Palanisamy Shanmugam

New atmospheric correction technique to retrieve the ocean colour from SeaWiFS imagery in complex coastal waters

There exists a large demand for an accurate atmospheric correction of satellite ocean colour data over highly turbid coastal waters, where the standard atmospheric correction (SAC) algorithms designed for open ocean water turn out to be unsuccessful because of eventual interference of elevated radiance from suspended materials and perhaps the shallow bottom with the corrections based on the two near-infrared bands at 765 and 865 nm in which the water-leaving radiances are discarded (or modelled) in order to estimate aerosol radiative properties and extrapolate these into the visible spectrum in the atmospheric correction of the imagery. Furthermore, in the presence of strongly absorbing aerosols (e.g. Asian dust and Sahara dust) the SAC algorithms often underestimate water-leaving radiance values in the violet and blue spectrum or completely fail to deliver the desired biogeochemical products for coastal regions. To make the satellite ocean colour data offer unrivaled utility in monitoring and quantifying the components of ecologically important coastal waters, this study presents a more realistic and cost-effective image-based atmospheric correction method to accurately retrieve water-leaving radiances and chlorophyll concentrations from SeaWiFS imagery in the presence of strongly absorbing aerosols over highly turbid Northwest Pacific coastal waters. This method is a modified version of the spectral shape matching method (SSMM) previously developed by Ahn and Shanmugam (2004 Korean J. Remote Sens. 20 289–305), re-treating the assumption of spatial homogeneity of the atmosphere using simple models for assessing the contributions of aerosol and molecular scattering. Because of the difficulties in making atmospheric measurements concurrently with each overpass of SeaWiFS the atmospheric diffuse transmittance values are dependent on a standard method with the SAC scheme designed for processing SeaWiFS ocean colour data. The new method is extensively tested under the presence of various atmospheric conditions using SeaWiFS imagery and the results are compared with in situ (ship-borne) measurements in highly turbid coastal waters of the Korean Southwest Sea (KSWS). Such comparison demonstrates the efficiency of SSMM in terms of removing the effects of strongly absorbing aerosols (Asian dust) and improving the accuracy of water-leaving radiance retrieval with an RMSE deviation of 0.076, in contrast with 0.326 for the SAC algorithm which masked most of the sediment-laden and aerosol-dominated coastal areas. Further comparison in the Yellow Sea waters representing a massive phytoplankton bloom on 27 March 2002 revealed that the SAC algorithm caused an excessive correction for the visible bands, with the 412 nm band being affected the most, leading to severe overestimation of chlorophyll concentrations in the bloom-contained waters. In contrast, the SSMM remained very effective in terms of reducing errors of both water-leaving radiance and chlorophyll concentration estimates.

A New Inversion Model to Retrieve the Particulate Backscattering in Coastal/Ocean Waters

Scientific implications and practical applications of spectral particulate backscattering (bbp(λ)) in oceanography are wide ranging, particularly in optical remote sensing as the light backscattered from various seawater constituents provides possibility to derive information about the particle properties of the water under investigation. Several inversion models have been previously developed for use with remote sensing reflectance (Rrs) data over open ocean waters; however, when applied to coastal waters, these models return bbp having large differences with in situ bbp values primarily because of the improper definitions of parameters of the functions describing the spectra of bbp(λ). The present study is aimed to develop a new inversion model with appropriate definitions of parameters of the functions to provide reliable retrievals of bbp in a variety of waters covering both the coastal and ocean environments. The new model is tested using large independent in situ data sets (NOMAD and Carder data sets) and simulated data provided by the IOCCG working group. When applied to these data sets, the new model outperformed the currently existing inversion models (e.g., GSM, LM and QAA models). The percent mean relative error (MRE) and root mean square error (RMSE) were found to be MRE -5.16 ~ 0.35% and RMSE 0.114 ~ 0.146 (in the 412-555 nm range) for the simulated data, MRE -0.14 ~ 2.42% and RMSE 0.125 ~ 0.157 for the NOMAD data, MRE -0.77% ~ 2.23% and RMSE 0.124 ~ 0.15 for the Korean regional data, and MRE 6.88% and RMSE 0.218 for the Carder data (for 490 nm only). Slopes close to unity, high R2 and low intercept values also indicated that the new model provides better performance over other models. The results further suggest that the new model is more robust and can be effectively applied to satellite ocean color data to retrieve the particulate backscattering coefficients in a variety of waters commonly found in the coastal and offshore domains.

Spatial and temporal aspects of phytoplankton blooms in complex ecosystems off the korean coast from satellite ocean color observations

Complex physical, chemical and biological interactions off the Korean coast created several striking patterns in the phytoplankton blooms, which became conspicuous during the measurements of ocean color from space. This study concentrated on analyzing the spatial and temporal aspects of phytoplankton chlorophyll variability in these areas using an integrated dataset from a Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Advanced Very High Resolution (AVHRR) sensor, and Conductivity Temperature Depth (CTD) sensor. The results showed that chlorophyll concentrations were elevated in coastal and open ocean regions, with strong summer and fall blooms, which appeared to spread out in most of the enclosed bays and neighboring waters due to certain oceanographic processes. The chlorophyll concentration was observed to range between 3 and 54 mg m-3 inside Jin-hae Bay and adjacent coastal bays and 0.5 and 8 mg m-3 in the southeast sea offshore waters, this gradual decrease towards oceanic waters suggested physical transports of phytoplankton blooms from the shallow shelves to slope waters through the influence of the Tsushima Warm Current (TWC) along the Tsushima Strait. Horizontal distribution of potential temperature (θ) and salinity (S) of water off the southeastern coast exhibited cold and low saline surface water (θ<19°C; S<32.4) and warm and high saline subsurface water (θ>12°C; S>34.4) at 75dBar, corroborating TWC intrusion along the Tsushima Strait. An eastward branch of this current was called the East Korean Warm Current (EKWC), tracked with the help of CTD data and satellite-derived sea surface temperature, which often influenced the dynamics of mesoscale anticyclonic eddy fields off the Korean east coast during the summer season. The process of such mesoscale anticyclonic eddy features might have produced interior upwelling that could have shoaled and steepened the nutricline, enhancing phytoplankton population by advection or diffusion of nutrients in the vicinity of Ulleungdo in the East Sea.

New model for subsurface irradiance reflectance in clear and turbid waters

Modeling of subsurface irradiance reflectance fields especially in turbid coastal, harbor and lagoon waters has important applications in ecology, engineering and optical remote sensing. The present study aims at exploring many possible causes of variation in the proportionality factor f and analyzing its effect on the subsurface irradiance reflectance in different waters. A new model is then developed to estimate this optical property as a function of the absorption coefficient (a), backscattering coefficient (bb), incident illumination condition, and other wavelength-depth dependent factors. Implementation of this new model is examined for five types of waters with varying turbidity and chlorophyll. Model results are verified with in situ measurements data and compared with the results from existing models. Formulas already proposed for estimating R in the previous studies and generally expressed by R = 0.33(bb/(a + bb)) or R = f (bb/(a + bb)) where f = 0.975-0.629 μ(0) (μ(0) is the incident photons just below the sea surface) work fairly well in clear oceanic waters, but yield large errors in turbid coastal and lagoon waters due to the use of a constant value ~0.33 or the dimensionless parameter f which does not account for certain processes in the model (e.g., multiple scattering, depth-dependent changes in the diffuse components of solar radiation, and spectral variation in f). By contrast, the new model estimates the reflectances having good agreement with in situ data from just below the water surface and throughout the water column. The improved performance of the present model is because it includes a parameterization of the proportionality factor f which varies with wavelength and depends on the sun angle, inherent optical properties, and diffuse attenuation coefficients. Knowledge related to interrelationships between inherent optical properties and apparent optical properties can be used to study the variability of the subsurface reflectance in homogeneous and stratified coastal waters with respect to many possible causes of its variations.

Monitoring of ocean surface algal blooms in coastal and oceanic waters around India.

The National Aeronautics and Space Administration’s (NASA) sensor MODIS-Aqua provides an important tool for reliable observations of the changing ocean surface algal bloom paradigms in coastal and oceanic waters around India. A time series of the MODIS-Aqua-derived OSABI (ocean surface algal bloom index) and its seasonal composite images report new information and comprehensive pictures of these blooms and their evolution stages in a wide variety of events occurred at different times of the years from 2003 to 2011, providing the first large area survey of such phenomena around India. For most of the years, the results show a strong seasonal pattern of surface algal blooms elucidated by certain physical and meteorological conditions. The extent of these blooms reaches a maximum in winter (November–February) and a minimum in summer (June–September), especially in the northern Arabian Sea. Their spatial distribution and retention period are also significantly increased in the recent years. The increased spatial distribution and intensity of these blooms in the northern Arabian Sea in winter are likely caused by enhanced cooling, increased convective mixing, favorable winds, and atmospheric deposition of the mineral aerosols (from surrounding deserts) of the post-southwest monsoon period. The southward Oman coastal current and southwestward winds become apparently responsible for their extension up to the central Arabian Sea. Strong upwelling along this coast further triggers their initiation and growth. Though there is a warming condition associated with increased sea surface height anomalies along the coasts of India and Sri Lanka in winter, surface algal bloom patches are still persistent along these coasts due to northeast monsoonal winds, enhanced precipitation, and subsequent nutrient enrichment in these areas. The occurrence of the surface algal blooms in the northern Bay of Bengal coincides with a region of the well-known Ganges–Brahmaputra Estuarine Frontal (GBEF) system, which increases supply of nutrients in addition to the land-derived inputs triggering surface algal blooms in this region. Low density (initiation stage) of such blooms observed in clear oceanic waters southeast and northeast of Sri Lanka may be caused by the vertical mixing processes (strong monsoonal winds) and the occurrence of Indian Ocean Dipole events. Findings based on the analyses of time series satellite data indicate that the new information on surface algal blooms will have important bearing on regional fisheries, ecosystem and environmental studies, and implications of climate change scenarios.

A new model for the vertical spectral diffuse attenuation coefficient of downwelling irradiance in turbid coastal waters: validation with in situ measurements.

The vertical spectral diffuse attenuation coefficient of Kd is an important optical property related to the penetration and availability of light underwater, which is of fundamental interest in studies of ocean physics and biology. Models developed in the recent decades were mainly based on theoretical analyses and numerical (radiative transfer) simulations to estimate this property in optically deep waters, thus leaving inadequate knowledge of its variability at multiple depths and wavelengths, covering a wide range of solar incident geometry, in turbid coastal waters. In the present study, a new model is developed to quantify the vertical, spatial and temporal variability of K(d) at multiple wavelengths and to quantify its dependence with respect to solar incident geometry under differing sky conditions. Thus, the new model is derived as a function of inherent optical properties (IOPs - absorption a and backscattering b(b)), solar zenith angle and depth parameters. The model results are rigorously evaluated using time-series and discrete in situ data from clear and turbid coastal waters. The K(d) values derived from the new model are found to agree with measured data within the mean relative error 0.02~6.24% and R² 0.94~0.99. By contrast, the existing models have large errors when applied to the same data sets. Statistical results of the new model for the vertical spectral distribution of K(d) in clear oceanic waters (for different solar zenith and in-water conditions) are also good when compared to those of the existing models. These results suggest that the new model can provide an improved interpretation about the variation of the vertical spectral diffuse attenuation coefficient of downwelling irradiance, which will have important implications for ocean physics, biogeochemical cycles and underwater applications in both relatively clear and turbid coastal waters.

Estimation of the spectral diffuse attenuation coefficient of downwelling irradiance in inland and coastal waters from hyperspectral remote sensing data: Validation with experimental data

Abstract A semi-analytical model is developed for estimating the spectral diffuse attenuation coefficient of downwelling irradiance (Kd(λ)) in inland and coastal waters. The model works as a function of the inherent optical properties (absorption and backscattering), depth, and solar zenith angle. Results of this model are validated using a large number of in-situ measurements of Kd(λ) in clear oceanic, turbid coastal and productive lagoon waters. To further evaluate its relative performance, Kd(λ) values obtained from this model are compared with results from three existing models. Validation results show that the present model is a better descriptor of Kd(λ) and shows an overall better performance compared to the existing models. The applicability of the present model is further tested on two Hyperspectral Imager for the Coastal Ocean (HICO) remote sensing images acquired simultaneously with our field measurements. The Kd(λ) spectra derived from HICO imageries have good agreement with measured data with the mean relative percent error of less than 12% which are well within the benchmark for a validated uncertainty of ±35% endorsed for the remote sensing products in oceanic waters. The model offers potential advantages for predicting changes in spectral and vertical Kd values in a wide variety of waters within inland and coastal environments.

A New Inversion Model to Estimate Bulk Refractive Index of Particles in Coastal Oceanic Waters: Implications for Remote Sensing

An inversion model is developed to estimate bulk refractive index (ηbp relative to water) for understanding the particle assemblages and dynamics in coastal oceanic waters. The model is based on an inversion of Mie theory combined with parameterizations such as the backscattering ratio, hyperbolic slope of the particle size distribution, and bulk density (relative to water). With the advent of high-frequency in-situ spectral attenuation and fluorescence-turbidity sensors, these parameters can be easily measured and used in the new model to estimate the ηbp values. To test the robustness of this model, the ηbp values estimated from the new model are verified with the Mie input refractive index values (relative to water) and those produced by an existing model. The new model is also applied to spatial, temporal, and vertical insitu profile data measured from turbid coastal waters off Point Calimere and clear waters off Chennai, southeast part of India. The ηbp values estimated by the present model are generally agreeable with the previously reported ηbp values (1.02-1.28) for these waters. By contrast, the existing model tends to provide relatively high ηbp values (1.10) for clear waters and low ηbp values (1.215) for relatively clear and sediment-laden waters. Application of these models to time-series in-situ data from moderately turbid and highly turbid waters reveals that the vertical distribution patterns of ηbp from the new model correspond better with turbidity patterns (with an increasing ηbp trend in sediment-laden bottom waters) that display large variations depending on the tidal cycles of the day. However, the existing model produces a very narrow range of ηbp values displaying nearly homogenous patterns regardless of the turbidity variation along the depth. The new model enables more reliable estimates of ηbp for living cells (1.02-1.07 because of higher water content) in clear waters, detrital particles (1.07-1.15) and minerals/mineral-detrital particles (>1.15 because of lower water content). These results suggest that the new model will have important implications for studies of the particle assemblages in coastal oceanic waters, with the feasibility of remote estimation of ηbp with the proof-of-concept approaches which will inspire further research into the nature of particles in the ocean and their variability on regional and global scales.

Detection of Ocean Wave Parameters Using Synthetic Aperture Radar (SAR) Data

This study describes the estimation of spherical wave parameters that appears in Synthetic Aperture Radar (SAR) images acquired over the coast of Chukk, Micronesia. The main causes for the interaction of SAR signals with ocean waves can be retrieved through the Bragg scattering mechanism. Dominant wavelengths were retrieved by means of Fast Fourier Transform (FFT) analysis in terms of peak frequency responses. Sea surface slopes were then obtained from the dispersion relation with consideration of different water wave conditions for each subset area, and wave heights were estimated with the use of dominant wavelengths and sea surface slopes. The work presented in this paper may be useful to retrieve the various wave parameters over different regions. The method used is a novel technique to correlate the relationship between SAR image parameters and dispersion relation.

Retrieval of ocean colour from high resolution multi-spectral imagery for monitoring highly dynamic ocean features

Retrieval of ocean colour information from a space borne Multi‐spectral Camera (MSC) on KOMPSAT‐2 is investigated to study and characterize small‐scale biogeophysical features that are very rich and dynamic in nature in the coastal oceans rather than the interior. Prior to the derivation of this information from space‐borne ocean colour observations, the path radiance largely from the atmospheric path and air–sea interface should be removed from the total signal recorded at the top of the atmosphere (TTOA ). In this study, the ‘path extraction’ method is introduced for the atmospheric correction of ocean colour images. The potential use of path extraction was demonstrated on Landsat TM and SeaWiFS images of highly turbid coastal waters of Korea. The path‐extracted water‐leaving radiance was then compared with the water‐leaving radiance spectra derived from the standard SeaWiFS atmospheric correction algorithm. It was noticed that the path‐extracted water‐leaving radiance resembled in situ spectra while the same was found highly degraded throughout the visible wavebands by adopting the standard SeaWiFS atmospheric correction algorithm. Algorithms for the retrieval of ocean colour information are explored from remotely sensed reflectance (Rrs ) in the visible wavelength bands of a Multi‐spectral Camera. A large set of remote sensing reflectances are generated by random number functions using an Rrs model, which relates bb /(a+bb ) to Rrs as functions of inherent optical properties, such as absorption and backscattering coefficients of six water components including water, phytoplankton (chl), dissolved organic matter (DOM), suspended sediment (SS) concentration, heterotropic organisms (he) and an unknown component, possibly represented by bubbles or other particulates unrelated to the first five components. Since the Kompsat‐2 MSC and Landsat‐5 TM bands are spectrally similar, these Rrs values are then modelled to the equivalent remote sensing reflectances at MSC and Landsat TM bands using a spectral band model. The empirical relationships between the spectral ratios of modelled Rrs (e.g. Rrs (MSC band1)/Rrs (MSC band2) and Rrs (MSC band1‐centre)/Rrs (MSC band2‐centre)) and chlorophyll concentrations are established in order to derive ⟨chl⟩ algorithms for both Landsat TM and MSC bands. Similarly, ⟨SS⟩ algorithms are obtained by relating a single band reflectance (e.g. Rrs (MSC band2) and Rrs (MSC Band2‐centre)) to the suspended sediment concentrations. Finally, a comparative analysis is made between the Landsat TM and MSC bands as well as narrow (centre‐wavelength) and broad band (full bandwidth) width of algorithms. From this study, it was observed that the Rrs spectra of three MSC spectral bands are found to be slightly superior to the Landsat TM bands in terms of spectral sensitivity to varying constituent concentrations. A small discrepancy between the reflectance ratios of broad and narrow bands was noticed in MSC and Landsat TM. The coefficient of determination (R 2) for log‐transformed data [⟨chl⟩ N = 500] was interestingly found to be R 2 = 0.90 for both Landsat TM and MSC. Similarly, the R 2 value for log‐transformed data [⟨SS⟩ N = 500] was 0.93 and 0.92 for Landsat TM and MSC, respectively. The modelled Rrs spectra were in good agreement with our in situ spectra obtained from the southern coastal Sea of Korea during 1998 and 1999. The algorithms presented are expected to explore the fine details of the complex coastal oceanic features from the ocean colour images of Multi‐spectral Camera and Landsat TM.