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Remote Sensing of Environment 130 (2013) 254–265

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Remote Sensing of Environment

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Airborne hyperspectral remote sensing to assess spatial distribution of water qualitycharacteristics in large rivers: The Mississippi River and its tributaries in Minnesota

Leif G. Olmanson a,⁎, Patrick L. Brezonik b, Marvin E. Bauer a

a Department of Forest Resources, University of Minnesota, St. Paul, MN 55108-6112, United Statesb Department of Civil Engineering, University of Minnesota, St. Paul, MN 55108-6112, United States

⁎ Corresponding author at: Department of Forest Reso1530 Cleveland Avenue North, St. Paul, MN 55108-611405 8081.

E-mail address: [email protected] (L.G. Olmanson

0034-4257/$ – see front matter © 2012 Elsevier Inc. Allhttp://dx.doi.org/10.1016/j.rse.2012.11.023

a b s t r a c t
a r t i c l e i n f o
Article history:Received 27 March 2012Received in revised form 23 November 2012Accepted 27 November 2012Available online 30 December 2012

Keywords:AirborneHyperspectral imageryImaging spectroscopyMississippi RiverRemote sensingRiverChlorophyllTurbidityMineral suspended sedimentWater qualityInherent optical propertiesCDOM

Aircraft-mounted hyperspectral spectrometers were used to collect imagery with high spatial and spectralresolution for use in measuring optically active water quality characteristics of major rivers of Minnesota.Ground-based sampling undertaken concurrent with image acquisition provided calibration data for chloro-phyll, suspended solids, turbidity and other measures of water clarity. Our approach identified the spectralcharacteristics that distinguish waters dominated by several inherent optical properties (IOPs), and we usedthose characteristics to develop models to map water quality characteristics in optically complex waters.For phytoplankton related variables (volatile suspended solids (VSS) and chlorophyll a (chl a)), the ratios ofthe scattering peak at the red edge (~700 nm) with the reflectance troughs caused by chlorophyll absorptionat ~670 nm and other plant pigment absorption peaks at 592 and 620 nm all were strong predictors of chl aand VSS (r2 values of 0.73–0.94). The scattering peak at ~700 nm was a strong predictor of variables related towater clarity (total suspended solids (TSS), turbidity and turbidity tube (T-tube)) (r2 values of 0.77–0.93). Formineral-based variables (nonvolatile suspended solids (NVSS) and the ratio NVSS:TSS), combinations of theTSS and chl a relationships described above were strong predictors (r2 values of 0.73–0.97) and the most robustbecause thismodel corrects for the scattering of phytoplankton at ~700 nm. Application of themethods to quan-tify spatial variations in water quality for stretches of the Mississippi River and its tributaries indicate thathyperspectral imagery can be used to distinguish andmap key variables under complex IOP conditions, particu-larly to separate and map inorganic suspended sediments independently of chlorophyll levels.

© 2012 Elsevier Inc. All rights reserved.

1. Introduction

Minnesota has 93,000 miles (150,000 km) of rivers and streams.They are highly important as transportation corridors and recreation-al resources that contribute significantly to the state's economy andtourism. Of the 17% of the state's river and stream miles assessedfor the 2010 Impaired Waters List, 40% were found to be impaired(Minnesota Pollution Control Agency, 2011). We explored the use ofaircraft-mounted remote sensing systems as a cost-effective way togather information to measure optically active water quality proper-ties of rivers relevant to the issue of river water impairment. Thispaper describes a general approach, as well as specific predictive rela-tionships, that can be used for such measurements.

We have had success previously using multispectral radiance infor-mation from Landsat imagery (e.g., Olmanson et al., 2008) to measurelakewater clarity. More recently, we showed that other satellite sensors(MERIS and MODIS) can provide accurate estimates of chlorophyll

urces, University of Minnesota,2, United States. Tel.: +1 651

).

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levels in large and moderately sized lakes (Olmanson et al., 2011). Weexpect that similar relationships exist in flowing waters, but comparedwith lakes, rivers and streams pose a more challenging set of problemsin applying remote sensing techniques to assess water quality. First,conditions in rivers and streams are temporally more dynamic andoften spatially more heterogeneous than those in lakes. Second, smallrivers and streams may be so shallow that light penetrates to the bot-tom, such that reflectance from thewater is a function of bottom condi-tions in addition to that of the water itself. Third, the spatial resolutionof most satellite sensors, including Landsat, is too coarse for small riversand streams. Finally, to measure water quality conditions other thanclarity, a better set of spectral bands is needed than what Landsat pro-vides. Although the MERIS and MODIS satellite sensors provide suchbands, their coarse spatial resolution makes them suitable only forvery large rivers or impoundments of large rivers.

Hyperspectral sensors, mounted in small aircraft can collect land-scape images with high spatial and spectral resolution. Such airbornesystems have been available for over two decades and have been usedfor mineralogical exploration (e.g., Abrams et al., 1977; Clark et al.,1990; Goetz & Srivastava, 1985), aswell as to determine the type, healthand condition of vegetation for environmental quality, forestry andagriculture purposes (e.g. Carroll et al., 2008; Gitelson & Merzlyak,

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255L.G. Olmanson et al. / Remote Sensing of Environment 130 (2013) 254–265

1996; Gitelson et al., 2002; Haboudane et al., 2002, 2004; Shah et al.,2002, 2004;Wessman et al., 1988). Several publications have describedthe application of such systems to measure water quality conditions inlakes (e.g., Ammenberg et al., 2002; Chipman et al., 2009; Hakvoortet al., 2002; Hoogenboom et al., 1998 and Moses et al., 2011), but onlya few publications (e.g., Senay et al., 2001 and Shafique et al., 2003)have focused on potentially more complex river systems. Phytoplankton,mineral suspended sediment, humic color or combinations of theseconstituents may dominate the optical properties of rivers dependingon watershed and flow conditions. Strong relationships were found inprevious studies between chlorophyll concentrations and turbidity orsuspended solids concentrations and reflectance data, but noneattempted to separate competing inherent optical properties (IOPs).

In this paper we describe the use of aircraft-mounted hyperspectralsensors on threemajor rivers inMinnesota (Mississippi,Minnesota, andSt. Croix) and some associated floodplain lakes with distinct and com-peting IOPs. Sufficient informationwas acquired in three separate aerialdata acquisitions to evaluate the usefulness of aircraft-mounted remotesensing as a supplement/complement to conventional ground-basedriver monitoring programs. For calibration purposes, water sampleswere collected concurrently with the remote sensing data acquisition,and in order to obtain a range of conditions for calibrations, we focusedour measurements around the confluences of river systems that havedifferent water quality characteristics. Imagery was collected duringAugust of 2004, 2005 and 2007, and each period represented differentflow and water quality regimes.

The overall goal was to develop reliable techniques for synopticmeasurements of key indicators of river water quality that can beused to complement data obtained by conventional ground-basedmethods. Our specific objectives were to: (1) identify spectral charac-teristics that distinguish waters dominated by different IOPs indicativeof important water quality characteristics; (2) develop predictive rela-tionships for these characteristics based on their individual spectral(reflectance) characteristics (or combinations thereof); (3) determinewhether and how accurately chlorophyll a, total suspended solids(TSS), and nonvolatile suspended solids (NVSS) can be mapped inde-pendently when competing IOPs are dominant and whether theproportion (NVSS/TSS) can be identified and mapped quantifiably;(4) develop an overall approach to measure and map water qualityvariables using aircraft-mounted spectrometers; and (5) evaluate theaccuracy and usefulness of aircraft-based hyperspectral remote sensingfor water quality studies of rivers. The work was conducted usingimages collected under clear (cloudless) conditions and processed bymethods similar to those developed for regional assessments of waterclarity and chlorophyll a from satellite imagery (e.g., Olmanson et al.,2008, 2011).

2. Background information: major rivers in Twin CitiesMetropolitan Area

The Mississippi River, which originates in Lake Itasca in northernMinnesota, is a moderate sized river (average flow of 11,700 cfs(3163 m3 s−1), Table 1) by river mile 871, where it reaches theMinneapolis–St. Paul (Twin Cities) Metropolitan Area (TCMA). Thedrainage basin of the Mississippi River above the TCMA (~49,000 km2

in area) includes much of central and north central Minnesota.

Table 1River discharge for Minnesota, Mississippi and St. Croix Rivers in the TCMA.

River Site Meandischarge (cfs) 26,604

Augudisch

Minnesota Jordan 8810–33% 3190Mississippi Anoka 11,700–44% 2770St. Croix Stillwater 6094–23% 3170

Land cover in the basin is mixed (37% forest and 21% cropland).In the TCMA (7700 km2) the land cover is 40% agricultural rowcrops, 30% urban and 13% forest. The Minnesota and St. Croix Riversare large tributaries that flow into the Mississippi in the TCMA andincrease the total river flow on average by more than a factor of two(Table 1).

The drainage basin of the Minnesota River covers 42,000 km2, in-cluding large portions of south-central and southwestern Minnesota,as well as small portions of Iowa and South Dakota. The basin has rel-atively flat to gently rolling hills with highly productive soils and isprimarily (75%) row-crop agricultural land. The river carries a highburden of suspended solids from stream bank and soil erosion thatis attributed to a combination of artificial drainage and steep slopesfrom deep incising of the Minnesota River Valley by Glacial RiverWarren (Belmont et al., 2011; Gran et al., 2009; Thorleifson, 1996).

The drainage basin of the St. Croix River covers 20,000 km2 ineastern Minnesota and northwestern Wisconsin. The basin is theleast developed of the three considered here and consists of 50% for-est, 15% grassland or pasture, and 15% agricultural row crops. Amongthe three rivers, the St. Croix has the lowest nutrient and suspendedsolids concentrations but has higher levels of natural coloreddissolved organic matter (CDOM).

Aside from their commercial and recreational importance, the riv-ers play important roles in transporting pollutants downstream. Mostof the treated wastewater from the TCMA enters the Mississippi Riverdownstream of a major treatment plant in St. Paul; among other con-sequences, this leads to high nutrient (nitrogen and phosphorus)concentrations that promote summertime algal blooms in the riverand downstream Lake Pepin (Engstrom et al., 2009).

The water chemistry/quality of these rivers is more complex thanthat of most lakes. Dominance by both phytoplankton, usually mea-sured in terms of chlorophyll a concentrations (sometimes called greenphase) and inorganic sediment, sometimes called brown phase andmea-sured in terms of Secchi depth, turbidity, or suspended solids concentra-tions,may occur depending onflowand seasonal dynamics. Typically, theMississippi River accounts for 40–45% of flow and 20% of incoming TSSload; theMinnesota River accounts for 25–30% of flow but 75% of incom-ing TSS; and the St. Croix River accounts for 25–30% offlowbut only 5% ofthe incoming TSS load (Metropolitan Council, 2004). Water quality con-ditions in the three rivers are highly diverse, and conditions in theMissis-sippi River downstream of the confluences with the two tributariesdepend on the relative flows of the rivers and the degree towhichmixingof the waters has occurred.

Water quality impairment issues occur in the Mississippi River as aconsequence of urban and agricultural runoff and inputs of treated mu-nicipal wastewater as the river flows through the TCMA.Major concernsexist about the effects of suspended solids and nutrient loadings on LakePepin, a natural lake in the main channel of the river ~40miles (70 km)downstream of the TCMA (Belmont et al., 2011; Engstrom et al., 2009;Mulla & Sekely, 2009). At a larger scale, there also is concern about con-tributions from Minnesota to the total nutrient load of the MississippiRiver to the Gulf of Mexico (Goolsby et al., 2001; Rabalais et al., 2002a,b; Turner & Rabalais, 1994). Under normal conditions Minnesota con-tributes ~3% of the total nitrogen flux and 2% of the phosphorus flux de-livered to the Gulf of Mexico by the Mississippi River (Alexander et al.,2008).

st 19, 2004arge (cfs) 9130

August 15, 2005discharge (cfs) 7370

August 30, 2007discharge (cfs) 8070

–35% 1840–25% 4160–52%–30% 4100–56% 1930–24%–35% 1430–19% 1980–24%

256 L.G. Olmanson et al. / Remote Sensing of Environment 130 (2013) 254–265

3. Methods

3.1. Imagery

Imagery was collected by the Center for Advanced Land Manage-ment Information Technologies (CALMIT, University of Nebraska,Lincoln) using AISA (Airborne Imaging Spectrometer for Application)hyperspectral imaging systems in a Piper Saratoga airplane using pro-cedures described by Chipman et al. (2009). The CALMIT systemevolved over the course of the study, as new equipment, softwareand techniques became available. Having different image characteris-tics complicated our analysis. AISA sensors collect spectral-radiancedata and compile imagery in the visible and near infrared portionsof the electromagnetic spectrum. They are “push-broom” systemswith a global positioning and inertial navigation system that simulta-neously record the aircraft position and attitude, along with upwell-ing radiance and downwelling solar irradiance. These raw dataproducts are used to create geospatially and radiometrically correctedimagery.

During the first acquisition period (August 19, 2004), an AISA-Classic sensor collected hyperspectral imagery in 16 (for 1 and 3 mresolution imagery) or 24 (for 2 m imagery) narrow bands (~5 nmbandwidth) selected to measure key water quality characteristics oversix segments (Fig. 1) that covered ~65 km of the Mississippi River andselected tributaries. A collaborative sampling effort involving crewsfrom the Minnesota Pollution Control Agency, Metropolitan Council,Minnesota Department of Natural Resources, Minnesota Departmentof Agriculture and the University of Minnesota collected water samplesand took in situ measurements including GPS coordinates at ~39 loca-tions along the river concurrently with the imagery acquisition.

During the second acquisition (August 15, 2005), an AISA-Eaglehyperspectral imager collected hyperspectral data at a spatial resolution

Fig. 1. Hyperspectral imagery flight

of 2 m in 86 contiguous bands: 2.5 nm bandwidth from 435 to 730 nm,20 nmbandwidth from730 to 900 nm, and 30 nm from900 to 960 nm.The data were recorded for 10 segments over a 60 km stretch of theMississippi River from Spring Lake (south of St. Paul) to Lake Pepin(Fig. 1). Sampling crews from the Minnesota Pollution Control Agencyand Metropolitan Council concurrently collected water samples at 26locations along the river.

Image acquisitions on August 30, 2007 also used an AISA-Eaglehyperspectral imager to collect hyperspectral data at 2 m spatialresolution in 97 contiguous bands (2.5 nm bandwidth from 435 to730 nm and 10 nm bandwidth from 730 to 950 nm) in 12 segmentsover a 100 km stretch of the Mississippi River from the Rum Rivernorthwest of the TCMA to downstream of the St. Croix River (Fig. 1).Sampling crews from the Minnesota Pollution Control Agency andthe Metropolitan Council simultaneously collected water samplesfrom 26 sites.

3.2. Water quality measurements

Because of the dynamic nature of rivers, sampling crews were onthe river to collect water samples and take in situ measurements asclose as possible to the time the aircraft collected imagery. All sam-ples were collected within a few hours of the remote sensing data ac-quisition and away from mixing zones subject to rapid temporal andspatial changes. Field measurements at each site included water clar-ity by turbidity tube (Myre & Shaw, 2006) and, if possible, Secchi disk(SD); high flow rates and high turbidity levels limit the feasibility ofSD measurements in some rivers. Samples were collected for turbidi-ty, total suspended solids (TSS), volatile suspended solids (VSS), chlo-rophyll a (chl a) and phaeophytin (Pheo) and were analyzed bylaboratories of the Metropolitan Council using standard methods:

lines for 2004, 2005 and 2007.


APHA-10200h (Greenberg et al., 1992), ASTM D3731-87 (ASTM, 1994),and USEPA-445 (U.S. EPA, 1992).

3.3. Image processing and classification

Image classification procedures were based on the methods ofOlmanson et al. (2001) and Kloiber et al. (2002), modified for thehyperspectral nature of the imagery and advances in software andcomputer hardware that enabled simpler or improved procedures.We used Leica Geosystems ERDAS Imagine and Esri ArcGIS forimage processing. Acquiring a representative sample for each sam-pling site from the image was a primary objective.

Each flight line image was corrected geospatially and radiometrical-ly to “at platform reflectance” using the raw data from the AISA system,but additional image preprocessing was necessary. Although the auto-mated geometric correction process yielded close results, additionalgeometric correctionwas performed using ~20well-distributed groundcontrol points (GCPs) from available orthorectified aerial photographywith a positional accuracy (RMSE) on the order of ±1 pixel (1–3 m).Nearest neighbor resampling was used to preserve the original pixelvalues.

Atmospheric correction was conducted on the images acquiredin 2007 using the FLAASH module in ENVI and standard atmos-pheric variables for mid-latitude summer and water retrieval usingthe band at 820 nm. Radiometric normalization, necessary for only afew nonconforming images which were identified by visuallyinspecting the imagery after mosaicking the images, was accomplishedusing pixel spectral data selected from the overlap area of both(“conforming” and “nonconforming”) images representing differentland cover and water areas. Regression analyses were performedwith the base (conforming) image as the dependent variable andthe image needing correction (nonconforming) as the independentvariable. The resulting models were used to normalize thenonconforming image to the base image.

Once image preprocessing was complete, a “water-only” imagewas produced by performing an unsupervised classification methodbased on ISODATA clustering. Because water has spectral characteris-tics distinct from terrestrial features, water pixels were groupedinto one or a few distinct classes that could be easily identified.We masked out terrestrial features to create a water-only image,performed an unsupervised classification on this image, and generat-ed spectral signatures of each class. We used these signatures, alongwith the locations where the pixels occurred, to differentiate classescontaining open water and shallow water (where bottom sedimentsand/or macrophytes affected spectral response). These areas tend tohave high spatial variability compared to open-water portions of therivers. Based on this analysis, we removed the affected pixels.

Next, the spectral-radiometric data from the “open water” imageswere extracted from the areas immediately around the sampling lo-cations to develop relationships with measured water quality data.For this purpose, we used the imagery and the GPS locations of eachsample to delineate a polygon in a spectrally-radiometrically similararea (identified visually using different band combinations stretchedto range of water only pixels) around each sample location. Kloiberet al. (2002) found that using at least nine pixels to extract spectralradiometric data from Landsat 30 m imagery improved correlationstrength with field data. The polygons we used had a minimum of200 pixels in narrow portions of the rivers or areas with high hetero-geneity and up to 1700 pixels in wider, more homogeneous areas.The signature editor in ERDAS Imagine was used to extract spectralradiometric data from the polygons. Mean band values from thepolygons were imported into Microsoft Excel, ratios and differences forband combinations were calculated, and data from the ground-basedmeasurements and samples were linked to the appropriate imagerysamples.

To develop models to predict water quality variables from the im-agery products, we performed step-wise forward regressions of theband datasets using the JMP 9 64-bit version statistical software pack-age of SAS Institute, Inc. Because different band sets had been usedin each acquisition period, a subset of 18 well-positioned bands(16 bands for 2004 1 and 3 m imagery) that occur at the same oralmost the same central wavelength for each dataset was used. Thebasis for selection of the bands is described in the Results anddiscussion section. Raw and log-transformed values of each waterquality variable were the dependent variables and single bands,band ratios and band differences were the independent variables.From these results, we used the one or two independent variablesthat were consistent for all three datasets and that contributedmost to the regression fit and applied them in a second multiple re-gression, as described by Olmanson et al. (2011). The models devel-oped for each variable and each image product were applied to eachpixel to create pixel-level water quality maps.

4. Results and discussion

4.1. Spectral characteristics of river water

Hyperspectral imagery can provide sufficient information to recon-struct the visible–near IR reflectance spectra of water bodies that inturn can be used to identify the spectral characteristics (e.g., reflectancepeaks and troughs) that distinguish waters dominated by the inherentoptical properties (IOPs) indicative of different water quality variables.Fig. 2 shows the characteristic reflectance spectra extracted from theAugust 30, 2007 hyperspectral imagery. Spectra from four locationsrepresent phytoplankton dominated waters: (1) hypereutrophic LakeConley (chl a=200 μg/L; TSS=48 mg/L with 42% NVSS), which is aMississippi River backwater lake upstream of the confluence with theSt. Croix River; (2) hypereutrophic Pig's Eye Lake (chl a=230 μg/L;TSS=51 mg/L with 58% NVSS), a backwater lake just downstreamfrom the Metropolitan Wastewater Treatment Plant (Metro); (3) theMississippi River upstream of the Rum River at river mile 872 (chla=78 μg/L; TSS=13 mg/L with ~60% NVSS); and (4) the MississippiRiver upstream of the Minnesota River at river mile 848 (chl a=45 μg/L; TSS=15 mg/L with ~40% NVSS). Flow at the last locationwas unusually low at the time of sampling because of flow restrictionsat St. Anthony Falls locks and dams (river mile 853.9) in Minneapolisnecessitated by a major bridge collapse downstream of the lock anddam (http://en.wikipedia.org/wiki/I-35W_Mississippi_River_bridge).The reflectance spectra of these waters show characteristic absorptionby chlorophyll and other plant pigments in the blue (400–500 nm)and red (600–700 nm) wavelengths, which results in low reflectance(i.e., troughs) at ~430 nm (not shown because of noise in that region)and ~670 nm and a phytoplankton scattering peak at ~700 nmresulting from a combination of scattering and decreasing absorp-tion by chlorophyll and increasing absorption by water (Vasilkov& Kopelevich, 1982).

The Minnesota River and the mixed waters of the Minnesota andMississippi Rivers at St. Paul (river mile 839) represent sediment-dominated waters with TSS concentrations of 61 and 45 mg/L (87 and82% NVSS), respectively. Although chl a levels were 40 and 55 μg/L,respectively, they were not high enough to overcome the dominanceby suspended sediment, and their reflectance spectra lack the absorp-tion characteristics (troughs at ~430 and ~670 nm) of chlorophyll andhave relatively high reflectances in the green and red region especiallyfrom 560 to 700 nm.

Cenaiko Lake, a small, mesotrophic water body east of the Missis-sippi River at river mile 865, represents relatively clear water. It hadlow concentrations of suspended matter (TSS=3 mg/L; NVSS=2 mg/L) and low chl a (8 μg/L). Although quantitative information isnot available on CDOM, the lake water was not visibly stained brown.Its reflectance spectrum is characterized by a reflectance peak near

http://en.wikipedia.org/wiki/I-35W_Mississippi_River_bridge

Fig. 2. Characteristic reflectance spectra extracted from 2007 hyperspectral imagery with 18 selected spectral bands indicated.


570 nm and declining reflectance at higher wavelengths. The lack ofthe characteristic peaks and troughs caused by chl a and theplankton/TSS scattering peak in the red and NIR range is notable. Fi-nally, the St. Croix River with low-to-moderate chl a (22 μg/L) and lowconcentrations of suspended matter (TSS=2 mg/L; NVSS=1 mg/L)represents a water body dominated by humic color (CDOM-richwater; color=30 chloroplatinate units, CPU). It had low reflectanceacross the entire spectrum, with characteristic absorption by CDOM inthe lower wavelengths; very small reflectance peaks are apparent inthe spectrum at 570 and 700 nm.

The distinctive features of reflectance spectra for waters dominat-ed by different optically-active constituents (chl a, inorganic sedi-ment, CDOM) provide insights regarding wavelength bands likely tobe useful for predictive models of these characteristics. We were un-able to find similar reflectance spectra in the literature for lakes withIOPs dominated by both phytoplankton and inorganic suspendedsolids, but Han (1997) conducted tank experiments with varying ad-ditions of soil sediment to clear and algae-laden waters and foundhyperspectra similar to those shown in Fig. 2. In general, addition ofsoil sediment to water containing high concentrations of algae causedsmaller increases in reflectance between 400 and 700 nm than simi-lar additions to water low in algae, and this was explained by the ab-sorption effects of chlorophyll.

Successful models using spectral-radiometric data to estimate waterquality variables should usewavelengths that identify key spectral char-acteristics without interference from competing optical features. Forchlorophyll, this means that algorithms commonly used for ocean CaseI waters (reflectance in the blue and green reflectance regions) willnot work well for inland (Case II) waters because these waters areinfluenced by suspended solids and CDOM derived from their water-sheds making them optically more complex (Morel & Prieur, 1977).CDOM and inorganic sediment have overlapping absorption featureswith chl a in the blue region. Successful chlorophyll models for thesewaters likely will use absorption characteristics from the red wave-lengths—the reflectance trough at ~670 nm and scattering by phyto-plankton at the red edge ~700 nm, where absorption by CDOM andsuspended solids is minimal (Gitelson, 1992; Moses et al., 2011). Theratio ~700 nmwith ~670 nmhas been shown to have a strong relation-ship with chl a in a variety of waters (including lakes, estuaries and riv-ers) over a wide range of concentrations (0.1 to 350 μg/L; Matthews,2011) and has been reported several times (Duan et al., 2007; Gitelsonet al., 1993; Gons, 1999; Menken et al., 2006; Mittenzwey et al., 1992;Moses et al., 2009).

Successful models for turbidity and TSS in optically complex waters(where both phytoplankton and inorganic sedimentmay dominate IOPfeatures) would avoid the absorption characteristics of chlorophyll inthe red and CDOM in the blue region and use the scattering peak at~700 or band combinations in the NIR and green regions. The scatteringpeak at ~700 was found to be strongly correlated with TSS by Härmäet al. (2001), Kallio et al. (2001) and Koponen et al. (2007) and for tur-bidity by Senay et al. (2001), and the difference (710–740) was founduseful by Shafique et al. (2003). The scattering peak at ~700 by itselfwas also found to work well for NVSS by Ammenberg et al. (2002).

CDOMmodels usually do not use the strong absorption of CDOM inbluewavelengths because chlorophyll and suspendedmatter also affectspectral reflectance at these wavelengths. Instead, CDOM (or humiccolor) usually is estimated using ratios of reflectance in the green andred regions (typically at wavelengths>550 nm), where CDOM absorp-tion of light still occurs but is much less intense. The physical basis forthese empirical relationships is not well understood.

4.2. Model development

Having different band sets and radiometric characteristics andcorrections for each set of image acquisitions complicated our analy-sis. Therefore, each acquisition dataset was analyzed individually, anda set of 18 well-positioned bands (16 bands for 2004 3-m imagery)that occur at the same or almost the same central wavelength foreach dataset was used. Because of poor quality 2004 imagery outsideof the TCMA (tree shadows and bottom effects are apparent in theCrow River images and differences in radiometric correction (dueto variable cloud cover) are apparent in the Blue Earth images),only the data from the TCMA for this year were included in the anal-ysis. The bands selected for analysis were derived based on the spec-tral characteristics of the data (Fig. 2) and bands used successfully byothers from the literature, as reviewed by Matthews (2011).

Several models developed from the images showed strong relation-ships between log transformed andnon-transformedwater quality data(T-tube, turbidity, TSS, VSS, NVSS and chl a) and reflectance values fromthe hyperspectral data; see Table 2 for a statistical summary for themodels, including ranges of the variables, bands in the best-fit relation-ships (consistent for all three datasets), r2, RMSE, and number of datapoints. Color data were collected during the 2004 acquisition, but therange in the data was too small (20–40 CPU) to allow development ofpredictive relationships. The non-transformed water quality data werenot normally distributed for some variables, especially chl a, but the

image of Fig.�2

Table 2Calibration statistics for water quality models.

LN of variable Bands 2004 2005 2007

N Range r2 RMSEa N Range r2 RMSEa N Range r2 RMSEa

T-tube (cm) 705 24 8–120 0.91 0.25 19 7–95 0.77 0.24 19 12–160 0.83 0.26Turbidity (NTU) 705 25 2–25 0.88 0.29 20 2–50 0.77 0.29 19 3–46 0.92 0.23TSS (mg/L) 705 25 4–80 0.83 0.28 20 4–95 0.77 0.32 19 2–61 0.93 0.25NVSS (mg/L) 705 & 705/670 25 3–63 0.95b 0.24 20 0–60 0.85b 0.27 19 1–53 0.97b 0.22NVSS/TSS (%) 705 & 705/620 25 50–83 0.80b 0.05 20 0–83 0.73b 0.1 19 30–87 0.91b 0.06VSS (mg/L) 705/670 25 1–17 0.80 0.26 20 4–81 0.94 0.17 19 1–28 0.73 0.25Chl a (μg/L) 705/670 25 14–210 0.75 0.43 20 31–830 0.93 0.21 19 8–230 0.83 0.32Chl a (μg/L) 705/620 25 14–210 0.76 0.38 20 31–830 0.91 0.21 19 8–230 0.92 0.21Chl a (μg/L) 705/592 25 14–210 0.90 0.25 20 31–830 0.92 0.19 19 8–230 0.85 0.27

a LN(variable).b R2.


log transformed data were or were nearly so, thus meeting theassumptions of regression. Olmanson et al. (2011) also found thatlog-transformed data produced models with improved fit in theimportant chl a range of 0–20 μg/L. Therefore, models based onlog-transformed data were used for map development.

Chl a, the most common water quality variable derived from highspectral resolution remote sensing, had strong relationships with theratio ~700:~670 nm for all three datasets, as also was found to bethe case in several previous studies (see previous discussion). Thestrength of this relationship is due to backscattering by phytoplank-ton that causes a peak ~700 nm in phytoplankton dominated watersand the chl a absorption maximum near 670 nm. For this dataset

Fig. 3. Scatter plots of in situ log transformed biophysical quantities versus corresponding(c) NVSS, (d) T-tube. For NVSS the multiple regression model from Table 2 was used.

even with a complex combination of IOP features, the 705:670 ratiowas strongly correlated with chl a, with r2 of 0.75–0.93 for thethree years of data. Two models that also used the peak at ~700 andother plant pigment absorption features seen in Fig. 2 also workedwell (Table 2): 705:592 and 705:620; e.g., see Fig. 3a. These wave-lengths are not in strong chl a absorption regions (620 nm is in theregion of phycocyanin absorption and 592 nm is in the region ofB-phycoerythrin absorption) and thus cannot be reliable for broadapplication even though they performed statistically well for thisdataset. The strength of these band ratios for chl a in this datasetstrongly suggests the presence of cyanobacteria in these watersbecause phycocyanin and B-phycoerythrin are both indicators of

band, band-ratio or model values for the August 30, 2007 imagery: (a) chl a, (b) TSS,

image of Fig.�3


cyanobacteria. The RMSE values reported for the predictive equationsin Table 2 are in LN(variable) and thus indicate that chl a can be esti-mated with an accuracy of ~21% (based on the 2005 and 2007 data).Some of the uncertainty reflected in the RMSE values likely arisesfrom uncertainties in the chl a analyses themselves, as well as fromdifferences between the water mass sampled by the ground crewsand that “sampled” by the imagery (caused by differences in thetiming of the two sampling methods).

TSS includes both organic and inorganic solids which potentiallyhave a variety of different combinations of IOP features. Remote sens-ing algorithms for TSS reported in the literature (Doxaran et al., 2005;Gitelson et al., 1993; Koponen et al., 2007) thus are less consistentand more condition-specific dependent on the dominant optical fea-tures. For our dataset strong relationships were found between TSSand reflectance in the band centered at 705 nm (Table 2); seeFig. 3b. This model avoids the absorption characteristics of chloro-phyll but uses the well-known scattering at ~700 nm of suspendedmaterial (including plankton) in water. As discussed earlier, otherworkers have found that the reflectance peak at ~700 works wellfor TSS (Härmä et al., 2001; Kallio et al., 2001; Koponen et al., 2007).

VSS, the mass of combustible (organic) suspended material thatpresumably is plankton and microbial detritus, is generally correlatedwith chl a. The relationships between VSS and band ratios thatworked well for chl a also were strong (Table 2): 705:592, r2=0.80–0.95; 705:620, r2=0.78–0.95; 705:670, r2=0.73–0.94. NVSS,the mass of inorganic suspended material, had strong relationshipswith reflectance in the band centered at 705 nm (r2 values of0.73–0.96). As discussed previously, this peak was found to be strong-ly correlated to NVSS by Ammenberg et al. (2002); however, this re-lationship could be condition-specific, depending on the dominantIOPs, and may not be robust in waters with low inorganic sediments.Therefore, for a more robust model we used the band centered at705 nm, which worked well for TSS, and the band ratio 705:670,which worked well for chl a. This model corrects for the scatteringcaused by phytoplankton at 700 nm and had even higher R2 values(Table 2); see Fig. 3c. The RMSE values reported for the predictiveequations in Table 2 indicate that NVSS can be estimated with an ac-curacy of ~22–27%.

Fig. 4. Maps of turbidity and chl a levels near the confluence of

The ratio NVSS:TSS (the fraction of TSS that is inorganic or mineral)could be useful to identify the dominant IOP (planktonic- versusmineral-based turbidity) in river waters. The predictive model for thisratio that worked best for all datasets used the band centered at705 nm, which worked well for TSS, and the band ratio 705:620,which worked well for chl a, (Table 2). The RMSE values reported forthe predictive equations in Table 2 indicate that NVSS:TSS can be esti-mated within ~5–10%.

Finally, turbidity and water clarity as measured by T-tube are gen-erally related to TSS. Strong relationships were found for both turbid-ity and T-tube (Table 2; see Fig. 3d for an example) with reflectancevalues for the band centered at 705 nm. These models are similar tothe best-fit models for TSS.

4.3. Water quality maps

Using the water quality models described above, we created pixel-level maps that captured the spatial heterogeneity of water quality inthe river systems we studied. During the first image acquisition(August 19, 2004), flows of the Minnesota and St. Croix Rivers wereslightly higher than the flow of the Mississippi River (Table 1), andthe Minnesota River IOP features were dominated by phytoplankton(green phase). As Fig. 4 shows, the high phytoplankton waters of theMinnesota River dominated water quality conditions in the MississippiRiver downstream the confluence with the Minnesota River duringthis time. Similarly, Fig. 5 shows that the phytoplankton (greenphase) waters of the Mississippi River were diluted by the relative-ly clear waters of the St. Croix River downstream of the confluenceof those two rivers.

During the second image acquisition (August 15, 2005), theMississippi River dominated flow (Table 1) andwater quality conditions,and the main channel had relatively low phytoplankton and sedimentlevels. However, as Fig. 6 shows, the backwater areas were dominatedby algal blooms. In addition, the figure shows that sediment picked upfrom the channel around Sturgeon Lake apparently settled as the riverflow velocity decreased in the upper part of Lake Pepin, and thelow suspended sediment concentrations (clearer water) enabled the

the Minnesota River and Mississippi River, August 19, 2004.

image of Fig.�4

Fig. 5. Maps of turbidity and chl a levels near the confluence of the Mississippi River and St. Croix River, August 19, 2004.


development of a large increase in phytoplankton further down-channelin the lake.

During the acquisition period onAugust 30, 2007, the largerflow fromthe Minnesota River (brown phase caused by a large rainfall event fromAugust 18 through August 20, 2007 that caused severe flooding insoutheastern Minnesota) dominated conditions in the Mississippi Riverdownstreamof the confluence (Fig. 7) becauseflowupstream in theMis-sissippi (green phase) was restricted by the closing of the St. AnthonyFalls locks and dams because of the I-35 bridge collapse. Further down-stream the relatively clear waters of the St. Croix River diluted theseconditions in the stretch of the river downstream of its entry to the Mis-sissippi (Fig. 8).

Fig. 6. Maps of turbidity and chl a levels in the Mississippi River from Spring Lake

The capability of airborne hyperspectral imagery to capture fine-scale variations in water quality conditions is illustrated by themaps in Fig. 9, which show the transition from sediment-dominatedwaters of the Mississippi River to phytoplankton-dominated waterof Pig's Eye Lake. Although turbidity is similar in the river and Pig'sEye Lake (with some variations within both water bodies (Fig. 9a)),phytoplankton is dominant in most of the lake and is much lower inthe river (Fig. 9b). An influx of water with a high load of inorganicsediment (i.e., high NVSS:TSS) from the river is evident in the south-western portion of the lake (Fig. 9c). The transition fromphytoplankton-dominated water at location “a” (Fig. 9b) to inorganic sediment-dominated water at location “e” is captured in the reflectance spectra

to Lake Pepin and the confluence with the St. Croix River, August 15, 2005.

image of Fig.�5image of Fig.�6

Fig. 7. Maps of turbidity and chl a levels near the confluence of the Minnesota River and Mississippi River, August 30, 2007.


extracted from the imagery (Fig. 10). Absorption characteristics ofchlorophyll are clearly visible at location “a” but diminish toward loca-tion “e”. This illustrates the vast amount of information available froma single image that would not be seen by conventional monitoring,which probably would involve only one sample for the entire area.

5. Conclusions

Airborne hyperspectral remote sensing was effective in capturinga much more comprehensive picture of river water quality thanis obtained by typical ground-based sampling, in which large areasare represented by a single sampling point that may represent only

Fig. 8. Maps of turbidity and chl a levels in the Mississippi River from the Ru

a small area. Airborne sensors are well-suited to capture conditionsin systems with extensive backwater areas (like the MississippiRiver and its backwater areas and tributaries in east-centralMinnesota)that are difficult to reach by conventional samplingmethods. Capturingthe spatial heterogeneity of such systems by conventional methodswould be prohibitively time consuming and expensive. High resolutionhyperspectral imagery is especially useful forwaters with complex IOPsbecause it can distinguish between the contributions of phytoplanktonand inorganic suspended sediments to water clarity.

The spectral characteristics of waters dominated by various IOPswere used to develop models to map water quality characteristics inoptically complex waters. Light absorption in the red wavelengths by

m River to past the confluence with the St. Croix River, August 30, 2007.


Fig. 9. Maps of Pig's Eye Lake showing the transition from conditions dominated by inorganic sediment to conditions dominated by phytoplankton: (a) turbidity, (b) chl a, and(c) NVSS/TSS; August 30, 2007.


chlorophyll in phytoplankton-dominated waters contrasted withthe lack of these absorption characteristics in inorganic sediment-dominated waters. The scattering peak at ~700 nm, above the regionwhere plant pigments absorb light, was a strong predictor for waterclarity variables (e.g., TSS and turbidity), and ratios of this scatteringpeak with the reflectance troughs caused by chlorophyll absorptionat ~670 nm and absorption by other pigments at 592 and 620 nmwere strong predictors of chl a and VSS (the last two wavelengthsbecause plankton blooms likely were dominated by cyanobacteria).The models that worked for these complex waters also workedwell for other researchers under varying water quality regimes andthus appear to be generally robust predictors. Our results for Pig'sEye Lake indicate that such predictive relationships can distinguish

Fig. 10. Reflectance spectra of the transition zone for conditions dominated by inorganic seLake, August 30, 2007 (Fig. 9B). Tabulated chl a, turbidity and NVSS/TSS values were calcul

key water quality variables under complex IOP conditions, i.e., inor-ganic suspended sediment and chlorophyll levels can be mappedindependently. For NVSS and the ratio NVSS:TSS, we found that themodel that combines the TSS and chl a relationships was themost ro-bust because it corrects for scattering by phytoplankton at ~700 nm.

Although airborne hyperspectral sensing has many advantagesfor river water quality assessments, it does have limitations, includingcost, which may be too high for local agencies with small budgets,and the need to collect data when atmospheric conditions are clear.Haze, cloud cover and cloud shadows can alter spectral-radiometricresponses, and such conditions should be avoided. Imagery thus maynot be available when needed. Although hyperspectral data are usefulin developingwater qualitymodels, they are not needed for operational

diment in the Mississippi River to conditions dominated by phytoplankton in Pig's Eyeated from reflectance spectra using the best predictive models (Table 2).



monitoring. Our findings show that only a few key spectral bandsare needed, and a multispectral system with well-positioned bandsthat capture key spectral characteristics and high spatial resolution(~1–3 m) would be sufficient. Technical advancements in satellites orunmanned drones with these characteristics may reduce the costs ofdata collection and make remote sensing a cost-effective and routinelyused technique for river water quality in coming years.

Acknowledgments

Support for data acquisition, processing and analysis was providedby funding from the Legislative-Citizen Commission on MinnesotaResources (LCCMR) Environment and Natural Resources Trust Fundto the Minnesota Pollution Control Agency and the University ofMinnesota Agricultural Experiment Station. For assistance with fielddata coordination, collection and sample analysis, special thanks goto Bruce Wilson (Minnesota Pollution Control Agency), Steve Kloiber(Metropolitan Council), and Kent Johnson (Metropolitan Council)and sampling crews from the Minnesota Pollution Control Agency,Metropolitan Council, Minnesota Department of Natural Resources,and Minnesota Department of Agriculture, which were dispatchedon short notice to collect water samples. Weather forecasts fromKARE 11 meteorologist Jonathan Yuhas were invaluable in planningfor data acquisitions under clear conditions.

References

Abrams, M. J., Ashley, R., Rowan, L. C., Goetz, A. F. H., & Kahle, A. B. (1977). Mappingof hydrothermal alteration in the Cuprite mining district, Nevada, using aircraftscanner imagery for the 0.46–2.36 μm spectral region. Geology, 5, 713–718.

Alexander, R. B., Smith, R. A., Schwarz, G. E., Boyer, E. W., Nolan, J. V., & Brakebill, J. W.(2008). Differences in phosphorus and nitrogen delivery to the Gulf of Mexicofrom the Mississippi River basin. Environmental Science and Technology, 42(3),822–830, http://dx.doi.org/10.1021/es0716103.

American Society of Testing Materials (1994). ASTM standards. Water and environmentaltechnology (I), Vol. 11.01, West Conshohocken, PA: ASTM International.

Ammenberg, P., Flink, P., Lindell, T., Pierson,D., & Strombeck, N. (2002). Bio-opticalmodel-ling combined with remote sensing to assess water quality. International Journal ofRemote Sensing, 23(8), 1621–1638, http://dx.doi.org/10.1080/01431160110071860.

Belmont, P., Gran, K. B., Schottler, S. P., Wilcock, P. R., Day, S. S., Jennings, C., et al. (2011).Large shift in source of fine sediment in the Upper Mississippi River. EnvironmentalScience and Technology, 45(20), 8804–8810, http://dx.doi.org/10.1021/es2019109.

Carroll, M. W., Glaser, J. A., Hellmich, R. L., Hunt, T. E., Sappington, T. W., Calvin, D., et al.(2008). Use of spectral vegetation indices derived from airborne hyperspectral imag-ery for detection of European corn borer infestation in Iowa corn plots. Journal of Eco-nomic Entomology, 101(5), 1614–1623, http://dx.doi.org/10.1603/0022-0493(2008)101[1614:UOSVID]2.0.CO;2.

Chipman, J. W., Olmanson, L. G., & Gitelson, A. A. (2009). Remote sensing methods forlake management: A guide for resource managers and decision-makers. Developedby North American Lake Management Society in collaboration with DartmouthCollege, University of Minnesota, University of Nebraska and University of Wisconsinfor the U.S. Environmental Protection Agency.

Clark, R. N., King, T. V. V., Klejwa, M., Swayze, G. A., & Verga, N. (1990). High spectralresolution reflectance spectroscopy of minerals. Journal of Geophysical Research —Solid Earth and Planets, 95, 12653–12680, http://dx.doi.org/10.1029/JB095iB08p12653.

Doxaran, D., Cherukuru, R., & Lavender, S. (2005). Use of reflectance band ratios toestimate suspended and dissolved matter concentrations in estuarine waters.International Journal of Remote Sensing, 26, 1763–1770, http://dx.doi.org/10.1080/01431160512331314092.

Duan, H. T., Zhang, Y. Z., Zhan, B., Song, K. S., & Wang, Z. M. (2007). Assessment ofchlorophyll—A concentration and trophic state for Lake Chagan using Landsat TMand field spectral data. Environmental Monitoring and Assessment, 129, 295–308,http://dx.doi.org/10.1007/s10661-006-9362-y.

Engstrom, D. R., Almendinger, J. E., & Wolin, J. A. (2009). Historical changes in sedimentand phosphorus loading to the Upper Mississippi River: Mass-balance reconstruc-tions from the sediments of Lake Pepin. Journal of Paleolimnology, 41, 563–588,http://dx.doi.org/10.1007/s10933-008-9292-5.

Gitelson, A. (1992). The peak near 700 nm on reflectance spectra of algae and water:Relationships of its magnitude and position with chlorophyll concentration. Inter-national Journal of Remote Sensing, 13(17), 3367–3373.

Gitelson, A., Garbuzov, G., Szilagyi, F., Mittenzwey, K., Karnieli, A., & Kaiser, A. (1993).Quantitative remote sensing methods for real-time monitoring of inland watersquality. International Journal of Remote Sensing, 14, 1269–1295.

Gitelson, A. A., & Merzlyak, M. N. (1996). Signature analysis of leaf reflectance spectra:Algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology,148, 494–500.

Gitelson, A. A., Zur, Y., Chivkunova, O. B., &Merzlyak,M.N. (2002). Assessing carotenoid con-tent in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 75,272–281.

Goetz, A. F. H., & Srivastava, V. (1985). Mineralogical mapping in the Cuprite miningdistrict, Nevada. Proceedings, Airborne Imaging Spectrometer Data Analysis Work-shop. Jet Propulsion Laboratory. (pp. 22–31) Pasadena, California: JPL Publication85-41.

Gons, H. J. (1999). Optical teledetection of chlorophyll a in turbid inland waters. Environ-mental Science and Technology, 33, 1127–1132, http://dx.doi.org/10.1021/es9809657.

Goolsby, D. A., Battaglin, W. A., Aulenbach, B. T., & Hooper, R. P. (2001). Nitrogen inputto the Gulf of Mexico. Journal of Environmental Quality, 30, 329–336.

Gran, K. B., Belmont, P., Day, S. S., Jennings, C., Johnson, A., Parker, G., et al. (2009). Geo-morphic evolution of the Le Sueur River, Minnesota, and implications for currentsediment loading. In L. A. James, S. L. Rathburn, & G. R. Whittecar (Eds.),Managementand restoration of fluvial systemswith broad historical changes and human impacts. Geol.Soc. Amer. Spec. Paper, 451. (pp. 119–130), http://dx.doi.org/10.1130/2008.2451(08).

Greenberg, A. E., Clesceri, L. S., & Eaton, A. D. (1992). Standard methods for the examina-tion of water and wastewater (18th Ed.). Washington, D.C.: American Public HealthAssociation.

Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004).Hyperspectral vegetation indices and novel algorithms for predicting green LAI ofcrop canopies:Modeling and validation in the context of precision agriculture. RemoteSensing of Environment, 90(3), 337–352, http://dx.doi.org/10.1016/j.rse.2003.12.013.

Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Inte-grated narrow-band vegetation indices for prediction of crop chlorophyll contentfor application to precision agriculture. Remote Sensing of Environment, 81(2–3),416–426.

Hakvoort, H., De Haan, J., Jordans, R., Vos, R., Peters, S., & Rijkeboer, M. (2002). Towardsairborne remote sensing of water quality in The Netherlands — Validation anderror analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 57, 171–183,http://dx.doi.org/10.1016/S0924-2716(02)00120-X.

Han, L. (1997). Spectral reflectance with varying suspended sediment concentration inclear and algal-laden waters. Photogrammetric Engineering and Remote Sensing, 63,701–705.

Härmä, P., Vepsalainen, J., Hannonen, T., Pyhalahti, T., Kamari, J., Kallio, K., et al. (2001).Detection of water quality using simulated satellite data and semi-empirical algo-rithms in Finland. Science of the Total Environment, 268, 107–121.

Hoogenboom, H. J., Dekker, A. G., & De Haan, J. F. (1998). Retrieval of chlorophyll andsuspended matter in inland waters from CASI data by matrix inversion. CanadianJournal of Remote Sensing, 24(2), 144–152.

Kallio, K., Kutser, T., Hannonen, T., Koponen, S., Pulliainen, J., Vepsalainen, J., et al.(2001). Retrieval of water quality from airborne imaging spectrometry ofvarious lake types in different seasons. Science of the Total Environment, 268, 59–77,http://dx.doi.org/10.1016/S0048-9697(00)00685-9.

Kloiber, S. M., Brezonik, P. L., Olmanson, L. G., & Bauer, M. E. (2002). A procedure forregional lake water clarity assessment using Landsat multispectral data. RemoteSensing of Environment, 82(1), 38–47, http://dx.doi.org/10.1016/S0034-4257(02)00022-6.

Koponen, S., Attila, J., Pulliainen, J., Kallio, K., Pyhälahti, T., Lindfors, A., et al. (2007). Acase study of airborne and satellite remote sensing of a spring bloom event in theGulf of Finland. Continental Shelf Research, 27, 228–244, http://dx.doi.org/10.1016/j.csr.2006.10.006.

Matthews, M. W. (2011). A current review of empirical procedures of remote sensingin inland and near-coastal transitional waters. International Journal of Remote Sensing,32:21, 6855–6899, http://dx.doi.org/10.1080/01431161.2010.512947.

Menken, K. D., Brezonik, P. L., & Bauer, M. E. (2006). Influence of chlorophyll andcolored dissolved organic matter (CDOM) on lake reflectance spectra: Implicationsfor measuring lake properties by remote sensing. Lake and Reservoir Management,22, 179–190.

Metropolitan Council (2004). Regional progress in water quality analysis of water qualitydata from 1976 to 2002 for the major rivers in the Twin Cities. Metropolitan CouncilEnvironmental Services regional report.

Minnesota Pollution Control Agency (2011). Minnesota's impaired waters and TMDLs:Impaired waters list. http://www.pca.state.mn.us/index.php?option=com_k2&Itemid=250&id=859&layout=item&view=item

Mittenzwey, K., Gitelson, A., Ullrich, S., & Kondratiev, K. (1992). Determination of chlorophylla of inland waters on the basis of spectral reflectance. Limnology and Oceanography, 37,147–149.

Morel, A., & Prieur, L. (1977). Analysis of variations in ocean color. Limnology andOceanography, 22, 709–722.

Moses, W. J., Gitelson, A. A., Berdnikov, S., & Povazhnyy, V. (2009). Satellite estimationof chlorophyll a concentration using the red and NIR bands of MERIS—The AzovSea case study. IEEE Geoscience and Remote Sensing Letters, 6(4), 845–849, http://dx.doi.org/10.1109/LGRS.2009.2026657.

Moses, W. J., Gitelson, A. A., Perk, R. L., Gurlin, D., Rundquist, D. C., Leavitt, B. C., et al.(2011). Estimation of chlorophyll—A concentration in turbid productive waters usingairborne hyperspectral data. Water Research, 46(4), 993–1004, http://dx.doi.org/10.1016/j.watres.2011.11.068.

Mulla, D. J., & Sekely, A. (2009). Historical trends affecting accumulation of sedi-ment and phosphorus in Lake Pepin. Journal of Paleolimnology, 41(4), 589–602,http://dx.doi.org/10.1007/s10933-008-9293-4.

Myre, E., & Shaw, R. (2006). The turbidity tube: Simple and accurate measurement ofturbidity in the field. Houghton, MI: Michigan Technology University.

Olmanson, L. G., Bauer, M. E., & Brezonik, P. L. (2008). A 20-year Landsat water claritycensus ofMinnesota's 10,000 lakes.Remote Sensing of Environment,112(11), 4086–4097,http://dx.doi.org/10.1016/j.rse.2007.12.013.

http://dx.doi.org/10.1021/es0716103http://dx.doi.org/10.1080/01431160110071860http://dx.doi.org/10.1021/es2019109http://dx.doi.org/10.1603/0022-0493(2008) 101[1614:UOSVID]2.0.CO;2http://dx.doi.org/10.1603/0022-0493(2008) 101[1614:UOSVID]2.0.CO;2http://dx.doi.org/10.1029/JB095iB08p12653http://dx.doi.org/10.1080/01431160512331314092http://dx.doi.org/10.1080/01431160512331314092http://dx.doi.org/10.1007/s10661-006-9362-yhttp://dx.doi.org/10.1007/s10933-008-9292-5http://dx.doi.org/10.1021/es9809657http://dx.doi.org/10.1130/2008.2451(08)http://dx.doi.org/10.1016/j.rse.2003.12.013http://dx.doi.org/10.1016/S0924-2716(02)00120-Xhttp://dx.doi.org/10.1016/S0048-9697(00)00685-9http://dx.doi.org/10.1016/S0034-4257(02)00022-6http://dx.doi.org/10.1016/S0034-4257(02)00022-6http://dx.doi.org/10.1016/j.csr.2006.10.006http://dx.doi.org/10.1016/j.csr.2006.10.006http://dx.doi.org/10.1080/01431161.2010.512947http://www.pca.state.mn.us/index.php?option=com_k2&Itemid=250&id=859&layout=item&view=itemhttp://www.pca.state.mn.us/index.php?option=com_k2&Itemid=250&id=859&layout=item&view=itemhttp://dx.doi.org/10.1109/LGRS.2009.2026657http://dx.doi.org/10.1016/j.watres.2011.11.068http://dx.doi.org/10.1007/s10933-008-9293-4http://dx.doi.org/10.1016/j.rse.2007.12.013


Olmanson, L. G., Brezonik, P. L., & Bauer, M. E. (2011). Evaluation of medium to lowresolution satellite imagery for regional lake water quality assessments. WaterResources Research, 47, W09515, http://dx.doi.org/10.1029/2011WR011005.

Olmanson, L. G., Kloiber, S. M., Bauer, M. E., & Brezonik, P. L. (2001). Image processingprotocol for regional assessments of lake water quality. Public report series #14WaterResources Center and Remote Sensing Laboratory, University of Minnesota, St. Paul,MN, 55108, October 2001 (15 pp.).

Rabalais, N. N., Turner, R. E., & Scavia, D. (2002). Beyond science into policy: Gulf ofMexico hypoxia and the Mississippi River. BioScience, 52, 129–142.

Rabalais, N. N., Turner, R. E., & Wiseman, W. J., Jr. (2002). Gulf of Mexico hypoxia, A.K.A. thedead zone. Annual Review of Ecology and Systematics, 33, 235–263, http://dx.doi.org/10.1146/annurev.ecolsys.33.010802.150513.

Senay, G. B., Shafique, N. A., Autrey, B. C., Fulk, F., & Cormier, S. M. (2001). The selectionof narrow wavebands for optimizing water quality monitoring on the Great MiamiRiver, Ohio using hyperspectral remote sensor data. Journal of Spatial Hydrology, 1,1–22.

Shafique, N. A., Autrey, B. C., Fulk, F. A., & Flotemersch, J. E. (2003). Hyperspectralremote sensing of water quality parameters for large rivers in the Ohio RiverBasin. First Interagency Conference on Research in the Watersheds. Benson, Arizona,October 27–30, 2003. Washington, DC: USDA Agric. Res. Serv.

Shah, C. A., Arora, M. K., Robila, S. A., & Varshney, P. K. (2002). ICA mixture model basedunsupervised classification of hyperspectral imagery. Proceedings of 31st Interna-

tional Workshop on Applied Imagery and Pattern Recognition. IEEE Computer Society,Washington DC, USA, 16–18 October 2002. (pp. 29–35) Los Alamitos, CA: IEEEComputer Society Press.

Shah, C. A., Arora,M. K., &Varshney, P. K. (2004). Unsupervised classification of hyperspectraldata: An ICA mixture model based approach. International Journal of Remote Sensing,25(2), 481–487, http://dx.doi.org/10.1080/01431160310001618040.

Thorleifson, L. H. (1996). Review of Lake Agassiz history. In J. T. Teller, L. H. Thorleifson,G. Matile, & W. C. Brisbin (Eds.), Sedimentology, geomorphology and history ofthe central Lake Agassiz Basin — Field trip guidebook B2; Geological Association ofCanada/Mineralogical Association of Canada Annual Meeting, Winnipeg, Manitoba,May 27–29.

Turner, R. E., & Rabalais, N. N. (1994). Coastal eutrophication near the Mississippi Riverdelta. Nature, 368, 619–621, http://dx.doi.org/10.1038/368619a0.

U.S. Environmental Protection Agency (1992). Methods for determination of chemicalsubstances in marine and estuarine environmental samples. EPA/600/R-92/121(95 pp.).

Vasilkov, A., & Kopelevich, O. (1982). Reasons for the appearance of the maximumnear 700 nm in the radiance spectrum emitted by the ocean layer. Oceanology, 22,697–701.

Wessman, C. A., Aber, J. D., Petersen, D. L., & Melillo, J. M. (1988). Remote-sensing ofcanopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature,335, 154–156, http://dx.doi.org/10.1038/335154a0.

http://dx.doi.org/10.1029/2011WR011005http://dx.doi.org/10.1146/annurev.ecolsys.33.010802.150513http://dx.doi.org/10.1080/01431160310001618040http://dx.doi.org/10.1038/368619a0http://dx.doi.org/10.1038/335154a0

Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: The ...1. Introduction2. Background information: major rivers in Twin Cities Metropolitan Area3. Methods3.1. Imagery3.2. Water quality measurements3.3. Image processing and classification

4. Results and discussion4.1. Spectral characteristics of river water4.2. Model development4.3. Water quality maps

5. ConclusionsAcknowledgmentsReferences

remote sensing of environment - unicamp...airborne hyperspectral remote sensing to assess spatial...

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