Indian Journal of Marine Sciences Vol. 32(4), December 2003, pp. 279-284
Remote sensing of diffuse attenuation coefficient (K490) using IRS-P4 Ocean Colour Monitor (OCM) sensor *Prakash Chauhan, Arvind Sahay, A. S. Rajawat & Shailesh Nayak Marine & Water Resources Group, Space Applications Centre, Ahmedabad 380015, India *[ E-mail:
[email protected] ] Received 27 December 2002, revised 4 September 2003 An algorithm relating the diffuse attenuation coefficient K(490) at 490 nm wavelengths to the ratio of normalized water-leaving radiance [Lwn(443)/Lwn(555)] has been developed through regression analysis of radiometric profiles obtained from three oceanographic cruises conducted in the north eastern Arabian Sea and off Orissa and Andhra coast in the Bay of Bengal. The regional K(490) algorithm has been applied on to the IRS-P4 Ocean Colour Monitor (OCM) satellite data and a limited validation of the algorithm has been performed for case I and case II waters off Gujarat coast in the Arabian Sea. The results of the satellite validation experiment conducted in the Arabian Sea for K(490) algorithm show that the algorithm captures very well the in situ K(490) values (RMS=0.011 m-1). The algorithm has also been validated for an independent in situ data set collected during SK-186 data collection campaign in the Arabian Sea; a good correlation was obtained for this data (r2 = 0.80, N=41). [Key words : Arabian Sea, Bay of Bengal , diffuse attenuation coefficient, IRS-P4 OCM]
The diffuse attenuation coefficient of oceanic water is a property that can be inferred from the ocean color satellite data. Light availability is a critical regulator of oceanic and coastal production of phytoplankton1 . The measurement of vertical diffuse attenuation coefficient (Kd ) is of particular interest for this purpose, which defines the presence of light versus depth. Optical classification of oceanic water types has been attempted by relating the diffuse attenuation coefficient to the plant pigment content2-3 . The diffuse attenuation coefficient K is of significance to a variety of problems associated with the penetration of natural light into the ocean and is also an important variable in evaluating propagation of artificial light in seawater for various optical, communication and surveillance systems. The knowledge of the optical attenuation within upper ocean water column is also useful to understand the warming of the upper ocean, which occurs through the absorption of solar irradiance (400-800 nm). The SeaWiFS derived global K (490) maps have been used for the calculation of solar subsurface heating for the prediction of sea surface temperature (SST) and mixed layer depth (MLD) using ocean general circulation models 5 . Using the values of vertical diffuse attenuation coefficient Kd (λ), (here λ is wavelength of light), estimates of the depth of informative (for remote sensing) layer, sometimes called penetration depth, but more often
attenuation depth, zK(λ) can be obtained. It is an important factor since 90% of the information detected by optical remote sensing instruments comes from above a depth where downward irradiance has fallen to a level, which is 37 % of its value just below the water surface. Satellite based remote sensing of the diffuse attenuation coefficient K has been demonstrated for CZCS data 5 . An operational K (490) algorithm was developed for Nimbus-7 CZCS data relating K (490), to the ratio of water leaving radiances Lw (443)/Lw (550), at wavelengths of 443 and 550 nm as: K (490) = 0.022 + 0.088 [Lw (443)/Lw (550)]-1.491 m-1 … (1) A large number of CZCS data sets were processed using this algorithm, and root mean square (rms) error in K (490) estimates from CZCS data was found to be less than 20 % based on direct comparison with in situ radiometric profiles6 . Recently SeaWiFS project of NASA has been providing the diffuse attenuation coefficient at 490 nm, K (490), as one of the standard ocean data product. SeaWiFS is using a revised K(490) algorithm7 . These relationships have been shown to be robust (in Case 1 and Case 2 waters) if accurate water leaving radiances can be derived from satellite ocean color data 8,9 .
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The objective of this work to develop a regional K(490) algorithm for IRS-P4 Ocean Colour Monitor (OCM) sensor specifications and for the bio-optical conditions in the Arabian Sea and the Bay of Bengal. Development of a new regional K(490) algorithm is based on normalized water leaving radiance (Lwn ) ratio of 443 nm to 555 nm bands of the OCM sensor. For this purpose we have analyzed radiometric profiles of spectral downwelling irradiance Ed (λ, z), spectral upwelling radiance Lu (λ, z) together with concurrent above water values of spectral surface irradiance Es (λ) from data collected in the north eastern Arabian Sea and coastal waters off Orissa and Andhra coast in the Bay of Bengal during the period November 2001 to March 2002. The algorithm has been tested on the IRS-P4 OCM satellite data over the Arabian Sea. Further, the developed algorithm has been validated for an independent data set collected in the Arabian Sea to ascertain its validity. Materials and Methods In this study, radiation data set obtained during cruises conducted in the north eastern Arabian Sea and coastal waters off Orissa and Andhra coast in Bay of Bengal (Fig. 1) have been analyzed. The area covered during the Arabian Sea cruise of ORV Sagar
Kanya (SK-171) was northeastern Arabian Sea, which lies between 15°-22°N and 68°E-74°E. The cruise was conducted on board ORV Sagar Kanya, during the period of November 3-17, 2001. The bio-optical conditions for this cruise represents both case 1 and case 2 waters between Goa and Porbandar in the waters of Arabian Sea. Total numbers of 24 stations were covered, three casts of radiation data were collected at each station. The bathymetry for these stations varied between 28 m in coastal regions to 3400 m in deep sea. Upwelling radiance Lu (λ), downwelling irradiance Ed (λ), and surface irradiance Es (λ), were measured with Satlantic underwater radiometer consisting of two units, i) SeaWiFS Profiling Multi-channel Radiometer (SPMR) which is lowered under the water to measure downwelling irradiance (Ed ) and upwelling radiance (Lu ), ii) SeaWiFS Multi-channel Surface Reference (SMSR) which is kept on the deck to measure downwelling irradiance (Es ), above the water surface. Radiation data of two cruises Bay of Bengal cruises of R.V. Samudra Kaustabh (ST-149 and ST-151) was obtained from the coastal waters of Orissa and Andhra coast. The ST-149 cruise was conducted during 27th January to 3rd February 2001 along the east coast of India around Sagar Island, off Gopalpur
Fig. 1— Radiometric sampling locations on the west coast of India during SK-171 cruise and on the east coast of India during ST-149 and ST-151 cruise
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coast, Orissa (Fig. 1). The other cruise, ST-151 was conducted during March 02-08, 2002, covering coastal regions around Visakhapattanam, extending up to Godavri river delta, off Andhra coast. Measurements similar to the Arabian Sea cruise were using same Satlantic underwater radiometer. Total numbers of 15 sampling stations with 3 casts of radiometer data were collected during these two cruises. The data were collected within 200 m depth contours from the coast, comprising typical case 2 bio-optical conditions. Each calibrated set of profiles of Ed (z, λ), Lu (z,λ) and scalar irradiance Es (λ), were analyzed using the integral least-squares solution of Muller10 to determine profiles of Kd (z, λ) and KL (z, λ), and the vertical attenuation coefficients for Ed (z, and Lu (z, λ), respectively. The remote sensing K (490), was calculated by averaging Kd (z, 490 nm) over the first optical attenuation length i.e.
1 K (490 ) = Z 90
Z 90
∫K
d
(z , 490 ) dz
… (2)
0
where, Z90 is the first attenuation depth i.e. the depth at which the surface light field reduces to 1/e or 37% of its surface value. The Normalized water leaving radiances Lwn (λ) for λ = 412, 443, 490, 510 and 555 nm are calculated as: Lwn (λ) = Lu (0 -, λ)*tf (λ)* [F0 (λ)/Es (λ)]
… (3)
-
where, Lu (0 ,λ) and Es (λ) are taken from the analysed profiles, tf(λ) is the upward Fresnel transmittance through the air-sea interface for radiance, and F0 (λ) is the mean extraterrestrial solar flux. The logarithmic regression model relating K(490) to the ratio Lwn (443)/Lwn (555) may be expressed as: ln[K(490) – 0.022] = ln(A) + B ln [Lwn (443)/Lwn (555)] … (4) where, A and B are coefficients of the model and the attenuation coefficient of pure water at 490 nm, Kw (490) is 0.022 m-1 , is the minimum possible value of K(490). A total number of 110 profile measurements were used to determine best-fit coefficients for the above-mentioned equation using a simple linear regression. Using the values of vertical diffuse attenuation coefficient K(λ), we can estimate the depth of informative (for remote sensing) layer, called sometimes penetration depth, but more often attenuation depth, zK(λ). It is an important factor since
90% of the information detected by optical remote sensing instruments comes from above a depth where downward irradiance has fallen to a level which is 1/e=0.36788 less than its value just below the water surface. It is calculated as: zk (λ) = 1/ Kd (λ)
… (5)
Through Kd (λ) the attenuation depth depends on wavelength of light, on the inherent optical properties on the water body and on the angular distribution on downwelling solar irradiance (solar zenith angle, cloudiness). The variation range for oceans is easy to describe on the basis of Kd values for oceanic and coastal waters, given by Jerlov’s water types2,10. Results and Discussion K(490) algorithm for the Indian Waters Total numbers of 39 sample location data were pooled together from waters of the Arabian Sea and Bay of Bengal. At each station 3 casts of radiometric measurements were collected, amounting to 117 radiometric profiles covering diverse bio-optical conditions. Out of 117 profiles only 110 profiles were used for further processing as 7 profiles were rejected due to poor data quality (data loss at certain depths). The 110 profiles of Satlantic radiometer were combined to assemble a regression sample of size N=110. The range of K(490) for all the casts were between 0.033 to 0.61 m-1 . The higher values of the K(490) correspond to coastal waters and the lower values corresponds to more clear waters of the open oceans. The sample data set shows a wide range of K(490) variability covering clear open ocean waters to nearshore turbid waters of the Bay of Bengal. The linear least-square fit to this data is: ln[ Kˆ (490) – 0.022] = −2.08638 − 1.63683. ln [Lwn (443)/Lwn (555) ]
… (6)
2
with the correlation coefficient, r =0.94. The scatter of these data is shown in Fig. 2, together with the best-fit regression line defined by Eq. (6). This equation is transformed to the following form as:
Kˆ (490) =0.022 + 0.124 [Lwn(443)/Lwn(555)]-1.64 m-1 … (7) The graphical relationship between Kˆ (490) for the Indian waters and [Lwn (443)/Lwn (555)] is shown in Fig. 3. The linear residual standard deviation of Kˆ (490) (standard error of the estimate) associated with Eq. (7) is
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S Kx
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=
[ Kˆ n (490 ) − K n ( 490 )]2 n= 1 N −1 N
∑
1/ 2
= 0.029 m -1
Figures 2 and 3 show scatter at different levels of K(490). In the log-log display of Fig.2 the largest deviations from the regression fit occur for three points of higher values of K(490).
Fig. 2— Scatter plot comparing K(490) and the normalized ratio Lwn(443)/Lwn(555). The solid line is the least-squares fit to the data. The resulting regression equation is ln[ Kˆ (490)– 0.022] = −2.0863 –1.6368 ln [Lwn(443)/Lwn(555)] with r2=0.94; and the standard error of the estimate is 0.029 m-1
Fig. 3 — Linear K(490) versus a logarithmic scaling Lwn(443)/Lwn(555) display of the data and regression fit (solid line) from Fig. 2
Validation of the K(490) algorithm The developed algorithm for K (490) for the west and east coast of India was applied to IRS-P4 OCM satellite data. The OCM data were processed to estimate normalized water leaving radiance in 443 and 555 nm bands 11 . The OCM image data corresponding to ship cruise period of SK-171 (November 3-17, 2001) were used for this analysis. The OCM image data for this period was used to obtain the K(490) images using Eq. (7). The OCM derived K(490) estimates were compared with in-situ measurements made during the cruise. The in-situ measurements were obtained using multi channel underwater radiometer and K(490) values were calculated using the profiles of downwelling irradiance Ed (z, λ) at different depths. Table 1 shows the results of the comparative analysis of K(490) values obtained by OCM measurements and in situ observations. Six match up locations could be obtained during the SK-171 cruise period. A root mean square (RMS) error of 0.011m-1 was estimated between OCM derived and in situ measurements. This match up data set mainly corresponds to open ocean clear waters of the west coast of India. Figure 4 shows the OCM derived spatial distribution of K(490) values on November 4, 2001 off west coast of India. As shown in Fig. 4 the coastal waters correspond to higher values of K (490) representing the low light penetration regions, with a K(490) value more than 0.1 m-1 . The clear open ocean waters in the part of the Arabian Sea shows K (490) values in the range of 0.01 to 0.09 m-1 . The algorithm was further tested for its validity with respect to an independent data set. A separate data set of optical measurements collected in the north eastern Arabian Sea during January 3-18, 2003 onboard ORV Sagar Kanya (SK-186) were used for the assessment of the performance of the developed algorithm. The data set comprised of 41 open ocean
Table 1 — Comparison of OCM derived K(490) and in situ measurements on the west coast of India Station no.
1 2 3 4 5 6
Position Latitude Longitude (0N) (0E) 15.66 17.60 19.42 21.67 20.81 17.66
72.03 70.48 68.86 68.46 70.46 72.66
Date
In situ K(490) m-1
OCM derived K(490) m-1
04 Nov 2001 06 Nov 2001 08 Nov 2001 10 Nov 2001 12 Nov 2001 14 Nov 2001
0.044 0.039 0.042 0.073 0.063 0.054
0.052 0.043 0.041 0.091 0.051 0.042
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Fig. 4 — IRS-P4 Ocean Colour Monitor (OCM) derived map of K(490) for November 4, 2001 along the west coast of India
station radiometric profiles. The normalized water leaving radiance (Lwn ) for 443 and 555 nm were measured using SPMR under water radiometer and corresponding in situ K (490) values were computed. Equation (7) was used to compute the modeled K (490) values from the SK-186 set of normalized water leaving radiance ratio of 443/555 nm spectral bands. Figure 5 shows the scatter plot of measured and modeled K (490) values for 41 radiometric profiles of SK-186 data set. A good correlation between measured and modeled values was obtained with a correlation coefficient r2 = 0.80. The range of the in situ K (490) values for this data set varied between 0.04 to 0.17. The regional K(490) algorithm has been developed making use of radiometric data collected on the west and east coast of India. The developed algorithm has shown good results with rms error of 0.011 m-1 , when implemented for the satellite based retrieval of diffuse attenuation coefficient K (490) using IRS-P4 OCM data. Algorithm was also tested against an independent in situ data set of mainly case 1 waters off Gujarat coast in the Arabian Sea. A good correlation
Fig. 5 — Validation of K(490) algorithm with independent data set of SK-186 cruise conducted on the west coast during January 3-18, 2003. The in-situ measured K(490) values shows a good correlation with the values predicated by the developed algorithm (r2=0.80, N=41).
between in situ and modeled K (490) values was obtained for this analysis with a coefficient of determination r2 =0.80 for 41 samples. The developed algorithm is being used to generate the water clarity maps for the coastal and oceanic waters of the
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Arabian Sea and Bay of Bengal. These products are very useful for the determination of the depth of euphotic zone, which supports the plant life in the ocean. Acknowledgement Authors are thankful to Dr. K. N. Shankara, Director, Space Application Centre (SAC), Ahmedabad, Dr. A. K. S. Gopalan, former Director, SAC and Dr. R. R. Navalgund, Director National Remote Sensing Agency (NRSA), Hyderabad for their incessant encouragements and invaluable suggestions towards this work. Thanks are also due to the Dy. Director General, Marine Wing, GSI, for facilitating R. V. Samudra Kaustubh for collecting the in situ measurements. References 1 Tyler J E, The in-situ quantum efficiency of natural phytoplankton populations, Limnol. Oceanogr., 20 (1975) 976-980. 2 Jarlov N G, Optical oceanography, (Elsevier, New York) 1976, pp.118-122. 3 Smith R C & Baker K, Optical classification of natural waters, Limnol. Oceanogr., 23 (1978) 260-267.
4 Rochford P A, Kara A B, Wallcraft A J & Arnone R A, Importance of solar subsurface heating in ocean general circulation models, J. Geophys. Res., 106 (2001) 3092330938. 5 Austin R W & Petzold T J, The determination of the diffuse attenuation coefficient of sea water using Coastal Zone Colour Scanner, in Oceanography from space, edited by J F R Gower, (Plenum, New York) 1981, pp.239-256. 6 Mueller, J L, An optical climatology of the Northeast Pacific Ocean, CHORS Tech Memo 001-93, (San Diego State University Centre for Hydro-optics and Remote Sensing, San Diego, California,), 1933, pp. 11. 7 Muller, J L & Trees C C, Revised SeaWiFS prelaunch algorithm for the diffuse attenuation coefficient K(490), NASA Tech Memo 104566, Case Studies for SeaWiFS Calibration and Validation: Part 4, 41 (1996) 18-21. 8 McClain C R, Ainsworth E J, Barnes R A, Eplee Jr R E, Patt F S, Robinson W D, Wang M, & Bailey S W, SeaWiFS post launch calibration and validation analyses: Part 1, NASA Tech. Memo., 2000-206892, 9 (2000) pp. 82. 9 O’Reilly, J E, SeaWiFS post launch calibration and validation analyses: Part 3, NASA Tech. Memo. 2000206892, 11 (2000) pp. 49. 10 Mueller, J L, Integral method for analyzing irradiance and radiance attenuation profiles. (CHORS Tech. Memo. 00791, San Diego State University Centre for Hydro-Optics and Remote Sensing, San Diego, California), 1991, pp. 10. 11 Chauhan P, Mohan M, Kumari B, Nayak S & Matondkar P, Surface chlorophyll-a estimation using IRS-P4 OCM data in the Arabian Sea, Int. J. Remot. Sens., 23 (2002) 1663-1676.
