Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with MODIS full-spectral data

Remote Sensing of Environment 114 (2010) 2048–2058

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Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with MODIS full-spectral data Chuanmin Hu a,⁎, Jennifer Cannizzaro a, Kendall L. Carder a,b, Frank E. Muller-Karger a, Robert Hardy a,c a b c

College of Marine Science, University of South Florida, 140 Seventh Avenue, South, St. Petersburg, FL 33701, USA SRI International, 450 8th Avenue, SE, St. Petersburg, FL 33701, USA Florida Fish and Wildlife Research Institute, 100 Eighth Avenue, SE, St. Petersburg, Florida 33701-5095, USA

a r t i c l e

i n f o

Article history: Received 6 December 2009 Received in revised form 11 April 2010 Accepted 18 April 2010 Keywords: Trichodesmium Sargassum Cyanobacteria bloom Remote sensing MODIS MERIS FAI Hyperspectral

a b s t r a c t Remote detection of the Trichodesmium spp. cyanobacteria blooms on the west Florida shelf (WFS) has been problematic due to optical complexity caused by sediment resuspension, coastal runoff, and bottom interference. By combining MODIS data measured by the ocean bands and land bands, an approach was developed to identify surface mats of Trichodesmium on the WFS. The approach first identifies possible bloom patches in MODIS FAI (floating algae index) 250 m resolution imagery derived from the Rayleigh-corrected reflectance at 667, 859, and 1240 nm. Then, spectral analysis examines the unique reflectance characteristics of Trichodesmium at 469, 488, 531, 551, and 555 nm due to specific optical properties (absorption, backscattering, and fluorescence) of the unusual pigments in Trichodesmium. These spectral characteristics (i.e., high–low–high–low–high reflectance at 469-488–531–551–555 nm, respectively) differentiate Trichodesmium mats unambiguously from other features observed in the FAI imagery, such as Sargassum spp. Tests in other coastal locations show that the approach is robust and applicable to other optically complex waters. Results shown here can help study Trichodesmium bloom dynamics (e.g., initiation and bloom formation) and may also help design future sensors to better detect and quantify Trichodesmium, an important N2 fixer in the global oceans. © 2010 Elsevier Inc. All rights reserved.

1. Introduction Nitrogen fixation by the marine cyanobacterium (also called bluegreen algae), Trichodesmium spp., plays a significant role in global nitrogen and carbon cycles (Capone et al., 1997; Gruber and Sarmiento, 1997; Karl et al., 1997). On the west Florida shelf (WFS), Trichodesmium blooms (mostly Trichodesmium erythraeum) occur regularly, and have been proposed to serve as a significant nitrogen source under oligotrophic conditions for the toxic phytoplankton species Karenia brevis (Walsh and Steidinger, 2001). Timely observation of Trichodesmium blooms can improve understanding of nutrient cycling and the dynamics of K. brevis red tides. Yet, to date it has been difficult to obtain timely and synoptic assessments of the distribution of Trichodesmium. No satellite-based observations of Trichodesmium blooms validated with field data have been reported in the literature for the optically complex yet ecologically important coastal ocean environment on the WFS. Detection of Trichodesmium blooms from space has been of interest since the 1980s (Dupouy et al., 1988; Borstad et al., 1992). Early attempts used empirical algorithms developed for the Coastal Zone

⁎ Corresponding author. Tel.: +1 727 5533987. E-mail address: [email protected] (C. Hu). 0034-4257/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2010.04.011

Color Scanner (CZCS) (Dupouy et al., 1988; Subramaniam and Carpenter, 1994). More recent efforts focused on the inherent and apparent optical properties (spectral absorption, backscattering, and reflectance) of Trichodesmium (Subramaniam et al., 1999a,b) and on the development of empirical (Subramaniam et al., 2002) and semianalytical algorithms (Westberry et al., 2005) for application to multispectral data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). These approaches were designed for waters where optical properties are dominated by phytoplankton. In coastal oceans, nonphytoplankton constituents such as resuspended sediments, colored dissolved organic matter (CDOM) from terrestrial runoff, and the shallow ocean bottom all can affect the optical signal (spectral reflectance) measured by satellites, rendering these algorithms inadequate. Such optical complexity is especially true for the WFS where sandbar features at 35 m deep can be visualized in satellite imagery for very clear waters (Hu, 2008; Fig. 1a), and very turbid features (e.g., Fig. 1c) are apparent for waters where winter storms and hurricanes induce significant sediment resuspension or CDOMrich coastal runoff (Hu et al., 2004; Hu and Muller-Karger, 2007). The empirical approach of Subramaniam et al. (2002) classified Trichodesmium pixels in the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery by using several threshold values and spectral shapes in the normalized water-leaving radiance (nLw) data. The approach appeared robust in phytoplankton-dominated waters, yet

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Fig. 1. MODIS/Aqua enhanced RGB (ERGB; R: nLw_551; G: nLw_488; B: nLw_443) imagery showing clear and turbid waters on the WFS (a and c) and the Trichodesmium classification results (b and d; green: Trichodesmium; grey: non-Trichodesimium; black: clouds or low-quality data) using an empirical algorithm (Subramaniam et al., 2002). In (d), the locations of near-concurrent (± 1 week) in situ samples taken for microscopic analyses are annotated: white “×” indicates no presence of Trichodesmium; other letters represent different Trichodesmium concentrations in cells L− 1 as follows: P — present (b 103); L — low (103–104); M — medium (104–105; H — high (105–106); V — very high (N 106). The clear water in (a) reveals bottom sandbar features up to 35 m deep where MODIS fluorescence data show low phytoplankton biomass (Hu, 2008), and thus the corresponding Trichodesmium classification result in (b) is a false-positive detection due to the bottom interference. Likewise, the approach misclassified Trichodesmium-rich waters (red letters) as Trichodesmium-free, resulting in false-negative detection. Note that these are two extreme cases for illustration purpose only and they should not be interpreted as the typical performance of the Subramaniam et al. (2002) algorithm.

suffered from the optical complexity of the WFS. Although a comprehensive evaluation is yet to be performed, Fig. 1 presents two examples of the empirical classification results where falsepositive detection resulted from shallow-water bottom interference (Fig. 1a and b) and false-negative detection resulted from turbid coast runoff (Fig. 1c and d). Although visual examination of the sandbar features in Fig. 1a can easily rule out the false-positive detection results in Fig. 1b, it is generally difficult to discount such results when the bottom effects are less apparent due to increased water turbidity. A more recent semi-analytical approach attempted to determine Trichodesmium-specific optical properties (absorption and backscattering coefficients) among other variables such as phytoplankton absorption, CDOM absorption, and particulate backscattering (Westberry et al., 2005). While plausible for open ocean waters where sediment concentration is low, bottom interference is nil, and CDOM absorption often co-varies with other properties, the approach is likely to fail for optically complex coastal waters due to the same reasons as for the empirical approach. Indeed, using a similar semianalytical approach Hu et al. (2003) showed that on the WFS it is often inherently difficult to explicitly partition the optical signal into three individual components: phytoplankton absorption, CDOM absorption, and particulate backscattering. In many cases the algorithm attributed the total absorption to CDOM only, resulting in false phytoplankton retrievals. Similar findings were also found in a recent algorithm round-robin evaluation (IOCCG, 2006), where errors in the retrieved phytoplankton absorption were generally larger than in the retrieved CDOM absorption. Adding another Trichodesmium-specific component will make the optical partitioning more difficult or even impossible. Clearly, new algorithms and more wavelengths are required. Trichodesmium have intracellular gas vacuoles allowing them to adjust buoyancy. They can form dense patches near the surface under calm conditions (Borstad et al., 1992). The vertical migration of Trichodesmium colonies in the course of a day is likely due to ballasting through physiological adjustments and changes in the relative concentration of carbohydrate and protein in the cell (Villareal &

Carpenter, 1990, 2003; Capone et al., 1997). The surface patches have distinctive reflectance signals in the near infrared (NIR) wavelengths where the influence of CDOM, suspended sediment, and shallow bottom may all be small or even negligible. A simple difference between the NIR and red bands, for example by using the first two bands of the Advanced Very High Resolution Radiometer (AVHRR) flying on the NOAA polar orbiting weather satellites, can detect these surface floating features (Capone et al., 1998; Subramaniam et al., 1999b). However, the band-differencing approach is strongly subject to effects of changing environmental (aerosol type and thickness) and observing (solar and viewing geometry) conditions, and a priori knowledge is required to determine if the detected surface features are Trichodesmium. Recently, a floating algae index (FAI) was developed using satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat to detect floating macroalgae such as Ulva prolifera (previously known as Enteromorpha prolifera) or Sargassum spp. in open ocean environments (Hu, 2009). The FAI is defined as the Rayleigh-corrected reflectance in the NIR (859 nm for MODIS and 825 nm for Landsat), referenced against a linear baseline between the red and shortwave infrared (SWIR) bands. Observations and model simulations show that the FAI is robust under various environmental and observing conditions. The approach was applied to document, assess, and study over 9 years cyanobacteria blooms in the third largest freshwater lake of China (Taihu Lake) between 2000 and 2008, because these blooms formed surface mats under calm conditions (Hu et al., 2010a). It is therefore logical to assume that a similar approach may also work for the WFS to detect Trichodesmium blooms. However, Sargassum slicks and patches in the Gulf of Mexico (GOM) also have elevated reflectance in the NIR (Gower et al., 2006; Hu, 2009), which may be falsely recognized as Trichodesmium in the FAI imagery. It is important to assess whether Trichodesmium mats and Sargassum exhibit different optical characteristics and therefore can be identified separately in satellite imagery. The optical properties of Trichodesmium have been documented since the 1970s (Shimura and Fujita, 1975; Borstad et al., 1992;

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Navarro Rodriguez, 1998; Subramaniam et al., 1999a,b; Steinberg et al., 2004). Unlike most other phytoplankton species, Trichodesmium colonies have the following optical characteristics: 1) increased absorption in the near-UV and blue wavelengths due to high amounts of CDOM and water-soluble, UV absorbing pigments in the cells, 2) absorption maxima around 495, 550, and 625 nm due to the presence of the PUB (phycourobilin), PEB (phycoerythrobilin), and PC (phycocyanin) pigments, respectively; 3) increased backscattering across all wavelengths due to changes of the refraction index within intracellular gas vacuoles; and 4) fluorescence peaks at 570 nm and 640 nm due to the presence of phycoerythrin and allophycocyanin pigments, respectively. Fig. 2a shows examples of the absorption and reflectance spectra of Trichodesmium blooms observed off Florida. Consistent with previously reported absorption properties of Trichodesmium (Navarro Rodriguez, 1998; Subramaniam et al., 1999a,b), local absorption maxima due to Chl, PUB, PEB, and PC are evident in the spectra.

Correspondingly, the remote sensing reflectance (Rrs) spectrum shows minima at similar or slightly shifted wavelengths due to modulations by backscattering and fluorescence. These optical characteristics result in several reflectance curvatures (i.e., the high–low–high–low–high reflectance features) in the blue-green wavelengths which might be differentiated by the corresponding MODIS bands. Indeed, although the local reflectance maximum in the blue wavelengths is at 478 nm (9 nm higher than the MODIS 469 nm band), data collected from coastal waters off Puerto Rico (Fig. 2b) shows a local reflectance maximum at the MODIS band center wavelength, 469 nm. Further, the surface aggregation of the Trichodesmium colonies leads to enhanced NIR reflectance found in typical sub-aerial vegetation. Collectively, these spectral characteristics may lead to an improved detection scheme. In this work, based on these unusual Rrs spectral characteristics, an approach was developed to detect Trichodesmium bloom mats on the WFS using MODIS data. Two steps were involved in the approach: first, MODIS FAI imagery products were generated and examined for surface patches; then, spectral shapes of patches were analyzed. Our purpose here is not to provide comprehensive statistics of bloom occurrences or occurrence dynamics, but rather to demonstrate a simple yet practical means to identify surface Trichodesmium blooms in optically complex coastal regions. Further, results from the spectral analysis may be used to help design future satellite ocean color instruments for identifying Trichodesmium blooms. 2. Data and methods 2.1. MODIS data processing MODIS Level-0 data were obtained from the U.S. NASA's Goddard Space Flight Center (GSFC). The data were processed to Level-1 (calibrated spectral radiance) using the software package SeaDAS (version 5.3), applying the radiometric calibrations updated in June 2009. Then, data from 16 spectral bands, at spatial resolutions ranging from 250 m to 1 km (Table 1), were corrected for ozone, water vapor absorption, and for molecular (Rayleigh) scattering using software provided by the MODIS Rapid Response Team at GSFC. The Rayleighcorrected spectral reflectance or Rrc was obtained as follows:   Rrc;λ ðθ0 ; θ; ΔϕÞ = πLt;λ * ðθ0 ; θ; ΔϕÞ = F0;λ × cosθ0 −Rr;λ ðθ0 ; θ; ΔϕÞ ð1Þ

Fig. 2. (a) Absorption (ap) and remote sensing reflectance (Rrs) spectra measured from Trichodesmium mats on the west Florida shelf and in the Florida Keys, respectively. The former was measured from a filtered Trichodesmium sample collected at 26.38°N, 83.25°W on 13 January 2000, while the latter was measured above a Trichodesmium patch at 24.63°N, 81.08°W on 1 July 1997. The vertical dashed lines denote the local absorption maxima due to Chl (chlorophyll-a), PUB (phycourobilin), PEB (phycoerythrobilin), and PC (phycocyanin) pigments. Also shown are some of the spectral bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instruments. The 10 nm ocean bands centered at 443, 488, 531, 551, and 667 nm have 1 km nadir resolutions. The land bands at 469 (459–479), 555 (545–565), 645 (620–670), and 859 (841–876) nm have spatial resolutions of 500, 500, 250, and 250 m, respectively. Local reflectance maxima are found at 478 nm (Rrs = 0.0138), 528 nm (Rrs = 0.0151), and 558 nm (Rrs = 0.0147). Local reflectance minima are found at 490 nm (Rrs = 0.0134) and 550 nm (Rrs = 0.0144). (b) The same (Rrs) spectrum in (a) is overlaid with the Rrs spectrum collected from coastal waters off Puerto Rico at 17.77°N 67.0°W in October 1997 (figure adapted from Fig. 25 of Navarro Rodriguez (1998). Data courtesy of Prof. Roy Armstrong of University of Puerto Rico). Local reflectance maxima are found at 469 nm (Rrs = 0.0333), 526 nm (Rrs = 0.0365), and 562 nm (Rrs = 0.0382). Local reflectance minima are found at 497 nm (Rrs = 0.0282) and 546 nm (Rrs = 0.0350). Note that the blue-wavelength maximum is shifted from 478 nm to 469 nm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

where λ is the wavelength, L*t is the calibrated sensor radiance after correction for ozone and other gaseous absorption, F0 is the extraterrestrial solar irradiance, (θ0, θ, Δϕ) represent the solar/ viewing geometry, and Rr is the reflectance due to Rayleigh (molecular) scattering. For simplicity, the dependence of Rrc on (θ0, θ, Δϕ) is omitted in the next sections. The correction of Rayleigh scattering would remove most of the atmospheric “color”, because aerosol reflectance is more spectrally flat. The reflectance data (dimensionless) were mapped to a cylindrical-equidistant projection at 250 m resolution (the 500 m and 1 km data were interpolated) using in-house software. FAI was derived as the difference between Rrc,859 and a linear baseline formed between Rrc,667 and Rrc,1240 where the numbers in the subscripts denote wavelengths in nanometers: ′

FAI = Rrc;859 −Rrc;859 ;   R′rc;859 = Rrc;667 + Rrc;1240 −Rrc;667 × ð859−667Þð1240−667Þ:

ð2Þ

The second equation above provides a linearly interpolated Rrc at 859 nm from the two neighboring bands (667 and 1240 nm), where (859 − 667)/(1240 − 667) represents the linear interpolation coefficient. The baseline subtraction serves as a simple but effective atmospheric correction to remove most of the aerosol effects, which

16

862 877 1 km 0.62 516 1.0 1.0 743 753 1 km 1.02 586 0.9855 0.9929

15 14

673 683 1 km 0.87 1087 0.9776 0.9902

13

662 672 1 km 0.95 910 0.9797 0.987

12

546 556 1 km 2.1 750 0.985 0.9898

2051

have near-linear spectral shapes between the red (667 nm) and shortwave infrared (SWIR, 1240 nm) wavelengths. Note that Rrc,645 in the original FAI design (Hu, 2009) was replaced with Rrc,667 in the current work, because the latter was lower (Fig. 2) and therefore provided an enhanced FAI over floating algae. SeaDAS (version 5.3) was also used to process the calibrated Level-1 data to Level-2 data products (spectral remote sensing reflectance or Rrs,λ, sr− 1) and mapped to the same cylindrical-equidistant projection at 250 m resolution. During this process, vicarious calibration gains obtained from a Marine Optical Buoy (MOBY) near Hawaii were used to adjust the total radiance (Lt,λ) measured by the MODIS instruments in all spectral bands, including the land bands of MODIS/Aqua (Table 1). The algorithms in SeaDAS were designed for the global oceans, and the generation of Rrs,λ was to provide reliable spectral reflectance over suspicious patches to help diagnose the phytoplankton type.

483 493 1 km 3.21 802 0.9795 0.9938

11 10 9

438 448 1 km 4.19 838 0.9848 0.9942 405 420 1 km 4.49 880 0.971 1.001

8 7

2105 2155 500 0.1 110 1.115 1.0 1628 1652 500 0.73 275 1.0 1.0

5

1230 1250 500 0.54 74 1.055 1.0

4 3

GSD Ltypical SNR Gain_A Gain_T

620 670 250 2.18 128 1.0049 1,0

2 1

λ(nm)

841 876 250 2.47 201 1.0304 1.0

459 479 500 3.53 243 1.002 1.0

545 565 500 2.9 228 0.9842 1.0

6

526 536 1 km 2.79 754 0.987 0.9901

2.2. In situ data

Band

Table 1 MODIS spectral band ground sample distance (GSD, or nadir resolution) and signal-to-noise ratio (SNR). SNR is specified for the given “typical” top-of-atmosphere (TOA) radiance (Ltypical in mW cm−2 µm−1 sr− 1). Noise-equivalent radiance (NEΔL in mW cm−2 µm−1 sr− 1) can be derived as Ltypical/SNR. Bands 1–7 were designed for land/cloud/aerosol studies, while bands 8–16 were designed for ocean studies. These sensor specification data were obtained from the NASA Goddard Space Flight Center. The last two rows list the ocean vicarious calibration gains used in the SeaDAS (version 5.3) Level-1 to Level-2 processing to retrieve Rrs,λ from the calibrated total radiance (i.e., Lt,λ data were adjusted by these gain factors before processing). Note that for the first 7 spectral bands (designed for land and atmosphere), ocean vicarious calibration was applied to MODIS/Aqua (Gain_A) only, and not to MODIS/Terra (Gain_T).

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Reflectance and absorption spectra of Trichodesmium bloom patches were collected from the Florida Keys and WFS using NASA standard protocols (Fig. 2). Because of the sporadic nature of blooms and due to limited cruise opportunity, only one Rrs spectrum and one absorption spectrum were available for the past 15 years from this area. For the same reasons, hyperspectral data from bloom patches are scarce in the literature. A reflectance spectrum collected from coastal waters off Puerto Rico (Navarro Rodriguez, 1998) was obtained to demonstrate the spectral curvatures in the blue-green wavelengths and to validate the MODIS observations. Trichodesmium cell count data for Florida waters were obtained from a database compiled by the Florida Fish and Wildlife Research Institute (FWRI). The data have been collected by FWRI, Mote Marine Laboratory, University of South Florida, local environmental groups, fishermen, and other volunteers since 1954. The data cover various spatial and temporal frequencies and are largely limited to waters within a few kilometers of the shoreline. Occasionally, samples from offshore waters were collected through the ECOHAB (Ecology and Oceanography of Harmful Algal Blooms) and MERHAB (Monitoring and Event Response for Harmful Algal Blooms) research programs. Most water samples were collected from the surface. Standard taxonomic analyses and cell counts were used to determine abundance of the various organisms. Some samples were counted live, some using the Utermöhl method with a light microscope that was sometimes inverted, and some samples were preserved in Lugol's solution for subsequent counting. Due to the poor coverage in space and time, these data were only useful to validate the MODIS observations in a qualitative manner. 3. Results 3.1. Blooms on the WFS Fig. 3 shows MODIS FAI images in which slicks and patches are clearly visible in coastal waters of the WFS near the two largest estuaries of Florida, Tampa Bay and Charlotte Harbor. The features in Fig. 3 show positive FAI values because of their elevated reflectance in the NIR (859 nm) above the baseline (667 and 1240 nm). Some of these features are outlined in red for further analysis. In contrast, the background pixels (dark blue) show relatively homogenous, negative FAI values. Although clouds also showed high FAI values, the features in Fig. 3 also appeared in FAI imagery of adjacent days, suggesting that they are some form of floating algae. However, from the FAI imagery alone it is difficult to tell if these algae features are Trichodesmium blooms or surface floating Sargassum spp. (a brown macroalgae often found in the GOM, see Gower et al., 2006). The latter can also appear as thin slicks or small patches in FAI imagery (Hu, 2009).

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Fig. 3. MODIS FAI imagery showing patches and slicks on the central WFS off Tampa Bay and Charlotte Harbor. Spectral analyses of the features outlined in red circles showed reflectance curvatures between 469 and 555 nm that suggest that these features are Trichodesmium blooms. Locations of near-concurrent (± 1 week) in situ samples taken for microscopic analyses are annotated: white “×” indicates no presence of Trichodesmium; other letters represent different Trichodesmium concentrations in cells L− 1 as follows: P — present (b 103); L — low (103–104); M — medium (104–105; H — high (105–106); V — very high (N 106). The areas in (a) and (b) are bounded by 27.4°N–28.8°N and 83.7°W–82.4°W, while in (c) and (d) are bounded by 26.0°N 27.4°N and 82.9°W–81.6°W. Note that Fig. 3a corresponds to Fig. 1d.

Spectral analysis of the SeaDAS-derived Rrs,λ data may help differentiate between Trichodesmium blooms and Sargassum. For the positive FAI features, SeaDAS algorithms unfortunately often rendered negative Rrs in the blue, or created a cloud or atmospheric correction failure mask because of the elevated NIR and SWIR reflectance. To overcome difficulties caused by such artifacts, an approach similar to the nearest neighboring method of Hu et al. (2000) was used, in which atmospheric properties (including aerosol reflectance and transmittance) in nearby clear-water pixels were used as the surrogates for the aerosol properties over the suspicious features. The resulting Rrs,λ for the spectral range (400– 1300 nm) are shown in Fig. 4. For comparison, Rrs,λ from Sargassum slicks in the western GOM identified from MODIS imagery on 2 June 2005 (see also Gower et al., 2006) was derived with the same technique outlined above and shown in Fig. 4d. All bloom features in Fig. 3 show relatively high reflectance in the green (531, 551, and 555 nm) and NIR (758, 859, and 869 nm) MODIS bands. In contrast, none of the Sargassum Rrs,λ spectra we have examined show reflectance maxima in the green bands, suggesting that the bloom patches in Fig. 3 may not be Sargassum. Rrs spectra in Fig. 5 focus on some details between 400 and 900 nm. Because of a lack of concurrent in situ reflectance data, historical Rrs,λ data collected from Trichodesmium patches in the Florida Keys and in Puerto Rico (PR) coastal waters (as shown in Fig. 2) were plotted to compare with those observed from MODIS. Similarity in the spectral curvatures between 469 and 555 nm (i.e. high–low–high–low–high at

469–488–531–551–555 nm, respectively) between the in situ Trichodesmium Rrs,λ and MODIS Rrs,λ was found, regardless of their magnitudes. Specifically, Rrs,λ show two local minima, one at 488 and one at 551 nm. Although the Florida Keys Rrs,λ showed a local maximum at 478 nm, the same local maximum occurred at 469 nm in the PR spectrum, which coincides with the MODIS land band center wavelength at 469 nm. Note that these spectral curvatures are absent in the nearby bloom-free waters. MODIS Rrs,748 was significantly lower than expected for a dense mat of floating algae (e.g., in situ Rrs,NIR is comparable to or higher than Rrs,555). This is possibly due to mixed MODIS pixels. The MODIS 748 nm band has spatial resolution of about 1 km. Trichodesmium blooms are known to be patchy. Thus, the 1 × 1 km2 pixel may contain both Trichodesmium patches (high Rrs,748) and Tricho-free water (Rrs,748 ≈ 0), resulting in reduced Rrs,748 as compared with that from a pure Trichodesmium patch. Similar mixed-pixel effect is much less apparent in the blue-green wavelengths, because Tricho-free water showed relatively high Rrs at these wavelengths. The similarity in the spectral curvatures (not the magnitudes) between the MODIS Rrs and in situ Trichodesmium Rrs strongly suggests that the surface features identified from the MODIS FAI imagery (Fig. 3) are Trichodesmium bloom mats. Near-concurrent (within ±1 week) in situ data from the FWRI database show moderate to high concentrations of Trichodesmium (see annotated letters in Fig. 3). Thus, we may conclude that the bloom features in MODIS FAI imagery (Fig. 3) are indeed Trichodesmium blooms.

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Fig. 4. Average spectral remote sensing reflectance (Rrs,λ, sr− 1) from bloom features shown in dotted circles in Fig. 3. The vertical bars are standard deviations from the sampled MODIS pixels. These were derived from MODIS full-spectral data using SeaDAS and a nearest-neighbor atmospheric correction. In (d), Rrs,λ from Sargassum slicks in the western Gulf of Mexico on 2 June 2005 are shown. Note the lack of reflectance peaks in the green bands (531, 551, and 555 nm) in the Sargassum spectra.

The two-step approach (i.e., identify bloom features on the FAI imagery, followed by Rrs spectral analysis) is effective in identifying Trichodesmium bloom mats on the WFS. The implementation of the nearest-neighbor atmospheric correction in the spectral analysis, however, is not straightforward. For example, for all 16 spectral bands, the following data must be obtained from SeaDAS processing: top-of-atmosphere (TOA) at-sensor radiance, Rayleigh-scattering radiance, aerosol-scattering radiance, ozone attenuation, sun-topixel attenuation, pixel-to-satellite attenuation, sun glint radiance, and whitecap radiance. The time-consuming processing is feasible for case studies, but impractical for processing of large amounts of data. To achieve more efficient processing, the spectral shapes in the Rayleigh-corrected Rrc,λ were examined to determine whether the spectral curvatures in the Rrs,λ obtained using the SeaDAS/nearestneighbor method could be reproduced with the simplified Rrc,λ. Fig. 6 shows the Rrc(λ) difference between the individual Trichodesmium

pixels and nearby water pixels. It is clear that the spectral curvatures between 469 and 555 nm are retained in the Rrc,λ difference spectra. In contrast, such curvatures are not present in Sargassum Rrc,λ difference spectra. Therefore, the Rrc,λ difference method can be used effectively as an alternative method to replace the labor intensive SeaDAS/nearest-neighbor processing. 3.2. Blooms in other coastal waters Trichodesmium blooms have been reported in various coastal waters, including those off Florida's east coast (Subramaniam et al., 2002), in the Arabian Sea (Capone et al., 1998; Subramaniam et al., 1999b; Sarangi et al., 2004, 2005; Gomes et al., 2008), and in the Northwest African Upwelling (Ramos et al., 2005). To test the effectiveness of our approach, we processed selected MODIS data for the periods when Trichodesmium blooms were reported in those

Fig. 5. Spectral remote sensing reflectance (Rrs,λ, sr− 1) from individual MODIS pixels of Trichodesmium patches and Sargassum slicks. Also plotted are the in situ Rrs,λ from Trichodesmium bloom patches in the Florida Keys and in Puerto Rico (PR) coastal waters (the same spectra as in Fig. 2b). Note the remarkable similarity in the spectral curvatures between 469–555 nm (i.e., high–low–high–low–high at 469–488–531–551–555 nm, respectively) between the in situ Rrs,λ and MODIS Rrs,λ from 22 May 2004 for Trichodesmium blooms (outlined in the dashed circle). MODIS Rrs,λ of Trichodesmium blooms from other days show lower magnitude but similar curvatures between 469 and 555 nm (dotted circle).

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Fig. 6. Difference between Rrc (Rayleigh-corrected reflectance) of blooms and nearby bloom-free waters for the individual MODIS pixels from the various Trichodesmium bloom patches on the WFS and Sargassum slicks in the West Gulf of Mexico (WGOM) and South Atlantic Bight (SAB). Also plotted are the in situ Rrs,λ from a Trichodesmium bloom patch measured in the Florida Keys on 1 July 1997. Note that the spectral curvatures between 469 and 555 nm (i.e., high–low–high–low–high at 469–488–531–551–555 nm, respectively) observed from Rrs,λ of the Trichodesmium blooms in Fig. 5 are retained in the Rrc difference spectra, while such curvatures are lacking for the Sargassum slicks. To prove that such a lack of curvature in the Sargassum spectra is not a coincident, two cases of Sargassum spectra are presented here.

areas. For reported blooms before 1999 (e.g., Florida's east coast) when MODIS data were not available, data from similar periods in other years were processed and analyzed. This was necessary because although data from SeaWiFS were available since October 1997, the

lack of spectral bands at N1000 nm made it impossible to implement a SeaWiFS FAI. Fig. 7 shows an example of Trichodesmium blooms found off Florida's east coast. The MODIS FAI image on 21 September 2006 shows extensive patches and slicks, and the spectral curvatures between 469 and 555 nm suggest that these floating algae are Trichodesmium rather than Sargassum. Similarly, for the reported Trichodesmium blooms off India's coast in the North Arabian Sea (Sarangi et al., 2004, 2005), the MODIS FAI image on 5 May 2002 and the spectral analysis in Fig. 8 show typical FAI patches/slicks and spectral curvatures unique to Trichodesmium. MODIS FAI imagery between 31 July and 3 August 2004 did not reveal any suspicious patches or slicks for the Trichodesmium bloom near the Northern African Upwelling, where a Trichodesmium bloom on 1 August 2004 was reported (Ramos et al., 2005). Data from the QuikSCAT satellite for the central WFS between late July and early August showed wind speeds of 5–10 m s− 1, suggesting that high winds could have prevented the accumulation of Trichodesmium in surface mats. The wind effect on the formation and detection of bloom mats was demonstrated for the cyanobacteria blooms in Taihu Lake of China (Hu et al., 2010a). 4. Discussion 4.1. Accuracy and limitations

Fig. 7. Top: MODIS/Aqua FAI image on 21 September 2006 shows floating algae patches and slicks in coastal waters off Florida's east coast. These features appear to be Trichodesmium blooms as revealed by the spectral curvatures between 469 and 555 nm shown in the bottom panel (i.e., high–low–high–low–high at 469–488–531–551555 nm, respectively). The mean and standard deviation (vertical bars) of the spectra were derived from algae pixels outlined in the red circle. The area is between 28.9oN– 29.9oN and 81.4oW–80.4oW.

The similarity between the spectral curvatures between 469 and 555 nm of the patches identified in the FAI imagery and those of Trichodesmium seems to unambiguously identify Trichodesmium blooms in MODIS imagery. However, because the derived MODIS reflectance curvatures strongly depend on the radiometric calibration of the individual spectral bands, can this similarity be simply a coincidence due to potentially erroneous calibration? Indeed, the 500 m and 250 m MODIS bands at 469-, 555-, 645- and 859 nm were designed for land applications, and it might be possible that their calibrations are not consistent with the 1 km resolution ocean bands, resulting in artificial curvatures. For example, the large difference between the 859 nm land band and the 869 nm ocean band (Figs. 4– 8) may be a result of inconsistent calibrations. However, after carefully examining the MODIS calibration procedures and analyzing MODIS spectral data from other surface features (see below), we discounted the possibility that the MODIS spectral curvatures in the blue-green wavelengths were due to calibration artifacts. First, MODIS Level-1 data for all spectral bands were calibrated by the MODIS Calibration Support Team (MCST) using an onboard solar diffuser (Jack Xiong, NASA/GSFC, personal communication). This

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Third, if the spectral curvatures were induced by calibration artifacts, they would also appear from other types of floating algae. However, such curvatures were lacking in the Sargassum Rrc spectra and in algae-free shallow waters, and they were also lacking in at least two other types of algae blooms, namely the green macroalgae Ulva prolifera and the cyanobacterium Microcystis aeruginosa. Both can aggregate on the water surface and result in positive FAI slicks/ patches. Fig. 9 shows data from the Yellow Sea and Taihu Lake (China) to illustrate the lack of spectral curvatures from U. prolifera and M. aeruginosa blooms. The massive blooms of U. prolifera in the Yellow Sea in summer 2008 and of M. aeruginosa in Taihu Lake in spring 2007 resulted in significant economic and management burdens to local government and management agencies (Hu and He, 2008; Guo, 2008; Yang et al., 2008). Because of the elevated reflectance at 859 nm, the FAI approach was used to quantify these blooms (Hu et al., 2010a,b). The reflectance spectra for the blue-green wavelengths, however, do not show spectral curvatures between 469–555 nm such as those described above for Trichodesmium blooms (Figs. 3–8). M. aeruginosa contains only the PC (phycocyanin) pigment and no PUB (phycourobilin) or PEB (phycoerythrobilin) pigments (Raps et al., 1985). Blue light in freshwater lakes is highly absorbed by CDOM and therefore not available for the algae — the PC pigment can harvest light at ∼620 nm. Therefore, the spectral curvatures of Trichodesmium blooms containing the PUB and PEB pigments are not observed in the M. aeruginosa spectra. A similar argument can also apply to U. prolifera. Indeed, in situ reflectance data collected from either U. prolifera. or M. aeruginosa blooms showed smooth spectral shapes in the blue-green

Fig. 8. Top: MODIS/Terra FAI image on 5 May 2002 shows floating algae patches and slicks in coastal waters of the North Arabian Sea. These features appear to be Trichodesmium blooms as revealed by the spectral curvatures between 469 and 555 nm shown in the bottom panel (i.e., high–low–high–low–high at 469–488–531–551– 555 nm, respectively). The mean and standard deviation (vertical bars) of the spectra were derived from algae pixels outlined in the red circle. The area is between 20.7°N– 21.7°N and 69°E–70°E. The blooms during April and May 2002 were first reported by Sarangi et al (2004, 2005).

should provide a self-consistent calibration, at least to first order, across all spectral bands. The calibrated spectral radiance data (Lt,λ) were used to generate the Rrc difference spectra in Figs. 6–8 (Eq. 1), which showed the spectral curvatures in the blue-green wavelengths. In the SeaDAS processing from Level-1 Lt,λ to Level-2 Rrs,λ, Lt,λ data were adjusted by vicarious calibration gains specifically designed for the ocean, using a radiative transfer model (the same model used in SeaDAS for atmospheric correction) and measurements from a Marine Optical Buoy near Hawaii (Table 1). The vicarious calibration was performed not only to the ocean bands, but also to the land bands of MODIS/Aqua. Thus, the results shown in Figs. 4 and 5 should be valid in the blue-green wavelengths to the extent of the vicarious calibration accuracy. Second, even if the vicarious calibration gains are slightly off by about 1%, the spectral curvatures would unlikely be altered. MODIS/ Terra land bands were not vicariously calibrated (i.e., gain = 1.0, Table 1), but the spectral curvatures in the blue-green wavelengths (Fig. 8) are nearly identical to those obtained from MODIS/Aqua measurements. Results from near concurrent MODIS/Aqua and MODIS/Terra measurements over suspicious bloom patches indeed showed similar spectral curvatures in the blue-green wavelengths. The likelihood that both sensors (one vicariously calibrated and the other not calibrated) show similar curvatures by coincidence due to calibration errors should be small.

Fig. 9. Top: Rrc difference spectra from surface mats of the green macroalgae U. prolifera in Qingdao coastal waters (Hu and He, 2008) do not show spectral curvatures between 469 and 555 nm; Bottom: Similarly, Rrc spectra from surface mats of the cyanobacteria Microcystis aeruginosa in Taihu Lake (Hu et al., 2010a) do not show the spectral curvatures either. The mean and standard deviation (vertical bars) of the spectra were derived from multiple algae pixels from the MODIS images. Note that the spectra band at 748 nm for the latter case was saturated and therefore not shown in the figure.

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wavelengths (M-X. He, Ocean University of China, personal communication; Y. Li, Nanjing Normal University, personal communication). Although in situ reflectance spectra of Sargassum could not be found in refereed or grey literature, we believe that this brown macroalgae should also have smooth spectral shape in the blue-green wavelengths, similar to that of U. prolifera. The in situ Rrs spectrum of a Trichodesmium patch from the Florida Keys showed high–low–high–low–high reflectance at 478–490–528– 550–558 nm instead of the MODIS wavelengths at 469–488–531– 551–555 nm (Fig. 2). If MODIS relative spectral response functions were used to generate the bandpass weighted Rrs,λ, the following values were obtained for the 469–488–531–551–555 bands, respectively: 0.0131, 0.0135, 0.0151, 0.0145, 0.0145 sr− 1. Not only is Rrs,551 equal to Rrs,555, but also Rrs,469 is lower than Rrs,488. The exact reason of this discrepancy between the in situ and MODIS Rrs curvatures is unclear, but is possibly due to changes in the absorption peaks/ troughs and/or in the backscattering peaks/troughs (note that the in situ and MODIS spectra were measured at different locations and times). In the published literature (see Table 1 of Navarro Rodriguez, 1998 and Table 1 of Subramaniam et al., 1999a), the wavelengths of local reflectance maxima/minima and pigment absorption peaks could differ by 5–10 nm. The exact locations of reflectance maxima/ minima should also be determined by the relative proportion of the various pigments, which depend on light and nutrient regimes. Subramaniam et al., 1999a showed that both PUB/PEB and (PUB + PEB)/Chl ratios can vary by at least a factor of 2 for samples measured at the same time of the day, and the average ratios also showed diurnal cycles (Fig. 2 of Subramaniam et al., 1999a). Unfortunately, no more in situ Rrs data from the west Florida shelf (including the Florida Keys) was available to evaluate if and how the positions of the reflectance maxima/minima may change. However, in situ Rrs from a Trichodesmium bloom patch off Puerto Rico showed the local reflectance maximum at 469 nm (Fig. 2b), with the MODIS bandpass weighted Rrs,λ values as: 0.0330, 0.0301, 0.0362, 0.0353, and 0.0365 sr− 1 for the 469–488–531–551–555 nm MODIS bands, respectively. Thus, it is possible that the Trichodesmium patches on the WFS and in other coastal waters (Figs. 3, 7, 8) may show similar spectral curvatures as those from this latter case, which are reflected in the MODIS Rrs and Rrc data. Indeed, for many other images with similar features on the WFS, these spectral curvatures always appeared in the blue-green wavelengths with no exception. In summary, three lines of evidence prove that the spectral curvatures in the blue-green MODIS wavelengths from the suspicious Trichodesmium patches are not due to potential calibration artifacts, but are realistic: 1) While MODIS Rrs was derived from vicariously calibrated data for all spectral bands, such a vicarious calibration was not applied to MODIS Rrc data. Yet both showed spectral curvatures in the blue-green wavelengths for all bloom patches that were thought to be Trichodesmium; 2) Likewise, MODIS/Aqua and MODIS/Terra were calibrated independently, yet they both showed similar spectral curvatures; 3) The most convincing evidence, however, is that these spectral curvatures disappeared for other types of blooms (Sargassum, U. prolifera, and M. aeruginosa) and for bloom-free waters, including the shallow, clear waters where reflectance in the blue-green wavelengths was also elevated. Thus, in conclusion, the spectral curvatures in the blue-green wavelengths derived from MODIS full-spectral data can be used to distinguish Trichodesmium blooms from other blooms unambiguously, and these signatures are not due to incorrect sensor calibrations. The 2-step approach to identify Trichodesmium surface mats is immune to interference from resuspended sediments, high CDOM concentrations, and/or bottom reflectance. These conditions are often found in coastal oceans, especially on the WFS (e.g., Fig. 1). Sedimentrich waters and bottom reflectance would result in a larger increase in the 667 nm bands than in the 859- and 1240 nm bands, leading to lower FAI values. This effect is shown in all FAI images above, as

evidenced by the low (often negative) FAI values near the coasts. These low-FAI pixels can be easily ruled out as containing potential Trichodesmium mats. The influence of CDOM to FAI is minimal because CDOM absorption decreases exponentially with increasing wavelengths and its effect in the FAI wavelengths can often be neglected. Thus, such optical complexity would not create any difficulty to the approach. Another advantage of the 2-step approach is that it avoids the problems of false cloud-masking by the SeaDAS default algorithms. Because the surface Trichodesmium mats exhibit high reflectance in the NIR and SWIR wavelengths, cloud-masking algorithms using threshold values, such as those used in SeaDAS, often regard these high reflectance pixels as clouds. Correspondingly, no nLw (or reflectance) data can be generated, not to mention applying an inversion algorithm to the nLw data. The 2-step approach, in contrast, is applied to the Rayleigh-corrected reflectance data, thus avoiding cloud-masking. For the same reason, however, visual examination is required to rule out clouds. However, the 2-step approach has inherent limitations to detect Trichodesmium blooms with low concentrations when they do not form surface mats or the high-concentration layer is submersed in water. The concentrations at which surface mats form, under favorable conditions (low wind, stratified ocean), are currently unknown. Subramaniam et al. (1999b) reported 16,900 trichomes ml− 1 (corresponding to Chl ∼ 4.25 mg m− 3) from a surface Trichodesmium patch in the Arabian Sea. Chl of ∼ 4.25 mg m− 3 is perhaps the lower detection limit for the FAI-based approach, while previous empirical and semi-analytical optical models (Subramaniam et al., 2002; Westberry et al., 2005) can detect blooms at lower concentrations in phytoplankton-dominated waters. If Trichodesmium blooms do not form surface mats, the first step of our approach, i.e., identify positive FAI slicks/patches, will simply fail. Likewise, when they form subsurface mats (e.g., Carpenter and Price, 1977), our approach is also limited by the light penetration depth. According to the MODIS specifications (Table 1), noise-equivalent radiance (NEΔL) at 859 nm is 0.0123 mW cm−2 µm−1 sr− 1. Rrs,859 of the identified Trichodesmium bloom is about 0.01 sr− 1 (Fig. 5), corresponding to about 0.69 mW cm−2 µm−1 sr− 1 under typical solar illumination conditions. Then, for a water attenuation of 4.85 m− 1 and assuming a minimal detectable signal of at least twice the noise, the depth beyond which the cyanobacteria mats cannot be detected by the MODIS 859 nm band is ln(0.69/0.0246)/(2 × 4.85) ≈ 0.3 m. The same analysis to the 469- and 555 nm bands showed 8–9 m depth limit to resolve the spectral curvatures, suggesting that future effort may be dedicated to quantifying these curvatures in the blue-green in order to detect subsurface blooms. Wind plays an important role in modulating the formation and dissipation of the cyanobacteria surface mats. In Taihu Lake where cyanobacteria blooms of M. aeruginosa occur every year, it was found that surface mats were formed for wind speed b2.5 m s− 1 but they were mostly dissipated for wind speed N3 m s− 1 (Hu et al., 2010a). Hourly wind speeds obtained from a NOAA NDBC station at Venice, Florida (27.07°N 82.45°W) near our study area were examined. Although all identified Trichodesmium blooms in Fig. 3 were associated with wind speed b5 m s− 1, the results were not conclusive. For example, for the bloom event in Fig. 3d that started on 11 July 2007 in the south of Charlotte Harbor and ended on 27 August 2007, no Trichodesmium patches could be found on any of several cloud-free days when wind speed was also b5 m s− 1 during the 5 h period before the MODIS measurements. Nevertheless, because a bloom event typically lasts for at least several weeks and wind speed is often low on the WFS for all seasons, the likelihood of missing a significant bloom event is small. The NDBC data showed that during the summer months of 1999–2008, wind speed was b5 m− 1 for N70% of the time and was b3 m− 1 for about 40% of the time (Fig. 10). The favorable wind condition for Trichodesmium surface aggregation, combined

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Fig. 10. Climatological mean and standard deviation of ocean surface wind as measured by a U.S. NOAA NDBC station along the west-central Florida coast. For the summer months, the probabilities to have wind b 5 m s− 1 and b 3 m s− 1 are N 70% and ∼40%, respectively.

with a 30% cloud-free probability, makes our 2-step approach practically feasible for the WFS. In summary, the approach used here will underestimate Trichodesmium bloom occurrences. However, because of the spectral signatures used from the blue-green to the NIR wavelengths, the possibility of a false-positive identification is nil for any coastal waters. The identified Trichodesmium blooms can serve as ground truth data to validate other approaches that are targeted to detect Trichodesmium colonies submersed in water instead of floating on the surface. Such ability will also greatly enhance our confidence in tracing bloom advections, studying bloom dynamics, and planning for targeted field research. In particular, because Trichodesmium can vertically migrate through the course of a day (Villareal and Carpenter, 1990, 2003; Capone et al., 1997), the identified blooms can serve as the basis for hindcast analyses of bloom dynamics. On the other hand, with some a priori knowledge of the bloom occurrence (e.g., bloom location), the lack of spectral curvatures between 469 and 555 nm may be used to identify Sargassum in the GOM as well as in the Sargasso Sea.

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Fig. 11. Rrc difference spectra from a Trichodesmium bloom observed on 2 November 2009 in the north of the Florida Keys (25.33°N–25.73°N, 82.24°W–81.85°W). The mean and standard deviation (vertical bars) of the spectra were derived from multiple algae pixels from MODIS and MERIS data collected on the same day. Note the absence of spectral curvatures in the blue-green wavelengths (outlined in the dotted circle) in MERIS data. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

proposed a list of bands for future sensors. These bands encompassed nearly all MODIS and MERIS bands. For example, in the blue-green wavelengths six bands were proposed at 460, 475, 490, 510 (or 515, or 520), 545 (or 555), and 565 nm, respectively. From the Trichodesmium spectra shown in Fig. 2, local reflectance maxima and minima occurred at 478–490–528–550–558 nm for the Florida Keys bloom patch and at 469–497–526–546–562 nm for the Puerto Rico bloom patch. Combined with the Lee et al. (2007) analysis, these results suggest future sensors should have spectral bands at 460, 469, 478, 490, 497, 510, 527, 546, 550, 558, and 565 nm in the blue-green wavelengths. Further, the sensitivity (or SNR) should be optimized for ocean observations as compared with the MODIS land bands. The current planning by the U.S. NASA to launch hyperspectral sensors in the next decade makes it possible to implement better detection methods to identify the various phytoplankton functional groups from space. 5. Conclusion

4.2. Sensor design The elevated signal in the NIR from the Trichodesmium mats was expected, yet the MODIS spectral curvatures in the blue-green were a surprise because the 469- and 555 nm bands were not designed for the ocean. The results strongly suggest that future ocean color sensors need to include spectral bands designed to identify phytoplankton functional groups, including Trichodesmium and Karenia brevis (Craig et al., 2006), among many others (Nair et al., 2008). Among the various existing ocean color sensors, MERIS (Medium Resolution Imaging Spectrometer, 2002-present) is equipped with more spectral bands (15 bands between 400 and 900 nm) specifically designed for the ocean. MERIS bands at 620- and 709 nm are unique and particularly useful for shallow-water bottom correction and detection of intense blooms (including those floating to form surface mats, see Gower et al., 2005, 2006). In addition, MERIS bands were designed to be programmable, but such capacity has never been realized since its launch in 2002. Currently, the number of bands between 443- and 560 nm is insufficient to detect spectral curvatures found from Trichodesmium blooms. Fig. 11 shows the MERIS and MODIS spectra from a Trichodesmium bloom north of the Florida Keys on 2 November 2009. The spectral curvatures between 469- and 555 nm in the MODIS data are not present in the MERIS data due to the limited number of spectral bands between 469- and 555 nm. Using derivative analysis and hyperspectral data collected from both optically shallow and optically deep waters, Lee et al. (2007)

Using data collected from space and other platforms and by combining MODIS data from the land bands and ocean bands, an approach was demonstrated for the detection of Trichodesmium spp. surface mats on the west Florida shelf and in other coastal waters around the world. The 2-step approach first used the FAI (floating algae index) imagery to examine the MODIS spectral signals in the Red-NIR-SWIR to detect algae mats floating on the water surface. Then, spectral curvatures in the blue-green were examined to determine if the detected blooms were from a Trichodesmium aggregation or from other types of algae (e.g., macroalgae of Sargassum spp. or Ulva prolifera, or other types of cyanobacteria). The PUB and PEB pigments of Trichodesmium resulted in spectral curvatures between 469- and 555 nm (specifically, high–low–high– low–high at 469–488–531–551–555 nm, respectively) that can confirm the presence of Trichodesmium in MODIS imagery without ambiguity. Future ocean color sensors should include more spectral bands in the blue-green wavelengths to improve their ability to differentiate various phytoplankton functional groups from space. Acknowledgements This work was supported by the US NASA Biology and Biogeochemistry Program, the US NOAA satellite oceanography program, the US EPA Gulf of Mexico program, and the South Carolina Sea Grant Consortium. We thank the Florida Fish and Wildlife Research Institute

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(FWRI) for compiling and providing in situ data of Trichodesmium concentrations, and NOAA NDBC for providing surface wind data. We are indebted to Dr. Roy Armstrong of the University of Puerto Rico for the in situ reflectance data, and to three anonymous reviewers and the associate editor for their critical comments to help improve the quality of this work. References Borstad, G. A., Gower, J. F. A., & Carpenter, E. J. (1992). Development of algorithms for remote sensing of Trichodesmium blooms. In E. J. Carpenter, & D. G. Capone (Eds.), Marine Pelagic Cyanobacteria: Trichodesmium and Other Diazotrophs (pp. 193−210). New York: Springer. Capone, D. G., Zehr, J., Paerl, H., Bergman, B., & Carpenter, E. J. (1997). Trichodesmium: A globally significant marine cyanobacterium. Science, 276, 1221−1229. Capone, D. G., Subramaniam, A., Montoya, J. P., Voss, M., Humborg, C., Johansen, A. M., et al. (1998). An extensive bloom of the N2-fixing cyanobacterium Trichodesmium erythraeum in the central Arabian Sea. Marine Ecology Progress Series, 172, 281−292. Carpenter, E. J., & Price, C. C., IV (1977). Nitrogen fixation, distribution, and production of Oscillatoria (Trichodesmium) spp. in the western Sargasso and Caribbean Sea. Limnology and Oceanography, 22, 60−72. Craig, S. E., Lohrenz, S. E., Lee, Z. P., Mahoney, K. L., Kirkpatrick, G. J., Schofield, O. M., et al. (2006). Use of hyperspectral remote sensing reflectance for detection and assessment of the harmful alga, Karenia brevis. Applied Optics, 45, 5414−5425. Dupouy, C., Petit, M., & Dandonneau, Y. (1988). Satellite detected cyanobacterial bloom in the southwestern tropical Pacific: Implication for oceanic nitrogen fixation. International Journal of Remote Sensing, 9, 389−396. Gomes, H. D. R., Goes, J. I., Matondkar, S. G. P., Parab, S. G., Al-Azri, A. R. N., & Thoppil, P. G. (2008). Blooms of Noctiluca miliaris in the Arabian Sea — An in situ and satellite study. Deep-Sea Research Part I, 55, 751−765. Gower, J., King, S., Borstad, G., & Brown, L. (2005). Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer. International Journal of Remote Sensing, 26, 2005−2012. Gower, J., Hu, C., Borstad, G., & King, S. (2006). Ocean color satellites show extensive lines of floating Sargassum in the Gulf of Mexico. IEEE Transactions Geoscience and Remote Sensing, 44, 3619−3625. Gruber, N., & Sarmiento, J. L. (1997). Global patterns of marine fixation and denitrification. Global Biogeochemical Cycles, 11, 235−266. Guo, L. (2008). Doing battle with the green monster of Taihu Lake. Science, 317, 1166. Hu, C., Carder, K. L., & Muller-Karger, F. E. (2000). Atmospheric correction of SeaWiFS imagery over turbid coastal waters: A practical method. Remote Sensing Environmental, 74, 195−206. Hu, C., He, M. -X. (2008). Origin and offshore extent of floating algae in Olympic sailing area. Eos. AGU Trans., 89(33), 302–303. Hu, C., Lee, Z. P., Muller-Karger, F. E., & Carder, K. L. (2003). Application of an optimization algorithm to satellite ocean color imagery: A case study in Southwest Florida coastal waters. In R. J. Frouin, Y. Yuan, & H. Kawamura (Eds.), SPIE proceedings 4892Ocean Remote Sensing and Applications. (pp. 70−79) Bellingham, WA: SPIE. Hu, C., Muller-Karger, F. E., Vargo, G. A., Neely, M. B., & Johns, E. (2004). Linkages between coastal runoff and the Florida Keys ecosystem: A study of a dark plume event. Geophysical Research Letters, 31, L15307, doi:10.1029/2004GL020382 Hu, C., & Muller-Karger, F. E. (2007). Response of sea surface properties to Hurricane Dennis in the eastern Gulf of Mexico. Geophysical Research Letters, Vol. 34, L07606, doi:10.1029/2006GL028935 Hu, C. (2008). Ocean color reveals sand ridge morphology on the west Florida shelf. IEEE Geoscience and Remote Sensing Letters, 5, 443−447. Hu, C. (2009). A novel ocean color index to detect floating algae in the global oceans. Remote Sensing Environmental, 113, 2118−2129. Hu, C., Lee, Z., Ma, R., Yu, K., Li, D., & Shang, S. (2010). Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China. Journal Geophysical Research, 115, C04002, doi:10.1029/2009JC005511

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