Water Quality Change in Reservoirs of Shenzhen China IMPORTANT

Science of the Total Environment 328(2004) 195–206

0048-9697/04/$ - see front matter� 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.scitotenv.2004.02.020

Water quality change in reservoirs of Shenzhen, China: detectionusing LANDSATyTM data

Yunpeng Wang*, Hao Xia, Jiamo Fu, Guoying Sheng

State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences,P.O. Box 1131, Guangzhou 510640, PR China

Received 8 April 2003; received in revised form 17 November 2003; accepted 13 February 2004

Abstract

The objective of this research is to explore a precise and fast way of monitoring water chemical and biochemicalquality in the reservoirs of Shenzhen, China. Water quality change in 1988 and 1996 are detected by synthesizingsatellite data and ground-based data. One scene Thematic Mapper(TM) image in winter of 1996 was acquired andthe simultaneous in situ measurement, sampling and analysis were performed. Main methods include radiometriccalibration of TM remote sensor, atmospheric correction to image data and statistical model construction. The resultsindicate that satellite-based estimates and in situ measured water reflectance have very high correlation, and the rootmean square differences between two kinds of indices are close to 0.02–0.03 for each TM band in Visible-NearInfrared(VI-NIR) range. Statistical relationship between calibrated image data(average of 5=5 pixels) of TM bandsand laboratory analyzed data of water samples indicated reflectance of TM band 1 to band 4 and organic pollutionmeasurements such as TOC, BOD and COD had higher correlation. The same scene TM data in the winter of 1988was processed in the same procedure. Results indicate that water quality of most reservoirs have become worse.Water of eastern reservoirs near Dongjiang River is characterized with higher TOC and TSS, and water of westernreservoirs is characterized with higher BOD and COD.� 2004 Elsevier B.V. All rights reserved.

Keywords: Water quality change; Remote sensing; Landsat-5 TM image; Reservoir water; Shenzhen; Biochemical oxygen demand;Chemical oxygen demand

1. Introduction

Reservoir water is the main source of drinkingwater, industrial water and agricultural water inShenzhen and Hong Kong. There is an importantneed for ongoing water quality monitoring toensure the water standard continuous to be active.

*Corresponding author. Tel.:q86-20-8290170; fax:q86-20-85290706.

E-mail address: [email protected](Y. Wang).

Most reservoirs accumulate the runoff formed ofrainwater, such as Tiegang reservoir and Shiyanreservoir, and some reservoirs accumulate thewater from rivers with long distance drainage,such as Shenzhen reservoir whose main water isfrom the Dongjiang River. Shenzhen is one areawith the most rapid development speed of econo-my and urbanization of China in the past 20 years.Local industrial developments and populationincreases have affected reservoir ecosystem and

196 Y. Wang et al. / Science of the Total Environment 328 (2004) 195–206

water quality. Now, and in the future, there isywillbe the need for more high quality water.Traditional water quality monitoring depends on

in situ measurements or sequent laboratory analysisof the samples. This kind of point sampling meth-ods may give accurate measurements, but they aretime and money consuming. Further and mostimportantly, they can’t give the real-time spatialoverview that is necessary for the global assess-ment and monitoring of water quality(Brivio etal., 2001).Satellite remote sensing may provide suitable

ways to integrate limnological data collected fromtraditional in situ measurements. Since the 1980s,with improvement of sensor spatial and spectralresolution, satellite remote sensing has been usedto monitor inland water by using correlationbetween broad-band reflectance and other proper-ties of the water column, including Secchi diskdepth, chlorophyll concentrations, pigment load,total suspended sediments, temperature and waterquality data analyzed in a laboratory(Schiebe etal., 1992; Dekker and Peters, 1993; Schneider andMauser, 1996; Zilioli and Brivio, 1997; Fraser,1998; Giardiano et al., 2001; Kloiber et al., 2000,2002).The Landsat-5 TM(Thematic Mapper) images

were selected to acquire broadband reflectancedata. All bands of TM Band 1(TM1) to TM Band7 (TM7) were used, and the most common wereTM1-TM4 and TM6. TM1-4 bands known asvisible-near infrared bands are in the spectral rangewhere light passed through the water, providingsome information about water quality(Giardianoet al., 2001; Kloiber et al., 2000, 2002).Previous studies prove TM1-4 bands may reflect

the routine parameters of water quality includingaquatic humus, chlorophyll-a, phytoplankton, dis-solved organic matter, suspended matter, etc.(Dek-ker et al., 1992; Lathrop, 1992; Dekker and Peters,1993; Lavery et al., 1993; Pattiaratchi et al., 1994;Cox et al., 1998; Brivio et al., 2001; Giardiano etal., 2001; Stadelmann et al., 2001; Kloiber et al.,2002). In this paper, a study of statistical modelbetween reflectance of TM band 1–4 and chemicalmeasurements of water quality including totalorganic carbon(TOC), chemical oxygen demand(COD) and biochemical oxygen demand(BOD)is presented for the reservoirs of Shenzhen. In

doing so, the paper provides the first informationon the statistical model between water chemicaland biochemical measurements and the satelliteestimated reflectance of TM bands and the prelim-inary application of the water quality change inthe reservoirs of Shenzhen.

2. Study area

The study area is in the north of Shenzhen,Guangdong Province, south of China(Fig. 1).This is a tropical area with abundant rainfall.According to the Shenzhen meteorological station,the annual precipitation of Shenzhen varies from1600–2000 mm, average annual precipitation is1933.3 mm, and in most area of Shenzhen theaverage annual precipitation is over 1700 mm(http:yywww.szlib.gov.cnyszglyqixiangyqixiang.htm). There are more than 30 reservoirs in Shen-zhen and they accumulate water throughout year.The five largest reservoirs Tiegang, Shiyan, Xili,Yantian and Shenzhen, which account for over80% water column of the surface water in Shen-zhen, are studied here. The typical characteristicsof these reservoirs are the heavy organic pollutionand eutrophication, and the main pollutants includetotal nitrogen and phosphorous, BOD and volatilehydroxybenzene(Annul Report of Shenzhen Envi-ronmental Protection Bureau, 2002).

3. Material and methods

One scene TM image was acquired and thesimultaneous in situ spectral measurement, watersampling and laboratory analysis were performed.Image process methods include radiometric cali-bration of TM remote sensor and atmosphericcorrection to image data. Multiple regression meth-ods were used for the statistical model construc-tion. The water quality change was detected bycomparing two scenes of TM data through thesame procedures.

3.1. Satellite data and spectral measurement

The date of the TM image is for the 10 March1996, a time when the composition of reservoirwater is relatively stable in the dry season. Thisdate is chosen because the image is cloud-free and

197Y. Wang et al. / Science of the Total Environment 328 (2004) 195–206

Fig. 1. Location of study area, Shenzhen, Guangdong, South of China.

in good quality (Detailed in http:yywww.rsgs.ac.cn). For comparing the radiation ofwater in remote sensor and the in situ measure-ment, a digital count and related parameters of theimage were used. A sub-image in size of1200=700 pixels was extracted, which coveredthe most of the study area.For determining the relationship between the

satellite data and in situ measurement, the surfaceradiation of water was measured during satelliteoverpass. The in situ measurements were takenusing a HG-100 portable spectroradiometerdesigned by our institute. It is a spectrally basedcolorimeter, which can perform complete radio-metric measurements in the range of 400–860 nmwith a spectral bandwidth of 10–2 nm accuracy.This instrument can mount different optics(cor-responding to 18) and remote cosine receptor(RCR) fields of view (FOV). A sun-photometerwas used to measure the atmospheric transmittancein different wavelengths from visible to near infra-red range.

3.2. Sampling and analysis

The sample gathering sites were arranged tocoincide with satellite passages along a section inthe north–south direction. Sampling number varies

from 8 to 10 according to the reservoir size. Theposition of the sampling ship was geolocated by aportable Global Position System(GPS: ModelGM-100 of Beijing InterGPS Corporation). Ateach sampling site, water was collected with adark-colored bottle from depths of 0 and 2 m.Then an integrated water sample of two depthswas taken in situ for the further analysis in thelaboratory. The interval between sampling posi-tions was maintained as short as possible, andthese time was usually between 30 and 60 min. Inthe field, all the sample were stored in a tempera-ture- maintaining(5"2 8C) container with a blackcover to avoid sample deterioration from biologicalactivity. At the laboratory, all sample bottles werestored in the refrigerator at the same temperatureprior to analysis.Laboratory analysis includes TOC, COD and

BOD. All analytical methods used were ChinaStandard Method(GB Method), which were asfollows:

1. TOC: Determination of TOC by non-dispersiveinfrared absorption method(GByT 13 193-1991).

2. COD: Determination of the chemical oxygendemand, Dichromate method(GByT 11 914-1989).


Table 2The atmospheric transmittanceT values in TM bands meas-lx

ured by a sun-photometer(March 10, 1996)

Band 1 2 3 4

Tlx 0.6705 0.7433 0.8402 0.9905

Table 1Radiometric calibration coefficients and exo-atmospheric solar irradiances from Landsat-5 TM(Thome et al.(1993) and Palmer(1984))

TM band Center wavelength Gain,G Offset, DSL0 Solar Irradiance,E0(mm) (countsyWm sr mm )y2 y1 y1 (counts) (Wm sr mm )y2 y1 y1

1 0.4863 1.6599 2.523 1959.22 0.5706 0.85099 2.417 1827.43 0.6607 1.2411 1.452 1550.04 0.8382 1.2277 1.854 1040.85 1.677 9.2526 3.423 220.756 2.223 17.550 2.633 74.960

1 Gs(y7.84=10 )(days since launch)q1.409y5




5 Gs(7.88=10 )(days since launch)q7.235y5

7 Gs(7.15=10 )(days since launch)q15.63y5

3. BOD: Determination of biochemical oxygendemand after 5 days, Dilution and seedingmethod(GByT 7488-1987).

The other determination methods were also GBMethods, which would not be listed here.

3.3. Calibration and atmospheric correction

For comparing the spectral properties of waterreceived by satellite and the in situ measurements,the satellite image data needed to be calibratedand corrected for atmospheric effects. The objec-tive of absolute radiometric sensor calibration of asatellite is to produce accurate calibrated data inSI units for broad usage(Brivio et al., 2001).Compared to soil and vegetation, the fraction oflight reflected from water is very small(Maul,1985). Therefore, accurate absolute radiometriccorrection of the sensor is critical(Gordon, 1987).Thome et al. have done the long-term research ofTM and ETM data calibration(Thome et al., 1993;Thome, 2001). In this study, Eq.(1) is used tocalibrate the digital signal levels(DSLs), whichare then converted to(Lambertian) apparent reflec-tance by Eq.(2) with the coefficients in Table 1.

DSLyDSL0L*s (1)G

2pd L*Sr*s (2)

T E mlx 0 s

L* is the radiance (Wm sr mm ); d : isy2 y1 y1S

Earth–Sun distance normalized with respect tomean of 1.0 A.U;m : is cosine of solar zeniths

angle; E : Exo-atmospheric solar irradiance0

(Wm mm ); T : is the atmospheric transmit-y2 y1lx

tance along the path from the sun to the groundsurface measured by sun-photometer simultaneous-ly while the satellite over-passing, which is shownin Table 2.The haze effect is thought to be the most

important atmospheric effect due to the scatteringand absorption of the radiation by molecules andaerosols(Kaufman, 1989). Many atmospheric hazeremoval techniques have been developed for theuse of digitally remote-sensed data(Kaufman andSendra, 1988; Caselles and Lopez Garcia, 1989;Richter, 1990; Fraser et al., 1992; Gilabert et al.,1994). These techniques are grouped into twoclasses: a simple dark object subtraction(DOS)


Fig. 2. Scatter plot of satellite estimated reflectance vs. in situmeasured reflectance of water in three TM bands(42 sites ofreservoirs, Shenzhen).

Fig. 3. The root mean square errors of water reflectance dif-ferences in TM band 1–4 between satellite and ground meas-urement, 42 locations in Shenzhen.

method and using atmospheric transmission modelcombining with in situ field measurements.Hence, an improved dark-object subtraction

technique for atmospheric haze correction is usedbased on the method of Chavez and Teillet aftercomparing different atmospheric correction meth-ods (Chavez, 1988, 1996; Teillet and Fedosejevs,1995). Eq. (3) is used to compute apparent reflec-tances at the sensor form DSLs extracted from theimagery for the dark target and average back-ground:

2pd DSL-DSLŽ .S 0r *s (3)ag E m GT0 s lx

This equation is used to obtainr * values forag

the dark target DSLs and the average backgroundDSLs in each of Landsat TM band 1, 2, 3 and 4.The radiometric calibration gain and offset coeffi-cients,G and DSL , listed in Table 1 were deter-0

mined by Thome et al.(1993) and Palmer(1984).E is the exo-atmospheric solar irradiance. The0

recommended gain coefficients are given in thelower part of Table 1. A sun angle computationwill give m for the scene center and ds for thes

date of image acquisition by the remote sensor.

4. Results

4.1. Satellite estimated and in situ measured reflec-tance of water

A 5=5 pixel window, corresponding to the areaof 150=150 m, was extracted from the images foreach of the locations in each reservoir. AverageDLS of 25 pixels within the window was convertedto radianceL*, reflectancer *. Also, the groundag

reflectance(r *) was calculated by DOS methodsg

as the above description.Satellite estimated and in situ measured reflec-

tance of water in 42 sites are compared in a scatterplots in Fig. 2. The results show that the computedvalues are close to the measured values, in general,and present a good fit(Fig. 2). The correlationanalysis results show that the correlation coeffi-cient square(R ) for TM1 of 42 samples is2

TM1

0.638, 0.607 for TM2 and 0.780 for TM3. Thesatellite-based estimates and in situ measured waterreflectance are significantly correlated: thet-sig-nificance test of correlation analysis was used(ts8.18)t s2.704,P-0.01).0.01 40( )

For assessing the absolute error of this method,the root mean square(RMS) errors in TM band1–4 for the 42 sites were computed. RMS is thestandard deviation of the differences between sat-ellite estimated and in situ measurements of waterreflectances. The bar graph of TM band 1–4 ispresented in Fig. 3. It shows that on average, theroot mean square differences between the twokinds of indices are close to 0.02–0.03 in VI-NIR


Fig. 4. Scatter plot of predicted values and analyzed values ofTOC (40 samples,rs0.911,Ps0.01).

Fig. 5. Scatter plot of predicted values and analyzed values ofBOD (40 samples,rs0.841,Ps0.01).

range. It indicates that satellite estimated reflec-tance of water presents a high accuracy in TMband 1–4.

4.2. Water quality models

Statistical techniques were used to determinethe relationships between the satellite estimatedreflectances or their ratios and water quality para-meters just like in previous studies(Baban, 1993;Mayo et al., 1995; etc.). A few previous studiesused non-linear power models to address the cur-vilinear behavior of this relationship(Lathrop,1992; Cox et al., 1998). In this study, somemultiple regression methods including linear, expo-nential and log transformations were used to exam-ine the statistical models between water parametersanalyzed in laboratory(TOC, BOD, and COD)and remote sensing reflectance values in TM 1–4.Through comparing, we chose the multiple linearregression method because of its higher multiplecorrelation coefficient. The regression equations ofTOC, BOD and COD and the atmosphericallycorrected reflectance of TM band 1–3(r1, r2 andr3) were as follows:

TOCs6.41y85.29r1y2.05r2y24.96r3

(MRs0.829,Ns40,

standard error of estimates0.25) (4)

BODs1.79y0.789r1q52.36r2y3.28r3

(MRs0.707,Ns40,


CODs2.76y17.27r1q72.15r2y12.11r3

(MRs0.626,Ns40,


Where r1, r2 and r3 are the atmosphericallycorrected reflectance of TM band 1–3. MR is themultiple linear correlation coefficient andN issample number.Scatter plots of predicted values from the Eqs.

(4)–(6) vs. analyzed values of TOC, BOD andCOD are shown in Figs. 4–6. The correlationcoefficients are 0.911(Ps0.01) for TOC,0.841(Ps0.01) for BOD and 0.791(Ps0.01) for


Fig. 6. Scatter plot of predicted values and analyzed values ofCOD (40 samples,rs0.791,Ps0.01).

Fig. 7. Scatter plot of atmospherically corrected reflectance ofTM1 vs. TM2 for water, shadow and vegetation(1988 and1996)

COD. The results indicate that reflectance of TMband 1–3 and organic pollution measurementssuch as TOC, BOD and COD have relativelyhigher correlation. Eqs.(4)–(6) are proved effec-tive to be used in predicting the water qualityparameters of TOC, BOD and COD.

4.3. Water quality change detection in 1988 and1996

The same scene TM data in the same season of1988 (January 10) was processed in the sameprocedure on the basis of radiometric calibrationand atmospheric correction. The calibration andatmospheric correction is of critical importance forthe multi-temporal remote sensing data while usingin change detection. For verifying the result of thecalibration and atmospheric correction methods, AScatter plot of atmospherically corrected reflec-tance of TM1 vs. TM2 for some dark objects suchas water, shadow and dense vegetation in 1988and 1996 was constructed, showing in Fig. 7. Thereflectance(i.e. TM1 vs. TM2) of these darkobjects in different years(1988–1996 with theinterval of 8 years) is distributed in the same areasgenerally (Fig. 7). The distributions of shadowand dense vegetation are very close in 1988 and

1996, while the distribution of water is relativelydispersive. It is due to the change of water quality.The results show that calibration and atmosphericcorrection to TM data is effective.Regression Eqs.(4)–(6) were applied to the

whole surface of the reservoirs and produced thedensity-sliced map of TOC, BOD and COD. Thesemeasurements are used for water classificationaccording to the Standard of State EnvironmentProtection Bureau, China. Fig. 8 is a comparisonof two BOD maps of Shiyan reservoir in 1988 and1996, which were calculated by Eq.(5).The BOD value varies from 2 to 3 mg l iny1

1988, and the high BOD area locates in the middleto northern parts of the reservoir(Fig. 8). How-ever, in 1996, BOD value varies from 2.5 mg ly1

to more than 3.5 mg l , and the BOD value iny1

most of the reservoirs is larger than 3 mg l . They1

map of COD shows the same characteristic inspace distribution. It indicates the pollution ofShiyan reservoir has increased from 1988 to 1996.The other reservoirs in the western area of Shen-zhen show the same trend, and the main source ofthe pollution is from the agriculture pollution.Comparison of two TOC maps of five reservoirs

in 1988 and 1996(Fig. 9) reveals that TOC valuevaries from 2 to 3.5 mg l in 1988, and the highy1

TOC areas locate in the Northern parts of Shiyanreservoir and the southern part of Shenzhen reser-voir. However, in 1996, TOC value in all reservoirs


Fig. 8. BOD maps of Shiyan reservoir in 1988(up) and 1996(bottom), calculated from TM atmospherically corrected reflectancethrough statistical model(Eq. (5)).

were higher, varying from 3.5 mg l to morey1

than 5 mg l , and the TOC value in most of they1

reservoirs was larger than 4 mg l . The charac-y1

teristic in space distribution indicates that thepollution of all reservoirs in Shenzhen haveincreased from 1988 to 1996. With the reservoirsin the eastern area of Shenzhen showing relatively

worse trend, as the source of the pollution is fromincreased agriculture, industry and urban pollution.

5. Conclusions

A precise and fast way of water quality moni-toring was explored in some reservoirs of Shen-


Fig. 9. TOC maps of reservoirs in Shenzhen in1988(up) and 1996(bottom), calculated from TM atmospherically corrected reflec-tance through statistical model(Eq. (4)). 1. Shiyan Reservoir; 2. Tiegang Reservoir; 3. Xili Reservoir; 4. Yantian Reservori; 5.Shenzhen Reservoir.


zhen. Water quality change in 1988 and 1996 aredetected by remote sensing. One scene TM imagein winter of 1996 was acquired with simultaneousin situ measurement, sampling and analysis. Theradiometric calibration of TM remote sensor andatmospheric correction to image data and statisticalmodel construction were performed. The resultsindicate that satellite-based estimates and in situmeasured water reflectance have very high corre-lation, and the root mean square differencebetween two kinds of indices are close to 0.02–0.03 for each TM band in VI-NIR range.The study shows that satellite estimated reflec-

tance of water presents a high accuracy in TMband 1–4. Correlation between calibrated imagedata (average of 5=5 pixels) of TM bands andlaboratory analyzed data of water samples wereperformed and results indicate that reflectance ofTM band 1 to band 4 and TOC, BOD and CODhave higher correlation. The statistical relationshipbetween satellite-based reflectance and organicpollution measurements including TOC, BOD andCOD were constructed. The standard errors ofestimation of these models vary from 0.24 to 0.30.The same scene TM data in the same season of1988 was processed in the same procedure on thebase of calibration and atmospheric correction tothe remote sensed data. Results indicate waterquality of most reservoirs has become worse.Water of eastern reservoirs near Dongjiang Riveris characterized with higher TOC and TSS, andwater of western reservoirs is characterized withhigher BOD and COD.This study demonstrates the possibility, accura-

cy, potential and effectiveness for determining thewater quality measurements of reservoir such asTOC, BOD and COD with a little ground proof.Hence this technique has many advantages, suchas more convenient, providing the spatial distri-bution of water parameters and relatively less timeand money consuming comparing with traditionalmethods. Some factors must be considered whilethis technique will be used. These factors include:

● A composite sample from relative large areasin one location should be used for the labora-

tory analysis instead of a single sample, becausethe spatial resolution of TM.

● The size of the location pixels should be eval-uated. Of course, single pixel DSL analyzedcould not be compared with a ground samplebecause of the fluidity of the water. Someexperts take the scale of 3=3 pixels(Giardianoet al., 2001). According to our experience,larger scales should be considered, such as5=5 pixels in this research.

● In situ measurements of radiation and atmos-pheric conditions are vitally important both forthe calibration and correction to remotelysensed data and the statistical model construc-tion of water quality. In a new area, it isnecessary to perform the in situ measurementsof spectra and atmospheric conditions, eventhrough the same technique and procedure areused. Combining with the actual measurement,different methods of calibration and atmospher-ic correction should be compared in a new areaor reservoir to determine the better strategy.

● The complexity of water quality affects theaccuracy of this technique. TSS should be takenaccount in, especially in the shallow area ofthe water. Seasonal variation is another impor-tant factor to the remote sensor and hydraulicconditions. The calibration parameters shouldbe adjusted in different seasons. This study isundertaken in dry season, the calibration para-meters and statistical models should be modi-fied while being used in humid season.

● Multi-regression method should be proved andmodified by some ground sample analysisresults and applied regionally. Finally, althoughthe satellite data can be used to reflect thewater quality parameters such as Secchi diskdepth, chlorophyll concentrations, pigmentload, total suspended sediments, temperatureand some chemical properties, and this tech-nique is valuable and important for remoteareas where direct access is not easy and wherethe sum of sampling and analytical chemistrycost is high, it should be emphasized that thistechnique cannot substitute the traditional meth-ods because some parameters of water qualitysuch as heavy metals, nitrate, phosphate and


organic pollutants cannot be determined byremote sensing.

Acknowledgments

This work was supported by National NaturalScience Foundation of China(Grant No.40171077, 49901014), National Key BasicResearch Programming Project(Grant No.2001CB209101) and Natural Science Foundationof Guangdong Province(Grant No. 010505). Wewant to greatly thank Kurtis J. Thome and MagalyKoch for providing some materials of TM datacalibration and Kim Neth for improving Englishof this paper. We also want to thank two anony-mous reviewers for their constructive comments.

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Water Quality Change in Reservoirs of Shenzhen China IMPORTANT

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