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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