Dierdre A. Toole,
David A. Siegel,
David W. Menzies,
Michael J. Neumann,
and Raymond C. Smith
All the authors are with the Institute for Computational Earth System Science, University of California, Santa Barbara, Santa Barbara, California 93106-3060. USA
D. A. Toole (dede@icess.ucsb.edu) is also with the Interdepartmental Graduate Program in Marine Science.
D. A. Siegel is also with the Department of Geography and the Donald Bren School of Environmental Science and Management.
R. C. Smith is also with the Department of Geography.
Dierdre A. Toole, David A. Siegel, David W. Menzies, Michael J. Neumann, and Raymond C. Smith, "Remote-sensing reflectance determinations in the coastal ocean environment: impact of instrumental characteristics and environmental variability," Appl. Opt. 39, 456-469 (2000)
Three independent ocean color sampling methodologies are compared
to assess the potential impact of instrumental characteristics and
environmental variability on shipboard remote-sensing reflectance
observations from the Santa Barbara Channel, California. Results
indicate that under typical field conditions, simultaneous
determinations of incident irradiance can vary by 9–18%, upwelling
radiance just above the sea surface by 8–18%, and remote-sensing
reflectance by 12–24%. Variations in radiometric determinations
can be attributed to a variety of environmental factors such as Sun
angle, cloud cover, wind speed, and viewing geometry; however, wind
speed is isolated as the major source of uncertainty. The
above-water approach to estimating water-leaving radiance and
remote-sensing reflectance is highly influenced by environmental
factors. A model of the role of wind on the reflected sky radiance
measured by an above-water sensor illustrates that, for clear-sky
conditions and wind speeds greater than 5 m/s, determinations of
water-leaving radiance at 490 nm are undercorrected by as much as
60%. A data merging procedure is presented to provide sky radiance
correction parameters for above-water remote-sensing reflectance
estimates. The merging results are consistent with statistical and
model findings and highlight the importance of multiple field
measurements in developing quality coastal oceanographic data sets for
satellite ocean color algorithm development and validation.
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For intercomparison purposes, the TSRB
radiance determinations are also propagated across the air–sea
interface without an initial extrapolation to the surface.
Mean normalized bias (MNB),
normalized rms error, slope, and r2 values for
the linear regression of various radiance measurements versus PRR
Lu(0+, λ)
measurements. The PRR Lu(0-,
λ) estimates were extrapolated to and propagated across the
air–sea interface before comparison (n = 84).
Mean normalized bias (MNB),
normalized rms error, slope, and r2 values for
the linear regression of various remote-sensing reflectance estimates
versus PRR Rrs(0+, λ)
estimates. PRR Rrs(0+, λ)
estimates are derived from PRR Ed(0+,
λ) deck cell measurements and PRR
Lu(0-, λ) measurements
extrapolated to and propagated across the air–sea interface
(n = 84).
Table 5
Rrs(0+, 490)
Intercomparison Results Separated by Environmental Conditions
For intercomparison purposes, the TSRB
radiance determinations are also propagated across the air–sea
interface without an initial extrapolation to the surface.
Mean normalized bias (MNB),
normalized rms error, slope, and r2 values for
the linear regression of various radiance measurements versus PRR
Lu(0+, λ)
measurements. The PRR Lu(0-,
λ) estimates were extrapolated to and propagated across the
air–sea interface before comparison (n = 84).
Mean normalized bias (MNB),
normalized rms error, slope, and r2 values for
the linear regression of various remote-sensing reflectance estimates
versus PRR Rrs(0+, λ)
estimates. PRR Rrs(0+, λ)
estimates are derived from PRR Ed(0+,
λ) deck cell measurements and PRR
Lu(0-, λ) measurements
extrapolated to and propagated across the air–sea interface
(n = 84).
Table 5
Rrs(0+, 490)
Intercomparison Results Separated by Environmental Conditions