P Shanmugam

An optical system is developed with the aim to detect and monitor three major algal blooms (including harmful algal blooms “HABs”) over ecologically relevant scales around India and to strengthen algal forecasting system. This system is designed to be capable of utilizing remote sensing, in situ, and radiative transfer techniques to provide species-specific data necessary for increasing capabilities of an algal forecasting system. With the ability to measure in-water optical properties by means of remote sensing and in situ observations, the optical system developed infers the desired phytoplankton signal from spectral distributions and utilize these data in a numerical classification technique to generate species-specific maps at given spatial and temporal scales. A simple radiative transfer model is adopted for this system to provide a means to optimally interpolate to regions with sparse in situ observation data and to provide a central component to generate in-water optical properties from remotely sensed data. For a given set of inherent optical properties along with surface and bottom boundary conditions, the optical system potentially provides researchers and managers coverage at different locations and depths for tracking algal blooms in the water column. Three major algal blooms focused here include Noctiluca scintillans/miliaris, Trichodesmium erythraeum, and Cochlodinium polykrikoides, which are recurring events in coastal and oceanic waters around India. Because satellite sensors provide a synoptic view of the ocean, both spatially and temporally, our initial efforts tested this optical system using several MODIS-Aqua images and ancillary data. Validation of the results with coincident in situ data obtained from either surface samples or depth samples demonstrated the robustness and potential utility of this approach, with an accuracy of 80–90% for delineating the presence of all three blooms in a heterogeneous phytoplankton community. Despite its limitation in detecting specific species during their prebloom phases and indicating whether a particular bloom is toxic or harmful, the proposed optical system will provide managers with the specific phytoplankton bloom maps to structure monitoring efforts and a powerful tool for studying the dynamics of algal blooms at various temporal and spatial scales.

Scattering phase function plays a crucial role in studies and calculations based on radiative transfer theory in water as well as atmosphere. A model based on Mie theory is developed for estimating the particulates-in-water scattering phase function for forward angles (0.1° - 90°). Particle size distribution (PSD) slope (ξ) and bulk refractive index (n) are chosen as key inputs for this proposed model. The PSD slope can be estimated from the attenuation spectrum measured directly in-situ and the bulk refractive index can be calculated by an inversion model using measured backscattering ratio (BP) and PSD slope. The attenuation spectrum and backscattering ratio can be easily measured in-situ using commercially available instruments in real time. The entire range of forward angles is divided into two ranges and phase function is modeled separately in the ranges 0.1° - 5° and 5° - 90°, from numerically calculated Volume Scattering Function (VSF) using Mie theory. The division boundary is decided owing to the fact that the scattering phase functions, for different oceanic conditions, exhibit a change in slope at approximately 5°. Performance of the present model is evaluated by comparing with existing empirical and analytical models as well as measured phase functions. The proposed phase function model shows a considerable improvement upon existing models, and will have important applications in remote sensing applications and underwater studies.

Underwater radiometers are generally calibrated in air using a standard source. The immersion factors are required for these radiometers to account for the change in the in-water measurements with respect to in-air due to the different refractive index of the medium. The immersion factors previously determined for RAMSES series of commercial radiometers manufactured by TriOS are applicable to clear oceanic waters. In typical inland and turbid productive coastal waters, these experimentally determined immersion factors yield significantly large errors in water-leaving radiances (Lw) and hence remote sensing reflectances (Rrs). To overcome this limitation, a semi-analytical method with based on the refractive index approximation is proposed in this study, with the aim of obtaining reliable Lw and Rrs from RAMSES radiometers for turbid and productive waters within coastal and inland water environments. We also briefly show the variation of pure water immersion factors (Ifw) and newly derived If on Lw and Rrs for clear and turbid waters. The remnant problems other than the immersion factor coefficients such as transmission, air-water and water-air Fresnel's reflectances are also discussed.

Ocean surface algal blooms (OSABs) are an emerging and progressively more important phenomenon in many regional waters. Current techniques perhaps delineate the spatial distributions of OSABs from the above-atmosphere spectral radiances or Raleigh corrected radiances, so far optical spectral characterization of the OSABs has been impossible due to the inability of the atmospheric correction algorithm to account for their enhanced radiances at the near-infrared (NIR) bands. An innovative algorithm, referred as the 'OSABT', is developed to provide essential data for detection and optical spectral characterization of OSABs in coastal and open ocean waters. The algorithm is applied to MODIS-Aqua imagery from the Arabian Sea and its results are systematically studied with regard to the spectral characteristics of OSABs, its evolution stages, sub-pixel variability, and atmospheric correction issues. The effectiveness of the OSABT is tested for several other regional waters on MODIS-Aqua data, where spatial bloom characteristics are previously known, and their optical spectra are featured with enhanced radiance at the NIR bands and reduced radiance at the red bands. The spectral signatures differ for each OSABs, and correlate to their evolution stages as well. These results support future applicability of this algorithm to derive the water-leaving radiance and new indices from the OSAB pixels for qualitative and quantitative assessment of the optical and biological OSAB properties as well as their associated ecological activities (e.g., growth, aggregation, density and impacts). The advantage of the OSABT is that it can be applied rather generally to other satellite-based sensors with no short-wave infrared bands, provided that suitable radiometric calibration coefficients are determined for using the OSABT. © 2013 IEEE.

Total alkalinity (TA) is a key parameter to understand the dynamics of biogeochemical properties in the global ocean and the effects of climate change on ocean acidification, ocean carbon cycle, and carbonate chemistry. To date, global surface ocean distributions of TA were investigated using multiple regional regression approaches which require smoothening techniques due to severe boundary effects in different oceanic regions/basins across latitudes/longitudes. To reduce the uncertainties and produce spatially and temporally consistent TA products, a novel single linear regression (SLR) approach was developed in this study to estimate TA fields in the global surface ocean waters. The SLR formulation was derived using the continuous in-situ measurements of sea surface salinity (SSS) collected from the different oceans. The performance of the SLR was assessed using independent in-situ/satellite derived TA data and the results from three existing algorithms. In general, the SLR-based global surface ocean TA fields from both in-situ and satellite data agreed well with in-situ measured TA data with a mean relative error less than 1%, which is much lower compared to the error with the existing algorithms. Studies were also conducted to examine the spatiotemporal variability and trends in the global surface ocean climatology of SSS and TA fields in the context of current climate change impacts.

The paper "A new theory and its application to remove the effect of surface-reflected light in above-surface radiance data from clear and turbid waters" by Pravin Jeba Dev and Palanisamy Shanmugam (Journal of Quantitative Spectroscopy & Radiative Transfer, 142 (2014) 75-92), has some minor errors and inaccurate expressions in one of the discussion sections. However, these errors do not have any effect on the new theory and its results. We apologize to the concerned editors, reviewers and the readers of the paper. © 2014 Elsevier Ltd.

One of the central goals of using satellite ocean color remote sensing is to detect and identify different phytoplankton groups (size classes) and describe their variability continuously and synoptically for various applications including marine ecosystem dynamics, carbon and biogeochemical cycles, and related fields in oceanography. Taking the advantage of phytoplankton communities having distinct optical properties, this study presents a new model to explicitly detect and differentiate between three phytoplankton size-classes namely, picophytoplankton, nanophytoplankton, and microphytoplankton, based on distinct differences in the optical signatures of these phytoplankton groups in a wide variety of coastal and oceanic waters. The model is based on the assumption that there is a significant relationship between chlorophyll-. a concentration, and total as well as the size-fractioned absorption coefficients of phytoplankton. The new model is validated using three different in-situ datasets collected from a wide variety of locations in the global and regional oceans (including turbid coastal and eutrophic waters) and its results are further compared with those of the existing two- and three-component models. The new model performs better than other models in terms of yielding more accurate estimates of the total and size-dependent phytoplankton absorption coefficients across the entire visible wavelengths. Since satellite observation of 'ocean color' as detected by a remote sensor provides an estimate of the chlorophyll-. a concentration, commonly used as an index of phytoplankton biomass, the new model is also applied to regional and global images of seasonal climatology over a decade of satellite ocean color observations provided by the MODIS-Aqua sensor. When applied to the MODIS-Aqua images of the Arabian Sea dominated by spatially intense algal blooms, the present model is generally excellent at predicting and describing the spatial distribution of these phytoplankton groups within cyclonic eddies and adjacent regions in the Arabian Sea. Conversely, size-fractioned phytoplankton absorption coefficients derived from global images of seasonal climatology are found to vary depending on the season and ocean basin. These global images imply that when phytoplankton abundance increases, larger size-classes are added incrementally to a background of smaller cells. Further examination of these data showed that picophytoplankton population is generally low, although dominating a major part of the surface ocean during summer and winter. Nanophytoplankton and microphytoplankton populations are high in surface waters of the North and South Atlantic, North and South Pacific Oceans, Arabian Sea, and equatorial region, showing an increasing trend in summer and a decreasing trend in winter in each hemisphere. These results suggest that the new model is an important tool which will inspire further research to investigate different phytoplankton size classes and their variability on regional and global scales.

The Moderate-resolution Wide-wavelengths Imager (MWI) is the ocean color sensor onboard the Chinese Tiangong-2 Space Lab, which was launched on Sept. 15, 2016. The MWI is also an experimental satellite sensor for the Chinese next generation ocean color satellites, HY-1E and HY-1F, which are scheduled for launch around 2021. With 100m spatial resolution and 18 bands in the visible light and infrared wavelengths, MWI provides high quality ocean color observations especially over coastal and inland waters. For the first time, this study presents some important results on water color products generated from the MWI for the oceanic and inland waters. Preliminary validation in turbid coastal and inland waters showed good agreement between the MWI-retrieved normalized water-leaving radiances (Lwn) and in situ data. Further, the MWI-retrieved Lwn values compared well with the GOCI-retrieved Lwn values, with the correlation coefficient greater than 0.90 and mean relative differences smaller than 26.63% (413 nm), 4.72% (443 nm), 3.69% (490 nm), 7.15% (565 nm), 9.45% (665 nm), 8.11% (682.5 nm), 14.68% (750 nm) and 18.55% (865 nm). As for the Level 2 product (e.g, total suspended matter TSM) in turbid Yangtze River Estuary and Hangzhou Bay waters, the relative difference between MWI and GOCI-derived TSM values was ~18.59% with the correlation coefficient of 0.956. In open-oceanic waters, the retrieved MWI-Chla distributions were well consistent with the MODIS/Aqua and VIIRS Chla values products and resolved finer spatial structures of phytoplankton blooms. This study provides encouraging results for the MWI’s performance and operational applications in oceanic and inland regions.

This paper aims to describe the variability of particulate absorption properties using a unique hyperspectral dataset collected in the Red Sea as part of the TARA Oceans expedition. The absorption contributions by phytoplankton (aph) and non-algal particles (aNAP) to the total particulate absorption coefficients are determined using a numerical decomposition method (NDM). The NDM is validated by comparing the NDM derived values of aph and aNAP with simulated values of aph and aNAP are found to be in excellent agreement for the selected wavelengths (i.e., 443, 490, 555, and 676 nm) with high correlation coefficient (R2), low root mean square error (RMSE), mean relative error (MRE), and with a slope close to unity. Further analyses showed that the total particulate absorption coefficients (i.e., ap(443)average = 0.01995 m−1) were dominated by phytoplankton absorption (i.e., aph(443)average = 0.01743 m−1) with a smaller contribution by non-algal particles absorption (i.e., aNAP(443)average = 0.002524 m−1). The chlorophyll a is computed using the absorption based Line Height Method (LHM). The derived chlorophyll-specific absorption ((a*ph = aph(λ)/ChlLH)) showed more variability in the blue part of spectrum as compared to the red part of spectrum representative of the package effect and changes in pigment composition. A new parametrization proposed also enabled the reconstruction of a*ph(λ) for the Red Sea. Comparison of derived spectral constants with the spectral constants of existing models showed that our study A(λ) values are consistent with the existing values, despite there is a divergence with the B(λ) values. This study provides valuable information derived from the particulate absorption properties and its spectral variability and this would help us to determine the relationship between the phytoplankton absorption coefficients and chlorophyll a and its host of variables for the Red Sea.

Sea-surface salinity (SSS) can be measured from space using a microwave sensor. However, achieving the desired accuracy in SSS retrieval is challenging due to the lower sensitivity of the brightness temperature to SSS especially at low sea-surface temperature conditions. The retrieval accuracy can be further degraded due to the atmospheric and sea-surface effects (including emission and reflection), which require more accurate correction methods based on the radiative transfer model. In this article, a vector radiative transfer model (VRTM) was developed based on a matrix operator method that considers the ocean–atmosphere system under non-raining conditions. The results from this model were compared with measurement data provided by the Soil Moisture and Ocean Salinity (SMOS) satellite sensor and the results from two other RT models (RT4 model and a forward model of the European Space Agency, ESA). Statistical evaluation of these results revealed that estimation errors of top of atmosphere (TOA) radiance by the VRTM model was less than 0.3% as compared to the RT4 model results. The difference of the brightness temperatures predicted by the VRTM model and measured by the SMOS was within 1.5 K which was better than the ESA’s forward model predictions. These results suggest that the VRTM is relatively more accurate and has high computational efficiency for simulating the TOA brightness temperature for various scientific research and remote-sensing applications.