Application of Landsat 5 and Landsat 7 images data ...

Application of Landsat 5 and Landsat 7 images data for water quality mapping in Mosul Dam Lake, Northern Iraq Mohammed F. O. Khattab & Broder J. Merkel

Arabian Journal of Geosciences ISSN 1866-7511 Volume 7 Number 9 Arab J Geosci (2014) 7:3557-3573 DOI 10.1007/s12517-013-1026-y

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Author's personal copy Arab J Geosci (2014) 7:3557–3573 DOI 10.1007/s12517-013-1026-y

ORIGINAL PAPER

Application of Landsat 5 and Landsat 7 images data for water quality mapping in Mosul Dam Lake, Northern Iraq Mohammed F. O. Khattab & Broder J. Merkel

Received: 13 February 2013 / Accepted: 30 June 2013 / Published online: 24 July 2013 # Saudi Society for Geosciences 2013

Abstract Mosul Dam Lake is the main reservoir in Iraq, supporting the water demand of Mosul, Baghdad, and other cities. The aim of this study is to derive simple and accurate algorithms for the retrieval of water quality parameters for Mosul Dam Lake from Landsat 5 and Landsat 7 reflectance data. The water quality measurements were performed in situ during March and July 2011. These measurements included temperature, turbidity, Secchi disk, chlorophyll-a, nitrate, nitrite, phosphate, total inorganic carbon, dissolved organic carbon, total dissolved solids, and pH. In order to properly use the values of reflectance bands, images enhancement techniques have been used. The field measurements were compared with reflectance values of Landsat 5 and Landsat 7 bands using different band combination of empirical algorithms. Generally, the results of analysis showed significant correlation between these models and water quality parameters with R2 >0.7 and p0.9 and p0.9, and values of the root mean square error ranged from 0.9 to 0.001. ArcGIS 10 was used to simulate the distribution values of water quality parameters calculated from spectral values of TM5 and ETM+ bands. The results of spatial analysis demonstrate that it is possible to use the TM5 and ETM+ images to evaluate the water quality for Mosul Dam Lake. M. F. O. Khattab (*) : B. J. Merkel Hydrogeology Department, TU Bergakademie Freiberg, Freiberg, Germany e-mail: [email protected] B. J. Merkel e-mail: [email protected] M. F. O. Khattab Remote Sensing Center, Mosul University, Mosul, Iraq

Keywords Water quality . ETM+ . TM5 . Mosul Lake . Mapping Nomenclature ATCOR Atmospheric/topographic correction Chl-a Cholorophyll-a DOC Dissolved organic carbon EC Electric conductivity LUT Image Enhancement via Lookup Table NDWI Normalized difference water index NIR Near infrared NO2 Nitrite NO3 Nitrate p Significance pH Potential of hydrogen PO4 Phosphate 2 R Coefficient of determination RMSE Root mean of squared error SEE Standard error of the estimate SLC Scan line corrector SWIR Shortwave infrared TDS Total dissolved solids TIC Total inorganic carbon TM5 Thematic Mapper sensor ETM+ Enhanced Thematic Mapper Plus ρ Band reflectance

Introduction Generally, water quality is explained in terms of physical, chemical, and biological parameters. As a result of rapid urban population growth and increasing industrial, agricultural, and urbanization activities as well as global climate changes, lakes and reservoirs are facing many challenges, the most important being the increase of nutrients and sediment concentration (Kondratyev et al. 1998; He et al. 2008).

Author's personal copy Arab J Geosci (2014) 7:3557–3573

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Therefore, the monitoring of water quality is a very necessary task for each country to supply water quality data to government and the relevant institutions (Chen et al. 2007). The conventional techniques for monitoring water quality are usually expensive (particularly for developing countries), time consuming, and unable to provide spatial and temporal outlook for the water bodies with limited sample points. The remote sensing technique may offer an appropriate method to integrate water quality data collected from traditional in situ measurements. Satellites images have already been widely used in monitoring many substances in water bodies (Wu et al. 2009). Landsat 5 and Landsat 7 provide a wellcalibrated continuous data set of moderate spatial resolution (30 m), multispectral images, repetition rate of 16 days, dating back to 1984 (Thematic Mapper sensor (TM5)) and 1999 (Enhanced Thematic Mapper Plus (ETM+))(Rogan and Chen 2004). Also, the TM5 and ETM+ images are characterized by reliable geometric integrity and validated radiometric quality and they are freely available; therefore, these images are perfect for the study of natural resources (Moran et al. 2001). Mosul Dam Lake is one of the largest

Fig. 1 Location map of study area

artificial water reservoirs in Iraq. This lake contributes to the supply of drinking water to Mosul, Baghdad, and other cities. The main goals of this study were to explore empirical algorithms for the retrieval of temperature, turbidity, Secchi disk, chlorophyll-a, nitrate, nitrite, phosphate, total inorganic carbon, dissolved organic carbon, total dissolved solids, and pH data for Mosul Dam Lake from TM5 and ETM+ images and to determine both the spatial and temporal distributions of water quality. An additional aim was to map the water quality parameters and display the possibility of using the models discovered instead of the conventional techniques to monitor the water quality of Mosul Dam Lake.

Study area Mosul Dam Lake is located on the Tigris River about 60 km north of Mosul City (Fig. 1). This lake, which was constructed in 1985, is one of the largest artificial water storage facilities in Iraq. The surface area of the lake is about

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385 km2 at the maximum operation level (330 m above sea level) with a total storage volume of 13.13 billion m3 and a maximum water depth of 80 m and its drainage basin reaches to 4,200 km2 inside Iraq (Kelley et al. 2007; Al-Taiee and Sulaiman 1990). The Tigris River discharges between 60 and 5,000 m3/s of water into the Mosul Dam Lake. The outflow has ranged between 100 and 1,000 m3/s since the construction of the dam in 1985 (AL-Obaydy 1996). The study area is a semiarid region, where the average rainfall between 1985 and 2006 was 382 mm/year and the average mean maximum temperature during July and January between 1985 and 2008 was 43.1 and 6.3 °C, respectively.

measure temperature, pH, and EC. Chlorophyll-a (Chl-a) was measured using probe at the March sites. For Chl-a analysis for the July sites, water samples were collected and analyzed according to the standard methods for the examination of water and wastewater (Eaton et al. 2005). NO3, NO2, and phosphorous were measured by means of a HACH Portable Colorimeter DR/NO2890 in the field (Shugar et al. 2000). The TIC and DOC were measured by liquiTOC device. DOC was measured after filtering the water through 0.45 μm filters. TDS were measured during March using a probe. TDS values were calculated as the sum of mass concentration of ions determined by ion chromatography (Gros et al. 2008). For July sites, the turbidity (by a probe) and Secchi disk were measured.

Methods Satellite data In the current study, water quality samples and Landsat satellite data were used to derive simple and accurate algorithms for the retrieval of water quality parameters for Mosul Dam Lake from Landsat 5 and Landsat 7 reflectance data. Collection of water quality samples Seven samples were collected in March 2011 and five during July 2011 (Fig. 2). The sampling took into consideration the monitoring of seasonal changes and the trophic situation of the lake, and samples covered the mesotrophic and eutrophic areas at the Dohuk River outlet (Khattab and Merkel 2012). Water was collected from the surface of the lake using sterile 100 ml glass bottles. Chlorophyll summer samples were transferred to the College of Science Department of Biology at Mosul University in Iraq. Samples for TIC and DOC were transferred to the geological department of TU Bergakademie Freiberg, Germany. Water quality parameters The Multi-Parameters Water Quality Sonde (YSI 6600 V2) and Multi Water Quality Checker (Model U-52) were used to Fig. 2 Locations of water quality samples

The main goal of this research is to determine the empirical formulas for the spectral properties of water, derived from satellite data and water quality parameters. Multi-data satellite images from a Landsat sensor were used to achieve this purpose. Two satellite images from Landsat 5 (equipped with TM5) and Landsat 7 (equipped with ETM+ instrument) were used in the present study. Images were acquired in March and July 2011 (Table 1). The quality of these images was good, with cloud cover ≤4.54 %. Satellite data processing All the images used in the current study were downloaded from the US Geological Survey (USGS) database at http://glovis.usgs.gov/. The Landsat program as an L1T product is used to catalogue the images, where images are georeferenced with a level of precision exceeding 0.44 pixels (13.4 m) (NASA 2006; Pardo-Pascual et al. 2012). Unfortunately, the images from Landsat 7 which were used in this research were suffering from the scan line corrector (SLC). The following paragraphs explain the processing operations which have been used to address the SLC problem and atmospheric effects on satellite data.


3560 Table 1 Attributes of Landsat 7 ETM+ and Landsat 5 TM bands

ETM+/TM5 Date

Band no.

ETM+/TM5 Wavelength (nm)

ETM+/TM5 Ground resolution (m)

March 5, 2011/July 3, 2011

1 2 3 4 5 6(L/H) 7 Pan

450–520/450–520 530–610/530–610 630–690/630–690 780–900/760–900 1,550–1,750/1,550–1,750 10,400–12,500/10,400–12,500 2,090–2,350/2,080–2,350 520–900/Nil

30/30 30/30 30/30 30/30 30/30 60/120 30/30 15/Nil

Gap-fill procedure The Landsat 7 ETM+ SLC failed on May 31, 2003, which resulted in the loss of approximately 22 % of the pixels in any ETM+ images (Zhu et al. 2012). Many methods have been developed to solve the SLC problem. One of these methods, recommended by USGS and NASA, includes combining an SLC-off image with one or more SLC-on images to produce a scene without gaps (Zhu et al. 2012; Storey et al. 2005). This method was applied in the current study through a series of steps, as follows (Fig. 3). A. Image selection Image selection is the first step in the gap-fill procedure for Landsat 7 images, as finding suitable images leads to good results. Two Landsat 7 (ETM+) images have been used to fill the gap area of the ETM+ scene acquired in March 2011. The ETM+ images which were used in this work (the fill images) were acquired on February 17, 2011 and March 2, 2010. Image selection for gap-filling was based on minimal cloud, same season, and small time interval. B. Image enhancement via LUT Before the gap-filling operation, the radiometric modification method is recommended to match the SLC-on images to the SLC-off image (Boloorani et al. 2008; Zhu et al. 2012; Storey et al. 2005). The method was used in this application, including the use of histogram matching. Histogram matching is widely used as a radiometric enhancement technique and is incorporated in many image processing software packages (Liang et al. 2001; Yang and Lo 2000). This method is based on the idea of matching the histogram of one image to that of another by creating an LUT that will transform the histogram of an SLC-on image to match that of an SLC-off image. In this research, the LUT has been built for each band in isolation and then the image enhancement via LUT for SLC-on image bands has been done, which produces very close brightness distribution between them. C. SLC-off image filling The method of filling gaps based on histogram matching is very simple and easy to apply (Chen et al. 2011). The fundamental concept of SLC-off to SLC-on

gap-filling is to fill gap areas in an ETM+ image by getting the values of pixels from the fill image or images, which have already been enhanced (Boloorani et al. 2008). The conditional formula (if SLC-off image >0, use SLC-off image data, else, use SLC-on image) was used to fill gaps for each band individually. The previous formula was applied twice: firstly between the original image (gap image) and a fill image, and secondly between the resulting image from the previous stage and the other image. After application of this procedure, the product image is without gap (Fig. 4). Atmospheric correction Electromagnetic radiation that is reflected by the earth and sensed by satellite sensors must pass though the atmosphere, which produces overlap between the reflected radiation and atmospheric components (atmospheric effects). These atmospheric effects include the absorption and scattering operations of the energy, which are detected by satellite sensors (Jones 2010; Tyagi and Bhosle 2011; Noyola-Medrano and Rojas-Beltrán 2010). Therefore,

Fill images

SLC-off

(ETM+)

image (ETM+)

Histogram Matching

Creating Lookup Table (LUT)

Image Enhancement via Lookup Table (LUT)

Filling SLC-off Image

The Resulting Image

Fig. 3 Flow chart for the gap-fill procedure


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Fig. 4 Result of gap-filling procedure for one band: a original image and b image after gab-filling

picking up quantitative data accurately and precisely from the remote sensing sensors requires atmospheric correction (Chander et al. 2009; Liang et al. 2001). In order to improve the quality of the images used, the atmospheric effects of satellite data (TM5 and ETM+) were corrected by using an ACTOR module (Fig. 5). The ACTOR module, recently ACTOR 2, ACTOR 3, and ACTOR 4, is used for calculating a ground reflectance and emissivity images for spectral bands of satellites. The modules are designed by Dr. Richter of the German Aerospace Center—DLR (San and Suzen 2010). The module process includes turning the digital pixel value of the original image into a percentage eflectance value. The resulting value represents the magnitude Fig. 5 Comparison of TM5 and ETM+ images (bands 1, 2, and 3) after and before atmospheric correction: a TM5 image before atmospheric correction; b TM5 image after atmospheric correction; c ETM+ image before atmospheric correction; and d ETM+ image after atmospheric correction

after the removal of mainly the influence of haze (aerosol practices and water vapor) of solar energy sensed by the satellites (Jones 2010). Waterline extraction The step of waterline (coastline) extraction and delineation for water bodies is significant. The waterline is the basis for measuring and characterizing land and water through its involvement in many applications such as coastal erosion, coastal zone management, watershed definition, environmental pollution, and the evaluation of water bodies (Bouchahma and Yan , 2012; Bagli and Soille 2003; Liu and Jezek 2004).


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Furthermore, the waterline is one of the 27 most important geographic features on the earth's surface, according to the International Geographic Data Committee (Li et al. 2002). The waterline is defined as a spatially continuous boundary between water and an exposed land mass (Liu et al. 2011; Ryu et al. 2002). Traditionally, the waterline was manually extracted by cartographers through visual interpretation of aerial photographs. This technique is difficult, costly, and time consuming and is sometimes impossible (Bagli and Soille 2003; Liu et al. 2011). In addition to those mentioned above, there are other restrictions such as being unable to monitor the seasonal changes of water bodies, and this method has become obsolete (Alesheikh et al. 2007). As a result of the increasing availability of satellite images, accompanied by growing spatial and spectral resolution, geographical information systems, and image processing techniques, waterline extraction based on satellite sensor is now common (Bagli and Soille 2003). Waterline extraction from satellite images assists with finding solutions to many problems, especially in the field of generating and updating the waterline maps (Alesheikh et al. 2007).

TM-5 image

ETM+ image

ETM+ image filling

Atmospheric Correction

ρgreen-ρNIR ρgreen+ρNIR

T M5

NDWI

ETM+

ρNIR-ρSWIR ρNIR+ρSWIR

Threshold segmentation

Binary image (water and land)

Raster to vector

Automatic waterline extraction Various methods have been developed to extract the waterline from satellite images. In this research, a set of steps was followed to extract and measure the waterline (coastline) of Mosul Dam Lake from satellite images (Fig. 6). The images used to extract the waterline of Mosul Dam Lake are the same images that were captured during water quality sampling. These images have been corrected as described in the preceding paragraphs. In this research, NDWI was used to delineate the surface water of the lake. The NDWI is one of the successful methods used to extract waterline from satellite images. The main goal of the arithmetic operation to find the spectral index is to produce a single number from two or more spectral bands (Ji et al. 2009). In the current study, the NDWI used for TM5 and ETM+ images was as follows: NDWIETMþ ¼ ðρNIR−ρSWIRÞ=ðρNIR þ ρSWIRÞ

ð1Þ

NDWITM5 ¼ ðρgreen−ρNIRÞ=ðρgreen þ ρNIRÞ

ð2Þ

where ρNIR, ρSWIR, and ρgreen are the reflectance of the near-infrared band (band 4), the shortwave infrared (band5), and the green (band2), respectively. The histogram profiles of TM5 (band 4 and band 2) and ETM+ (band 4 and band 5) are shown in Fig. 7. The bimodal distribution of histogram bands used to calculate the NDWI for Mosul Dam Lake is presented in Fig. 7. Designing the NDWITM5 by green and INR bands aimed to maximize the reflectance properties of water

Change measurement

Fig. 6 Flow chart for extracting waterline from images

(Bouchahma and Yan 2012). The NDWITM5 gives a good result when the sediment exposure is dry (Murray et al. 2012). Thus, the NDWITM5 is appropriate to extract the waterline from the TM5 images captured during July (summer) 2011. The NDWIETM+ used is similar to Gao's index (Gao 1996), where the SWIR is sensitive to soil moisture, plant water content, and high absorption by water (even turbid water) (Alesheikh et al. 2007; Sarodja 2011). As a result of these characteristics, the waterline extraction during March 2011 by applying Gao's index is more correct. The values of NDWI are in a range of −1 to 1. The values that are greater than 0 refer to water area while values which are less than or equal to 0 refer to non-water (McFeeters 1996; Ji et al. 2009). Figure 8 shows the waterlines extraction for Mosul Dam Lake during March 5, 2011 and July 3, 2011. The surface area of the lake was 142 km2 during March and 284.539 km2 during July, where the difference in the surface area of the lake during the two periods was more than 33 %. Regression analysis There are many algorithms used in the literature to examine the relationship between water quality parameters (chlorophyll-a and Secchi disk) and remote sensing data (Giardino et al. 2001;


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Fig. 7 Histograms of TM5 bands (b2 and b4) and ETM+ bands (b4 and b5) for Mosul Dam Lake

Zhang et al. 2003; Yüzügüllü and Aksoy 2011). In this study, the algorithms for regression analyses were created to examine the relationship between reflectance of TM5 and ETM+ bands and water quality parameters of Mosul Dam Lake in Northern Iraq. The water quality parameters used in this analysis were temperature, pH, EC, Chl-a, NO3, NO2, PO4, TIC, DOC, and TDS during March. The reflectance values of TM5 and ETM+ bands were used after stage image correction, as described the previous paragraph. During July, the quality parameters were temperature, turbidity, Secchi disk, pH, EC,

Fig. 8 Coastline of Mosul Dam Lake

Chl-a, NO3, NO2, PO4, TIC, DOC, and TDS. The performance of these equations was checked depending on R2, the standard error of the estimate (SEE), and p values. SPSS 11.5 was used to calculate mathematical parameters. The equations for the regression algorithms for TM5 and ETM+ bands and water quality parameters are presented in Tables 2 and 3, respectively. Results of the analysis give valid relational models, depending on R2 and p, between reflectance of TM5 and ETM+ bands and water quality parameters. The spectral data of bands used were significantly correlated with water


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quality parameters with R2 values >0.8 and p values 0.71 (Table 2). Traditionally, the algorithm for Chla is based on blue and green bands (Gilerson et al. 2010). In this research, the traditional algorithm was proved with a linear equation (R2 =0.99 and p=0.01). Although improved results have been found in many other studies, the ratio (band 1/band 3) of TM5 is closely related to Secchi disk (Fuller et al. 2004). The result of analysis showed that the Secchi disk was strongly correlated with band 3, and the exponential model was the best to simulate this relationship (R2 =0.88 and p=0.01). The turbidity was correlated strongly with the ratio (band 2/band 3) and band 4 (R2 =0.97 and p=0.02). Clear water has higher reflectance in the green band and lowers in the red and NIR bands, while the opposite can occur in turbid water (Duong 2012; Wua et al. 2008). The multiple linear models were the best fit to simulate the variation in EC and TDS values in situ with band reflectance. The EC was clearly correlated with the ratio (band 2/band 3), where R2 =0.84 and p=0.02, while the TDS was correlated with the ratios (band 2/band 3), (band 1/band 3) and band 4 (R2 =0.99, p=0.02, and SEE=0.0004). Generally, an increase in water salinity leads to significant changes in the amount of reflected radiation within the visible and nearinfrared bands (Ahmed 2001; Pegau et al. 1997). The surface temperature of the lake was correlated with the ratio (band

6/band 7). The R2 value was 0.72 and p=0.06. Commonly, the thermal infrared band (band 6) of Landsat TM5 is used to detect the land surface temperature (Sobrinoa et al. 2004). Relative to other TM5 bands, the thermal infrared band has the lowest radiometric sensitivity and lowest resolution (120×120 m). Furthermore, the SWIR wavelength TM5 sensors (bands 5 and 7) are most strongly correlated with temperature (Southworth 2004). Nitrite and nitrate had positive significant relationship with the red band and (band 2/band 3), respectively. The linear mathematical formula was clear to simulate the relationship between NO2 and band 3, while the power model was a better fit to simulate the relationship between NO3 and the ratio (band 2/band 3). Values of R2 ranged between 0.92 for nitrite and 0.93 for nitrate, while values of p were equal to 0.009 and 0.006 for NO2 and NO3, respectively. The sensitivity of the green−red band to nitrite and nitrate concentrations during the summer season is most likely due to their association with chromophoric dissolved organic matter (Chen et al. 2009; Para et al. 2010). There was a positive linear relationship (R2 =0.75) between phosphate and band 5. This is consistent with recent studies which have shown the existence of a strong correlation between phosphorous and longer wavelength bands (bands 5 and 7) (Sridhar et al. 2009; Vincent 2010). The DOC was significantly inversely correlated (R2 =0.78 and p=0.04) with band 5. The DOC absorbs light and therefore reduces the reflectance (Guan 2009; Hirthle and Rencz 2003). TIC was positively correlated with the NIR band. The growth model has been used to simulate this relationship (R2 =0.74 and p=0.05). An increase in inorganic substances (dissolved and suspended) in water likely shows good reflectance in the red-IR region (630–2,500 nm) (Aenugu et al. 2011; Bendiganavale and Malshe 2008). The pH was inversely correlated with band 5 (R2 =0.75 and p=0.05). The

Table 2 Regression algorithms for TM5 bands and water quality parameters for Mosul Dam Lake R2

SEE

p value

Water quality parameter

Equation

Secchi disk

=3119.27 e− 0.233b3

0.88

0.311

0.01

EC NO2 NO3 PO4 Turbidity DOC TIC Chl-a Temperature pH TDS

=0.5185−0.3679/(b2/b3) =0.109−0.003b3 =0.2394×(b2/b3)6.7012 =−3.783+0.264b5 =3 5.121−14.489(b2/b3)−0.911b4 =0.696+21.8637/b5 =e(2.6239+ 0.0107b4) =111.236−27.416(b1/b2)−70.17(b2/b1)−0.448b2 =25.16+19.272(b7/b6) =9.738−0.084b5 =−0.149+0.104(b2/b3)−0.025(b1/b3)+0.004b6

0.84 0.92 0.93 0.75 0.99 0.78 0.74 0.99 0.72 0.75 0.99

0.007 0.002 0.103 0.476 0.802 0.126 0.041 0.014 0.25 0.087 0.0004

0.02 0.009 0.006 0.05 0.009 0.04 0.05 0.01 0.06 0.05 0.02

Author's personal copy Arab J Geosci (2014) 7:3557–3573 Table 3 Regression algorithms for ETM+ bands and water quality parameters for Mosul Dam Lake

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Water quality parameter

Equation

R2

SEE

p value

EC NO2 NO3 PO4 DOC

=−0.585+0.002b4 +0.008 b62 +0.322(b3/b2) =−0.658+0.008b4 +0.012(b1/b3)+0.569(b62/b61) =1.782+75.469 ln(b62/b61) =−0.081−0.008b3 +0.018b4 =0.678+0.015b4 −0.019b5 +0.027b7

0.95 0.99 0.60 0.96 0.94

0.033 0.013 1.512 0.029 0.042

0.015 0.002 0.040 0.001 0.019

TIC Chl-a Temperature pH TDS

=11.771+0.051b4 +0.555b1 +0.089b5 =−15.16+0.449b1 −1.252(b3/b1) =−7.463+0.119b61 +0.066b62 −0.017b5 =−200.914+304.26(b62/b61)−95.202(b62/b61)3 =−0.920−0.002b2 +0.01b62 +0.001b4

0.94 0.88 0.97 0.87 0.96

1.046 0.942 0.119 0.074 0.009

0.020 0.013 0.006 0.015 0.009

correlation between pH and reflectance of SWIR 1 (band 5) could be as a result of the change in concentration of DOC. There is generally a positive relationship between pH and DOC (Clarke et al. 2005). ETM+ algorithms During March 2011, water quality parameters which included temperature, pH, EC, Chl-a, NO3, NO2, PO4, TIC, DOC, and TDS were measured in situ. Results of regression analysis for ETM+ bands and water quality parameters were better than those for TM5 based on R2 and p values. Earth observations obtained by ETM+ are more precise than those of TM5 because the ETM+ data have greater spatial resolution (15 m panchromatic band) and improved radiometric response compared with the previous TM. Therefore, the algorithms based on surface reflectance for ETM+ are more quantitative and accurate (Liang et al. 2001; Goward and Williams 1997). Mostly, the water quality parameters were clearly related to the reflectance of ETM+ bands (R2 >0.9 and p0.9 and RMSE