Sediment Myint Nan IJRS

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int. j. remote sensing, 2002, vol. 23, no. 16, 32293249

Quantication of surface suspended sediments along a river dominated

coast with NOAA AVHRR and SeaWiFS measurements: Louisiana,

USA

S. W. MYINT

Department of Geography, University of Oklahoma, Norman, OK 73019, USA;e-mail: [email protected]

and N. D. WALKER

Coastal Studies Institute/Dept of Oceanography and Coastal Sciences,

Louisiana State University, Baton Rouge, LA 70803, USA;e-mail: [email protected]

(Received 22 May 2000; in nal form 8 August 2001 )

Abstract. The ability to quantify suspended sediment concentrations accuratelyover both time and space using satellite data has been a goal of many environ-mental researchers over the past few decades. This study utilizes data acquiredby the NOAA Advanced Very High Resolution Radiometer (AVHRR) and the

Orbview-2 Sea-viewing wide eld-of-view ( SeaWiFS) ocean colour sensor, coupledwith eld measurements to develop statistical models for the estimation of near-surface suspended sediments and suspended solids. Ground truth water sampleswere obtained via helicopter, small boat and automatic water sampler within afew hours of satellite overpasses. The NOAA AVHRR atmospheric correctionwas modied for the high levels of turbidity along the Louisiana coast. Modelswere developed based on the eld measurements and reectance/radiance meas-urements in the visible and near infrared Channels of NOAA-14 and Orbview-2SeaWiFS. The best models for predicting surface suspended sediment concentra-tions were obtained with a NOAA AVHRR Channel 1 (580680 nm) cubic model,Channel 2 (7251100 nm) linear model and SeaWiFS Channel 6 (660680 nm)power model. The suspended sediment models developed using SeaWiFS Channel5 (545565 nm) were inferior, a result that we attribute mainly to the atmosphericcorrection technique, the shallow depth of the water samples and absorptioneVects from non-sediment water constituents.

1. Introduction

Louisiana coastal waters receive inorganic sediments from the Mississippi River,

the largest river in North America and sixth largest worldwide in terms of discharge.

Annual water and sediment discharges average 18 400 m3 s1 and 210106 tons yr1

(Milliman and Meade 1983) The discharge and sediment load of the Mississippi


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S. W. Myint and N. D. Walker3230

Figure 1. Map of the study region showing where the Mississippi River and Atchafalaya

River enter into the northern Gulf of Mexico along the Louisiana coastline.

River bird-foot delta and the Atchafalaya River delta, 200 km to the west ( gure 1) .

The Atchafalaya River (including the Red River ow) carries about 30% of the

volume and 50% of the suspended sediment load of that in the Mississippi River

(Mossa and Roberts 1990). The low salinity, high turbidity plumes of these two

rivers are discharged into very diVerent coastal environmental settings. The main

branch of the Mississippi River ows into the Gulf of Mexico through several passes,

intersecting a headland that projects about 60 km south of the mainland. The

Atchafalaya River water and sediments are discharged through Atchafalaya Bay


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Quantication of suspended sediments from satellite 3231

of NOAA AVHRR and radiance measurements of the Orbview-2 SeaWiFS. Satellite-

acquired reectance measurements can provide valuable information on the distribu-

tion of river water and sediments on the continental shelf as well as on the circulation

processes aVecting the fate of river-borne material. The development of better tech-

niques for quantifying water constituents using satellite reectance measurements

will lead to major improvements in the understanding and modelling of sedimentre-suspension and transport in river-inuenced coastal environments.

Many researchers have used optical remote sensing techniques to study the

spatial extent and temporal changes in suspended sediments of reservoirs, lakes and

rivers and the coastal ocean (e.g. Goldman et al. 1974, Gagliardini et al. 1984, Rouse

and Coleman 1976, Curran and Novo 1988, Stumpf 1988, Stumpf and Pennock

1989, Moeller et al. 1993, Wang et al. 1996, Walker 1996, Walker and Hammack

2000). Riverine and coastal waters are comprised of a diverse array of living, non-

living and once living material that vary over time and space. The main constituents

are suspended inorganic matter, suspended organic matter, phytoplankton , dissolvedorganic matter and detritus (Kondratyev and Filatov 1999). In the coastal zone of

Louisiana, suspended sediments (inorganic matter) are a large component of the

in-water substances with sediment loads in the Mississippi and Atchafalaya River

typically ranging from 100 to 400 mg l1 (Mossa 1990, Walker 1996, Allison et al.

2000). Concentrations of suspended inorganic matter due to river discharge as well

as from the re-suspension of unconsolidated bottom sediments from wind-waves are

relatively high (Huh et al. 1991, Moeller et al. 1993, Huh et al. 1996, Walker 1996,

Allison et al. 2000, Walker and Hammack 2000, Huh et al. 2001). The contribution

of suspended organic matter may also be relatively high as a result of the high levelsof phytoplankton production (Rabalais et al. 1991) as well as from detritus derived

from the extensive marshes. SAIC (1989) showed that a major optical property of

the Louisiana shelf waters is the large amount of yellow substances found in the

estuarine and river discharges.

The determination of suspended sediments or total suspended solids from water

reectance is based on the relationship between the scattering and absorption proper-

ties of water and its constituents (Maul 1985). Most of the scattering is caused by

suspended sediments whereas the absorption is controlled by chlorophyll a and

colored dissolved or particulate matter. The spectral behaviour of sediments isdependant both on the particle size distribution and mineral composition (Maul

1985, Novo et al. 1989). For any given concentration, ne-grained material contains

more particles and thus scatters more than would an equal weight of coarse-grained

material. Sediment type also eVects the relationship between reectance and sus-

pended sediment concentration due to the unique reectance spectra of diVerent

sediments. For example, Novo et al. (1989) found that a sample of white clay was

four times more reective at 600 nm than red silt. Moore (1977) demonstrated that

the peak in reectance shifts to longer wavelengths as the suspended sediment

concentration increases (Curran and Novo 1988). The absorptive in-water compon-ents such as chlorophyll a and CDOM have been shown to lower the reectance in

b i l ( d ) h b i V f


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later applied by Froidefond et al. (1993) and Walker (1996), among others. The

modication we present here enables development of statistical models that estimatesuspended sediment concentrations in the highly turbid waters of the Atchafalaya

River outow region (Walker and Hammack 2000, Walker 2001). The models are

then applied to independent data collected in subsequent years to evaluate the

robustness and accuracy of these algorithms under diVerent environmental condi-tions. Statistical models for estimating suspended sediment concentrations are also

developed for the Orbview-2 SeaWiFS, using the standard NASA atmospheri c

correction formulation (Gordon and Wang, 1994).

The primary objectives of this study were:

(1 ) To develop improved techniques for the quantication of suspended sedi-

ments in turbid coastal environments (Case 2 waters) using visible and near

infrared Channels of NOAA AVHRR and Orbview-2 SeaWiFS.

(2) To apply these models to independent datasets to evaluate the robustness and

accuracy for estimating suspended sediment concentrations under varyingenvironmental conditions.

(3 ) To identify and evaluate the main sources of error in model development.

2. Methodology

The main steps undertaken were the (a) collection of ground truth measurements

coincident with clear-sky satellite overpasses, (b) atmospheric correction of the

satellite data to obtain water reectances, (c) investigation of relationships between

the in-situ measurements and the satellite-derived water reectances, (d) development

of statistical models, and (e) application, testing and assessment of the new modelson independent datasets.

2.1. Satellite data overview

The satellite data used in this study were received and analysed at the Earth

Scan Laboratory, Coastal Studies Institute, Louisiana State University. Reectance

measurements from the NOAA-14 Advanced Very High Resolution Radiometer(AVHRR) were used as they were obtained with the highest sun angle, in mid-

afternoon. The AVHRR sensor has two Channels appropriate for analysing sus-

pended material, Channel 1 in the red visible portion from 580 to 680 nm, andChannel 2 in the near infrared from 725 to 1100 nm (Table 1). SeaWiFS radiance

measurements are obtained in six visible channels and two near infrared channels

from 402 to 885 nm (Table 1) . The SeaWiFS channels oVer increased radiometric

Table 1. NOAA-14 AVHRR and SeaWiFS ocean colour scanner spectral Bands/Channels.

NOAA-14 AVHRR SeaWiFSChannel/band wavelength (nm) Wavelength (nm)

1 580680 4024222 7251100 4334533 3550 3930 480 500


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sensitivity over NOAA AVHRR visible and near infrared channels (Stumpf 1992) as

the sensors primary purpose is to estimate chlorophyll a and ocean productivity

(OReilly et al. 1998). Both sensors have pixel sizes of approximately 1.1 km at nadir.

2.2. Field data

A crucial step in the quantitative use of remote measurements is the collectionof in situ water samples coincident with clear-sky satellite overpasses. It is important

that the measurements be made as close together in time as possible. Helicopters

provide an eYcient platform for collection of water samples for several reasons.

First, one can sample a large area rapidly, enabling the sampling of a range of water

types. Second, one can locate areas of uniform colour indicating some homogeneity

in the water mass. Third, if clouds are in the eld of view, one can locate cloud-free

areas more easily at the elevated altitude. The ground truth data used in this study

were obtained by helicopter, by high speed boat and by automatic water samplers.

For the NOAA AVHRR model development, two helicopter trips were performedover the Atchafalaya region on 26 April 1996 and 21 June 1996, with data collection

lasting about four hours, centred on the satellite overpass time. Additional ground

truth water samples were obtained by automatic water samplers in West Cote

Blanche Bay west of Atchafalaya Bay on 26 March and 27 March 1998 within one

hour of satellite overpass. On 21 March 2001, additional water samples were collected

by a small fast coastal research vessel in Fourleague Bay and on the inner shelf, east

of Atchafalaya Bay. Also on 21 March, water samples were collected by a research

cruise on the inner shelf near the Mississippi bird-foot delta. The water samples

collected on 21 March 2001 were used to test the NOAA AVHRR models. TheSeaWiFS ground truth data were obtained on 26 April 2000, west of the Mississippi

River delta, in Barataria Bay and on the inner shelf seaward of the Bay. As of yet,

additional ground truth data have not been obtained to test the SeaWiFS suspended

sediment model.

The 500 ml water samples, obtained from the top 0.5 m of water, were refrigerated

in the dark and processed within a few days of collection. The concentrations of

total suspended solids (TSS) were determined by ltering through GF/F glass ber

lters following the methods described in USGS (1987). Filters were dryed at 60C

for 12 hours and re-weighed. The inorganic sediment fraction or suspended sediment(SS) was determined by ashing the lters at 500C for 12 h and re-weighing. For the

water samples analysed, the inorganic fraction contributed 80% or more to the total

suspended solid (TSS) weight when TSS exceeded 10 mg l 1 . At lower TSS concentra-

tions, the organic fraction was as much as 50%. Visual examination of the lter

papers before ashing revealed that the organic material was primarly phytoplankton

(Walker and Hammack 2000).

2.3. Atmospheric correction of the data

The atmospheri c correction of NOAA AVHRR Channel 1 and 2 data wereperformed using a technique described by Stumpf and Pennock (1989) and Stumpf

( ) hi h i i i f h i l i di


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Channel 2 reectances are subtracted from Channel 1 reectances. The purpose of

this step is to remove contamination from aerosols and sunglint. However, in doingso one assumes that the water reectance in Channel 2 is zero (Gordon and Morel

1983). Then, a clear-water pixel is identied in the area of interest and the reectance

value of this pixel is subtracted from the entire scene. This last step removes contam-

ination due to Rayleigh and aerosol scattering. This technique has been used success-fully in studies of Mobile Bay and Delaware Bay (Stumpf 1992) and the Mississippi

plume region (Walker 1996). For our study area, the technique required modications

because the Channel 2 measurements in the Atchafalaya Bay region were non-zero

(gure 2(b)), thus invalidating the assumption that water reectance in Channel 2 is

negligible. This problem was solved by modifying the bias correction technique for

use with Channel 1 alone. In short, step 2 was omitted from the above procedure.

This modied technique was successful in retaining the water reectance patterns

within the Atchafalaya and adjacent bays and on the inner shelf. Three new variables

were created in the atmospheric correction process. The variable created by sub-tracting Channel 2 from Channel 1 is called Ch1-Ch2. The atmospherically corrected

Channel 1 and 2 variables are called Ch1W and Ch2W.

Determination of water column reectance Rd for each Channel was based on

the following steps:

Rd#R

d=R

c-R

bias

where:

Rc=

CA( 1)

T0

(1)T1

(1)-

A(2)

T0

(2)T1

(2)

D*(1/r

2)*(1/cosh)

A(l)=albedo for Channel l, where A=G*C+I (from Kidwell 1998), G and I

are calibration coeYcients, and C=count value (01023), T0

(l)=exp.{-(tr(l)/2+

t0

(l))/cosh}, T1

(l)=exp.{-tr(l)/2+t

0(l)}, ( 1/r2 )={1+0.0167 cos j }2 and j=2

(3.1416)(D-3)/365, D is the Julian day, cos h=cosine of solar zenith angle at

scene centre, tr(l)=Rayleigh optical depth for Channel l, t

0(l)=ozone and water


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vapor absorption optical depth for Channel l, Rbias=residual reectance dened as

Rc

for a clear atmosphere over clear water near the area of interest.

The SeaWiFS data were atmospherically corrected using the SeaSpace TerascanTMversion of the NASA Goddard SEADAS code based primarily on Gordon and Wang

(1994). The NASA atmospheric correction software computes water leaving radiance

for channels 1 to 5 and total radiance for 6 to 8. As in the Stumpf and Pennock(1989) technique, the assumption is made that water leaving radiance in the near IR

channels is zero and the water leaving radiance in channels 15 is lowered accord-

ingly. Throughout the paper, NOAA AVHRR data is presented as reectance meas-

urements in percent and SeaWiFS data is presented as radiance measurements with

units of mW cm2 mm 1 sr1 . These are the standard outputs of our TerascanTMimage processing software. The reader is referred to Froidefond et al. (1993) for a

discussion of conversion techniques from NOAA AVHRR reectance to radiance

values.

3. Results

3.1. Model development

Correlation, linear and nonlinear regression techniques were used to quantify the

relationships between the NOAA AVHRR satellite measurements and the in situ

measurements of total suspended solids (TSS) and suspended sediments (SS). The

SeaWiFS sensor had not been launched at the time of these helicopter over-ights.

Linear correlations between TSS, SS and the three variables derived from NOAA

AVHRR reectance data were investigated using the near-simultaneou s measure-

ments of satellite and eld data collected on 26 April 1996 (table 2) and on 21 June1996 (table 3) . The separate correlation analyses provided information on the

strength and reliability of the relationships among the variables. Separate analyses

for 26 and 27 March 1998 were not performed since only a single measurement was

available for each date.

Table 2. NOAA-14 correlation matrix of variables using the 26 April 26 1996 eld data.

TSS SS Ch1Ch2 Ch1W Ch2W

TSS 1SS 0.999 1Ch1Ch2 0.677 0.680 1Ch1W 0.833 0.836 0.967 1Ch2W 0.939 0.941 0.862 0.963 1

Notes: Correlations are all signicant at the 0.01 level (Pearson Correlation), N=19.

Table 3. NOAA-14 correlation matrix of variables using the 21 June 1996 eld data.

TSS SS Ch1Ch2 Ch1W Ch2W


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Signicant linear correlations (at the 0.01 level ) were determined between the

satellite reectances, TSS and SS. The linear correlations were highest between Ch2Wand the TSS/SS on both dates. The correlations were relatively low between the

Ch1Ch2 variable and the TSS/SS measurements. The linear correlations were

highest using the eld measurements of 21 June 1996 when the Atchafalaya River

discharge was relatively high compared with the April 1996 dataset. River dischargeon 21 June was 11 558 m3 s1 and on 26 April it was 5920 m3 s1 .

The ground truth measurements of TSS and SS are shown in gure 3 (see

gure 2 for locations). TSS ranged from 5140 mg l1 and SS ranged from 0 to

120 mg l1, indicating that inorganic sediments dominated the water constituents. A

similar graph was constructed for the satellite reectance variables (gure 4). The

Ch1W reectance measurements were higher and exhibited a larger range in values

than those of Ch2W, a result that would be expected for this shorter wavelength

Channel. By comparing gures 3 and 4, it is evident that the Ch1Ch2 variable does

not follow the curves of TSS and SS above concentrations of about 60 mg l1

. Weattribute this result to the failure of the atmospheric correction scheme at the higher

levels of TSS and SS.

Scatter-plots of satellite reectances (independent variables) and the eld measure-

ments of suspended sediments (dependant variables) demonstrated that the relation-

ships were non-linear using Ch1W reectances and linear using Ch2W reectances

(gure 5). Previous algorithms developed using NOAA AVHRR Ch1 measurements

were also non-linear in form (Stumpf 1992, Walker 1996, Walker and Hammack

2000).

The SPSS software package was used to determine the best predictive modelsfor the estimation of suspended sediments and suspended solids from the reectance

measurements. Both linear and nonlinear regression techniques were investigated.


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Figure 4. Satellite reectances (%) corresponding to the eld measurements of gure 3.Sample points 121 were obtained in June and 2244 were obtained in April.

The nonlinear models that were investigated included logarithmic, inverse, quadratic,

cubic, power, compound, S-curve, logistic, growth and exponential. Initially, models

were chosen based mainly on the coeYcient of determination (R2 ) values and F-

ratios. Table 4 depicts the best model results using Ch1W and Ch2W as independent

variables.

From the statistical results shown in table 4 and graphical data displays, the

most robust models were chosen for the estimation of both suspended sediments

(SS) and total suspended solids (TSS) using Ch1W and Ch2W. In gure 6, the

predicted concentrations of SS computed from the two diVerent reectance models

are shown. Table 5 corresponds to gure 6, listing summary statistics for the chosen

models including R2 , the standard error of the estimate (SEE), root mean square

error (RMS), F-ratio and the tabled F value.

The cubic models ( SS1C, TSS1C) yielded the best estimates of suspended sediment

and suspended solids using Ch1W (table 5, gure 6). The linear models (TSS2L,

SS2L) gave the best results using Ch2W (table 5, gure 6).

The equations for the selected predictive models for TSS and SS are given below:

TSS=-10.26+(14.8288 Ch1W)-(3.1684 (Ch1W)2)+(0.2691 (Ch1W)3)

SS=-10.746+(12.7179 Ch1W)-(2.7548 (Ch1W)2 )+(0.2353 (Ch1W)3 )

TSS=-8.1358+(20.0235 Ch2W)

SS=-9 3438+(17 3593 Ch2W)


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Figure 5. Scatter-plot of NOAA AVHRR (top panel ) Ch1W reectances (%) and ( bottompanel) Ch2W reectances (%) against suspended sediment (SS) concentrations(mg l1): April/June 1996.

more similar to the eld measurements, particularly at the higher levels of suspended


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Table 4. Linear and nonlinear estimations for TSS and SS using Ch1W and Ch2W.

LIN QUA CUB EXP POW

TSS Ch1W R2 0.71 0.92 0.95 0.91 0.83F-ratio 96.97 213.63 218.03 393.54 192.99

SS Ch1W R2 0.70 0.92 0.95 0.83 0.78F-ratio 94.86 217.05 224.38 194.13 140.13

TSS Ch2W R2 0.91 0.91 0.95 0.83 0.87F-ratio 379.52 207.56 224.77 193.92 268.53

SS Ch2W R2 0.90 0.91 0.95 0.71 0.76F-ratio 349.14 189.80 233.48 96.92 129.12

Notes: LIN=Linear model; QUA=Quadratic model; CUB=Cubic model; EXP=Exponential model; and POW=Power model.

sediment levels were highest. The Ch2W model yielded higher levels of SS in these

regions. This was also observed in the actual Ch2 reectance measurements ( gure 2 ).

It is most likely that the diVerence may be attributed to diVerential absorption by

non-sediment water constituents including gelbstoVe, marsh detrital material and

phytoplankto n (Wang et al. 1996). Absorption eVects would have impacted Ch1

more than Ch2, lowering reectance values in Ch1. Additional information on water

constituents will be needed to clarify the observed diVerences between reectance

patterns of the two channels.

3.2. Application and testing of the models

Water samples were obtained on 21 March 2001 in the Atchafalaya Bay region

and in the Mississippi River plume, coincident with a clear sky NOAA AVHRR

image. The Atchafalaya and Mississippi Rivers were in ood with discharges

of 10214m3 s1 and 24 209 m3 s 1 (http://www.mvn.usace.army.mil), respectively.

Twelve surface samples were collected in the Atchafalaya region and six in the

Missisissippi Plume region ( Locations, gures 8 and 9). The satellite reectance

values were extracted from individual pixels closest to the location of the watersamples. The models developed in previous secions were then applied to this

independent set of data.

The estimates of SS and TSS from the Ch1W and Ch2W models were better

than expected. RMS errors were lowest using the Ch2W linear algorithm and ranged

from 5.87.2 mg l1 (table 6). The Ch1W cubic algorithm produced concentration

estimates with RMS values of 10.610.9 mg l1 . Bias values were negative using

Ch1W (-22 to -24 mg l1 ) and positive using Ch2W ( 1321 mg l 1). The relatively

high bias values are attributable to the larger range of SS and TSS encountered on

21 March 2001.The spatial distribution of surface suspended sediments hindcast using the NOAA

A d l h f h A h f l i ( ) d f


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Figure 6. Model estimates of SS concentrations (mg l1 ) using the NOAA AVHRR (top

panel) Ch1W cubic model and (bottom panel) Ch2W linear model, compared witheld measurements of SS. SS1C and SS2L refer to the suspended sediment predictionsusing the Ch1W cubic model and the Ch2W linear model, respectively.


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Table 5. Summary statistics for the non linear cubic regression model using NOAA AVHRRCh1W and the linear regression model using NOAA AVHRR Ch2W data.

Variable Models R2 SEE RMS F-ratio Tabled F0.05

Ch1W TSS 0.95 9.09 7.69 218.03 4.08

SS 0.95 6.94 6.60 224.38 4.08Ch2W TSS 0.91 10.38 10.14 379.52 4.08

SS 0.90 9.38 9.16 349.14 4.08

Figure 7. Model-estimated regional SS concentrations (mg l 1 ) on 26 April 1996 using (leftpanel) the Ch1W cubic model and (right panel) the Ch2W linear model. Contourintervals of 10, 25, 50 and 100 mg l 1 are shown.


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Figure 9. Model-estimated regional SS concentrations (mg l1) for the Mississippi deltaregion on 21 March 2001 using (left panel) the Ch1W cubic model and (right panel)the Ch2W linear model. Contour intervals of 10, 25, 50 and 100 mg l1 are shown.The location of ground truth data collection is depicted using dots.

Table 6. RMS and bias values for the estimation of TSS and SS from NOAA-14 AVHRRCh1W and Ch2W reectance data on 21 March 2001.

RMS Bias

TSS Ch1W 10.94 -22.19SS Ch1W 10.57 -24.02TSS Ch2W 7.18 21.07SS Ch2W 5.81 13.44

Extensive chlorophyll blooms have been previously observed in Vermilion Bay during

eld sampling, however, water samples were only obtained from Fourleague Bay in

March 2001. In the Mississippi delta region (gure 9), the surface sediment distribu-

tion patterns were similar, but somewhat higher using the Ch2W linear algorithm.

The Ch2W reectance data and predictions were observed to contain more noise

and more atmospheric contamination in the 21 March 2001 image, compared with

those of 1996.

The March 2001 data set provided the opportunity to improve the SS algorithms

due to the larger range of sediment concentrations encountered in the eld (maximum

ground truth data was 209 mg l1 in March 2001). Scatter-plots of Ch1W and

Ch2W reectances with SS are shown in gure 10. The summary statistics for the

new SS regression models, developed by combining eld and satellite measurements

from 1996 and 2001, are summarized in table 7. The cubic model using Ch1W yielded

a coeYcient of determination (R2 ) of 0.9. The linear model using Ch2W yielded a


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Figure 10. Scatter-plot of NOAA AVHRR ( top panel ) Ch1W reectances (%) and ( bottom

panel) Ch2W reectances (%) against SS concentrations (mg l 1 ) using all eldmeasurements: 19962001.


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Table 7. Summary statistics of suspended sediment regression models using all eld datawith NOAA AVHRR Ch1W and Ch2W variables.

LIN QUA CUB EXP POW

Ch1W R2 0.64 0.88 0.90 0.81 0.73

F-ratio 104.07 206.39 174.15 242.18 154.56Ch2W R2 0.93 0.94 0.94 0.66 0.73

F-ratio 816.15 436.11 285.79 110.22 156.55

Notes: LIN=Linear model; QUA=Quadratic model; CUB=Cubic model; EXP=Exponential model; and POW=Power model.

to the interior of the bay (gure 11). Two additional samples were obtained to the

east of the line within the central bay region. The samples in the vicinity of the tidal

pass were excluded from the analysis as they were too close to land to be valid with

the nominal 1.1 km pixels of SeaWiFS. The correlation matrix obtained for theradiance values, SS and TSS is shown in table 8. The concentration of suspended

sediments (SS) and total suspended solids (TSS) were found to be highly correlated

(0.998 ). Channel 6 ( 670 nm) was more highly correlated with SS and TSS than was

Channel 5 (555 nm). Correlation coeYcients between Ch 6 radiances, TSS and SS

were 0.85 (table 8). Correlation coeYcients between the Ch 5 and TSS/SS were only0.460.47. The relatively high correlations with Channel 6 were not unexpected as


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Table 8. Correlation matrix showing the relationship between TSS, SS, SeaWiFS 555 nmand SeaWiFS 670 nm radiances on 26 April 2000.

TSS SS SeaWiFS-555 SeaWiFS-670

TSS 1

SS 0.998** 1SeaWiFS-555 0.456* 0.468* 1SeaWiFS-670 0.845** 0.853** 0.552* 1

Notes: **Correlation is signicant at the 0.01 level (Pearson Correlation), N=20.*Correlation is signicant at the 0.05 level (Pearson Correlation), N=20.

it is most similar to NOAA AVHRR Ch1 measurements. The relatively poor perform-

ance of Channel 5 may be partially attributed to over-correction for the atmosphere

by the standard NASA algorithm, although SS concentrations were not as high as

in the Atchafalaya region (maximum of 48 mg l1 ). Another source of error may

have been the use of water samples from the upper 0.5 m of the water column, if the

Ch5 radiances represented an integration over a deeper water column.

Statistical models were then developed using the radiance values of the 555 nm

and 670 nm Channels (table 9 ). For estimation of SS, the power model out-performed

other models. Only the model using the 670 nm Channel is considered a reasonable

approximation of the surface suspended sediment levels (gure 12) . The model-

estimates of suspended sediment concentrations are compared with actual values in

gure 13. The RMS error for this model was 8.24 mg l 1 .

4. Discussion and conclusions

The technique of ground truthing satellite data via helicopter and high speed

boat proved very eVective for obtaining water samples close in time to the satellite

reectance measurements. This is of great importance in coastal regions where

reectance patterns change rapidly as a result of water movements due to tidal and

wind-driven currents, mixing and re-suspension processes.

The near simultaneous acquisition of eld measurements and regional synoptic

satellite data enabled the development and testing of statistical models for estimating

near-surface total suspended solids (TSS) and suspended sediments (SS) in surface

Table 9. Summary statistics of the selected regression models using SeaWiFS 555 and 670 nmChannel data.

LIN QUA CUB EXP POW

TSS 555 nm R2 0.21 0.56 0.56 0.37 0.52F-ratio 4.73 10.73 6.91 10.49 19.42

SS 555 nm R2

0.22 0.56 0.57 0.42 0.58F-ratio 5.04 10.98 7.14 13.01 24.90

TSS 670 R2 0 72 0 72 0 72 0 79 0 83


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Figure 12. Scatter-plot of satellite-derived normalized radiances (670 nm) and SSconcentrations (mg l1 ) along transect line shown in gure 11.

Figure 13. Model estimates of SS concentrations (mg l1 ) using the SeaWiFS 670 nm powermodel compared with eld measurements of SS.


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for estimating suspended sediments that was applicable to imagery acquired several

years later.

The NOAA AVHRR Ch1W non-linear model and the Ch2W linear model yielded

R2 values from 0.90.95 for SS and TSS. Subsequent testing with an independent

dataset yielded acceptable RMS values (


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