Dr. Srinivas Kolluru Profile

Dr. Srinivas Kolluru

postdoctoral research scholar at Harbor Branch Oceanographic Institute

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Publications (3)

Proceedings Article | 12 June 2023 Presentation + Paper

Decomposition of the total non-water absorption coefficient for retrieval of absorption subcomponents

Srinivas Kolluru, Shirishkumar Gedam, Michael Twardowski

Proceedings Volume 12543, 125430D (2023) https://doi.org/10.1117/12.2663892

KEYWORDS: Absorption, Ocean optics, Algorithm development, Optical properties, Data modeling, Absorption spectrum, Spectral models, Light absorption, Error analysis

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The total non-water absorption coefficient of seawater, 𝑎_𝑝𝑔(𝜆) (𝑚⁻¹) (light absorption coefficient after subtraction of pure water contribution) provides information about the amount of light absorbed by various optically significant substances in natural waters (𝜆 is wavelength). Partitioning 𝑎_𝑝𝑔^(𝜆) into phytoplankton, 𝑎_𝑝ℎ^(𝜆)and colored detrital matter, 𝑎_𝑑𝑔(𝜆) is useful to understand the light interaction with the distribution and variability of constituent matter, light availability at various depths, and ecological and biogeochemical cycles, as these constituents represent pools of carbon and other elements. Some of the existing partitioning methods either require ancillary inputs or assume limited shapes for constituents absorption in deriving the 𝑎_𝑝ℎ(𝜆) and 𝑎_𝑑𝑔(𝜆) from 𝑎_𝑝𝑔(𝜆). In this study, we propose a decomposition method of 𝑎_𝑝𝑔(𝜆) using a spectral optimization routine utilizing a spectral library consisting of various shapes for both phytoplankton and colored detrital matter absorption components. The proposed method does not require any ancillary inputs in deriving absorption of the constituent subcomponents. Performance of the proposed method is evaluated using two dataset compilations covering a very wide range of water types with sampling locations. Among various parameterizations tested in the decomposition method, the parameterization with the phytoplankton shape model of Ciotti et al. (2005) combined with exponential, stretched exponential, and hyperbolic shapes for colored detrital matter resulted in lower Mean Absolute Percentage Errors (MAPE) consistently across all sites. The good performance of the proposed method is characterized by average MAPE values of 17% and 13% and average percentage absolute errors (%AE) of 15.5% and 11.5% for the derived 𝑎_𝑝ℎ(443) and 𝑎_𝑑𝑔(443) respectively. The proposed method exhibited better performance with 7 - 10% lower average spectral MAPE (MAPE averaged over all wavelengths) values compared to two other existing partitioning algorithms in optically complex waters. The proposed method can be used for deriving the constituents absorption from input data of 𝑎_𝑝𝑔(𝜆) collected from various oceanographic and remote-sensing platforms. Since apg is a core product of several semi-analytical ocean color inversion algorithms, this approach has relevance to the future hyperspectral NASA PACE ocean color imager, as it is directly adaptable to hyperspectral reflectance data.

Proceedings Article | 10 October 2018 Presentation + Paper

Inherent optical properties retrieval from deep waters using Multi Verse Optimizer

Srinivas Kolluru, Gedam Shirishkumar S., A. Inamdar

Proceedings Volume 10784, 107840F (2018) https://doi.org/10.1117/12.2325613

KEYWORDS: Optimization (mathematics), Particle swarm optimization, Absorption, Water, Backscatter, Sensors, Optical properties, Remote sensing, Spectral models, Genetic algorithms

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Proceedings Article | 7 May 2016 Paper

Classification of case-II waters using hyperspectral (HICO) data over North Indian Ocean

Srinivasa Rao N., Ramarao E. P., Srinivas K., Deka P. C.

Proceedings Volume 9878, 98780X (2016) https://doi.org/10.1117/12.2223712

KEYWORDS: Ocean optics, Sensors, Reflectivity, Fluctuations and noise, MODIS, Remote sensing, Algorithm development, Spatial resolution, Statistical modeling, Particles

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