aerosol models are inadequate, or that their use is a pitfall in atmospheric correction.

Calibration of SeaWiFS. I. Direct techniques

We present an overview of the calibration of the Sea-viewing Wide Field-of View Sensor (SeaWiFS) from its performance verification at the manufacturer's facility to the completion of its third year of on-orbit measurements. These calibration procedures have three principal parts: a prelaunch radiometric calibration that is traceable to the National Institute of Standards and Technology; the Transfer-to-Orbit Experiment, a set of measurements that determine changes in the instrument's calibration from its manufacture to the start of on-orbit operations; and measurements of the sun and the moon to determine radiometric changes on orbit. To our knowledge, SeaWiFS is the only instrument that uses routine lunar measurements to determine changes in its radiometric sensitivity. On the basis of these methods, the overall uncertainty in the SeaWiFS top-of-the-atmosphere radiances is estimated to be 4-5%. We also show the results of comparison campaigns with aircraft- and ground-based measurements, plus the results of an experiment, called the Southern Ocean Band 8 Gain Study. These results are used to check the calibration of the SeaWiFS bands. To date, they have not been used to change the instrument's prelaunch calibration coefficients. In addition to these procedures, SeaWiFS is a vicariously calibrated instrument for ocean-color measurements. In the vicarious calibration of the SeaWiFS visible bands, the calibration coefficients are modified to force agreement with surface truth measurements from the Marine Optical Buoy, which is moored off the Hawaiian Island of Lanai. This vicarious calibration is described in a companion paper.

Atmospheric Correction of Ocean Color Imagery: Test of the Spectral Optimization Algorithm with the Sea-Viewing Wide Field-of-View Sensor

We implemented the spectral optimization algorithm [SOA; Appl. Opt. 37, 5560 (1998)] in an image-processing environment and tested it with Sea-viewing Wide Field-of-View Sensor (SeaWiFS) imagery from the Middle Atlantic Bight and the Sargasso Sea. We compared the SOA and the standard SeaWiFS algorithm on two days that had significantly different atmospheric turbidities but, because of the location and time of the year, nearly the same water properties. The SOA-derived pigment concentration showed excellent continuity over the two days, with the relative difference in pigments exceeding 10% only in regions that are characteristic of high advection. The continuity in the derived water-leaving radiances at 443 and 555 nm was also within ~10%. There was no obvious correlation between the relative differences in pigments and the aerosol concentration. In contrast, standard processing showed poor continuity in derived pigments over the two days, with the relative differences correlating strongly with atmospheric turbidity. SOA-derived atmospheric parameters suggested that the retrieved ocean and atmospheric reflectances were decoupled on the more turbid day. On the clearer day, for which the aerosol concentration was so low that relatively large changes in aerosol properties resulted in only small changes in aerosol reflectance, water patterns were evident in the aerosol properties. This result implies that SOA-derived atmospheric parameters cannot be accurate in extremely clear atmospheres.

Atmospheric Correction of SeaWiFS Imagery: Assessment of the Use of Alternative Bands

Spatial inhomogeneity, or speckling, frequently occurs in Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data products such as water-leaving radiance and chlorophyll concentration. We have found that this effect may be caused by high-altitude aerosols or thin cirrus clouds or even by digitization errors. For the scenes evaluated, whitecaps were ruled out as a likely cause of these errors. We tried to avoid using the 765-nm band, which is affected by O(2) absorption and is more sensitive to digitization errors, by instead using the 670-nm band in the atmospheric correction and found that speckling for either cloud-free areas or cloud-adjacent areas was significantly reduced.

Changes in the Radiometric sensitivity of SeaWiFS determined from lunar and solar-based measurements

We report on the lunar and solar measurements used to determine the changes in the radiometric sensitivity of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Radiometric sensitivity is defined as the output from the instrument (or from one of the instrument bands) per unit spectral radiance at the instrument's input aperture. Knowledge of the long-term repeatability of the SeaWiFS measurements is crucial to maintaining the quality of the ocean scenes derived from measurements by the instrument. For SeaWiFS bands 1-6 (412-670 nm), the change in radiometric sensitivity is less than 0.2% for the period from November 1997 through November 1998. For band 7 (765 nm), the change is approximately 1.5% and for band 8 (865 nm) approximately 5%. The rates of change of bands 7 and 8, which were linear with time for the first eight months of lunar measurements, are now slowing. The scatter in the data points about the trend lines in this analysis is less than 0.3% for all eight SeaWiFS bands. These results are based on monthly measurements of the moon. Daily solar measurements using an onboard diffuser show that the radiometric sensitivities of the SeaWiFS bands have changed smoothly during the time intervals between lunar measurements. Because SeaWiFS measurements have continued past November 1998, the results presented here are considered as a snapshot of the instrument performance as of that date.

Atmospheric correction of ocean color imagery: use of the junge power-law aerosol size distribution with variable refractive index to handle aerosol absorption

When strongly absorbing aerosols are present in the atmosphere, the usual two-step procedure of processing ocean color data-(1) atmospheric correction to provide the water-leaving reflectance (rho(w)), followed by (2) relating rho(w) to the water constituents-fails and simultaneous estimation of the ocean and aerosol optical properties is necessary. We explore the efficacy of using a simple model of the aerosol-a Junge power-law size distribution consisting of homogeneous spheres with arbitrary refractive index-in a nonlinear optimization procedure for estimating the relevant oceanic and atmospheric parameters for case 1 waters. Using simulated test data generated from more realistic aerosol size distributions (sums of log-normally distributed components with different compositions), we show that the ocean's pigment concentration (C) can be retrieved with good accuracy in the presence of weakly or strongly absorbing aerosols. However, because of significant differences in the scattering phase functions for the test and power-law distributions, large error is possible in the estimate of the aerosol optical thickness. The positive result for C suggests that the detailed shape of the aerosol-scattering phase function is not relevant to the atmospheric correction of ocean color sensors. The relevant parameters are the aerosol single-scattering albedo and the spectral variation of the aerosol optical depth. We argue that the assumption of aerosol sphericity should not restrict the validity of the algorithm and suggest an avenue for including colored aerosols, e.g., wind-blown dust, in the procedure. A significant advantage of the new approach is that realistic multicomponent aerosol models are not required for the retrieval of C.

Remote sensing of ocean color and aerosol properties: resolving the issue of aerosol absorption

Current atmospheric correction and aerosol retrieval algorithms for ocean color sensors use measurements of the top-of-the-atmosphere reflectance in the near infrared, where the contribution from the ocean is known for case 1 waters, to assess the aerosol optical properties. Such measurements are incapable of distinguishing between weakly and strongly absorbing aerosols, and the atmospheric correction and aerosol retrieval algorithms fail if the incorrect absorption properties of the aerosol are assumed. We present an algorithm that appears promising for the retrieval of in-water biophysical properties and aerosol optical properties in atmospheres containing both weakly and strongly absorbing aerosols. By using the entire spectrum available to most ocean color instruments (412-865 nm), we simultaneously recover the ocean's bio-optical properties and a set of aerosol models that best describes the aerosol optical properties. The algorithm is applied to simulated situations that are likely to occur off the U.S. East Coast in summer when the aerosols could be of the locally generated weakly absorbing Maritime type or of the pollution-generated strongly absorbing urban-type transported over the ocean by the winds. The simulations show that the algorithm behaves well in an atmosphere with either weakly or strongly absorbing aerosol. The algorithm successfully identifies absorbing aerosols and provides close values for the aerosol optical thickness. It also provides excellent retrievals of the ocean bio-optical properties. The algorithm uses a bio-optical model of case 1 waters and a set of aerosol models for its operation. The relevant parameters of both the ocean and atmosphere are systematically varied to find the best (in a rms sense) fit to the measured top-of-the-atmosphere spectral reflectance. Examples are provided that show the algorithm's performance in the presence of errors, e.g., error in the contribution from whitecaps and error in radiometric calibration.

Remote sensing of ocean color: assessment of water-leaving radiance bidirectional effects on atmospheric diffuse transmittance

Two factors influence the diffuse transmittance (t) of water-leaving radiance (L(w)) to the top of the atmosphere: the angular distribution of upwelling radiance beneath the sea surface (L(u)) and the concentration and optical properties of aerosols in the atmosphere. We examine these factors and (1) show that the error in L(w) that is induced by assuming L(u) is uniform (i.e., in treating the subsurface reflectance by the water body as Lambertian) is significant in comparison with the other errors expected in L(w) only at low phytoplankton concentration and then only in the blue region of the spectrum; (2) show that when radiance ratios are used in biophysical algorithms the effect of the uniform- L (u) approximation is even smaller; and (3) provide an avenue for introducing accurate computation of the uniform L(u) diffuse transmittance into atmospheric correction algorithms. In an Appendix the reciprocity principle is derived for a medium in which the refractive index is a continuous function of position.

Atmospheric correction of ocean color sensors: analysis of the effects of residual instrument polarization sensitivity

We provide an analysis of the influence of instrument polarization sensitivity on the radiance measured by spaceborne ocean color sensors. Simulated examples demonstrate the influence of polarization sensitivity on the retrieval of the water-leaving reflectance rho(w). A simple method for partially correcting for polarization sensitivity--replacing the linear polarization properties of the top-of-atmosphere reflectance with those from a Rayleigh-scattering atmosphere--is provided and its efficacy is evaluated. It is shown that this scheme improves rho(w) retrievals as long as the polarization sensitivity of the instrument does not vary strongly from band to band. Of course, a complete polarization-sensitivity characterization of the ocean color sensor is required to implement the correction.

Simultaneous determination of water-leaving reflectance and aerosol optical thickness from Coastal Zone Color Scanner measurements

We present an iterative algorithm for the simultaneous determination of water-leaving reflectances and aerosol optical thickness from the total outgoing radiances measured in the Coastal Zone Color Scanner (CZCS) visible and near-infrared channels. Numerical experiments were carried out to investigate the feasibility of the algorithm. The results show that the errors in determined water-leaving reflectance at 0.443 microm are approximately 10% in most cases. The errors in determined water-leaving reflectance at 0.55 microm are in the 4.05-7.2% range. The errors in the simultaneously determined aerosol optical thickness for all CZCS visible and near-infrared channels are less than approximately 10%.

Satellite image analysis techniques applied to oceanography

Infrared and visible spectral sensors on spacecraft can provide imagery of ocean conditions equivalent to a global network of in situ sensors. Oceanographers can now view all the world’s oceans during one day’s travel of a polar-orbiting satellite. The surface areas of complex ocean regions that have strong thermal and chemical gradients, such as the Gulf Stream in the Atlantic and the Kuroshio in the Pacific, are displayed in minute detail in the imagery provided by satellite infrared and visible sensors. Because of the vast quantities of data involved in satellite infrared and visible imagery, computer techniques must be used to derive oceanographic information. A variety of basic computer image analysis techniques are required to do so. These basic techniques include selective enhancement, geographic registration, absolute ocean radiation and multiple-image composition. Through the use of these techniques, satellite and conventional oceanographic data can be integrated to form synergistically a powerful analytical tool for modern ocean research.

Analysis of the influence of O(2) A-band absorption on atmospheric correction of ocean-color imagery

Two satellite-borne ocean-color sensors scheduled for launch in the mid 1990's each have a spectral band (nominally 745-785 nm) that completely encompasses the O(2) A band at 762 nm. These spectral bands are to be used in atmospheric correction of the color imagery by assessment of the aerosol contribution to the total radiance at the sensor. The effect of the O(2) band on the radiance measured at the satellite is studied with a line-by-line backward Monte Carlo radiative transfer code. As expected, if the O(2) absorption is ignored, unacceptably large errors in the atmospheric correction result. The effects of the absorption depend on the vertical profile of the aerosol. By assuming an aerosol profile-the base profile-we show that it is possible to remove most of the O(2)-absorption effects from atmospheric correction in a simple manner. We also investigate the sensitivity of the results to the details of the assumed base profile and find that, with the exception of situations in which there are significant quantities of aerosol in the stratosphere, e.g., following volcanic eruptions or in the presence of thin cirrus clouds, the quality of the atmospheric correction depends only weakly on the base profile. Situations with high concentrations of stratospheric aerosol require additional information regarding vertical structure for this spectral band to be used in atmospheric correction; however, it should be possible to infer the presence of such aerosol by a failure of the atmospheric correction to produce acceptable water-leaving radiance in the red. An important feature of our method for removal of the O(2)-absorption effects is that it permits the use of lookup tables that can be prepared in the absence of O(2) absorption by the use of more efficient radiative transfer codes.