nt 109 (2007) 207–220www.elsevier.com/locate/rse

Remote Sensing of Environme

Monitoring turbidity in Tampa Bay using MODIS/Aqua 250-m imagery

Zhiqiang Chen ⁎, Chuanmin Hu, Frank Muller-Karger

Institute for Marine Remote Sensing (IMaRS), College of Marine Science, University of South Florida 140 Seventh Ave. S., St. Petersburg, FL 33701, USA

Received 22 July 2006; received in revised form 27 December 2006; accepted 29 December 2006

Abstract

We developed an approach to map turbidity in estuaries using a time series (May 2003 to April 2006) of 250-m resolution images from theModerate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua satellite, using Tampa Bay as a case study. Cross-calibration of theMODIS 250-m data (originally designed for land use) with the well-calibrated MODIS 1-km ocean data showed that the pre-launch radiometriccalibration of the 250-m bands was adequate. A simple single scattering atmospheric correction provided reliable retrievals of remote sensingreflectance at 645 nm (0.002bRrs(645)b0.015 sr−1, median bias=−7%, slope=0.95, intercept=0.00, r2=0.97, n=15). A more rigorousapproach, using a multiple scattering atmospheric correction of the cross-calibrated at-sensor radiances, retrieved similar Rrs(645). Rrs(645)estimates, after stringent data quality control, showed a close correlation with in situ turbidity (turbidity=1203.9×Rrs(645)

1.087,0.9b turbidityb8.0 NTU, r2=0.73, n=43). MODIS turbidity imagery derived using the developed approach showed that turbidity inHillsborough Bay (HB) was consistently higher than that in other sub-regions except in August and September, when higher concentrations ofcolored dissolved organic matter seem to have caused underestimates of turbidity. In comparison, turbidity in Middle Tampa Bay (MTB) wasgenerally lowest among the Bay throughout the year. Both Old Tampa Bay (OTB) and Low Tampa Bay (LTB) showed marked seasonal variationswith higher turbidity in LTB during the dry season and in OTB during the wet season, respectively. This seasonality is linked to wind-drivenbottom resuspension events in lower portion of the Bay and river inputs of sediments in the upper portion of the Bay. The Bay also experiencessignificant interannual variation in turbidity, which was attributed primarily to changes in wind forcing. Compared with the once-per-month, non-synoptic in situ surveys, synoptic and frequent sampling facilitated by satellite remote sensing provides improved assessments of turbidity patternsand thus a valuable tool for operational monitoring of water quality of estuarine and coastal waters such as in Tampa Bay.© 2007 Elsevier Inc. All rights reserved.

Keywords:MODIS; Aqua; Ocean color; Remote sensing reflectance; Atmospheric correction; Bio-optical inversion; Turbidity; River runoff; Sediment resuspension;Tampa Bay estuary

1. Introduction

Turbidity is a fundamental index used to assess coastal andestuarine water quality conditions that affect light attenuationand therefore the productivity of planktonic and benthic algae(Cloern, 1987; Cole & Cloern, 1987; Fisher et al., 1999;Pennock & Sharp, 1994), seagrass and coral reefs (Anthonyet al., 2004; Moore et al., 1997). Variations in turbidity also helpunderstand the distribution of total suspended solids or sed-iments (TSS), and therefore processes like coastal erosion andmobilization of chemicals or pollutants (Heyes et al., 2004).

Turbidity shows a wide range in spatial and temporal vari-ability in coastal and estuarine waters. Rivers deliver terrestrial

⁎ Corresponding author. Tel.: +1 727 553 1186.E-mail address: zchen@marine.usf.edu (Z. Chen).

0034-4257/$ - see front matter © 2007 Elsevier Inc. All rights reserved.doi:10.1016/j.rse.2006.12.019

materials to estuaries typically following seasonal patterns, butalso in events that may trigger marked interannual variability(Doxaran et al., 2006; Pribble et al., 2001). Currents and waveslead to suspension of bottom sediments, changing turbidity inresponse to storms and other wind events at tidal and subtidalfrequencies (Cloern et al., 1989; Uncles et al., 2002; Wolanski& Spagnol, 2003). Human activities such as transportation anddredging also influence the magnitude and distribution ofturbidity (Schoellhamer, 1996). As a result, conventionalsampling methods often fail to characterize turbidity dynamicsbecause of the limitations in temporal and spatial sampling(Chen et al., in revision).

Satellite-borne sensors could be an ideal tool to assess tur-bidity because they provide synoptic and frequent mappingcapabilities. However, satellite platforms also have limitationssuch as in spatial resolution, revisit time, accessibility to data,

208 Z. Chen et al. / Remote Sensing of Environment 109 (2007) 207–220

sensor calibration and image processing softwares (Doxaranet al., 2002; Miller et al., 2005; Ruddick et al., 2003; Stumpf &Pennock, 1989). For example, although Landsat ETM+provides high resolution (30-m) imagery, the revisit time is∼16 days. This long revisit time, together with possible cloudcover, makes it inadequate to resolve turbidity dynamics incoastal and estuarine waters. In contrast, some sensors designedto observe marine waters do have shorter revisit time, e.g., near-daily overpasses, but the spatial resolutions are typically toocoarse (∼1 km/pixel) to characterize fine-features in turbidity(e.g., the Advanced Very High Resolution Radiometer —AVHRR, the Sea-viewing Wide Field-of-View Sensor —SeaWiFS, and ocean color bands of the Moderate ResolutionImaging Spectroradiometer — MODIS). Although the Euro-pean Medium Resolution Imaging Spectrometer (MERIS) hasfull-resolution of 300-m bands with near-daily coverage, thecost of acquisition of these data has limited its wide application.

The MODIS flown aboard the Aqua spacecraft, launched inMay, 2002, provides near-daily coverage of the subtropicalocean and has two bands that observe the Earth at 250-mresolution (band 1: 645 nm, from 620–670 nm; band 2: 859 nm,from 841–876 nm). These bands have sufficient sensitivity todetect a wide range of changes in color of estuarine waters (Huet al., 2004). Several studies have demonstrated the potential ofthese bands to monitor water quality in coastal and estuarinewaters (Hu et al., 2004; Miller & Mckee, 2004). Similar cov-erage is possible from the MODIS on the Terra satellite, but atpresent both the MODIS/Terra 1-km data and 250-m are notwell-calibrated and containmore noise compared to theMODIS/Aqua data (Hu et al., 2004). Thus we focused this study on usingMODIS/Aqua data, but similar approach developed in this studyshould be applicable to MODIS/Terra data as well.

Three key issues must be resolved before MODIS 250-mdata can be routinely applied in coastal studies as proposed byHu et al. (2004). First, the reliability of at-sensor radiances atthe top of atmosphere (TOA) has to be assessed. The MODIS250-m bands were originally designed to serve as “sharpeningbands” to detect land, aerosol, and cloud features. Therefore,there has been no effort to apply an oceanic vicarious calibrationsuch as done for the ocean color 1-km bands. However, remotesensing of aquatic environments demands rigorous and accuratesensor calibration because a 5% error in at-sensor radiancesmay result in a 50% remote sensing reflectance (Rrs) error. Inaddition, like most sensors Aqua signals may degrade with time,thus correction for potential degradation effects is prerequisiteto use the MODIS 250-m data for a consistent time seriesanalysis. Second, a method has to be available to estimate Rrs

of aquatic environments from at-sensor radiances. Accurateretrieval of Rrs is critical as all other satellite products arederived from Rrs estimates. Although several atmospheric cor-rection schemes were developed for the 250-m bands (Hu et al.,2004; Miller & Mckee, 2004), the accuracy of these methodshas remained untested and requires a full validation using insitu Rrs measurements. Finally, a robust bio-optical algorithm(an algorithm to convert Rrs to water quality parameters such asturbidity or TSS) needs to be developed and validated as wellfor a study region.

The objective of the study was to develop an approach forroutine application of MODIS 250-m data in monitoring tur-bidity in Tampa Bay, Florida (USA), for better characterizing itstemporal and spatial variability, and thereby also provide apathfinder approach to better study other estuarine environ-ments. To this end, we systematically evaluated the integrity ofthe methods developed for applying MODIS 250-m data. Wefirst cross-calibrated MODIS 250-m bands with the well-calibrated 1-km ocean color bands. Second, we validated anatmospheric correction algorithm using in situ Rrs measurements.Third, we developed and validated an empirical algorithm byapplying rigorous data quality control process to convert theatmospherically corrected Rrs to turbidity. Finally, to evaluate thealgorithm and potential for operational application of MODIS250-m data, a time series of MODIS/Aqua 250-m imagescollected between May 2003 and April 2006 were processed.Then, composites of MODIS turbidity images were created torepresent calendar monthly means for each year and long-termcross-year monthly means (i.e. averaging turbidity over thesame months across all years). The variation patterns observedin the MODIS turbidity composites were compared with in situobservations from an independent monitoring program.

2. Materials and methods

2.1. Tampa Bay estuary

Tampa Bay is Florida's largest open water estuary, featuringa surface area of ∼1000 km2. It is conventionally subdividedinto 4 mainstem sub-regions, namely Old Tampa Bay (OTB),Hillsborough Bay (HB), Middle Tampa Bay (MTB), and LowerTampa Bay (LTM) (Fig. 1). The average water depth is ∼4.0 mwith a navigation channel N10 m running along the centralportion of the Bay (Fig. 2). This channel plays an importantrole in the circulation patterns and water quality distributionacross Tampa Bay (Bendis, 1999; Weisberg & Zheng, 2006).

In the decades prior to the 1980's, Tampa Bay was heavilypolluted by nutrient loading from sources like sewage andwastewater, which caused significant eutrophication problems.The high phytoplankton concentrations decreased water clarityin the Bay and thus led to substantial losses of seagrass cover(Janicki et al., 2001; Johansson, 2000). Since then, ecosystemrestoration efforts have gradually improved the water quality ofTampa Bay and some of the seagrass has recovered (Lewiset al., 1998; Tomasko et al., 2005). At present variation in waterquality in Tampa Bay is suggested generally due to seasonal andinter-annual variability in precipitation or river runoff to the Bay(Janicki et al., 2001; Lipp et al., 2001; Schmidt & Luther, 2002).

2.2. Field data

Turbidity data (reported in nephelometric turbidity units orNTU) were obtained from the Environmental Protection Com-mission of Hillsborough County's (EPCHC) Tampa Bay waterquality monitoring program (Boler et al., 1991). The EPCHCconducts monthly surveys that span 3 weeks with each weekcovering approximately one segment of the Bay (Fig. 1). Water

Fig. 2. NOAA/USGS merged bathymetric/topographic digital elevation model(DEM) of Tampa Bay at 30 m spatial resolution (Gesch & Wilson, 2001). Datawere binned to 250-m to match the MODIS 250-m resolution. Grey and whiterepresent land and missing data, respectively.

Fig. 1. MODIS 250-m image of Tampa Bay showing the four sub-regions of theBay and major tributaries. The sub-regions are Old Tampa Bay (OTB),Hillsborough Bay (HB), Middle Tampa Bay (MTB), and Lower Tampa Bay(LTB). The rivers are the Hillsborough River (HB), the Alafia River (AR), theLittle Manatee River (LMR), and the Manatee River (MR). The EnvironmentalProtection Commission of Hillsborough County's (EPCHC) water qualitymonitoring stations are overlaid with various symbols to indicate differentsampling times in each month: diamond (OTB) generally in the first week,triangle (HB) in the second week, and square (MTB and LTB) in the last week.Five stations marked with white crosses and labeled with numbers are alsooverlaid (stations 92, 23, 14, 40, 55 from the EPCHC program). The inset showsthe location of the Tampa Bay estuary in the state of Florida, USA.

Table 1Symbols, definitions and units

Symbols Definitions Units

t0 Diffuse transmittance from the sunto the ground due to Rayleighand aerosol scattering

Dimensionless

tv Diffuse transmittance from theground to a sensor due to Rayleighand aerosol scattering

Dimensionless

toz Diffuse transmittance from the sun to theground and from the ground to a sensordue to absorption by ozone

Dimensionless

La Radiance from aerosol scattering andaerosol-Rayleigh interaction

mW cm−2 μm−1 sr−1

Lr Radiance from Rayleigh scatteringin the absence of aerosols

mW cm−2 μm−1 sr−1

Lt Total at-sensor radiance mW cm−2 μm−1 sr−1

Lw Water-leaving radiance at the sea surface mW cm−2 μm−1 sr−1

Lgn Normalized sun glint radiance if there wereno atmosphere and solar irradiance F0=1

Sr−1

θ0 Solar zenith angle Degreeθv Sensor zenith angle DegreeF0 Adjusted extraterrestrial solar irradiance mW cm−2 μm−1

For simplicity, the wavelength dependency of all terms (except solar and sensorangles) is suppressed.

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samples were collected at mid-depth of a station where depthwas greater than 3 m, otherwise only surface samples werecollected.

In the lab, the turbidity of water samples was measuredwith a Hach® Model 2100N Turbidimeter. The delay betweensample collection and analysis was usually b24 h. Turbidityreadings are expected stable within this timeframe based onprotocols sanctioned by the US Environmental ProtectionAgency (EPA 180.1) (Joe Barron/EPCHC, personal communi-cation). The device measures light intensity (peak spectralresponse is ∼570 nm) at 3 different angles (90° scattered,forward scattered, and transmitted light). The light scattered at90° normalized to the total scattered light at 3 angles wascalibrated with four standard solutions ranging from 2 to2000 NTU (Hach® Model 2100N Turbidimeter manual, 1999).Thus, turbidity measures a bulk scattering by particles in watersamples, and has been frequently used as an indicator of

sediment concentrations. However, the relationship betweenturbidity and sediment concentration varies depending onsediment properties (e.g., size distribution, shape, composition,and refractive index) (Twardowski et al., 2001; Ulloa et al.,1994) and measurement uncertainties. For example, when

210 Z. Chen et al. / Remote Sensing of Environment 109 (2007) 207–220

sediment concentrations are low, relative uncertainty in mea-surements of sediment concentrations using the traditional drymethod can be larger (Christian & Sheng, 2003).

Remote sensing reflectance (Rrs, sr−1) data were collected

during several field surveys on 21–22 October 2003, 1–3 June2004, 12–14 October 2004, 13 December 2005, and 27February 2006, respectively, using a handheld PR650 (PhotoResearch Inc.) or Analytical Spectral Device (ASD Inc.) spec-troradiometer, following the method described in Hu et al.

Fig. 3. (a) MODIS at-sensor radiance at 667 nm (1-km) on 13 December 2004, 18:43the same satellite pass. The dashed lines on the 250-m image are due to sensor scan erand 250-m images, respectively) were used for cross-calibration in this and other te

(2004). The in situ hyperspectral Rrs data were integrated overthe relative spectral response (RSR) function of MODIS band 1to obtain Rrs(645) for MODIS validations.

2.3. Cross-calibration of the MODIS 250-m with 1-km bands

Vicarious calibration of satellite at-sensor radiance is theprocedure used to adjust a measured radiance to a predictedradiance, based on well-calibrated measurements and a radiative

GMTover the study area; (b) MODIS at-sensor radiance at 645 nm (250-m) fromrors. Data in the rectangular box offshore (10×10 and 40×40 pixels for the 1-kmn images collected between 2003 and 2006.

Fig. 4. Scatter plots of the at-sensor radiance (mW cm−2 μm−1 sr−1) in the two250-m bands (645 nm in (a) and 859 nm in (b)) as measured by the sensor andpredicted by the calibrated 1-km ocean color bands. The dashed lines are 1:1lines. (See Table 2 to for statistics).

Table 2Comparison of the 250-m at-sensor radiances (mW cm−2 μm−1 sr−1) betweenthe measurements and those predicted from the 1-km ocean color bands

Band Ratio a % difference b Slope Intercept r2 RMSEc n

Lt(645) 0.96 3.61 0.91 0.07 0.997 0.04 11Lt(859) 1.01 1.30 0.97 0.01 0.995 0.02 11a Median ratio between the measured and predicted radiances, indicating

overall biases.b Median absolute percentage difference (MPD) between the measured and

predicted at-sensor radiance, indicating typical uncertainties (Bailey & Werdell,2006).c RMSE represents root mean square errors of the linear regression fitting, in

units of mW cm−2 μm−1 sr−1.

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transfer model. This procedure is critical because the pre-launchlaboratory calibration of ocean color sensors typically hasuncertainties of 2–5%, which can translate to 20–50% relativeerrors in the retrieved Rrs after atmospheric correction. Thevicarious calibration and the subsequent atmospheric correctionusing identical radiative transfer codes can be considered to be a“self-tuning” process.

Vicarious calibration of ocean color sensors is typicallyperformed at a well-characterized open ocean site (e.g., spatiallyhomogeneous water away from land), and with well-calibratedinstrument (e.g., the Marine Optical Buoy or MOBYat Hawaii,USA). When this option is not available, an alternative is tocalibrate one sensor against another well-calibrated sensor, forexample calibrating the Moderate Optoelectrical Scanner(MOS) on the Indian Remote Sensing Satellite (IRS-P3) usingSeaWiFS (Wang & Franz, 2000) or calibrating Landsat/ETM+using SeaWiFS (Hu et al., 2001b). We hereby refer to thecalibration using observations from a satellite sensor as a cross-calibration to differentiate a calibration using in situ measuredradiances. We took an approach similar to that of Hu et al.(2001b) to calibrate the 250-m bands using MODIS/Aqua 1-kmocean bands. Because all MODIS bands have identical solar/viewing geometry, the procedure is simpler than those used tocross-calibrate two sensors on separate satellites as in Hu et al.(2001b). In short, the 250-m data are adjusted according to theat-sensor radiances predicted by the 1-km data. For clarity webriefly describe this cross-calibration procedure below.

After correction for atmospheric light absorption due toozone (Hu et al., 2004), at-sensor radiances, Lt, can be predictedas (for brevity the wavelength dependency is suppressed here):

Lt ¼ Lr þ La þ tv � Lw; ð1Þ

(Definitions of terms are provided in Table 1.)In this equation the effects of sun glint and whitecaps are

omitted because during the quality control step data associatedwith these artifacts can be discarded. The effects of water vapor,oxygen absorption, or light polarization are negligible forthe 250-m bands (e.g., Meister et al., 2005). The terms on theright-hand side of Eq. (1) were estimated from the MODIS/Aqua 1-km ocean bands in the following way:

1. A group of MODIS images from May 2003 to April 2006were chosen for the cross-calibration based on followingcriteria: 1) cloud free in the entire study area (central westFlorida shelf); 2) large dynamic range in Lt to apply thecross-calibration over a wide range of radiances; 3) thescenes are as evenly distributed as possible in time series.Ultimately, eleven images were selected randomly from thegroup for this study.

2. For each image, a clear-water ocean area adjacent to TampaBay was chosen as a calibration site (Fig. 3). The choice of aclear-water site ensures a better performance of standardatmospheric correction schemes and avoids possible satura-tion issue at 667 nm because Lt(667) typically ranges from0.9–1.6 mW cm−2 μm−1 sr−1, as shown in Fig. 4, which isbelow the saturation radiance specified for this band(4.2 mW cm−2 μm−1 sr−1, Franz et al., 2006).

3. MODIS 1-km Level 1 data were processed using SeaDAS4.8 to estimate Lr, La, tv, and Lw for each of the 1-kmwavelengths (λ=412, 443, 488, 531, 551, 667, 678, 748,869 nm, respectively), using the similar procedure describedby Hu et al. (2001b) for SeaWiFS, including adjustment tothe center wavelength.

4. These multi-spectral data were used to construct anartificial hyperspectral dataset by interpolation, covering400 to 900 nm. Lw(859) for the clear-water site wasassumed to be equal to zero, and Lw(645) was approximatedas 1.30×Lw(667) according to extensive in situ hyperspec-tral measurement collected from the West Florida shelf

Fig. 5. Remote sensing reflectance in Tampa Bay at band 1 (Rrs(645), sr−1)

from in situ measurements and MODIS estimates derived using 1) multiplescattering atmospheric correction of the cross-calibrated at-sensor radiance(filled circles); and 2) single scattering atmospheric correction of the original(i.e., pre-launch calibration) at-sensor radiance (open circles). The dashedand dotted line represents the 1:1 ratio (Comparison results are listed inTable 3).

Fig. 6. Relationship between MODIS remote sensing reflectance at band 1 (Rrs

(645), sr−1) and in situ turbidity (NTU) in a log–log scale. The power functioninstead of a linear function is used here to take into account of the non-linearinteraction between Rrs(645) and turbidity when turbidity is high.

212 Z. Chen et al. / Remote Sensing of Environment 109 (2007) 207–220

(Cannizzaro et al., in press). Sensitivity analyses indicatedthat results were similar for factors in the range 1.0–1.4,because Lw(645) typically accounts for b5% of Lt(645).A 40% increase in Lw(645) would increase Lt(645) by∼b2%. At the clear-water location the contribution ofLw (645) to Lt(645) is expected to be even smaller. Lee et al.(2005) used a threshold of 1.2 to approximate a Rrs rela-tionship between at 640 nm and at 667 nm, which is close toour value.

5. The simulated hyperspectral data (except the Lw term) wereintegrated over the bandpass of the 250-m bands, modulatedby the relative spectral response (RSR) functions

LxðbandÞ ¼RLx λð Þ � S λð Þ � dλR

S λð Þ � dλ; ð2Þ

where S(λ) is the RSR function of a 250-m band, x can be “r” or“a” from (1), and “band” is 645 or 859 nm, i.e. the centerwavelengths of the 250-m resolution bands.

2.4. Satellite image processing

MODIS L1B direct broadcast data were captured in real-timeby an X-band antenna located at the University of South Florida(USF) in Saint Petersburg, Florida. For regions without a localantenna, historical data can be obtained from the NASA

Table 3Comparison between in situ versus MODIS remote sensing reflectance at645 nm (Rrs(645), sr−1), with the latter derived using 1) single scatteringapproximation and the original at-sensor radiance (i.e., pre-launch calibration)and 2) multiple scattering method and the cross-calibrated at-sensor radiance

Calibration Ratio % difference Slope Intercept r2 RMSE n

Pre-launch 0.93 8 0.95 0.00 0.97 0.0007 15Cross-calibration 1.05 10 1.04 0.00 0.98 0.0007 15

The ratio, % difference, and RMSE are defined in the same way as in Table 2.

Goddard Space Flight Center's Level 1 and atmosphere archiveand distribution system (LAADS) (http://ladsweb.nascom.nasa.gov/data).

Clouds were masked according to the TOA reflectance (sr−1)at 859 nm,

Rt ¼ LtF0 � cosh0

; ð3Þ

with a threshold of 0.018 sr−1. This filters out most clouds andsevere sun glint contaminations.

Atmospheric correction was then carried out using themethod of Hu et al. (2004) summarized as the followingequation:

Rrs 645ð Þ ¼Lt 645ð Þtoz 645ð Þ−

Lt 859ð Þtoz 859ð Þ−Lr 859ð Þ

� �� �� F0 645ð Þ

F0 859ð Þ−Lr 645ð Þt0 645ð Þ � tv 645ð Þ � F0 645ð Þ � cosh0

:

ð4Þ

Both single scattering approximation and multiple scattering“exact” calculations (used for cross-calibrated at-sensorradiances) were used to estimate Lr.

2.5. Satellite — in situ comparison

In coastal and estuarine waters, it is difficult to make directcomparisons between satellite and in situ observations due tothe high spatial and temporal variability in water and at-mosphere properties. To accomplish this, satellite data must becollected within certain hours of in situ measurements (Bailey& Werdell, 2006) to be considered as “concurrent”. Here thenumber of hours was chosen to be 2 for turbidity comparisonbut 4 for Rrs validation to allow for a larger number of matchingobservations. Second, to minimize sensor noise (Hu et al.,2001a), a 3×3 pixel box centered on an in situmeasurement sitewas sampled. Within this box, there had to be at least 4 valid(see below) pixels, and the coefficient of variance of the validpixels had to be b0.4, following the value used by Harding et al.

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(2005). The median value of the valid pixels was then chosen ina comparison to in situ data.

Besides above general quality controls, some more rigorousquality controls were applied during MODIS-in situ comparisonto remove those suspicious MODIS observations. By trialand error, pixels that meet one of the following criteria wereconsidered invalid:

Fig. 7. Long-term cross-year (May 2003 to April 2006) monthly means of turbidity dmask of shallow water (bottom depth b2.8 m). Grey color represents land.

1) Water depth b2.8 m (large bottom reflectance contamina-tion). This value was chosen as such that it yielded a closerelationship between turbidity and Rrs(645) and further in-creasing this depth threshold did not significantly improvethe relationship. Interestingly, this value is very close to thepredicted light penetration depth at 645 nm (1 / Kd(645),where Kd(645) is the diffuse attenuation coefficient of the

erived from MODIS 250-m data. White color within Tampa Bay represents the

Fig. 7 (continued ).

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water column that can be approximated as absorptioncoefficient of water molecules for medium to low turbidand CDOM waters: ∼0.33 m−1) (Lee et al. 2005; Pope &Fry, 1997);

2) θvN50° (large scan edge);3) (Rt(859)−Rr(859))N0.019 sr−1 (large aerosol conditions);4) Normalized sun glint radiance (Lgn)N0.0001 sr

−1, followingthe method of Wang and Bailey (2001) (large sun glintcontamination).

3. Results

3.1. Cross-calibration and validation

Fig. 4 shows the comparison between the ozone-corrected at-sensor radiances in the 250-m bands and those predicted fromthe 1-km bands. Comparison statistics are presented in Table 2.The comparison clearly shows that both MODIS 250-m bandshave excellent linear relationship with the estimates derived

Fig. 8. Long-term cross-year monthly means of turbidity (May 2003 and April2006) derived from MODIS at several stations in Tampa Bay (see Fig. 1 forstation locations). Station 55 was affected by high CDOM and cloud coverduring late summer (i.e. no valid data in August, and possible underestimate ofturbidity in September).

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from the 1-km bands, with median ratios (overall biases) of 0.96and 1.01 for the 645 nm and 859 nm bands, respectively. Themedian absolute percentage differences (MPD, overall uncer-tainties, Bailey & Werdell, 2006) were 3.61% and 1.30%,respectively. These uncertainties correspond to ∼2 counts forthe 645 nm band (Lt(645) is normally N60 counts with TSSranging between 2–15 mg L−1 in Tampa Bay) and b1 count forthe 859-nm band. From the slopes and intercepts of thelinear regressions, the vicarious gains and offsets for the two250-m bands are [1.0904, −0.067] and [1.0303, −0.0124],respectively.

The at-sensor radiances were adjusted using these gains andoffsets and then the rigorous atmospheric correction (usingmultiple scattering for Lr) was applied to process the MODISimagery available for the match-up of Rrs(645). Fig. 5 shows thecomparison of Rrs(645) between MODIS and in situ observa-tions and Table 3 presents the statistics of the comparison. Toreveal what improvement can be achieved using more rigorousmethods (i.e. cross-calibration and multiple scattering), thecomparison of Rrs(645) derived from a single scatteringatmospheric correction and the original at-sensor radiance (i.e.without cross-calibration) is also shown in Fig. 5 and Table 3.The improvement in terms of bias (5% versus −7%) or othermeasures is not significant (paired Student's t-test, pb0.001),indicating that the pre-launched radiometric calibration forMODIS 250-m bands and the single scattering atmosphericcorrection scheme are adequate for this time period. Therefore,the pre-launch calibrations (i.e., original at-sensor radiances)and the single scattering atmospheric correction scheme wereused to derive the entire time series of MODIS Rrs(645) imagerybetween May 2003 and April 2006.

3.2. Turbidity from satellite Rrs

After stringent quality control of the satellite data (seeMaterials and methods Section), a total of 43 satellite and in situmatching pairs were obtained. These matching stations aredistributed across the four bay sub-regions and throughoutfour seasons, thus representing an overall relationship be-tween MODIS and in situ measurements. Fig. 6 showsthat MODIS Rrs(645) ranging from 0.001 to 0.008 sr−1 isclosely correlated with in situ turbidity values from 0.9 to8.0 NTU (turbidity=1203.9×Rrs(645)

1.087, r2 =0.73, n=43).More importantly, this relationship appeared to be stable over2004 and 2005 (Fig. 6), indicating that the regression is time-independent. Indeed, the relationship derived for 2005 alone canaccurately predict 2004 turbidity (median predicted versusin situ ratio of ∼0.98, with median absolute percentage dif-ference/MPD of 10%). Thus the regression relationshipshowed in Fig. 6 and the rigorous data quality control criteria(see Materials and methods Section) were applied to the MODISRrs(645) imagery to obtain the time series of turbidity maps.

3.3. Image series of turbidity

The long-term cross-year monthly mean MODIS maps (May2003 to April 2006) help characterize the spatial and temporal

patterns of turbidity in Tampa Bay (Fig. 7). Turbidity inHillsborough Bay (HB) was consistently higher than in othersub-regions except in August and September. At this time ofyear, however, turbidity in HB may be underestimated due tohigh input of colored dissolved organic matter (CDOM) from“major” rivers (see Fig. 1 and discussion below). In comparison,turbidity in the upper Middle Tampa Bay (MTB, around Station14) was generally lowest as compared to estimates in other baysub-regions throughout the year. Both Old Tampa Bay (OTB)and Low Tampa Bay (LTB) showed marked seasonal variations.LTB, particularly near the bay mouth, showed high turbiditybetween November–March and lower turbidity between May–October, while opposite seasonality was observed in OTB. Abay-wide higher turbidity was observed in April.

This seasonality across the Bay becomes more apparent inextracted time series of turbidity at stations from the sub-regions(Fig. 8). For example, the upper bay (Sta. 55 in HB and Sta. 40in OTB; see Fig. 1 for locations) typically showed higherturbidity than MTB and LTB, particularly in the wet season(from May to October, turbidity N3.0 NTU). MTB (Sta. 14)showed the lowest turbidity and seasonality (typically∼2.0 NTU). LTB showed moderate turbidity but the largestseasonal variations.

The MODIS turbidity images also show that the Bayexperiences significant interannual variations in turbidity. Fig. 9shows the turbidity variations for April of 2004, 2005, and 2006with highest turbidity in April, 2005 and lowest turbidity inApril, 2006. The month of April was chosen because it typicallyis the month with highest wind speed and turbidity, andtherefore turbidity in this month can be used to highlight thepotential interannual variations. The observed interannualvariation is consistent with changes in wind forcing (Fig. 10).Based on the observations of Chen et al. (in revision), thethreshold of daily averaged wind speed, above which sedimentresuspension occurs, is about 6.0 m s−1. Thus there existed 7, 8,and 2 sediment resuspension events for April of 2004, 2005,and 2006, respectively. Indeed, previous studies showedthat wind is the most important factor controlling sediment

Fig. 9. Monthly turbidity images from MODIS 250-m data (top panels) and from interpolated in situ measurements (lower panels) for April of 2004, 2005, and 2006(no in situmeasurement was available for April 2006). Crosses in lower panels represent in situ sample stations with bottom depth N2.8 m, and the values shown are insitu turbidity measurements. The in situ “monthly” map is a graphical composite of single observations taken at different times in different parts of Tampa Bay and isnot a “true”monthly mean. The number of satellite images used to create turbidity composites for April 2004, 2005, and 2006 are 4, 5, and 3, respectively, for most bayareas. The days when images were used are superimposed on Fig. 10 using arrows. The variation in the number of observations across the Bay is shown in Fig. 11. Novalue is shown for April 2006.

216 Z. Chen et al. / Remote Sensing of Environment 109 (2007) 207–220

resuspension in Tampa Bay relative to other factors (e.g., tides)(Chen et al., in revision; Schoellhamer, 1995) and river inputs ofsediments seem to influence on turbidity only in the upperportion of the Bay, but have minimal or negligible impact inlower portion of the Bay (Chen et al., in press). In April, riverinputs of sediments are usually at minimum level, thus windforcing becomes a dominant factor determining turbidityvariations.

Fig. 9 also shows the contrast of turbidity between satelliteobservations and in situ measurements. Over larger scales, themonthly MODIS turbidity estimates were generally consistentwith those determined from in situ observations. For example,both in situ and MODIS data show turbidity in April, 2005 washigher than in April, 2004. However, there were some importantdifferences at finer scales. The differences are partly due toartifacts introduced by interpolation (Barnes, 1994) of the

Fig. 10. Daily averaged wind speed measured at Port of Manatee (27.64°N,82.56°W) in April 2004, 2005, and 2006 (data courtesy of NOAA). The dashedline represents 6.0 m s−1 above which a sediment resuspension is assumed tooccur. Superimposed arrows mark the days when MODIS had validobservations for 2004 (blue, 4 instances), 2005 (red, 5 instances), and 2006(black, 3 instances). (For interpretation of the references to color in this figurelegend, the reader is referred to the web version of this article.)

217Z. Chen et al. / Remote Sensing of Environment 109 (2007) 207–220

in situ data, but in great part due to the mismatch in samplingfrequencies between the two data sets. Unlike non-synoptic andsingle snapshot in situ measurements, MODIS composites werecreated using multiple and synoptic observations (Fig. 10).Similarly, while the single monthly in situ turbidity observationsare frequently within 1 standard deviation of the correspondingsatellite mean estimates (Fig. 11), MODIS mean turbidityshowed less short-term temporal variability but more pro-nounced seasonal and interannual variability than in situobservations. For example, high in situ turbidity values

Fig. 11. Example time series of monthly turbidity estimates derived from in situ and MMay 2003 to December 2005 (no in situ data available for 2006). The number of Mnumber of samples was N1, the standard deviation of the monthly mean is also shownmonth. Circles highlight the relatively high turbidity observed from late 2004 tomeasurements. The y-axis scale for station 55 is different from that in the other pan

(N10.0 NTU) were observed at Sta. 55 in October andDecember 2003, respectively, but the monthly MODIS meansshowed lower turbidity comparable to those seen in othermonths. These high turbidity events sampled by in situ mea-surements mask the broader and more persistent relative in-creases in turbidity observed during the wet season. Thereforethe present in situ sampling protocol is unable to effectivelydetect seasonal changes (Fig. 8). The high in situ turbidityobservations at Sta. 23 in late 2003 and at Sta. 92 in early 2004also obscure the overall increases in turbidity for late 2004 andearly 2005, which are, however, pronounced from MODIS data(Fig. 11). These improvements are clearly related to increasednumber of high-quality MODIS observations. MODIS typicallyhas N4 high-quality observations per month (about once perweek), and N10 observations per month were collectedfrequently between October and March (Fig. 11). These mul-tiple observations help minimize aliasing, thus construct morerealistic monthly means than using the single in situ monthlymeasurements. In addition to this increased temporal resolution,the satellite observations also provide higher detail of the spatialheterogeneity in turbidity patterns and help reduce introducedartifacts during spatially extrapolating a limited number ofin situ measurements using an interpolation method (e.g.,Barnes, 1994).

However, MODIS observations have limitations becausevalid satellite observations require cloudless skies and minimalsun glint interference. These requirements reduce the numberof MODIS observation, thus MODIS did not have valid obser-vations in some months of the wet season (Figs. 8 and 11)

ODIS measurements at selected stations shown in Fig. 1. The period covered isODIS observations during each month is shown on the right-hand side. Whereon the MODIS data. Please note that there is only one in situ observation in eachearly 2005 in Lower Tampa Bay, which are not observed with the in situ

els.

218 Z. Chen et al. / Remote Sensing of Environment 109 (2007) 207–220

or missed some larger sediment resuspension occurrences(Fig. 10).

4. Discussion

4.1. Calibration and algorithm issues

The cross-calibration and multiple scattering atmosphericcorrection schemes produced similar results to those obtainedwith the simpler atmospheric correction of data calibrated withpre-launch calibration coefficients. The agreement indicates thatthe radiometric calibration at 250-m bands is stable throughoutour 3-year study period and the pre-launch calibration is ade-quate for a consistent time series analysis during this period. Inthe long run, however, periodic cross-calibration of the 250-mbands using the 1-km bands is preferred because the latter arecorrected for sensor degradation effects using periodic lunar orsolar calibrations.

Also, at present atmospheric correction over coastal waters isstill a challenge due to non-zero Rrs in the near-infrared andblue-absorbing aerosols. Although atmospheric parameters inthe 250-m bands may be estimated from the 1-km ocean colorbands (e.g., Franz et al., 2006), the latter are known to haveproblems over coastal waters (Hu et al. 2000; Siegel et al.,2000). While the validation result showed good agreementbetween in situ and satellite Rrs using our simple schemes, apotential error source may be the “white aerosol” assumption,which may yield errors of several counts (e.g., Hu et al., 2001b)or in the order of 5–10% in the retrieved Rrs(645) in case ofnon-white aerosol. The black-pixel assumption (Lw(859)=0)may also be problematic in very turbid waters. In such cases, analternative correction may be effected by transferring nearbyaerosol reflectance from clear waters to the “turbid” pixels (e.g.,Hu et al., 2000; Miller &Mckee, 2004). Recently, Wang and Shi(2005) proposed to use longer wavelengths in the short-waveinfrared (SWIR) to circumvent this problem, but the increaseddistance between the wavelength of interest (645 nm) and thereference wavelength (1640 nm) may yield some errors in theaerosol reflectance extrapolation. Future sensors may includeanother SWIR band to facilitate a more accurate atmosphericcorrection.

5. Effects of colored dissolved organic matter (CDOM)

The low turbidity observed in Hillsborough Bay (HB) withMODIS in the wet season (e.g, August and September) is likelyan artifact due to the interference by CDOM. The CDOMabsorption coefficient at 645 nm (acdom(645)) in the wet seasoncan be ∼0.40 m−1 (Chen et al., in press), i.e. greater than theabsorption due to pure water (aw(645)∼0.33 m−1, Pope & Fry,1997). In theory, when bottom contribution is negligible, Rrs isproportional to bb / (bb+a) where bb is the total backscatteringcoefficient (dominated by particles in the red wavelengths),equivalent to turbidity, and a is the total absorption coefficient.Therefore, high CDOM absorption will reduce Rrs(645) andyield low, unrealistic turbidity retrievals. Similar effects havealso been observed elsewhere (e.g., Woodruff et al., 1999). This

problem was, however, limited to HB in the wet season only andis generally negligible in other bay segments and other seasons,since acdom(645) is typically b0.03 m−1 in those regions andperiods (Chen et al., in press). The similarity in large-scalepatterns between MODIS and in situ turbidity measurements(Fig. 11) also supports this conclusion.

6. Applicability to other regions

The validations and derived MODIS images show thatour approach successfully mapped turbidity in Tampa Bay, canthe same approach be applied to other estuaries? The approachwas based on the satellite-derived, validated Rrs, which isproportional to bb (equivalent to turbidity) for medium to lowturbid waters where both bottom and CDOM contributions atthe 645 nm wavelength are negligible. Under these circum-stances, Rrs is proportional to turbidity /aw where aw is aconstant (0.33 m−1). Therefore, the algorithm developed hereshould be site-independent and applicable to other estuariesas long as a same turbidity protocol (e.g., the same turbiditycalibration standards) is adopted, unless in extremely turbidwaters where Rrs becomes a non-linear function of bb so thealgorithm needs to be tuned to account for the non-linearity. Onthe other hand, because the mass-specific absorption andscattering coefficients of sediments can vary substantially froma region to another region (Babin et al, 2003; Chami et al., 2005),estimating sediment concentration from remote sensing doesrequire a regional bio-optical algorithm. Similarly, when appliedto other regions, those data quality control criteria empiricallydeveloped for the Tampa Bay may also need to be modifiedfor other regions.

7. Conclusions

We addressed a range of issues that advance the transition ofresearch towards operational application of MODIS 250-m datafor monitoring the turbidity of estuarine waters, using TampaBay, Florida, as a case study. This included cross-calibration ofthe MODIS 250-m bands with the 1-km ocean color bands and arigorous, multiple scattering atmospheric correction. Thecombination of the two led to no substantial improvementsover using the pre-launch calibration and a simple, single scat-tering atmospheric correction approach. We also found thatturbidity can be estimated with a simple algorithm that isrelatively stable from year to year, and possibly site-indepen-dent under certain circumstances. However, there still remainseveral issues to be addressed, specifically the interference ofhigh concentrations of colored dissolved organic matter(CDOM) on turbidity estimates, and the contamination of thesignal by shallow (b2.8 m) bright bottom reflectance.

The synoptic MODIS 250-m resolution images providedmultiple observations of turbidity of Tampa Bay per month.This time series showed some features that were similar to thoseinferred from the once per month in situ turbidity observations.However, the MODIS 250-m maps of turbidity helpeddifferentiate between short-time variability and seasonal andinterannual changes. MODIS data provided an improved

219Z. Chen et al. / Remote Sensing of Environment 109 (2007) 207–220

representation of the monthly “mean” state of turbidity acrossTampa Bay, as well as of seasonal and inter-annual variability.These results show that turbidity in Hillsborough Bay (HB) wasconsistently higher than in other sub-regions except in Augustand September, when higher concentrations of CDOM seemedto cause underestimates of turbidity. Turbidity in Middle TampaBay was generally lowest across the Bay throughout the year.Both Old Tampa Bay (OTB) and Low Tampa Bay (LTB)showed marked seasonal variations with higher turbidity in LTBduring the dry season and in OTB in wet season respectively.This seasonality is related to wind-driven bottom resuspensionevents in the lower portion of the Bay and river inputs ofsediments in upper portion of the Bay. The Bay also experienceslarge interannual variation, primarily caused by the changes inwind forcing over the region.

Because of the repeated, frequent, and synoptic coverage ofestuarine regions provided by MODIS satellite data across theglobe, and because these data are robust, low cost, and simple touse, satellite data from sensors such MODIS are importantcomplements of traditional in situ water quality surveys. Werecommend incorporating MODIS observations in operationalmonitoring of water quality of moderate size estuaries and ofcoastal regions.

Acknowledgements

This work was funded and logistically supported by theTampa Bay Integrated Science Project of the U.S. GeologicalSurvey (USGS), Coastal and Marine Geology Program.Additional financial support was provided by the USF-USGS Cooperative Graduate Assistantship Program, and bythe National Aeronautics and Space Administration (NASANAG5–10738). Logistical support was provided by theoperational section of the USGS Center for Coastal andWatershed Studies. MODIS data collection and processingwere made possible by the data distribution and softwaredevelopment efforts of the NASA GSFC MODIS Projectand affiliated Science Team members. We also particularlythank the Environmental Protection Commission of Hillsbor-ough County (EPCHC) for their willing to share TampaBay water quality monitoring data. We also thank two anony-mous reviewers for their comments and suggestions, whichsignificantly improve this manuscript. IMaRS contribution# 109.

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