Dynamic range and sensitivity requirements of satellite ocean color sensors: learning from the past

doi:10.1364/AO.51.006045

Dynamic range and sensitivity requirements of satellite ocean color sensors: learning from the past

Chuanmin Hu, Lian Feng, Zhongping Lee, Curtiss O. Davis, Antonio Mannino, Charles R. McClain, and Bryan A. Franz »View Author Affiliations

Applied Optics, Vol. 51, Issue 25, pp. 6045-6062 (2012)
http://dx.doi.org/10.1364/AO.51.006045

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Abstract

Sensor design and mission planning for satellite ocean color measurements requires careful consideration of the signal dynamic range and sensitivity (specifically here signal-to-noise ratio or SNR) so that small changes of ocean properties (e.g., surface chlorophyll-a concentrations or Chl) can be quantified while most measurements are not saturated. Past and current sensors used different signal levels, formats, and conventions to specify these critical parameters, making it difficult to make cross-sensor comparisons or to establish standards for future sensor design. The goal of this study is to quantify these parameters under uniform conditions for widely used past and current sensors in order to provide a reference for the design of future ocean color radiometers. Using measurements from the Moderate Resolution Imaging Spectroradiometer onboard the Aqua satellite (MODISA) under various solar zenith angles (SZAs), typical ( $L_{typical}$ ) and maximum ( $L_{\max}$ ) at-sensor radiances from the visible to the shortwave IR were determined. The $L_{typical}$ values at an SZA of 45° were used as constraints to calculate SNRs of 10 multiband sensors at the same $L_{typical}$ radiance input and 2 hyperspectral sensors at a similar radiance input. The calculations were based on clear-water scenes with an objective method of selecting pixels with minimal cross-pixel variations to assure target homogeneity. Among the widely used ocean color sensors that have routine global coverage, MODISA ocean bands (1 km) showed 2–4 times higher SNRs than the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (1 km) and comparable SNRs to the Medium Resolution Imaging Spectrometer (MERIS)-RR (reduced resolution, 1.2 km), leading to different levels of precision in the retrieved Chl data product. MERIS-FR (full resolution, 300 m) showed SNRs lower than MODISA and MERIS-RR with the gain in spatial resolution. SNRs of all MODISA ocean bands and SeaWiFS bands (except the SeaWiFS near-IR bands) exceeded those from prelaunch sensor specifications after adjusting the input radiance to $L_{typical}$ . The tabulated $L_{typical}$ , $L_{\max}$ , and SNRs of the various multiband and hyperspectral sensors under the same or similar radiance input provide references to compare sensor performance in product precision and to help design future missions such as the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission and the Pre-Aerosol-Clouds-Ecosystems (PACE) mission currently being planned by the U.S. National Aeronautics and Space Administration (NASA).

OCIS Codes
(010.4450) Atmospheric and oceanic optics : Oceanic optics
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(280.4788) Remote sensing and sensors : Optical sensing and sensors

ToC Category:
Image Processing

History
Original Manuscript: April 11, 2012
Revised Manuscript: June 14, 2012
Manuscript Accepted: July 10, 2012
Published: August 24, 2012

Virtual Issues
Vol. 7, Iss. 11 Virtual Journal for Biomedical Optics
November 9, 2012 Spotlight on Optics

Citation
Chuanmin Hu, Lian Feng, Zhongping Lee, Curtiss O. Davis, Antonio Mannino, Charles R. McClain, and Bryan A. Franz, "Dynamic range and sensitivity requirements of satellite ocean color sensors: learning from the past," Appl. Opt. 51, 6045-6062 (2012)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=ao-51-25-6045

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Abbott, M. R.
Ahmad, S. W. B. Z.
Aiken, J.
Al-Saadi, J.
Angal, A.
Arnone, R.
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Barnes, R. A.
Barnes, W. L.
Behnert, I.
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Brown, C. A.
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Carder, K.
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Chin, M.
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Corson, M.
Corson, M. R.
Cota, G.
Cullen, J. J.
Culver, M.
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Czajkowski, K.
Dall’Olmo, G.
Davis, C.
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Dekker, A. G.
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DiGiacomo, P.
Doerffer, R.
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Dungan, J. L.
Dye, J.
Edwards, D.
Eldering, A.
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Esaias, W. E.
Feldman, G. C.
Feng, L.
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Firestone, E. R.
Fischer, J.
Fishman, J.
Franz, B.
Franz, B. A.
Fukushima, H.
Gao, B.-C.
GEO-CAPE Atmospheric Science Working Group,
GEO-CAPE Ocean Science Working Group,
Giardino, C.
Goes, J.
Goode, W.
Gordon, H. R.
Green, R. O.
Gregg, W. W.
Harding, L.
Hawes, S. K.
Heil, C. A.
Herman, J.
Hooker, S.
Hooker, S. B.
Hu, C.
Huntington, J. F.
Huot, Y.
Iraci, L.
Jacob, D.
Johns, E.
Jordan, C.
Kahru, M.
Kamykowski, D.
Kawa, S. R.
Kelble, C.
Key, R.
Kishino, M.
Korwan, D. R.
Kruse, F. A.
Kuji, M.
Kwiatkowska, E. J.
Lee, Z.
Lee, Z. P.
Letelier, R.
Letelier, R. M.
Li, R. R.
Lian, F.
Lima, I.
Liu, X.
Lohrenz, S.
Lucke, R. L.
Magnuson, A.
Mahowald, N.
Mannino, A.
Maritorena, S.
McClain, C.
McClain, C. R.
McGlothlin, N. R.
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Milligan, A. J.
Miner, J.
Mitchell, B.
Montes, M. J.
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Morel, A.
Morrison, J. R.
Moses, W. J.
Mueller, J.
Muller-Karger, F. E.
Müller-Karger, F.
Nakajima, M.
Nakajima, T.
Nakajima, T. Y.
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Neu, J.
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P.,
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Schroeder, T.
Siegal, D.
Siegel, D. A.
Signorini, S. R.
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Snyder, W. A.
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Strömbeck, N.
Strutton, P.
Subramaniam, A.
Tantalo, T.
Taylor, C.
Tian, L.
Toole, D.
Tzortziou, M.
Uchiyama, A.
Vincent, R. K.
Wang, J.
Wang, M.
Weidemann, A.
Werdell, P. J.
Westberry, T. K.
Wettle, M.
Wiggert, J. D.
Wood, D. L.
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Yuan, X.
Zhou, B.

Appl. Opt.

Biogeosci. Discuss.

M. J. Behrenfeld, T. K. Westberry, E. S. Boss, R. T. O’Malley, D. A. Siegel, J. D. Wiggert, B. A. Franz, C. R. McClain, G. C. Feldman, S. C. Doney, J. K. Moore, G. Dall’Olmo, A. J. Milligan, I. Lima, and N. Mahowald, “Satellite-detected fluorescence reveals global physiology of ocean phytoplankton,” Biogeosci. Discuss. 5, 4235–4270 (2008). [CrossRef]

Chin. J. Oceanol. Limnol.

Z. Yu, X. Chen, B. Zhou, L. Tian, X. Yuan, and L. Feng, “Assessment of total suspended sediment concentrations in Poyang Lake using HJ-1A/1B CCD imagery,” Chin. J. Oceanol. Limnol. 30, 295–304 (2012). [CrossRef]

Deep Sea Research II: Top. Stud. Oceanogr.

C. R. McClain, S. R. Signorini, and J. R. Christian, “Subtropical gyre variability observed by ocean-color satellites,” Deep Sea Research II: Top. Stud. Oceanogr. 51, 281–301 (2004). [CrossRef]

IEEE Trans. Geosci. Remote Sens.

R. O. Green, B. E. Pavri, and T. G. Chrien, “On-orbit radiometric and spectral calibration characteristics of EO-1 Hyperion derived with an underflight of AVIRIS and in situ measurements at Salar de Arizaro, Argentina,” IEEE Trans. Geosci. Remote Sens. 41, 1194–1203 (2003). [CrossRef]
F. A. Kruse, J. W. Boardman, and J. F. Huntington, “Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping,” IEEE Trans. Geosci. Remote Sens. 41, 1388–1400 (2003). [CrossRef]
P. J. Curran and J. L. Dungan, “Estimation of signal-to-noise: a new procedure applied to AVIRIS data,” IEEE Trans. Geosci. Remote Sens. 27, 620–628 (1989). [CrossRef]

Int. J. Remote Sens.

S. Sathyendranath, L. Prieur, and A. Morel, “A three component model of ocean colour and its application to remote sensing of phytoplankton pigments in coastal waters,” Int. J. Remote Sens. 10, 1373–1394 (1989). [CrossRef]
T. Schroeder, I. Behnert, M. Schaale, J. Fischer, and R. Doerffer, “Atmospheric correction algorithm for MERIS above case-2 waters,” Int. J. Remote Sens. 28, 1469–1486 (2007). [CrossRef]

J. Appl. Remote Sens.

Z. Lee, B. Casey, R. Arnone, A. Weidemann, R. Parsons, M. J. Montes, B.-C. Gao, W. Goode, C. Davis, and J. Dye, “Water and bottom properties of a coastal environment derived from Hyperion data measured from the EO-1 spacecraft platform,” J. Appl. Remote Sens. 1, 011502 (2007). [CrossRef]

J. Geophys. Res.

K. L. Carder, F. R. Chen, Z. P. Lee, S. K. Hawes, and D. Kamykowski, “Semianalytic Moderate-Resolution Imaging Spectrometer algorithms for chlorophyll a and absorption with bio-optical domains based on nitrate-depletion temperatures,” J. Geophys. Res. 104, 5403–5421 (1999). [CrossRef]
C. Hu, Z. Lee, and B. Franz, “Chlorophyll a algorithms for oligotrophic oceans: a novel approach based on three-band reflectance difference,” J. Geophys. Res. 117, C01011 (2012). [CrossRef]
H. R. Gordon, “Atmospheric correction of ocean color imagery in the Earth Observing System era,” J. Geophys. Res. 102, 17081–17106 (1997). [CrossRef]

Limnol. Oceanogr.

J. R. Morrison, “In situ determination of the quantum yield of phytoplankton chlorophyll a fluorescence: a simple algorithm, observations, and a model,” Limnol. Oceanogr. 48, 618–631 (2003). [CrossRef]

Limnol. Oceanogr.: Methods

Y. Huot, C. A. Brown, and J. J. Cullen, “New algorithms for MODIS sun-induced chlorophyll fluorescence and a comparison with present data products,” Limnol. Oceanogr.: Methods 3, 108–130 (2005). [CrossRef]

Opt. Eng.

R. D. Fiete and T. Tantalo, “Comparison of SNR image quality metrics for remote sensing systems,” Opt. Eng. 40, 574–585 (2001). [CrossRef]

Opt. Express

W. J. Moses, J. H. Bowles, R. L. Lucke, and M. R. Corson, “Impact of signal-to-noise ratio in a hyperspectral sensor on the accuracy of biophysical parameter estimation in case II waters,” Opt. Express 20, 4309–4330 (2012). [CrossRef]

Remote Sens. Environ.

B.-C. Gao, “An operational method for estimating signal to noise ratios from data acquired with imaging spectrometers,” Remote Sens. Environ. 43, 23–33 (1993). [CrossRef]
M. Wettle, V. E. Brando, and A. G. Dekker, “A methodology for retrieval of environmental noise equivalent spectra applied to four Hyperion scenes of the same tropical coral reef,” Remote Sens. Environ. 93, 188–197 (2004). [CrossRef]
R. K. Vincent, X. Qin, R. M. L. McKay, J. Miner, K. Czajkowski, J. Savino, and T. Bridgeman, “Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie,” Remote Sens. Environ. 89, 381–392 (2004). [CrossRef]
C. Giardino, V. E. Brando, A. G. Dekker, N. Strömbeck, and G. Candiani, “Assessment of water quality in Lake Garda (Italy) using Hyperion,” Remote Sens. Environ. 109, 183–195 (2007). [CrossRef]
C. Hu, K. L. Carder, and F. E. Muller-Karger, “How precise are SeaWiFS ocean color estimates? Implications of digitization-noise errors,” Remote Sens. Environ. 76, 239–249(2001). [CrossRef]
R. M. Letelier and M. R. Abbott, “An analysis of chlorophyll fluorescence algorithms for the moderate resolution imaging spectrometer (MODIS),” Remote Sens. Environ. 58, 215–223 (1996). [CrossRef]
C. Hu, F. E. Muller-Karger, C. Taylor, K. L. Carder, C. Kelble, E. Johns, and C. A. Heil, “Red tide detection and tracing using MODIS fluorescence data: a regional example in SW Florida coastal waters,” Remote Sens. Environ. 97, 311–321 (2005). [CrossRef]

Other

S. B. Hooker, E. R. Firestone, W. E. Esaias, G. C. Feldman, W. W. Gregg, and C. R. McClain, “An overview of SeaWiFS and ocean color,” NASA Technical Memorandum, S. B. Hooker and E. R. Firestone, eds. (NASA Goddard Space Flight Center, 1992).
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C. Hu, F. Lian, and Z. Lee, “Evaluation of GOCI sensitivity for at-sensor radiance and GDPS-retrieved chlorophyll-a products,” submitted to Ocean Science J..

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