Brian Barnes
24 June, 2010
Performance of AVHRR cloud filter in extreme temperatures
Introduction
Accurate measurement of sea surface temperature is thus critical to research and monitoring of marine ecosystems. Toward that end, the National Oceanic and Atmospheric Association (NOAA) launched and maintain a series of Polar Orbiting Environmental Satellites. Onboard each of these satellites is an Advanced Very High Resolution Radiometer (AVHRR). This instrument has a swath width of approximately 2500 km and a spatial resolution at nadir of 1.09 km pixel -1 . The most recent AVHRR designs include 6 bands, primarily measuring in the infrared spectrum. All objects above absolute zero temperature emit radiation at a wavelength relative to their temperature (black-body radiation) [1].
Ideally, the measured infrared radiation incident on the satellite sensors has been emitted by the surface of the water, and thus carries information about the SST. The band measurements can then be converted to a temperature in Celsius using an algorithm developed by McClain et al. [2].
Even though the sensor will measure (and output) incoming radiation each of its bands at every pixel, often this data does not relay any information about the sea temperature. The most obvious example of this is when the satellite is measuring over land. Land pixels, being fixed in position, are easy to locate and remove from geo-referenced SST images by masking them with null data (land mask).
Clouds, however, present more of a challenge. Several of the bands on the AVHRR are intended to detect clouds (both day and night), but they are not foolproof. The problem exists that clouds are composed of water, and thus can emit radiation that appears consistent with very cold SST.
A cloud filter has been developed by Hu et al. [3] to mask any remaining pixels influenced by clouds.
This filter includes both median and climatological components. The median filter is based on the assumption that SST does not temporally change fast, and will thus mask a pixel if its SST value is two degrees from the median value for that pixel from all filtered images in the previous 4 days. The climatology filter uses a twelve-year weekly mean climatology maps, derived from previous filtered
AVHRR passes, which serve as the basis for comparison to recent data. If value of a pixel is four degrees from the climatological mean for that pixel, then it is discarded as cloud data. For the purposes of this paper, images including this mask will be considered filtered, while data without this mask are termed unfiltered or raw.
Although this cloud filter method is effective for most data, a problem arises during anomalously cold or warm events. If the temperature of the water is truly outside the median or climatology temperature threshold, it can be discarded as cloud data. Such anomalous events are among the most stressful times for marine life with respect to temperature. Further, when this data is incorrectly masked, it is not used in subsequent climatologies, thus artificially changing the threshold for correctly interpreting future anomalous events.
Any such system will inevitably present a balance between ensuring that all measurements are valid while minimizing the number of improperly masked pixels. However, the effect of extreme temperature events is especially profound in coastal marine ecosystems, such as the waters surrounding
Florida. Reef-building scleractinian corals, for example, typically live near their thermal maximum [4]. In the face of thermal stress (both warm and cold) these organisms will often expel their photosynthetic symbionts, relinquishing their primary food source [5-6]. Sustained bleaching often leads to death of the coral tissue [7]. Manatees are known to seek out warm water, often landing them in the outflows of power plants [8]. In waters much below 20 °C, manatees are susceptible to mortality due to cold [9].
Cold stunning of sea turtles and subsequent mortality has been seen in response to several cold temperature events, including in Florida [10]. Young sea turtles are negatively affected by temperatures

under 20 C, and are subject to mortality in temperatures below 10 C [11-12]. Damage and death to mangroves [13-14] and seagrasses [15] have also been observed in Florida waters during extreme cold events. Apart from being critical to ecosystem health overall, each of these species provides an indirect economic benefit to the state of Florida via tourism. As such, it is essential that SST products are capable of monitoring SST during extreme temperature events.
Objectives & Hypothesis
The imperative for this study was that the current cloud filters used in processing AVHRR data at the University of South Florida (USF) Institute for Marine Remote Sensing (IMaRS) incorrectly remove accurately measured cold SST data during an anomalously cold event in January 2010. As such, this project was developed to assess the extent to which the current cloud filter masks accurate data, and the effect that these improper masks may have on the overall climatology for the waters surrounding
Florida. The primary hypothesis was that climatologies created from this filtered AVHRR data overestimate the true climate, and could be improved through use of a manual filter.
Methods
All raw and matching filtered SST maps were acquired from the USF IMaRS. Images studied included those from the month of January, 1995-2010, and spanning the waters of Florida (24 to 31 N,
79 to 86 W), a total of 3,200 files. Any image where the swath covered less than approximately 25% of the image was discarded, leaving 2,703 images.
Raw images were first compared side-by-side to corresponding filtered images (figure 1a,b).
Where it was apparent that the filtered image was masking good SST data, the raw image was set aside for re-processing. This subjective determination of good data was based on the author’s integration of spatial (good SST data typically shows smooth and consistent spatial gradients), and temporal (whether a feature is persistent in temporally close images) information. Only the images where the author felt that a more appropriate cloud mask could be manually created were selected for reprocessing. This qualification is necessary in the event that a manually created mask would improperly filter more good data than the current mask.
These selected raw images were then loaded into ENVI (version 4.5) image processing software.
A mask for clouded pixels was created for each image using the built-in Region of Interest (ROI) tool
(Figure 1c). Data pixels not covered by this ROI were cloud free and was retained in a new image
(termed manual filter). Again, this determination was based on information about the spatial and temporal homogeneity of the feature. In the creation of these ROIs, special attention was given to coastal data as they are more likely to show temperature fluctuations in response to atmospheric weather patterns. This same ROI was then applied to the autonomously filtered image, and any pixels enclosed by the ROI (cloud pixels) were copied to the manually filtered image (figure 1d). In this way, the manually filtered image retained the best portions of both the raw and autonomously filtered images; maximizing the amount of data in the image, while the minimizing the number of mistakenly omitted good pixels.
Finally, two sets of weekly climatologies were created showing the mean of SST in January,
1995-2010. The first of these was constructed using only the images filtered autonomously by IMaRS.
The second climatology used the autonomously filtered images except in the case that a manually filtered image had been created as described above. In these instances, the autonomously filtered image was replaced with the corresponding manually filtered image and was subsequently used in the climatology. The deviation between two corresponding weekly climatologies was simply calculated by subtracting one from the other.

Results
Of the 2,703 images analyzed, it was determined that the filter underperformed in 500 images
(18.5 %). The majority of the images that required re-processing were from paths during extreme cold events in 2001, 2006, and especially 2010, indicating that the autonomous filter was primarily cutting cold SST data (see Figure 1). During such events, the filter began removing coastal pixels at water temperatures of ~15 °C when the true data showed SST up to 10 °C lower. Several re-processed images, however, showed the IMaRS filter was also incorrectly masking warm data, especially during warm spells in 2002 and 2007.
Overall, the IMaRS derived climatologies differed greatly from the manually created climatologies, especially for the first and second weeks of January. The most prominent differences between the two climatologies were at inshore pixels. In week 1, the IMaRS derived climatology showed overall higher average SST than that of the manually created climatology over the entire coast of Florida. In the Big Bend and Florida Bay region, the overage was up to 2 °C. Week 2 showed an underestimation by the IMaRS climatology (approximately 0.5 °C) in the near inshore regions, but an overestimation (up to 1 °C) in the further offshore regions (figure 2b). Finally, neither week three or four showed any aggregious, wide-spread differences between the two climatologies. It should be noted that 0.2 °C was the smallest unit of change detection in this study.
Discussion
Even a cursory comparison of raw verses filtered SST maps during a cold event presents evidence that the current filter is unintentionally masking real data. The current study indicates that at more than 1 in 5 images could be improved by manually removing clouds. However, given the relatively rare nature of severe cold water events in Florida winters, it is expected that number of inappropriately filtered images would be less in non-winter months. Nevertheless, it is clear the current filter mistakenly masks not only cold, but also warm water data. The most obvious examples of such were in the Big Bend region and the waters north of the Florida Keys. Not coincidentally, these areas are both bathymetrically shallow, and thus more heavily influenced by rapidly fluctuating air temperatures.
Although the misgivings of the current filter are quite striking within a single SST map, the difference between two climatologies is not as prominent. The most obvious reason for this is that inputs for the climatologies were identical in 81.5 % of the images. Further, the similarities between the climatologies can be attributed to the fact that the autonomous filter had improperly masked both warm and cold water – taking the mean of the masked data points understates the extent to which the autonomous filter was in error. Nevertheless, most of the coastal pixels show some slight (0.2 °C) deviation between the two climatologies created. Colder-than-expected water was often, unsurprisingly, seen around river outflows, owing to the mixing of cool riverine water with the marine in these regions. As expected, the most extreme differences in the performance of the two filters occur in the shallow regions with low seafloor slope. The maximum deviations, however, were only observed in the Florida Bay / Thousand Islands region, where numerous small islands may create mixed pixels and reduce the accuracy of the measurements.
Improving the current filter involves a balance between ensuring that all clouds are removed yet all good data are retained. Since the majority of the improperly masked data was in coastal regions, perhaps a bathymetry component needs to be included. As such, at a fixed depth (perhaps 20 m) the climatological filter would be relaxed, thus reducing the number of rejected data points. However, this would ultimately increase the number of accepted cloudy pixels. Further, this method would create an artificial line across which data is processed in different manners, resulting in unnatural divisions in SST maps. Another potential improvement would be to modify the median filter. Instead of using the median of the filtered images from the prior 4 days, perhaps it would be more appropriate to use the median of the prior unfiltered images. If a large region has shown no change according to the median

filter, perhaps the climatological filter could then be relaxed. A third improvement would be to implement a manual correction procedure similar to the one performed here, with the obvious drawback of time requirements and loss of autonomy. Finally, there could be inclusion of real-time validation with the array of National Data Buoy Center’s in situ SST measures.
When considering the extent of these extreme temperature events and the effect that it might have on marine life, it is important to remember that the satellite is measuring the temperature of a very small surface layer, not that of the entire water column, and these two values can differ significantly [16]. It is evident, nonetheless, that cold water events are not being monitored as well as is possible. Again, these anomalous temperatures represent the conditions that are most stressful to marine creatures. Further research and testing is required in order to more effectively monitor these events in real time, and direct ecological research and protection efforts in a timely manner.
Works cited
[1] D. B. Brace, et al., The laws of radiation and absorption; memoirs by Prévost, Stewart, Kirchhoff,
[2]
[3]
and Kirchhoff and Bunsen. New York, Cincinnati,: American Book Company, 1901.
E. P. McClain, et al., "Multi-channel improvements to satellite-derived global sea-surface temperatures," Advances in Space Research, vol. 2, pp. 43-47, 1982.
C. M. Hu, et al., "Building an Automated Integrated Observing System to Detect Sea Surface
[4]
[5]
Temperature Anomaly Events in the Florida Keys," Ieee Transactions on Geoscience and Remote
Sensing, vol. 47, pp. 2071-2084, Jul 2009.
P. Jokiel and S. Coles, "Response of Hawaiian and other Indo-Pacific reef corals to elevated temperature," Coral Reefs, vol. 8, pp. 155-162, 1990.
P. Jokiel and S. Coles, "Effects of temperature on the mortality and growth of Hawaiian reef corals," Marine Biology, vol. 43, pp. 201-208, 1977.
[6]
[7]
[8]
[9]
A. E. Douglas, "Coral Bleaching - how and why?," Marine Pollution Bulletin, vol. 46, pp. 385-392,
2003.
O. Hoegh-Guldberg, "Climate change, coral bleaching and the future of the world's coral reefs,"
Marine and Freshwater Research, vol. 50, pp. 839-866, 1999.
S. Shane, "Manatee use of power plant effluents in Brevard County, Florida," Florida Scientist, vol. 47, pp. 180-186, 1984.
A. B. Irvine, "Manatee metabolism and its influence on distribution in Florida," Biological
Conservation, vol. 25, pp. 315-334, 1983.
[10] B. E. Witherington and L. M. Ehrhart, "Hypothermic stunning and mortality of marine turtles in the Indian River Lagoon System, Florida," Copeia, vol. 1989, pp. 696-703, 1989.
[11] J. Davenport, "Temperature and the life-history strategies of sea turtles," Journal of Thermal
Biology, vol. 22, pp. 479-488, 1997.
[12] J. Davenport, et al., "The effects of current velocity and temperature upon swimming in juvenile green turtles chelonia mydas L.," Herpetological Journal, vol. 7, pp. 143-147, 1997.
[13] M. Storey and E. Gudger, "Mortality of fishes due to cold at Samibel Island, Florida, 1886-1936,"
Ecology, vol. 17, pp. 640-648, 1936.
[14] T. Savage, Florida mangroves as shoreline stabilizers. St. Petersburg,: Florida Dept. of Natural
Resources, Marine Research Laboratory, 1972.
[15] R. C. Phillips, Observations on the ecology and distribution of the Florida seagrasses. St.
Petersburg,: Florida State Board of Conservation, Marine Laboratory, 1960.
[16] P. Schluessel, et al., "On the bulk-skin temperature difference and its impact on satellite remote sensing of sea surface temperature," Journal of Geophysical Research, vol. 95, pp. 13,341-
13,356, 1990.

Figure 1 a) b) c) d)
AVHRR derived (n15) data from January 15, 2010, 10:37 GMT. a) Raw image showing cold water in coastal Florida regions. b) Autonomously filtered image accurately masking cloud data (bottom of image) but artificially masking cold, non-cloud data (inshore regions). c) Manually created ROI (white) over raw image to mask all clouds. d) Final manually filtered image containing all data from image c and the portion of image b underneath ROI.

Figure 2 a) b) c) d)
Differenc es between climatologies derived from 2001-2010 AVHRR SST maps: a) January 1-7, b)
January 8-14, c) January 15-21, d) January 22-28. Negative numbers indicate that the IMaRS climatology overestimated the true temperature.