Download This folder contains time-series imagery of surface chlorophyll

Gulf of Mexico surface chlorophyll concentration from SeaWiFS: A brief description
NOTICE:
Orbimage, Inc., and the University of South Florida’s Institute for Marine
Remote Sensing (IMaRS) should be acknowledged any time these images are used.
INTRODUCTION AND USE OF DATA:
This folder contains a time-series of imagery representing surface chlorophyll
concentration in the Gulf of Mexico, estimated from measurements of the Sea-viewing
Wide Field-of-view Sensor (SeaWiFS; data owned by Orbimage, Inc., see below).
SeaWiFS was launched onboard the SeaStar (OrbView-2) satellite in August 1997 by
Orbital Sciences Corporation (Orbimage). The SeaWiFS sensor has provided synoptic,
global estimates of chlorophyll concentration since September 1997. SeaWiFS is in a
sun-synchronous polar-orbit, from where it measures light intensity exiting from the top
of the atmosphere (TOA) in eight spectral channels (wavelengths) centered at 412, 443,
490, 510, 555, 670, 765, and 865 nm, respectively.
USE OF DATA ON THE DISK
The images, located under the directories “./imgs_weekly” and “./imgs_biweekly”
are in PNG format. This format may be viewed with a web browser such as Internet
Explorer or Netscape. Further, two html files are provided here to animate the time series
from a web browser, namely “weekly_seawifs.html” and “biweekly_seawifs.html”. The
individual PNG files can be animated directly from this CD-ROM. Note that due to
different screen resolution settings the animation buttons, which are on the bottom of the
web page, may not appear on the screen. In this case a user simply needs to scroll down
the page.
The chlorophyll concentration estimates (data) computed as a result of application of
bio-optical algorithms (see below) are log-stretched to a byte value (DN=0-255), and are
then stored in digital format in a PNG image which has a color lookup table (LUT, i.e., a
definition of color for each byte value). The formula used to convert chlorophyll
concentration at each image pixel to DN for the data contained in this CD-ROM was:
Pixel_DN_value = log(chlorophyll+1)/0.00519
1
Inversion of this formula can be used to extract chlorophyll data at any given location
from the PNG image.
The satellite data provided in this CD-ROM were captured in real-time using groundbased tracking antennae at the University of South Florida’s College of Marine Science
(Institute for Marine Remote Sensing, http://imars.marine.usf.edu). The data are
immediately processed, distributed (with password protection), and archived.
The images are weekly and bi-weekly composites in order to remove most of the
cloud cover, which may occupy a significant portion of the Gulf of Mexico in any daily
imagery, especially in summertime. We generated the composites in the following way.
We average data at each pixel from images collected over the specified period (one or
two weeks). The arithmetic average uses “valid” pixels only, i.e., pixels that are not
associated with several suspicious flags (these flags were generated during the daily data
processing for quality control purposes). For example, at a given location for a weekly
composite with seven images available, if there are only three valid pixels from the seven
images, the mean value for this location is computed as the sum of these three values
divided by three. If none valid pixel is found, a value (and the corresponding color)
representing “no data” is given for this location.
The data and images are property of Orbimage, Inc. Their use here is in accordance
with the SeaWiFS Research Data Use Terms and Conditions Agreement of the NASA
SeaWiFS project. These data should be used strictly for research and education purpose
only. Commercial users should contact Orbimage (http://orbimage.com) for permission.
DATA PROCESSING SUMMARY
Sophisticated algorithms are used to remove the color of the atmosphere (atmospheric
correction). Generally speaking, bio-optical algorithms are used to estimate near-surface
chlorophyll concentrations from the ocean’s color (spectral radiance). This works where
phytoplankton (or materials that covary with phytoplankton concentration) dominate the
color of the ocean because phytoplankton strongly absorb blue light. These algorithms
fail where other processes affect the color of the ocean as well, such as within river
plumes, near coasts where sediments are resuspended, over shallow bottoms, or where
peculiar phytoplankton blooms occur (such as coccolithophores or thrichodesmium
2
blooms) the chlorophyll estimates are erroneous. Users of these data need to exercise
caution and common sense when studying the Gulf of Mexico with these “chlorophyll
concentration” products since frequently the concentrations are in error, especially in
coastal zones.
The bio-optical algorithms can be found in the literature as well as in the SeaWiFS
Prelaunch
and
Postlaunch
Technical
Report
Series
(http://seawifs.gsfc.nasa.gov/SEAWIFS/). For clarity they are briefly described below.
There are three general “steps” to convert the sensor signal to surface chlorophyll
concentrations.
First, the sensor signal (a voltage) is radiometrically calibrated to a total radiance, Lt
(for brevity the wavelength dependency is omitted hereafter). This includes 1) correction
for various factors such as temperature effects, mirror effects, stray light effects, and out
of band response; 2) correction of sensor degradation effects through time by measuring
the moon every month; and 3) adjustment of the resulting signal to a modeled total
radiance according to concurrent field measurement with a marine optical buoy (MOBY)
near Hawaii. The field-measured signal is propagated to the TOA according to an
atmospheric correction model. This process is the so-called “vicarious calibration”, a
critical step to guarantee accurate retrievals of the surface signal (water-leaving radiance,
see next step). Note that the vicarious calibration is not a radiometric calibration, but a
calibration of the whole system (radiometric + atmospheric correction algorithm). As a
result, atmospheric effects must be removed (see next step) with the same (or a strictly
consistent) algorithm as used in the vicarious calibration.
Second, we obtain water-leaving radiance (Lw, radiance exited from the ocean as
detected by a sensor just above the surface. Note that directly reflected sky light by the
surface is not included in Lw) with an atmospheric correction algorithm from the
conceptual relationship: Lt = LA + tLw, where LA is radiance from the atmosphere
(including the surface reflected light) and t is the diffuse transmittance from the surface to
the sensor.
For clear water the sensor signal in the near-infrared (NIR) can be reasonably
assumed to come from the atmosphere alone (i.e., Lw(NIR)  0 so that LA(NIR) 
Lt(NIR)), and LA in other wavelengths can be derived from LA(NIR), based on pre-
3
computed lookup tables. t can be estimated from the same lookup tables in a similar
fashion. Lw in all wavelengths is then obtained. For turbid coastal waters where the
assumption of Lw(NIR)  0 no longer holds true, an iterative scheme is involved that uses
a fixed relationship between Lw in three wavelengths as a constraint. This may result in
some uncertainty if the actual relationship (mainly depends on particle size distribution in
the water) differs significantly from the assumed one.
Finally, from the spectral Lw, surface chlorophyll concentration is estimated with a
bio-optical algorithm. The algorithm is based on thousands of field measurements that
relate the ratio of Lw between two bands to chlorophyll concentration. The two bands
used in the OC4v4 algorithm are 555 nm and one of the three bands (443, 490, and 510
nm) that has the maximum Lw value.
The SeaWiFS chlorophyll concentration estimates were from the most recently
available software (SeaDAS4.4, released in summer 2002), which incorporated
improvements in both calibration and algorithms. The uncertainty in the retrieved
chlorophyll values for phytoplankton-dominated waters (typically found in the open
ocean or coastal upwelling region) is generally within 35% to 50%, with smaller RMS
errors. As mentioned above, in coastal waters where other constituents such as colored
dissolved organic matter (CDOM) or suspended sediments dominate the optical signal,
the uncertainty can be much larger.
Clearly, the capability to detect spatial features is of prime concern, and therefore the
images are extremely valuable even when the accuracy of the concentration estimates is
compromised. The SeaWiFS images provide a means to effectively trace water
circulation and oceanographic fronts. This is especially important in this study to identify
oceanographic features during summer, since sea surface temperature (SST) loses most
its contrast and therefore its ability to detect frontal features.
Dr. Chuanmin Hu and
Dr. Frank Muller-Karger
Institute for Marine Remote Sensing/IMaRS, College of Marine Science
University of South Florida, 140 7th Ave. South, St Petersburg, FL 33701
<<http://imars.usf.edu>>
4