Building an Automated Integrated Observing System to Detect Sea ...

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 2, FEBRUARY 2009

2

3 4 5

AQ1

AQ2 AQ3

AQ4 AQ5 AQ6

AQ7 AQ8

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Building an Automated Integrated Observing System to Detect Sea Surface Temperature Anomaly Events in the Florida Keys Chuanmin Hu, Frank Muller-Karger, Brock Murch, Douglas Myhre, Judd Taylor, Remy Luerssen, Christopher Moses, Caiyun Zhang, Lew Gramer, and James Hendee

Abstract—Satellite-derived sea surface temperature (SST) images have had limited applications in near-shore and coastal environments due to inadequate spatial resolution, incorrect geocorrection, or cloud contamination. We have developed a practical approach to remove these errors using Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS) 1-km resolution data. The objective was to improve the accuracy of SST anomaly estimates in the Florida Keys and to provide the best quality (in particular, high temporal and spatial resolutions) SST data products for this region. After manual navigation of over 47 000 AVHRR images (1993–2005), we implemented a cloud-filtering technique that differs from previously published image processing methods. The filter used a 12-year climatology and ±3-day running SST statistics to flag cloud-contaminated pixels. Comparison with concurrent (±0.5 h) data from the SEAKEYS in situ stations in the Florida Keys showed near-zero bias errors (< 0.05 ◦ C) in the weekly anomaly for SST anomalies between −3 ◦ C and 3 ◦ C, with standard deviations < 0.5 ◦ C. The cloud filter was implemented using IDL for near-real-time processing of AVHRR and MODIS data. The improved SST products were used to detect SST anomalies and to estimate degree-heating weeks (DHWs) to assess the potential for coral reef stress. The mean and anomaly products are updated weekly, with periodic updates of the DHW products, on a Web site. The SST data at specific geographical locations

were also automatically ingested in near real time into National Oceanic and Atmospheric Administration (NOAA)’s Integrated Coral Observing Network web-based application to assist in management and decision making through a novel expert system tool (G2) implemented at NOAA.

31 32 33 34 35

Index Terms—Advanced Very High Resolution Radiometer (AVHRR), cloud detection, coastal ocean observing system (COOS), Florida Keys, MODerate-resolution Imaging Spectroradiometer (MODIS), remote sensing, sea surface temperature (SST), water quality.

36 37 38 39 40

I. I NTRODUCTION

41

IE E Pr E oo f

1

1

Manuscript received December 19, 2007; revised February 5, 2008, May 11, 2008, and August 20, 2008. This work was supported in part by the National Aeronautics and Atmospheric Administration (NASA Ocean Biology and Biogeochemistry Program and Interdisciplinary EOS Program) and in part by the National Oceanic and Atmospheric Administration (NOAA) Coral Reef Conservation Program. C. Hu is with the Institute for Marine Remote Sensing, College of Marine Science, University of South Florida, Tampa, FL. F. Muller-Karger is with the School for Marine Science and Technology, University of Massachusetts Dartmouth, Dartmouth. B. Murch D. Myhre J. Taylor is with the Institute for Marine Remote Sensing, University of South Florida, Tampa, FL, and also with Orbital Systems, Ltd., Dallas, TX. R. Luerssen C. Moses is with the Jacobs Technology. C. Zhang is with the College of Oceanography and Environmental Science, Xiamen University, Xiamen, China. L. Gramer is with the Rosenstiel School of Marine and Atmospheric Science, University of Miami, Coral Gables, FL, and also with the National Oceanic and Atmospheric Administration. J. Hendee is with the Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2008.2007425

HE SOUTHWEST Florida coastal system encompasses 42 the Gulf and Atlantic waters of the Florida Keys, the Dry 43 Tortugas, and the Southwest Florida Shelf. Within this system is 44 the Florida Keys National Marine Sanctuary (FKNMS, Fig. 1), 45 an important marine protected area of the United States that 46 encompasses > 2800 nmi2 of islands and coastal waters and 47 houses a major coral reef system. Annually, the FKNMS at- 48 tracts 3 million tourists who spend approximately 1.2 billion 49 dollars [5]. The FKNMS is exposed to various local and remote 50 sources of pollution and runoff, including the South Florida 51 ecosystem [31], [24], [25] and the Mississippi River [26], [42]. 52 It is a system that, similar to other tropical coral reef systems, is 53 also exposed to the influence of climate change and fluctuations 54 of weather, including storms and warm and cold events. 55 Understanding the connection of the Florida Keys ecosystem 56 to other processes calls for an integrated research approach that 57 addresses processes taking place over synoptic scales and that 58 combines field surveys, autonomous in situ measurements, re- 59 mote sensing, modeling, advanced data integration techniques, 60 and routine operations. With operational remote sensing from 61 space, our ability to observe and study the coastal ocean has 62 been significantly enhanced with advances in technology and 63 science during the past decade. However, automated mecha- 64 nisms that integrate these concepts to monitor anomaly events 65 (e.g., temperature, turbidity, and phytoplankton bloom) at rel- 66 atively high resolution (1 km) in the Florida Keys ecosystem 67 still do not exist, although some sea surface temperature (SST) 68 data products at coarse resolution (50 km) have been automati- 69 cally generated by the U.S. National Oceanic and Atmospheric 70 Administration (NOAA) in the past years. This is primarily due 71 to the following: 1) the lack of reliable, routine, synoptic, and 72 high-resolution data products, and 2) the lack of a computing 73

T

0196-2892/$25.00 © 2008 IEEE

2

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 2, FEBRUARY 2009

IE E Pr E oo f

Fig. 1. Map of FKNMS showing various water zones. The map covers an area from 24.09◦ N to 26.23◦ N and from 83.22◦ W to 79.9◦ W. The circled crosses show locations of the NDBC stations. Base map courtesy of Kevin Kirsch (FKNMS).

facility or program dedicated to analyzing the data and issuing early alerts. 76 In this paper, we focus on improving satellite SST products 77 and demonstrate a system that integrates various in situ and 78 satellite data in real time for cross-validation and generating 79 alerts about environmental change. This is a first step to address 80 routine and synoptic water-quality monitoring in the Florida 81 Keys, including automated anomaly detection. In future work, 82 we will describe how to develop similar products using ocean 83 color data. 74 75

84 85

II. SST IN THE F LORIDA K EYS : C URRENT S TATUS AND R EQUIREMENTS

SST is a parameter that affects local processes of air–sea interaction and various elements of marine ecosystems including primary production and, in the aggregate, has important effects on climate change. Since it is easy to measure directly and using 90 remote sensing techniques, it is also an important index of vari91 ability of environmental health. Since the early 1980s, the series 92 of the Advanced Very High Resolution Radiometer (AVHRR) 93 sensors flown aboard the U.S. NOAA polar-orbiting satellites 94 has provided synoptic estimates of SST of the global ocean. 95 More recently, the U.S. NASA’s MODerate-resolution Imaging 96 Spectroradiometer (MODIS) sensors also provide near-daily 97 global and local SST observations at similar resolutions as 98 AVHRR (∼1 km/pixel at nadir). 99 An important emerging application is the continuous assess100 ment of conditions that affect coral reef health, by monitoring 101 the SST anomaly (SSTA). This concept has been implemented 102 by the NOAA/NESDIS Coral Reef Watch (CRW) team, with a 103 global shallow-water tropical coral-bleaching alert system that 104 utilizes AVHRR data resampled at 50-km spatial resolution 105 [16], [49]. These NOAA HotSpot and degree-heating week 106 (DHW) products have been successful in the prediction of 107 massive multispecies bleaching events on the Australian Great 86 87 88 89

Barrier Reef in 2002 [36] and in the eastern Caribbean in 2005 108 (NOAA/NESDIS, unpublished data). 109 In the Florida Keys, however, a single 50-km pixel may, de- 110 pending on its exact position, cover the warmer Florida Current 111 offshore, waters of the Pourtales Terrace (an intermediate shelf 112 in the Southern Straits of Florida off the reef tract), Florida Bay, 113 Hawk Channel, as well as the reef tract itself, each featuring 114 different local SST conditions. For example, as in the case of 115 the HotSpot product for Sombrero Key of the FKNMS, the 116 50-km pixel is chosen to monitor waters of the West Florida 117 Shelf, separated from the reef tract by some tens of kilometers, 118 and by a series of relatively narrow bridge channels between 119 the landmasses of the Florida Keys. Indeed, SST across a 120 50-km line drawn in almost any orientation in the Florida Keys 121 can vary by > 1 ◦ C during many times of the year. 122 Instead, the full-resolution AVHRR or MODIS SST imagery 123 (∼1 km) differentiates waters closer to each reef and could 124 help assess stresses at reef scales. Spatial and temporal scales 125 of significant sea temperature variability in the reef waters of 126 the FKNMS have been estimated using in situ data at depth 127 [33], [34]. These data show that onshore (upwelling) transport 128 processes can affect reef waters at inertial and tidal frequencies, 129 with length scales ranging from 1 m to 10 km, with significant 130 impact on the benthic ecosystem, and also on SST at the shal- 131 low reef crest. In addition, frontal meanders and mesoscale and 132 submesoscale eddies [11], [12] all have relatively small spatial 133 scales and rapid translation velocities [13], [22] that require 134 high spatial and temporal resolutions (e.g., 1 km daily). Another 135 example is the case of Mississippi waters being brought onshore 136 in the reef tract [14], [26], [42]. These anomalous waters may 137 not be detected by the 50-km pixels, yet these anomalous low- 138 salinity high-nutrient riverine waters can nonetheless have a 139 direct impact on the ecology of shelf and reef habitats at small 140 scales in the FKNMS. 141 In addition to being sensitive to anomalously warm waters, 142 corals can also be under stress or even bleached in unusually 143

HU et al.: BUILDING AN AUTOMATED INTEGRATED OBSERVING SYSTEM TO DETECT SST ANOMALY EVENTS

cold waters [23], [44]. A significant source of cold water 145 transport onto the reef tract is from offshore subthermocline 146 waters through upwelling, frontal meanders, and mesoscale and 147 submesoscale eddies. However, to date, there is no effort to 148 detect and monitor cold water events in the region. 149 An important question is whether the satellite-derived SST 150 products are accurate for a variety of oceanographic appli151 cations, particularly for monitoring anomaly events at local 152 scale. Studies of decadal climate changes require an accuracy of 153 ∼0.2 ◦ C [4], while studies of ocean fronts and upwelling require 154 accuracies of at least 0.5 ◦ C [54]. Assessment of anomaly events 155 and DHW require similar or higher accuracies. In open ocean 156 environments, the rms error of AVHRR SST data has been 157 estimated to be about 0.5 ◦ C [3], [27], [40]. Similar estimates 158 of accuracy have not been conducted for heterogeneous coastal 159 areas like the Florida Keys National Marine Sanctuary. 160 Improper detection of clouds is a common and significant 161 problem in achieving accurate SST images, because many 162 clouds escape traditional cloud detection and masking algo163 rithms. Most algorithms are based on radiance thresholds and 164 statistics of the multichannel signals, but they frequently fail, 165 allowing clouds to be identified as cold water and therefore 166 leading to negative residual errors. These errors are often easy 167 to recognize visually, but they often pass uncorrected in auto168 mated SST image processing. This can lead to significant bias 169 in “mean” SST products and erroneous time series. 170 Here, we offer an approach to address these issues. Our 171 objectives are to develop high-resolution validated SST data 172 products for the Florida Keys that are integrated with a com173 puter expert system to help monitor the environmental health of 174 the benthic habitats. We begin by introducing a practical cloud175 filtering algorithm based on statistics at each pixel location, fol176 lowed by accuracy evaluation of the data products from various 177 satellite sensors. Finally, we will introduce an experimental data 178 integration and broadcasting system designed for automated 179 detection of anomaly events.

AQ9

180

III. D ATA S OURCES

Satellite data from several AVHRR and MODIS (Terra and Aqua) sensors were collected by a local antenna starting from 1993 to 2003, respectively, with a nominal resolution of about 1 km/pixel. They were navigated and processed using methods 185 and algorithms described in Appendix A. The results were geo186 referenced SST products in both data (HDF computer files) and 187 imagery (PNG) formats. These products, however, frequently 188 contain cloud-contaminated pixels that need to be removed 189 before reliable time series of mean and anomaly products can 190 be derived (see succeeding discussions). 191 In situ temperature data in the Florida Keys were obtained 192 from the National Data Buoy Center (NOAA NDBC), from 193 thermal sensors mounted on Coastal-Marine Automated Net194 work (C-MAN) stations, towers, or marine buoys. The NDBC 195 C-MAN stations use tubes filled with antifreeze where temper196 ature is homogenous and the thermistors inside tubes measure 197 the average temperature of the water column. Other C-MAN 198 stations and other observing stations used thermistors at depths 199 of 0.5–1 m below the ocean surface. For simplicity, all stations 181 182 183 184

TABLE I NDBC STATIONS USED IN THIS PAPER

IE E Pr E oo f

144

3

are referred to as NDBC stations. Table I lists the information 200 about the stations from which SST data were used in this paper. 201 All data were collected at 1-h intervals and examined by NOAA 202 to identify problems using a series of quality control procedures 203 [15], [41]. 204

IV. P RACTICAL A PPROACH TO C LOUD F ILTERING : M ETHOD AND R ESULTS

A. Implementation

205 206 207

First, because SST shows clear seasonal patterns, a “clima- 208 tology” filter can be constructed. If the difference between a 209 pixel value and its climatological value is larger than a prede- 210 fined threshold, then the pixel may be flagged as cloudy. The 211 threshold value needs to be chosen carefully to be as effective 212 as possible yet sufficiently robust to keep all valid data. 213 Similarly, because SST changes slowly with time, a temporal 214 filter could be developed, where SST is compared with the 215 “median” state within a given period and the pixel is flagged 216 as cloudy if the difference is larger than a predefined threshold. 217 We implemented these approaches by determining the 218 threshold values and the temporal windows by trial and error, 219 using statistics derived from the NDBC and satellite observa- 220 tions. Fig. 2 shows an example of these tests based on two 221 NDBC stations. Nearly all data passed through the climatology 222 filter when the climatology window was one week and the 223 threshold value was 4 ◦ C. More importantly, the climatology 224 filter could not be further tightened because too many valid data 225 points would have been deleted otherwise. For similar reasons, 226 the temporal filter window was chosen to be ±3 days with a 227 threshold value of 2 ◦ C. 228 The filter was implemented to process the entire image series 229 covering the Florida Keys from late 1993 through December 230

4

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 2, FEBRUARY 2009

IE E Pr E oo f

Fig. 2. Histogram of the differences between hourly NDBC SST and corresponding climatology (1994–2005) value from the same week from the MLRF and DRYF stations. Nearly all the differences are within ±4 ◦ C. Of the 100 986 and 69 169 hourly SST measurements from the two stations, respectively, only 38 and 75 measurements show differences > ±4 ◦ C from their weekly climatology values, and only 268 and 40 measurements show differences > ±2 ◦ C from their ±3-day median values.

Fig. 3. Schematic of the cloud-filtering process for AVHRR and MODIS images, where ∆Tclim and ∆Tmedian are the threshold values determined as 4 ◦ C and 2 ◦ C, respectively, from trial-and-error using NDBC SST data. 231 232

2005 (> 47 000 images) using the following two steps and according to the schematic shown in Fig. 3.

233 234

1) For each location, a time series was extracted, and a 12-year weekly “climatology” was constructed. The entire series was screened for clouds, using the climatology and ∆Tclim threshold, and then used to construct the climatology again. This process was repeated three times, after which the result converged. 2) For each location, a moving window of ±3 days was used to estimate the median value from all “valid” pixels (as gauged by the climatology filter earlier). A pixel was flagged as cloudy if its value was away from the median value by >∆Tmedian .

235 236 237 238 239 240 241 242 243

In step 2), the median value was chosen because it was less sensitive to any “outlier” values in the temporal window than the mean. Indeed, comparison with concurrent NDBC data 247 from all stations in this paper showed that the “median” method 248 always yielded better results than the “mean” method in terms 249 of rms error and mean bias error. 250 A set of images “cleaned” of clouds was randomly chosen 251 from the > 47 000 images for visual examination. Fig. 4 252 shows an example where the cloud-contaminated pixels were 253 successfully flagged as clouds. Nearly all valid pixels passed 254 through the filter. Similar results were found for other images. 255 Occasionally, several pixels were mistakenly flagged as clouds, 256 and this happened most often near sharp fronts. However, con257 sidering the significant improvement over the original satellite 258 data set (see below), such adverse effects were negligible. 244 245 246

Fig. 4. Example of the filtering results for a cloud-contaminated image. The image was taken from the n12 AVHRR sensor on December 31, 2004 at 10:37 GMT. (a). Original image from the Terascan software after initial cloud filtering. Note the contaminated pixels (purple color) adjacent to and on clouds (gray color). (b) The same image after a temporal (±3 days) median filter (threshold: ±2 ◦ C). Most contaminated pixels were flagged as clouds, but some still remain unchanged. (c) The same image after a weekly climatology filter (threshold: ±4 ◦ C) and the ±3-day median filter, where all contaminated pixels are flagged as clouds.

B. Validation

259

In order to validate the effectiveness of the cloud filter, a 260 series of “matching pairs” was extracted at each NDBC station. 261

HU et al.: BUILDING AN AUTOMATED INTEGRATED OBSERVING SYSTEM TO DETECT SST ANOMALY EVENTS

5

IE E Pr E oo f

TABLE II STATISTICS OF THE SATELLITE SST VALIDATION AGAINST NDBC STATION DATA AFTER SATELLITE SST DATA PASSED THROUGH THE CLOUD FILTER. THE STATIONS ANNOTATED WITH ∗ ARE TOO CLOSE TO LAND, AND THEREFORE, DATA MAY BE CONTAMINATED BY LAND INTERFERENCE. HERE, SD STANDS FOR STANDARD DEVIATION, SLOPE AND INTERCEPT ARE THE LINEAR REGRESSION COEFFICIENTS BETWEEN THE T WO D ATA S ETS , r I S THE C ORRELATION C OEFFICIENT , AND N I S THE N UMBER OF M ATCHING P AIRS

Fig. 5. Validation of satellite SST (from AVHRR and MODIS) over two NDBC stations [(a) and (b)] MLRF1 and [(c) and (d)] DRYF1. Satellite SSTs before and after the cloud filter are shown in (a) and (c), and (b) and (d). Statistics of the validation is also annotated on the figure. The solid lines in (b) and (d) are 1 : 1 lines, and the dashed lines are linear regression lines.

SST from the satellite (SSTsat ) and NDBC (SSTNDBC ) were considered “concurrent” only when the two measurements were 264 within ±0.5 h. 265 Table II lists the statistics of the validation result. Except 266 for the three shallow stations (KYWF1, VCAF1, and VAKF1), 267 which are close to land and may have been contaminated by 268 land interference, rms and SD errors are generally smaller 269 than 1 ◦ C, with much smaller bias (mean rms = 0.90 ◦ C, 270 mean SD = 0.84 ◦ C, and mean bias = −0.29 ◦ C). Fig. 5 shows 271 two examples for MLRF1 and DRYF1 before and after the 272 cloud filter. Clearly, the cloud filter removed suspicious data 273 and improved the satellite SST accuracy significantly. 274 Can these results satisfy the need to accurately detect short275 term (weekly to monthly) SSTA events? Although the accuracy 276 of satellite SST can be regarded as generally satisfactory, 277 the slope values are less than 1.0, suggesting that at high 262 263

temperatures, the satellite tends to underestimate SST. Indeed, 278 for SST > 28 ◦ C, bias values for the two stations shown in 279 Fig. 5 are −0.54 ◦ C and −0.56 ◦ C, respectively. We are not 280 certain what would lead to this condition, but it is possible that 281 the atmospheric correction (e.g., correction for water vapor, 282 see [10]) attempted through application of the multichannel 283 sea-surface temperature (MCSST) algorithm leads to an error. 284 Alternatively, there may be residual errors from cloud conta- 285 mination (e.g., thin cirrus), or the algorithm may simply have 286 an error associated with the bulk versus skin difference related 287 to remote sensing applications [55]. However, as long as the 288 satellite SST is consistent through time, SST anomalies should 289 be accurate. This is clearly shown in the validation statistics 290 for the anomaly products at MLRF1 and DRYF1 (Fig. 6). 291 Compared with Fig. 5(b) and (d), the weekly data showed 292 smaller rms and SD errors. More importantly, the mean bias 293

6

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 2, FEBRUARY 2009

AQ10

biases among sensors were similar. The higher signal-to-noise 325 ratio of the MODIS sensors did not lead to significant improve- 326 ment in accuracy. However, for all cases, NOAA-12 (n12) was 327 slightly worse, while n17 was slightly better. For each station, 328 MODIS SST4 showed slightly smaller standard deviations than 329 other sensors. There appeared to be a systematic trend in the 330 bias errors between daytime and nighttime: Nighttime satellite 331 SST tended to have higher negative biases, which may result 332 from diurnal heating effects. Nevertheless, these errors are 333 small, and the results show that SST products from all sensors 334 are consistent and that they can therefore be combined for time- 335 series analyses in the Florida Keys. 336 E. Climatology and Anomaly Imagery

294 295 296

in the satellite weekly anomaly was nearly zero, suggesting that these satellite data can be used reliably for the detection of anomalous SST conditions.

297

C. Cloud Statistics

Satellite SST may be biased when using only a few images (snapshots) to represent the mean state within a certain time interval. We used the daily composite imagery from AVHRR and MODIS between 2004 and 2005 to evaluate the cloud302 free probability for each location (pixel), defined as the ratio 303 between the number of cloud-free days and the total number 304 of days for each of the four seasons when SST data were 305 available. Results showed that for the South Florida region, the 306 daily cloud-free probability is always > 50%, meaning that, on 307 average, there should be at least one cloud-free SST value every 308 two days for every location. 309 There are two implications from this result: 1) The weekly 310 or monthly time series of satellite SST is not biased due 311 to sampling frequency, and 2) the ±3-day time window in 312 the median filter to remove suspicious data is a reasonable 313 choice, because within seven days, there would be at least 314 four valid SST values to compute statistics, without counting 315 the availability of multiple passes (typically > 10) within a 316 day. However, in rare cases, persistent cloud cover occurred 317 for periods longer than a week at a given location. In these 318 conditions, the temporal median filter failed, but SST was still 319 improved by the climatology filter alone. 298

299 300 301

320

D. Sensor Consistency

We performed statistics on each individual sensor and on daytime and nighttime passes separately (Table III). MODIS SST4 data products (derived from the 4-µm channels from 324 nighttime passes) were also evaluated. Overall, the errors and

321

322 323

From the daily composite imagery, monthly and weekly 338 climatology, mean, and anomaly images were derived and used 339 to examine the spatial and temporal variability of SST in 340 Florida Bay and around the Florida Keys. Fig. 7 shows some of 341 the monthly climatology imagery, where temperature gradients 342 across isobaths are clearly visible. 343 An example of monthly anomalies for 2005 is shown in 344 Fig. 8. SSTA along the Keys showed patchiness. Cold waters in 345 January 2005 and warm waters in August 2005 were restricted 346 to waters shallower than 10 m. Clearly, these high-resolution 347 images provide a significant advantage to address the small- 348 scale variability that affects reef communities. 349 Coral-bleaching potential, based on the methods used for 350 the NOAA/NESDIS CRW DHW product [36], was derived 351 from the 1-km SST data after cloud screening and weekly 352 compositing (Fig. 9). Based on data from 97 sites surveyed by 353 the Florida Reef Resiliency Program carried out by The Nature 354 Conservancy (TNC) and the Florida Department of Environ- 355 mental Protection, mild-to-moderate coral bleaching occurred 356 in 2005. Areas near south Biscayne Bay reported the highest 357 amount of bleaching and were associated with positive SST 358 anomalies (SSTA > 1 ◦ C) and higher DHW values. Interest- 359 ingly, this area also experienced high negative SST anomalies 360 during winter [Fig. 8(a)]. The high-resolution SSTA and DHW 361 products detected smaller reef-scale heterogeneity in heating 362 and provided information useful to detect areas of increased 363 bleaching threat in south Biscayne Bay (Fig. 9). 364

IE E Pr E oo f

Fig. 6. Comparison of weekly mean SST and weekly SSTA from satellite and from NDBC stations for MLRF1 and DRYF1 between 1994 and 2005.

337

V. A UTOMATION C ONSIDERATIONS

365

The cloud filter was implemented to remove cloud- 366 contaminated data in real time and automatically. The temporal 367 median filter was adjusted to use the past four days to com- 368 pute the statistics. Results were similar to those obtained by 369 reprocessing the data three days later, when the ±3-day median 370 filter could be applied. Weekly and monthly mean and anomaly 371 products were generated automatically, while DHW products 372 were computed manually and visually examined to detect 373 suspicious features (http://imars.usf.edu/merged/). We suggest 374 that it is possible and desirable to implement a mechanism 375 for automatic anomaly detection that generates e-mail or cell- 376 phone alerts to resource managers or field assessment research 377 teams. 378

AQ11

HU et al.: BUILDING AN AUTOMATED INTEGRATED OBSERVING SYSTEM TO DETECT SST ANOMALY EVENTS

IE E Pr E oo f

TABLE III STATISTICS OF SATELLITE SST VALIDATION RESULTS FOR ALL AVHRR AND MODIS SENSORS

Fig. 7.

SST monthly climatology derived from AVHRR and MODIS after cloud filtering. Annotated on the images are the 10- and 30-m isobaths.

Fig. 8.

SSTA for January and August 2005, derived from all available AVHRR and MODIS data.

7

8

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 2, FEBRUARY 2009

Fig. 9. Comparison of (left) existing NOAA/NESDIS 50-km DHW product and (right) USF equivalent 1-km DHW product for the 3-day period ending on August 20, 2005. Land mask colors are reversed between the images with zero DHW shown as black in the NOAA image and white in the USF image. Original NOAA/NESDIS image can be found at http://www.osdpd.noaa.gov/PSB/EPS/SST/data2/dhwa.8.20.2005.gif.

Such an automated system is in the development by the Integrated Coral Observing Network (ICON) team at NOAA’s Atlantic Oceanographic and Meteorological Laboratory. The 382 system is based on an object-oriented expert system and data 383 analysis platform called G2 (Gensym Corporation) [20], [21]. 384 The ICON/G2 system uses data integration tools, fuzzy logic 385 and rule-based inferencing, in situ SST, and cloud-filtered satel386 lite SST to assess ecologically significant changes in coral reef 387 ecosystems. The ICON/G2 system recognizes potential threats 388 and changes on monitored reefs using ecological forecasts or 389 ecoforecasts. In general, ecoforecasts attempt to “now-cast” 390 (recognize) or forecast the occurrence of ecological and organ391 ismal conditions on the basis of available environmental data. 392 Reliable SST at reef scale is a decisive criterion upon which 393 several ecoforecasts in coral ecosystems rely. The NOAA ICON 394 project has implemented a variety of ecoforecasts for coral reefs 395 in the FKNMS that happen to be well monitored via the in situ 396 monitoring stations FWYF1, MLRF1, and SMKF1 (see 397 Table I). Use of the high-resolution SST data product described 398 here not only added more confidence in the in situ and satellite 399 SST accuracy through intercomparison for these sites but will 400 also allow for extension of the system to other sensitive coral 401 reef and sea-grass habitats within the FKNMS where in situ 402 data are not available. The ICON/G2 system has been operated 403 continuously in partnership between NOAA and the University 404 of South Florida (USF)’s Institute for Marine Remote Sensing 405 (IMaRS) since its inception in 2005. ASCII and graphic reports 406 are e-mailed to subscribers every day and are archived and made 407 available on the web. During this experimental phase, we are 408 still diagnosing and testing its performance, with plans to add 409 more satellite data products such as those from ocean color 410 measurements.

(see, e.g., [6], [7], [8], [9], [30], [37], [43], [46], [56], and oth- 419 ers). Some of the algorithms, mainly based on single-image sta- 420 tistics, have been implemented for operational data processing 421 at several satellite ground stations. Examination of the results 422 showed that these filters are often too strong near land and 423 therefore unlikely applicable to the Florida Keys. Furthermore, 424 there are also residual errors which required manual corrections 425 (see, e.g., [2] and [35]). The Cayula and Cornillon method 426 combines single-image-based statistics with referencing to a 427 neighboring image [7]. However, to our knowledge, this method 428 has not been implemented for operational data processing, 429 possibly due to the extra computational time required. 430 Our purpose was not to develop a sophisticated cloud- 431 filtering algorithm at the radiance level but to remove the cloud- 432 contaminated pixels in the image products in a practical way in 433 order to construct reliable time series of climatology and anom- 434 aly products. Indeed, quality-control filters for continuous data, 435 either from a moored buoy or from an automatic flow-through 436 system, are common. Yet, similar applications for operational 437 image processing could not be found in the current literature. 438 We implemented such a filter (a combination of climatology 439 and temporal median) based on rigorous statistical tests of 440 the in situ and satellite data. Because of the frequent satellite 441 coverage (multiple passes per day) and relatively slow SST 442 changes over daily scales in our study area, such a filter resulted 443 in significant improvement in cloud removal and SST data 444 product accuracy. The filter is also computationally efficient 445 to finish a 1000 × 1000 image within seconds. However, for 446 areas where persistent clouds exist or SST changes dramatically 447 either temporally or spatially, such a filter may induce large 448 errors or simply does not work. 449 The NASA Pathfinder SST (PFSST), which is derived from 450 the AVHRR Global Area Cover (GAC) data set at 4-km reso- 451 lution, uses a nonlinear algorithm (NLSST, see Appendix A) 452 that includes time- and water-vapor-dependent coefficients by 453 month, determined from the Pathfinder Matchup Data Base of 454 concurrent satellite and in situ measurements [30], [45]. Cloud 455 masking and other quality flags are determined by a series of 456 decision trees, which flag data with quality values from 0 to 7, 457 where 0 is cloud and 7 represents the highest quality. Cloud 458 detection uses threshold values, 3 × 3 uniformity test, and 459 “reference test” against the corresponding three-week Reynolds 460 SST field ±2 ◦ C. 461

IE E Pr E oo f

379 380 381

411 412

VI. D ISCUSSION A. Unique Data Product

Cloud contamination can be a serious problem in time-series analysis of infrared imagery. Most published cloud-detection algorithms are based on an individual image, while others check 416 on a reference image (either from a climatology, a coarse reso417 lution Reynolds SST or cloud-free microwave data, or a recent 418 image) to determine if the current pixel is cloud contaminated 413 414 415

AQ12

HU et al.: BUILDING AN AUTOMATED INTEGRATED OBSERVING SYSTEM TO DETECT SST ANOMALY EVENTS

9

IE E Pr E oo f

Fig. 10. Comparison between PFSST [(a) and (c), 4-km resolution] and USF SST [(b) and (d), 1-km resolution] for January 2005. The data sources were nearly identical in terms of the various AVHRR sensors used to build the data sets, but the Pathfinder data used reduced resolution GAC data recorded onboard, while the USF SST used the full-resolution data received at the USF ground station. The SST products were derived with different algorithms and each used different strategies to filter cloud contamination.

Fig. 11. Comparison of monthly mean PFSST (δ), USF SST (O), and NDBC SST between 1995 and 2005 for (a) MLRF1 and (b) DRYF1. Note that when these monthly mean products are used to derive monthly anomaly products, smaller SD values (i.e., higher consistency) will lead to smaller errors in the anomaly products.

The PFSST data set has been widely used for global and regional studies because of its wide availability and relatively high (4-km) resolution (see, e.g., [1]). However, preliminary 465 comparison between the PFSST and our cloud-filtered products 466 showed that although, for the offshore waters, the two data sets 467 were nearly identical, the PFSST data retained a high degree of 468 noise that smeared the SST gradients for coastal areas (Fig. 10). 469 This may be due to residual errors derived from the selection 470 of the warmest pixel onboard the satellite prior to recording the 471 GAC, in navigation errors associated with the AVHRR data, and 472 cloud filtering. Fig. 11 further shows that for coastal shallow 473 waters, although, during the summer months, PFSST appeared 474 to have smaller bias, SST from USF data (USF SST) was more 475 consistent (smaller SD). USF SST was also more accurate in 476 other times, particularly during the winter months when PFSST 477 often overestimated SST by 2 ◦ C or higher. The ability to 478 detect cold water events during winter months is also important 462 463 464

to predict coral stress and bleaching [23], [44]. Because our 479 goal is to derive reliable SSTA products, data consistency, as 480 measured by SD computed from the monthly means, is more 481 important than bias. Hence, USF SSTA products are more 482 robust than those from PFSST. For example, for the warmest 483 month (August) and from the multiyear monthly data, SD 484 values for the MLRF1 and DRYF1 stations are 0.18 ◦ C (n = 485 11) and 0.17 ◦ C (n = 9) for USF SST and 0.90 ◦ C (n = 11) 486 and 0.33 ◦ C (n = 9) for PFSST, respectively. Similarly, for 487 the coldest month (February), SD values for the two stations 488 are 0.32 ◦ C (n = 11) and 0.22 ◦ C (n = 9) for USF SST and 489 0.72 ◦ C (n = 11) and 0.35 ◦ C (n = 9) for PFSST, respectively. 490 Clearly, the application of PFSST to derive anomaly products 491 for coastal and shallow regions such as the Florida Keys will 492 lead to larger errors. 493 There are several other satellite-derived SST products that 494 provide various advantages but also have shortcomings. The 495

10

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 2, FEBRUARY 2009

Fig. 12. High-resolution (1-km) AVHRR SST imagery (NOAA-16 satellite) showing small-scale (∼1020 km, ∼< 1 ◦ C) frontal eddies (annotated with black arrows) along the shallow isobaths in the Florida Straits on January 22, 2002 at 7:36 GMT (a) and January 23, 2002 at 18:45 GMT. Note that the color scale is different from other figures. These eddies are due to shelf wave dynamics, and they cannot be detected by other coarser resolution data (4 km or worse). Note that the color scales are stretched to show the eddies more clearly.

low-resolution SST data from microwave sensors (e.g., TRMM Microwave Imager or TMI at 25-km resolution) or from some 498 global efforts (e.g., NOAA SST products) are available for the 499 world ocean, but they are not adequate for small-scale studies. 500 Similarly, high-resolution (120-m) SST data from the Landsat 501 thermal band [50] are limited due to cloud cover and the 502 16-day revisit cycles. Recently, Walker et al. [51] showed that 503 GOES-R (a geostationary sensor at 75◦ W) could provide high 504 temporal resolution (hours or better) SST at 4-km resolution 505 for the Gulf of Mexico and the Atlantic Ocean. Unfortunately, 506 these coarse-resolution data products are only available since 507 late 2002, which also contain residual errors from cloud con508 tamination, and their accuracy in shallow coastal waters needs 509 to be tested. The cloud-free TMI SST data could be merged 510 with the AVHRR SST (see, e.g., [19]), but TMI also has a 511 conservative land mask and there are no data near the coast. 512 Therefore, the current 1-km, cloud-filtered, and validated data 513 products may be the best available products for the region to 514 detect and monitor SSTA events. 515 The final SST products showed some small negative biases 516 in the high SST range (see, e.g., Fig. 5). Ideally, this type of 517 error should be addressed at the algorithm level as in [30], 518 where Kilpatrick et al. used a decision tree to discard and flag 519 invalid data. However, the purpose of our work was not to 520 develop an alternative comprehensive algorithm (such as the 521 Pathfinder algorithm) to process the raw satellite data but to 522 establish a consistent SST time series for a coastal region to 523 detect and monitor anomaly events. We emphasize that for this 524 purpose, “consistency” is the key. Indeed, the consistent time 525 series of the SST data set led to near-zero bias in the weekly 526 anomaly products (see, e.g., Fig. 6), showing its effectiveness 527 for our purpose. Alternatively, the SST data can be adjusted 528 using the regional slope/intercept values (Table II) to minimize 529 the bias. However, this adjustment will not affect the anomaly 530 products.

AQ13

AQ14

events during which corals may be under stress. Indeed, from 538 conversations with the local management groups, these high- 539 resolution products are highly demanded by the TNC through 540 the Florida Reef Resiliency Program and by the FKNMS re- 541 source managers (Brian Keller, FKNMS, personal comm.). 542 The problems the Florida Keys ecosystem is facing are 543 not unique; rather, they are typical in global scale for most 544 coral reef waters and coastal estuaries. Thus, can the same 545 concept described here be applied elsewhere? We applied the 546 data quality control and cloud-filtering procedures to the entire 547 AVHRR and MODIS data set captured by our antennas. Even 548 though the results for other areas did not go through rigorous 549 validation test as shown here, visual examination showed great 550 improvement in removing cloud contamination while retaining 551 valid data. 552 On the West Florida Shelf, the cloud-filtered SST data were 553 used to identify thermal fronts in fishery studies [52]. The high 554 temporal (near-daily) and spatial (1-km) resolution data are 555 critical to understand the scales of the circulation related to the 556 distribution of high trophic level pelagic animals. Similarly, the 557 small-scale (∼10-km) frontal eddies [32] in the Florida Keys 558 along the shallow isobaths can be clearly revealed by these 559 products (Fig. 12). In contrast, these features, while important 560 in fishery and other ecological studies [17], [47], cannot be 561 detected by the 4-km resolution data from either GOES or 562 PFSST. Note that the eddies in the lower Keys are remarkably 563 similar to those revealed by in situ surveys [17] targeted to 564 understand fish larvae transport. 565 The products are also useful to show diurnal SST changes 566 in shallow regions. Weekly climatologies from daytime and 567 nighttime data separately showed systematic differences of 568 0.2 ◦ C−0.5 ◦ C for most shallow areas (< 30 m), and such 569 differences can occasionally reach 1 ◦ C. This is important for 570 biological studies as well as for studies of thermal dynamics 571 of the upper ocean and air–sea interactions in shallow waters. 572 The diurnal differences may indicate that two separate data sets 573 for daytime and nighttime, respectively, should be generated for 574 climatology, mean, and anomaly. However, because the same 575 diurnal differences exist in both the climatology and the mean, 576 in principle, they are canceled out in computing the anomaly 577 (i.e., mean–climatology), and the daytime and nighttime anom- 578 alies are nearly identical to our results mentioned earlier. 579 There are numerous ground stations around the globe that 580 capture and process AVHRR Local Area Coverage (LAC) data. 581

IE E Pr E oo f

496 497

531

B. Other Applications

The high-resolution validated SST-anomaly data products provide complementary information to the existing NOAA 50-km products for monitoring the environmental health of 535 the coral reefs in the heterogeneous FKNMS, where spatial 536 scales of SST variability can be between 1 m and 10 km. Fur537 thermore, the high-resolution products also help monitor cold 532 533 534

HU et al.: BUILDING AN AUTOMATED INTEGRATED OBSERVING SYSTEM TO DETECT SST ANOMALY EVENTS

Furthermore, MODIS LAC data at global scale are freely 583 available at the U.S. NASA Goddard Space Flight Center 584 (GSFC). The cloud filter we developed can be implemented for 585 all coastal regions provided that cloud cover is not persistent 586 through time, but the parameters (running window and thresh587 old values) may need to be fine-tuned according to regional 588 oceanographic and meteorological conditions. Once cloud fil589 tering is implemented for a given region/site, the automated 590 anomaly alerting mechanism described earlier can be applied 591 with a similar system such as ICON/G2, even when in situ 592 measurements are not available due to technical difficulties. 582

593

11

TABLE IV AVHRR AND MODIS CHANNELS (SPECIFICATIONS FROM http://modis.gsfc.nasa.gov/ AND http://www2.ncdc.noaa.gov/docs/klm/. NE∆T DETERMINED AT 300 K)

VII. C ONCLUSION

Satellite SST data products are widely available from a variety of sources. However, products span a wide range of spatial resolutions (mostly ≥ 4 km) and contain various errors due to incorrect georeferencing (navigation) and cloud contamination. 598 Cloud contamination leads to underestimates of SST. The GAC 599 data, as shown in the PFSST products, tend to overestimate SST 600 in the Florida Keys, and the overestimate is not consistent in 601 time. This leads to errors if these products are used to monitor 602 anomaly events in the heterogeneous environment. 603 We demonstrated the importance of correcting the navigation 604 errors in high-resolution (∼1-km) AVHRR SST data (typically 605 several pixels in size but can be tens of pixels) but recog606 nized that this is not normally possible with the NOAA GAC 607 (> 4-km) data. Similarly, we have developed an effective cloud 608 filter that reduces cloud-contamination errors in AVHRR and 609 MODIS data while retaining valid pixels. The cloud filter 610 applied to both AVHRR and MODIS SST data was based on 611 statistics of cloud cover and of SST from both in situ and 612 satellite SST time-series observations. The process significantly 613 improved satellite SST retrievals. 614 The near-real-time improved SST data were used to derive 615 weekly anomaly and DHW images to assist in environmental 616 monitoring in the Florida Keys. Most of these products are up617 dated every week and accessible on a Web site (http://imars.usf. 618 edu/merged/), and ongoing efforts are dedicated toward the de619 velopment of an automated system to detect not only warm wa620 ter events but also cold water events. Combined with NOAA’s 621 ICON network through the G2 tool, these satellite data products 622 can play a significant role in the Integrated Ocean Observing 623 System in monitoring environmental health. We suggest that 624 this approach be applied to other LAC AVHRR and MODIS 625 data for both research and monitoring applications. 594

626 627

A PPENDIX S ATELLITE D ATA C OLLECTION AND P ROCESSING

The University of South Florida has collected, processed, and archived all regional AVHRR full-resolution data since 630 September 1993 and MODIS full-resolution data since 2003. 631 These data cover an area about ±30◦ of latitude and longitude 632 from the ground station location at 27.8◦ N and 82.6◦ W. 633 This study included all data collected through December 2005 634 over 47 000 individual AVHRR and MODIS images. Upon 635 collection, a series of processes was initiated by which the data 628 629

were first calibrated, georeferenced (navigated), processed to 636 SST, and then mapped to a series of tiles covering particular 637 geographic areas prior to cataloging, archiving, and serving 638 openly to the public via the Internet (http://imars.usf.edu). 639 The AVHRR images were first autonavigated using the 640 TeraScan software (version 3.2; SeaSpace Corporation). Be- 641 cause of the lack of positioning system on the satellites, this 642 step identified coastlines in the imagery and attempted to match 643 them with coastlines in an existing database. The navigation 644 error was typically a few 1-km pixels but could occasionally 645 reach tens of pixels. Therefore, all images were systematically 646 navigated manually to ensure proper georeferencing. Manual 647 navigation has also been used by others to assure accuracy in 648 high-resolution data (see, e.g., [2]). Each scene was examined 649 visually, and at least three areas where the coast was visible 650 were used as the reference to manually shift the image to fit 651 the coast. This is equivalent to making changes to the time, 652 pitch, roll, and yaw sensor attitude parameters. The parameters 653 were saved for use as required in subsequent reprocessing of the 654 data. Note that the manual navigation of AVHRR data to correct 655 residual autonavigation errors is tedious and also relatively 656 subjective. However, at present, this is inevitable due to higher 657 requirement of navigation accuracy on the high-resolution 658 (1-km) data. The MODIS instruments, in contrast, have accu- 659 rate positioning system where autonavigation errors are negli- 660 gible (∼< 150 m, [28]). 661 All data were reprocessed in 2006 to incorporate algo- 662 rithm updates and new calibration coefficients derived from 663 satellite—in situ matchups. AVHRR channels 1, 2, and 3a 664 (Table IV) were all calibrated prior to launch using an integrat- 665 ing sphere with 20 lamps as the light source. Channel 3a was 666 only used during daytime. Channels 3b, 4, and 5 were calibrated 667 prior to launch using a cold target, an external blackbody 668 (representing the Earth), and an internal blackbody. Postlaunch 669 calibration was performed using the internal warm body and 670 cold space. These calibration coefficients were routinely up- 671 dated by SeaSpace. 672 The algorithms to derive SST from the satellite measure- 673 ments and their theoretical background were published several 674 decades ago and were recently reviewed by Martin [39 , Ch. 7]. 675 In our processing, SST was derived using the MCSST algorithm 676

IE E Pr E oo f

595 596 597

AQ15

12

677 678

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 2, FEBRUARY 2009

based on the calibrated sensor radiances [38], [48]. [55]. The algorithm takes the form of

SST = C1 T4 + C2 (T4 − T5 ) + C3 (T4 − T5 )(sec θ − 1) + C4 (1)

ACKNOWLEDGMENT

743

The authors would like to thank J. Vazquez for providing detailed background on PFSST algorithms and also the three anonymous reviewers for their substantial comments and suggestions. The manual navigation of the > 47 000 AVHRR images would have been impossible without the hard work and contribution from many colleagues of the USF Institute for Marine Remote Sensing; thus, their effort, support, and good humor are greatly appreciated (IMaRS contribution #127).

744 745 746

IE E Pr E oo f

where T4 and T5 are the blackbody temperatures based on the radiances received in channels 4 and 5, θ is the satellite 681 zenith angle, and the coefficients C1 −C4 are determined by 682 least squares regression of the satellite SSTs derived from T4 683 and T5 against concurrent and collocated in situ SSTs, typically 684 obtained from the global ocean. 685 Cloud detection and masking were performed using the 686 TeraScan software prior to calculating the SST. A variety of 687 methods included in the software were tested, all operating 688 on a box with a default size of 3 × 3 pixels. First, all tests 689 were applied as appropriate for daytime or nighttime images, 690 and a center pixel failing any of the tests was marked as 691 cloud. One of the tests was a “threshold test,” based on the 692 assumption that clouds are brighter than the ocean in the visible 693 AVHRR bands and that the center pixel value must be less 694 than a user-specified value to be classified as noncloud. The 695 second test was based on comparing the maximum value in 696 the box in channel 2 (Table IV) to the center value. If the 697 difference was greater than a specified value (default 25%), the 698 pixel was marked as cloud. These two tests were performed on 699 daytime images only. The rest of the tests were performed using 700 the infrared channels. One method compared the maximum 701 channel 4 value for the box with the center value, and the 702 center pixel was marked as cloud if the difference was greater 703 than 0.45 ◦ C (or other user-specified value). Second, because 704 clouds are typically cooler than the water shown around them, 705 any channel 4 values less than a minimum value were marked 706 as cloud. Finally, a nighttime-only test was based on whether 707 the difference between the mean channel 3 and mean channel 4 708 was less than a predefined value, and if this condition was true, 709 then the center pixel was marked as cloud. The overall result 710 was that most cloud pixels were correctly identified. How711 ever, there were also frequent errors, particularly for nighttime 712 images. 713 Since summer 2003, USF has collected MODIS/Terra and 714 MODIS/Aqua data. These have been processed using software 715 developed at the University of Miami’s Rosenstiel School of 716 Marine and Atmospheric Science. MODIS channels 22 and 23 717 were used to derive SST at night (a product called “SST4” 718 by NASA; 4 stands for 4 µm) with a multichannel algorithm 719 similar to that used for AVHRR but with different parameteriza720 tion. Channels 31 and 32 were used to derive both daytime and 721 nighttime SSTs using a nonlinear algorithm (NLSST), which 722 is similar to the MCSST algorithm [see (1)], but it adjusts co723 efficients to account for variations in atmospheric water vapor 724 [10], [29], [30], [53], [54]. Because of accurate onboard exterior 725 orientation (position and attitude) measurement systems [28], 726 there was typically no major navigation error with MODIS SST 727 data. Cloud detection in the MODIS SST process used similar 728 algorithms as with AVHRR and, indeed, generated similar 729 residual errors. 679 680

More recent MODIS data have been processed using 730 SeaDAS4.8 (NASA software originally designed to process 731 SeaWiFS data). The SeaDAS approach identified clouds in 732 daytime images using the MODIS ocean color channels; the 733 nighttime images were not screened for clouds by SeaDAS. 734 After calibration, navigation, initial cloud screening, and 735 computing SST, the images were mapped to a series of equidis- 736 tant cylindrical projection tiles covering various smaller re- 737 gions. These products were further processed to remove the 738 residual cloud-contamination errors using procedures described 739 in Section IV. The final products are available freely to the 740 public and provided in near real time to assist fishery and 741 weather studies (see, e.g., [18]). 742

747 748 749 750 751

R EFERENCES

752

[1] I. M. Belkin and P. C. Cornillon, “Bering Sea thermal fronts from Pathfinder data: Seasonal and interannual variability,” Pac. Oceanogr., vol. 3, no. 1, pp. 6–20, 2005. [2] J. J. Bisagni, K. W. Seemann, and T. P. Mavor, “High-resolution satellitederived sea-surface temperature variability over the Gulf of Maine and Georges Bank region, 1993-1996,” Deep-Sea Res., Part 2, Top. Stud. Oceanogr., vol. 48, no. 1–3, pp. 71–94, 2001. [3] O. B. Brown, J. W. Brown, and R. H. Evans, “Calibration of advanced very-high resolution radiometer infrared observations,” J. Geophys. Res., vol. 90, no. C6, pp. 11 667–11 678, 1985. [4] K. S. Casey and P. Cornillon, “A comparison of satellite and in situ–based sea surface temperature climatologies,” J. Clim., vol. 12, no. 6, pp. 1848– 1863, Jun. 1999. [5] B. D. Causey, “The role of the Florida Keys National Marine Sanctuary in the South Florida Ecosystem Restoration Initiative,” in The Everglades, Florida Bay, and Coral Reefs of the Florida Keys, An Ecosystem Source Book, J. W. Porter and K. G. Porter, Eds. Boca Raton, FL: CRC Press, 2002, pp. 883–894. [6] J. F. Cayula and P. Cornillon, “Multi-image edge detection for SST images,” J. Atmos. Ocean. Technol., vol. 12, no. 4, pp. 821–829, Aug. 1995. [7] J. Cayula and P. Cornillon, “Cloud detection from a sequence of SST images,” Remote Sens. Environ., vol. 55, no. 1, pp. 80–88, Jan. 1996. [8] P. Y. Chen, R. Srinivasan, G. Fedosejevs, and B. Narasimhan, “An automated cloud detection method for daily NOAA-14 AVHRR data for Texas, USA,” Int. J. Remote Sens., vol. 23, no. 15, pp. 2939–2950, Aug. 2002. [9] M. Derrien, B. Farki, L. Harang, H. LeGléau, A. Noyalet, D. Pochic, and A. Sairouni, “Automatic cloud detection applied to NOAA11/AVHRR imagery,” Remote Sens. Environ., vol. 46, no. 3, pp. 246–267, Dec. 1993. [10] W. J. Emery, Y. Yu, G. A. Wick, P. Schluessel, and R. W. Reynolds, “Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor contamination,” J. Geophys. Res., vol. 99, pp. 5219–5236, 1994. [11] J. Fiechter and C. N. K. Mooers, “Simulation of frontal eddies on the East Florida Shelf,” Geophys. Res. Lett., vol. 30, no. 22, p. 2151, Nov. 2003. DOI: 10. 1029/2003GLO18307. [12] J. Fiechter and C. N. K. Mooers, “Primary production associated with the Florida current along the East Florida Shelf: Weekly to seasonal variability from mesoscale-resolution biophysical simulations,” J. Geophys. Res., vol. 112, no. C12, p. C12 002, Dec. 2007. DOI: 10.1029/2006JC003576.

753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793

HU et al.: BUILDING AN AUTOMATED INTEGRATED OBSERVING SYSTEM TO DETECT SST ANOMALY EVENTS

[13] J. Fiechter, B. K. Haus, N. Melo, and C. N. K. Mooers, “Physical processes impacting passive particle dispersal in the Upper Florida Keys,” Continental Shelf Res., vol. 28, no. 10/11, pp. 1261–1272, Jun. 2008. DOI:10. 1016/j. csr. 2008. 02. 018, in press. [14] P. S. Gilbert, T. N. Lee, and G. P. Podesta, “Transport of anomalous low-salinity waters from the Mississippi River flood of 1993 to the Straits of Florida,” Continental Shelf Res., vol. 16, no. 8, pp. 1065–1085, Jul. 1996. [15] D. B. Gilhousen, “Improved real-time quality control of NDBC measurements,” in Proc. Preprints 10th Symp. Meteorol. Observations Instrum., Phoenix, AZ, 1998, pp. 363–366. [16] M. W. Gleeson and A. E. Strong, “Applying MCSST to coral reef bleaching,” Adv. Space Res., vol. 16, no. 10, pp. 151–154, 1995. [17] H. C. Graber and C. B. Limouzy-Paris, “Transport patterns of tropical reef fish larvae by spin-off eddies in the straits of Florida,” Oceanography, vol. 10, no. 2, pp. 68–71, 1997. [18] S. L. Haines, G. J. Jedlovec, and S. M. Lazarus, “A MODIS sea surface temperature composite for regional applications,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 9, pp. 2919–2927, Sep. 2007. [19] R. He, R. Weisberg, H. Zhang, and F. Muller-Karger, “A cloud-free, satellite-derived, sea surface temperature analysis for the West Florida Shelf,” Geophys. Res. Lett., vol. 30, no. 15, pp. 1811–1815, Aug. 2003. [20] J. C. Hendee, E. Stabenau, L. Florit, D. Manzello, and C. Jeffris, “Infrastructure and capabilities of a near real-time meteorological and oceanographic in situ instrumented array, and its role in marine environmental decision support,” in Remote Sensing of Aquatic Coastal Ecosystem Processes, L. L. Richardon and E. F. LeDrew, Eds. Dordrecht, The Netherlands: Springer-Verlag, 2006. [21] J. C. Hendee, L. Gramer, J. Kleypas, D. Manzello, M. Jankulak, and C. Langdon, “The integrated coral observing network: Sensor solutions for sensitive sites,” in Proc. IEEE 3rd Int. Conf. ISSNIP, 2007, pp. 669–673. [22] G. L. Hitchcock, T. N. Lee, P. B. Ortner, S. Cummings, C. Kelble, and E. Williams, “Property fields in a Tortugas Eddy in the southern straits of Florida,” Deep-Sea Res., Part 1, Oceanogr. Res. Pap., vol. 52, no. 12, pp. 2195–2213, Dec. 2005. [23] O. Hoegh-Guldberg, M. Fine, W. Skirving, R. Johnstone, S. Dove, and A. Strong, “Coral bleaching following wintry weather,” Limnol. Oceanogr., vol. 50, no. 1, pp. 265–271, 2005. [24] C. Hu, K. E. Hackett, M. K. Callahan, S. Andréfouët, J. L. Wheaton, J. W. Porter, and F. E. Muller-Karger, “The 2002 ocean color anomaly in the Florida Bight: A cause of local coral reef decline?” Geophys. Res. Lett., vol. 30, no. 3, p. 1151, Feb. 2003. DOI: 10. 1029/2002GL016479. [25] C. Hu, F. E. Muller-Kager, G. A. Vargo, M. B. Neely, and E. Johns, “Linkages between coastal runoff and the Florida Keys ecosystem: A study of a dark plume event,” Geophys. Res. Lett., vol. 31, no. 15, p. L15 307, Aug. 2004. DOI: 10. 1029/2004GL020382. [26] C. Hu, J. Nelson, E. Johns, Z. Chen, R. Weisberg, and F. E. Muller-Karger, “Mississippi River water in the Florida Straits and in the Gulf Stream off Georgia in summer 2004,” Geophys. Res. Lett., vol. 32, no. 14, p. L14 606, Jul. 2005. DOI: 10. 1029/2005GL022942. [27] E. J. Kearns, J. A. Hanafin, R. H. Evans, P. J. Minnett, and O. B. Brown, “An independent assessment of Pathfinder AVHRR sea surface temperature accuracy using the Marine Atmosphere Emitted Radiance Interferometer (MAERI),” Bull. Amer. Meteorol. Soc., vol. 81, no. 7, pp. 1525–1536, Jul. 2000. [28] R. E. Wolfe, M. Nishihama, A. J. Fleig, J. A. Kuyper, D. P. Roy, J. C. Storey, and F. S. Patt, “Achieving sub-pixel geolocation accuracy in support of MODIS land science,” Remote Sens. Environ., vol. 83, no. 1, pp. 31–49, Nov. 2002. [29] K. A. Kilpatrick, G. P. Podesta, and R. E. EvansSea Surface Temperature global area coverage (GAC) processing, 1998. Version 4.0. [Online]. Available: http://www. rsmas. miami. edu/groups/rrsl/ pathfinder/Processing/proc_index. html [30] K. A. Kilpatrick, G. P. Podesta, and R. E. Evans, “Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database,” J. Geophys. Res., vol. 106, no. C5, pp. 9179–9197, Jan. 2001. [31] B. E. Lapointe, J. D. O’Connell, and G. S. Garrett, “Nutrient couplings between on-site sewage disposal systems, groundwaters, and nearshore surface waters of the Florida Keys,” Biogeochemistry, vol. 10, no. 3, pp. 289–307, Jun. 1990. [32] T. N. Lee, “Florida current spin-off eddies,” Deep-Sea Res., vol. 22, pp. 753–765, 1975. [33] J. J. Leichter and S. L. Miller, “Predicting high-frequency upwelling: Spatial and temporal patterns of temperature anomalies on a Florida coral reef,” Continental Shelf Res., vol. 19, no. 7, pp. 911–928, Jun. 1999.

[34] J. J. Leichter, G. B. Deane, and M. D. Stokes, “Spatial and temporal variability of internal wave forcing on a coral reef,” J. Phys. Oceanogr., vol. 35, no. 11, pp. 1945–1962, Nov. 2005. [35] R. M. Luerssen, A. C. Thomas, and J. Hurst, “Relationships between satellite-measured thermal features and Alexandrium-imposed toxicity in the Gulf of Maine,” Deep-Sea Res., Part 2, Top. Stud. Oceanogr., vol. 52, no. 19–21, pp. 2656–2673, Sep./Oct. 2005. [36] G. Liu, A. Strong, and W. Skirving, “Remote sensing of sea surface temperature during the 2002 (Great) Barrier Reef coral bleaching,” EOS Trans. AGU, vol. 84, no. 15, pp. 137–144, 2003. [37] G. Luo, P. A. Davis, L. B. Stowe, and E. P. McClain, “A pixel-scale algorithm of cloud type, layer, and amount for AVHRR data. Part I: Nighttime,” J. Atmos. Ocean. Techol., vol. 12, no. 5, pp. 1013–1037, Oct. 1995. [38] E. P. McClain, W. Pichel, and C. C. Walton, “Comparative performance of AVHRR-based multichannel sea surface temperature,” J. Geophys. Res., vol. 90, no. C6, pp. 11 587–11 601, 1985. [39] S. Martin, An Introduction to Ocean Remote Sensing. Cambridge, U.K.: Cambridge Univ. Press, 2004, p. 426. [40] P. J. Minnett, “Consequences of sea surface temperature variability on the validation and applications of satellite measurements,” J. Geophys. Res., vol. 96, no. C10, pp. 18 475–18 489, Jul. 1991. [41] “NDBC Technical Document 03-02,” Handbook of Automated Data Quality Control Checks and Procedures of the National Data Buoy Center, 2003. [Online]. Available: http://www. ndbc. noaa. gov/ handbook. pdf [42] P. B. Ortner, T. N. Lee, P. J. Milne, R. G. Zika, E. Ckarke, G. Podesta, P. K. Swart, P. A. Tester, L. P. Atkinson, and W. R. Johnson, “Mississippi River flood waters that reached the Gulf Stream,” J. Geophys. Res., vol. 100, no. C7, pp. 13 595–13 601, 1995. [43] R. W. Saunders and K. T. Kriebel, “An improved method for detecting clear sky and cloudy radiances from AVHRR data,” Int. J. Remote Sens., vol. 9, no. 1, pp. 123–150, Jan. 1988. [44] T. Saxby, W. C. Dennison, and O. Hoegh-Guldberg, “Photosynthetic responses of the coral Montipora digitata to cold temperature stress,” Mar. Ecol., Prog. Ser., vol. 248, pp. 85–97, 2003. [45] S. C. Shenoi, “On the suitability of global algorithms for the retrieval of SST from the north Indian Ocean using NOAA/AVHRR data,” Int. J. Remote Sens., vol. 20, no. 1, pp. 11–29, Jan. 1999. [46] J. J. Simpson, T. J. McIntire, J. R. Stitt, and G. L. Hufford, “Improved cloud detection in AVHRR daytime and night-time scenes over the ocean,” Int. J. Remote Sens., vol. 22, no. 13, pp. 2585–2615, 2001. [47] S. Sponaugle, T. Lee, V. Kourafalou, and D. Pinkard, “Florida current frontal eddies and the settlement of coral reef fishes,” Limnol. Oceanogr., vol. 50, no. 4, pp. 1033–1048, 2005. [48] A. E. Strong and E. P. McClain, “Improved ocean surface temperatures from space—Comparisons with drifting buoys,” Bull. Amer. Meteorol. Soc., vol. 65, no. 2, pp. 138–142, Feb. 1984. [49] A. E. Strong, G. Liu, J. Meyer, J. C. Hendee, and D. Sasko, “Coral reef watch 2002,” Bull. Mar. Sci., vol. 75, no. 2, pp. 259–268, Sep. 2004. [50] A. C. Thomas, D. Byrne, and R. Weatherbee, “Coastal sea surface temperature variability from Landsat infrared data,” Remote Sens. Environ., vol. 81, no. 2, pp. 262–272, Aug. 2002. [51] N. D. Walker, S. Myint, A. Babin, and A. Haag, “Advances in satellite radiometry for the surveillance of surface temperatures, ocean eddies and upwelling processes in the Gulf of Mexico using GOES-8 measurements during summer,” Geophys. Res. Lett., vol. 30, no. 16, p. 1854, Aug. 2003. DOI: 10. 1029/2003GL017555. [52] C. Wall, “Linkages between environmental conditions and recreational king mackerel catch off West-Central Florida,” M.S. thesis, College Mar. Sci., Univ. South Florida, Tampa, FL, Oct. 2006. [53] C. C. Walton, “Nonlinear multichannel algorithms for estimating sea surface temperature with AVHRR satellite data,” J. Appl. Meteorol., vol. 27, no. 2, pp. 115–124, Feb. 1988. [54] C. C. Walton, W. G. Pichel, F. J. Sapper, and D. A. May, “The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites,” J. Geophys. Res., vol. 103, no. C12, pp. 27 999– 28 012, 1998. [55] G. A. Wick, W. J. Emery, and P. Schluessel, “A comprehensive comparison between satellite-measured skin and multichannel sea surface temperature,” J. Geophys. Res., vol. 97, no. C4, pp. 5569–5595, 1992. [56] Z. Yang, G. Wood, and J. E. O’Reilly, “Cloud detection in sea surface temperature images by combining data from NOAA polar-orbiting and geostationary satellites,” in Proc. IEEE Geosci. Remote Sens. Symp., 2000, vol. 5, pp. 1817–1820.

IE E Pr E oo f

794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870

13

871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947

14

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 2, FEBRUARY 2009

Chuanmin Hu received the B.S. degree in physics from the University of Science and Technology of China, Hefei, China, the M.S. degree in physics from the Institute of Physics, Chinese Academy of Sciences, Beijing, China, and the Ph.D. degree in physics (ocean optics) from the University of Miami, Coral Gables, FL, in 1997. He is currently an Associate Research Professor with the College of Marine Science, University of South Florida, Tampa, where he is also currently the Executive Director of the Institute for Marine Remote Sensing. He has been a Principal and Coprincipal Investigator of several projects funded by the U.S. NASA, NOAA, and USGS to study river plumes, red tides, water quality and benthic habitat health, and connectivity of various ecosystems. His research is focused on the coastal ocean and particularly on the biooptical properties of river plumes and estuaries, to characterize how they are changing, and the reasons and consequences of such changes.

965 966 AQ17 967 968 969 970 971 972 973 974 975 976 977 978 979 980

Frank Muller-Karger received the Ph.D. degree in marine science and the M.S. degree in management from. He is currently the Dean of the School for Marine Science and Technology, University of Massachusetts Dartmouth, Dartmouth. His primary research interests include science education and oceanographic research of coastal zones, continental margins, and the contributions of the marine environment to the global carbon cycle. His research utilizes observational time series, satellite data, high-speed computing, and other new technologies to measure large-scale oceanographic phenomena. He is the author or coauthor of over 80 scientific publications. Dr. Muller-Karger has received several awards for his outstanding contributions in support of satellite technologies for ocean observation and his work on the U.S. Commission on Ocean Policy.

AQ18 981 Brock Murch, photograph and biography not available at the time of the

982 publication.

AQ19 983 Douglas Myhre, photograph and biography not available at the time of the

984 publication.

985 986 987 988 989 990 991 992 AQ20 993 994 995

Judd Taylor received the B.S. degree in information systems management from the University of South Florida (USF), Tampa, in 2002. He has been working with the Institute for Marine Remote Sensing (IMaRS), USF, since 2001. While still heavily involved with IMaRS, he is currently the Lead Software Engineer with the Orbital Systems, Ltd., Dallas, TX, a direct broadcast antenna systems manufacturer. His research interests include applications of MODIS data, geospatial data systems, and intelligent cloud-filtering algorithms.

AQ21 996 Remy Luerssen, photograph and biography not available at the time of the

997 publication.

Christopher Moses was born in Houston, TX. He received the B.A. degree in earth science and the M.S. degree in geology from Baylor University, Waco, TX, in 1996 and 1999, respectively, and the Ph.D. degree in marine geology and geophysics from the Rosenstiel School of Marine and Atmospheric Science, University of Miami, Coral Gables, FL, in 2005. Between 2005 and 2008, he was a Postdoctoral Fellow with the University of South Florida, Tampa, where he focused on the remote sensing of coral reefs and their environment. Currently, he is a Marine Geologist with Jacobs Technology contracted to the U.S. Geological Survey, St. Petersburg, FL.

998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010

Caiyun Zhang was born in Fujian, China. She received the B.S. degree in marine chemistry, the M.S. degree in physical oceanography, and the Ph.D. degree in environmental science from Xiamen University, Xiamen, China, in 1994, 1997, and 2006, respectively. She is currently an Associate Professor with the College of Oceanography and Environmental Science, Xiamen University. Her research interests focus on satellite oceanography and marine ecosystem dynamics, particularly on the physical–biological interactions, ocean-climate variability, and ecosystem response using in situ and satellite observations.

1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023

Lew Gramer was born in Norwalk, OH. He received the Science Baccalaureate in theoretical mathematics from Massachusetts Institute of Technology, Cambridge, in 1990 (’90 XVIII-A). He is currently working toward the Ph.D. degree in physical oceanography at the Rosenstiel School of Marine and Atmospheric Science, University of Miami, Coral Gables, FL. He is also currently with the National Oceanic and Atmospheric Administration as a Cooperative Institute Research Associate and Knowledge Engineer of the Coral Health and Monitoring Program. His research interests include the dynamics of coastal- and near-shore oceanic processes, and their impact on coral reef nutrient balances and ecology.

1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 AQ22 1036 1037

James Hendee was born in Denver, CO. He received the B.S. degree in marine biology from Florida State University, Tallahassee, FL, in 1971, the M.S. degree in marine biology from the University of Alaska Fairbanks, Fairbanks, AK, in 1984, and the Ph.D. degree in information systems from Nova Southeastern University, Fort Lauderdale-Davie, FL, in 2000. He has been with the Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, since 1990, where he is currently the Administrator and Founder of the Coral Health and Monitoring Program, which includes the Integrated Coral Observing Network. His recent research interests involve collaborations with several government agencies and academic institutions and includes instrument monitoring of coral reef ecosystems and utilizing artificial intelligence techniques to formulate timely ecological forecasts on coral reefs.

1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 AQ23

IE E Pr E oo f

948 949 950 951 952 953 954 955 956 957 958 AQ16 959 960 961 962 963 964