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Estimation of underwater visibility in coastal and inland waters using remote sensing data

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Abstract

An optical method is developed to estimate water transparency (or underwater visibility) in terms of Secchi depth (Z sd ), which follows the remote sensing and contrast transmittance theory. The major factors governing the variation in Z sd , namely, turbidity and length attenuation coefficient (1/(c + K d ), c = beam attenuation coefficient; K d  = diffuse attenuation coefficient at 531 nm), are obtained based on band rationing techniques. It was found that the band ratio of remote sensing reflectance (expressed as (R rs (443) + R rs (490))/(R rs (555) + R rs (670)) contains essential information about the water column optical properties and thereby positively correlates to turbidity. The beam attenuation coefficient (c) at 531 nm is obtained by a linear relationship with turbidity. To derive the vertical diffuse attenuation coefficient (K d ) at 531 nm, K d (490) is estimated as a function of reflectance ratio (R rs (670)/R rs (490)), which provides the bio-optical link between chlorophyll concentration and K d (531). The present algorithm was applied to MODIS-Aqua images, and the results were evaluated by matchup comparisons between the remotely estimated Z sd and in situ Z sd in coastal waters off Point Calimere and its adjoining regions on the southeast coast of India. The results showed the pattern of increasing Z sd from shallow turbid waters to deep clear waters. The statistical evaluation of the results showed that the percent mean relative error between the MODIS-Aqua-derived Z sd and in situ Z sd values was within ±25%. A close agreement achieved in spatial contours of MODIS-Aqua-derived Z sd and in situ Z sd for the month of January 2014 and August 2013 promises the model capability to yield accurate estimates of Z sd in coastal, estuarine, and inland waters. The spatial contours have been included to provide the best data visualization of the measured, modeled (in situ), and satellite-derived Z sd products. The modeled and satellite-derived Z sd values were compared with measurement data which yielded RMSE = 0.079, MRE = −0.016, and R 2 = 0.95 for the modeled Z sd and RMSE = 0.075, MRE = 0.020, and R 2 = 0.95 for the satellite-derived Z sd products.

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Abbreviations

TSI:

Trophic state index

SeaWiFS:

Sea-Viewing Wide Field-of-View Sensor

DN:

Digital number

TM:

Thematic mapper

GLMs:

Generalized linear models

GAMs :

Generalized additive models

AC-S:

Absorption and attenuation sensors

BB9:

Backscattering sensors

IOPs:

Inherent optical properties

ECO:

Environmental characterization optics

FLNTU:

Turbidity and fluorescence chlorophyll sensors

TSS:

Total suspended sediment

ABI:

Algal bloom index

RMSE:

Root mean square error

MRE:

Mean root error

NTU:

Nephelometric turbidity unit

SAV:

Submerged aquatic vegetation

CDOM:

Colored dissolved organic matter

a(λ):

Absorption coefficient

a-a w :

Particulate absorption coefficient

b b (λ):

Backscattering coefficient

c-c w :

Particulate attenuation coefficient

Z sd :

Secchi depth

K d :

Vertical diffuse attenuation coefficient

K d,PAR :

Vertical diffuse attenuation coefficient for photosynthetically active radiation

c(λ):

Beam attenuation coefficient

b(λ):

Scattering coefficient

1/(c + K d ):

Length attenuation coefficient

Γ :

Coupling coefficient

E d,λ :

Spectral downwelling irradiance

R rs :

Remote sensing reflectance

L t :

Total radiances

nL w :

Normalized water leaving radiance

N :

Number of data-points

R 2 :

Correlation coefficient

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Acknowledgements

This research was supported by the Department of Science and Technology (OEC/16-17/130/DSTX/PSHA) under the HSRS program and the Indian Institute of Technology Madras through the MHRD fellowship. We would like to extend our thanks to D. Rajshekhar, The Head, Vessel Management Cell (VMC), and the Director of National Institute of Ocean Technology (NIOT), for providing the Coastal research vessels (CRV Sagar Paschimi and CRV Sagar Purvi) to IIT Madras for carrying out various underwater light field measurements during the cruises and developing the bio-optical models. We gratefully acknowledge the contributors and scientists who contributed to NOMAD and the Ocean Biology Processing Group of NASA for the distribution of the NOMAD in situ data and the support of the SeaDAS 7.2 Software. We sincerely thank the two anonymous reviewers for their valuable comments and suggestions to improve the manuscript.

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  1. Department of Ocean Engineering, Ocean Optics and Imaging Laboratory, Indian Institute of Technology Madras, Chennai, 600036, India

    Anuj Kulshreshtha & Palanisamy Shanmugam

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Kulshreshtha, A., Shanmugam, P. Estimation of underwater visibility in coastal and inland waters using remote sensing data. Environ Monit Assess 189, 199 (2017). https://doi.org/10.1007/s10661-017-5905-7

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