Monitoring of regional lake water clarity using Landsat imagery

661 © IWA Publishing 2015 Hydrology Research | 46.5 | 2015 Monitoring of regional lake water clarity using Landsat imagery Matias Bonansea, Raquel Bazán, Claudia Ledesma, Claudia Rodriguez and Lucio Pinotti ABSTRACT The application of remote sensing technology to water quality monitoring has special significance for lake management at regional scale. Water clarity expressed in terms of Secchi disk transparency (SDT) is a highly useful indicator of trophic status and ecosystem health. In this study, we related Landsat TM and ETMþ data with ground observations to develop a model for the estimation of SDT which can be used as a standardized procedure for regional-scale lake clarity assessment in the central region of Argentina. Samples were taken from two reservoirs of the region. Pearson correlation coefficients and step-wise multiple regression analysis were used to evaluate correlation between Landsat bands and measured SDT. Results suggested that Landsat band 3 plus the ratio 1/3 was a consistent and reliable predictor of SDT (R 2 ¼ 0.80). The algorithm was validated (R 2 ¼ 0.81) and applied to the November 10, 2010 ETMþ image obtaining a map that characterized water clarity of reservoirs within the study area. The procedure presented here could become a low cost measurement tool for water management authorities and decision-makers, obtaining simpler and practical results for regional water clarity monitoring. Key words | algorithm, Landsat, remote sensing, reservoir, Secchi disk transparency, water clarity Matias Bonansea (corresponding author) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Departamento de Estudios Básicos y Agropecuarios, Facultad de Agronomía y Veterinaria (FAyV), Universidad Nacional de Río Cuarto (UNRC), Ruta Nacional 36 Km 601, (5800) Río Cuarto, Córdoba, Argentina E-mail: [email protected] Raquel Bazán Departamento de Ingeniería Química y Aplicada, Facultad de Ciencias Exactas Físicas y Naturales (FCEFyN), Universidad Nacional de Córdoba (UNC), Juan Filloy s/n, Ciudad Universitaria, (5000) Córdoba, Argentina and Instituto Superior de Estudios Ambientales (ISEAUNC), Secretaria de Ciencia y Tecnología (UNC), Juan Filloy s/n, Ciudad Universitaria, (5000) Córdoba, Argentina Claudia Ledesma Claudia Rodriguez Departamento Ciencias Básicas, FAyV, UNRC, Ruta Nacional 36 Km 601, (5800) Río Cuarto, Córdoba, Argentina Lucio Pinotti CONICET – Departamento de Geología, UNRC, Ruta Nacional 36 Km 601, (5800) Río Cuarto, Córdoba, Argentina INTRODUCTION Decision-makers are demanding new tools for regional areas, which create spatially irregular, non-random samples. monitoring and assessment of water quality. The conven- Many lakes are rarely or never monitored, so an accurate tional measurements of regional assessment are logistically assessment of their status and change over time cannot be challenging and expensive to perform regularly due to made. Satellite remote sensing has been shown to be a cost, lake accessibility and the number of water bodies powerful supportive tool for regional water quality assess- requiring repeated sampling (McCullough et al. b). As ment, reducing costs and allowing monitoring to occur a result, sample sizes must be limited and usually cannot simultaneously across an extensive area (Trivero et al. encompass each type of water body present in a region; ; Larsen et al. ; Doña et al. ). therefore, the status of the water system at a regional scale Among several satellite systems that have been used can be difficult to represent (Zhao et al. ). According for water quality monitoring, the Landsat system, which to McCullough et al. (a), these restrictions lead to field provides an unparalleled record of the status and assessments concentrated in developed, easily accessible dynamics of the Earth’s surface since 1972, is particularly doi: 10.2166/nh.2014.211 Downloaded from http://iwaponline.com/hr/article-pdf/46/5/661/369199/nh0460661.pdf by guest 662 M. Bonansea et al. | Hydrology Research Monitoring of regional water clarity by Landsat | 46.5 | 2015 useful for assessment of inland lakes (Kloiber et al. b; in models. Although most of the studies on regional lake Matthews ; Wulder et al. ; Chao Rodríguez et al. water clarity estimation by remote sensing were carried ). Most techniques for remote sensing of water quality out for the northern hemisphere, little has been done to construct reliable empirical relationships between Landsat develop appropriate regional assessment of water clarity data and ground observations of water quality parameters, in the southern hemisphere. including chlorophyll and phycocyanin concentrations The objective of this paper was to develop an algor- (Vincent et al. ; Tebbs et al. ), water clarity ithm to estimate water clarity which can be used as a expressed in terms of Secchi disk transparency (SDT) standardized procedure for regional-scale lake clarity (Domínguez Gómez et al. ; Zhao et al. ; McCul- assessment in the central region of Argentina. Thus, we lough et al. a), total suspended sediments (Kulkarni were able to obtain a single standardized method with ; Bonansea & Fernandez ), among others. In this constant coefficient values that could be used by water study, we focus on SDT estimation due to its simplicity management authorities and decision-makers to achieve and relatively low cost. Besides, this parameter, which is information for lakes not sampled, allowing an easier widely used and a common metric of lake water quality, comparison of water clarity from different lakes at a has strong ecological and economic implications, being a regional scale. highly useful indicator of trophic status and ecosystem health (Sriwongsitanon et al. ; Zhao et al. ; McCullough et al. b; Chao Rodríguez et al. ). According METHODOLOGY to Domínguez Gómez et al. (), the assessment of water clarity has a crucial impact on water quality moni- Study area toring because it shows, in a global way, all the components that can be found in water and the many The western region of Córdoba province, located in the cen- interactions existing among them. tral region of Argentina, is characterized by a mountainous Most studies related to water clarity estimation by system called Sierras Pampeanas which encompasses Landsat imagery have focused on generating empirical approximately a 500 km long and 150 km wide area. This models for the lake or reservoir where samples were area presents nine moderately eutrophic reservoirs greater taken (Domínguez Gómez et al. ; Giardino et al. than 0.3 km2 (Figure 1) which were built between 1930 ; Guan et al. ). However, there has been increas- and 1950 for multiple purposes such as water supply, ing focus on regional-scale assessment of water quality power generation, flood control, irrigation, tourism and rec- and few monitoring programs exist for this purpose. Pull- reational activities (Bazán et al. ; Mancini et al. ; iainen et al. () suggest that the estimation of water Ledesma et al. ). quality from remote sensing data for numerous lakes As part of a monitoring program, since the 1990s sev- could be achieved using ground observation data for eral physical, chemical and biological properties of two only a few representative lakes from the region. Kloiber multipurpose reservoirs of the area (Río Tercero and Los et al. (b) and Olmanson et al. () described a prac- Molinos reservoirs) have been surveyed. Río Tercero reser- tical and efficient procedure for Landsat imagery for voir (32 110 S, 64 230 W) which is the largest artificial routine, regional-scale assessments of lakes for water reservoir in the province, has a surface area of 46 km2, a clarity, and Kloiber et al. (a) used this approach to volume of 733 hm3 and maximum and mean depths of measure spatial patterns and temporal trends in a large 46.5 and 12.2 m, respectively. In 1986, a nuclear power number of lakes. McCullough et al. (a) have shown plant (CNE: 600 MWa) was installed. Water for cooling that Landsat TM can be used to predict regional water the nuclear reactor is taken from the middle section of clarity in Maine lakes located in the northeastern United the reservoir and is returned to the western basin by a W W States, and those predictions are more accurate when 5 km long open-sky channel (Bonansea et al. ). Los average depth and watershed wetland area are included Molinos reservoir (31 490 S, 64 320 W), which is used to Downloaded from http://iwaponline.com/hr/article-pdf/46/5/661/369199/nh0460661.pdf by guest W W 663 Figure 1 M. Bonansea et al. | | Hydrology Research Monitoring of regional water clarity by Landsat | 46.5 | 2015 Principal reservoirs of the western region of Córdoba province and position of sampling sites in Los Molinos and Río Tercero reservoir. supply drinking water to Córdoba city (with 1.4 million 2 resolution is 30 m for the visible through middle infrared inhabitants), has an area of 21.1 km , an average depth of channels and 120 m for the thermal infrared band (Loveland 16.3 m and a maximum volume of 399 hm3 (Bazán et al. & Dwyer ). The ETMþ sensor has a similar suite of ). bands as TM, but with a 60 m thermal band and an Water clarity was estimated in the field by measuring additional 15 m panchromatic band. Both sensors present SDT using a standard 20 cm diameter Secchi disk at a revisit time of 16 days and a radiometric resolution of nine sampling sites in Río Tercero and five sites in Los 256 digital numbers. Molinos reservoir (Figure 1). Coordinates of sample sites The criteria for image selection were: existing in situ were recorded using a Global Positioning System (GPS) data of both reservoirs in ±4 days to the satellite passes device. (time window) obtaining reasonable results for empirical relationships between SDT and Landsat imagery; no Satellite data heavy rainfall prior to the image data to minimize the effects of changes in water surfaces that disturb the esti- We used images from Landsat TM and Landsat ETMþ mates; 0% haze or cloud cover when possible. To detect (Path: 229; Row: 82) downloaded from the USGS Global haze and cloud cover, which affect spectral-radiometric Visualization TM responses and cause erroneous results, an RGB band sensor is equipped with multi-spectral scanning equipment, combination (1,6,6) was used (Olmanson et al. ). which operates on seven spectral bands located between The selected criteria are in agreement with different the visible and infrared regions of the spectrum. The spatial authors (Kloiber et al. b; Sriwongsitanon et al. ; Viewer (http://glovis.usgs.gov). Downloaded from http://iwaponline.com/hr/article-pdf/46/5/661/369199/nh0460661.pdf by guest The 664 M. Bonansea et al. | Hydrology Research Monitoring of regional water clarity by Landsat | 46.5 | 2015 Tebbs et al. ). Thus, from the pool of suitable images, Geometric correction was applied to each scene, we selected one Landsat TM image and two ETMþ resulting in a root mean square error (RMSE) of pos- images (Table 1). itional accuracy of less than 0.5 pixel, guaranteeing a precise geometric match between images. Since May 2003, ETMþ images have a permanent failure known as Image pre-processing Scan Line Corrector (SLC): off, characterized by wedgeshaped gaps (Chen et al. ). Using a methodology The electromagnetic radiation signals collected by satellites adapted from the SLC Gap-Filled Products, Phase One in the solar spectrum are modified by scattering and absorp- Methodology article (USGS ), SLC failure was cor- tion by gases and aerosols while traveling through the rected predicting the best closest value of the missing atmosphere from the Earth’s surface to the sensor (Song pixels. To delineate the lake surface masks, producing et al. ). Atmospheric corrections to satellite data are ‘water-only’ images and isolating anomalously pixels that therefore important for correcting these effects, so that infor- do not belong to the reservoirs, the normalized difference mation from multitemporal data set with variable aerosol water index (NDWI) algorithm proposed by McFeeters loading can be sensibly compared. Using the Second Simu- () was applied. According to Ji et al. () and lation of the Satellite Signal in the Solar Spectrum (6S) Alcântara et al. (), the NDWI can be used successfully (Vermote et al. ), atmospheric correction was carried in delineating water bodies and monitoring the water area out. changes. The importance of applying the 6S model to improve the estimates of water lake clarity was described in Sri- Algorithm development wongsitanon et al. (). These authors suggest that the 6S model can remove the additive effects provided by As the locations of sampling points were georeferenced, it atmospheric rayleigh and aerosol scattering which influ- was possible to compare matchups between field data ence the visible Landsat bands (band 1–3). On the other and corresponding Landsat reflectance values. To deter- hand, the corrected reflectance values of the near infrared mine which spectral band or band ratio was the best and infrared bands (band 4–7) tend to be higher than the predictor of SDT, Pearson correlation coefficients and uncorrected reflectance values. This is because the near backward step-wise multiple regression analysis were car- infrared and middle infrared wavelengths are affected by ried out between in situ SDT (dependent variable) versus atmospheric absorption while the influence of air mol- atmospherically corrected reflectance values of Landsat ecules and aerosol particle scattering are negligible in bands or band ratios (independent variables). Applying these ranges. Since the 6S model can remove these effects, the Pearson correlation analysis, we assume that a high reflectance values within these bands were then increased level of correlation between variables is implied by a cor- (Sharma et al. ; Sriwongsitanon et al. ; Homem relation coefficient (r) greater than 0.5 in absolute terms Antunes et al. ). (Gupta ). The backward step-wise multiple regression analysis was performed using the thresholds for factor Table 1 | Landsat data set and sampling date in Río Tercero and Los Molinos reservoirs removal with a significance level of p-value more than 0.05. If the p-value is less than the threshold, it means Río Tercero reservoir Los Molinos reservoir Time relationship is then reliable to be used for prediction (Sriwongsitanon et al. ). Thus, we could identify the Acquisition Landsat Sampling window Sampling window image date sensor date (days) date (days) 4 09-28-2006 ETM þ 09-24-2006 10-01-2006 3 12-09-2006 TM 12-09-2006 0 12-11-2006 2 11-10-2010 ETM þ 11-10-2010 0 11-10-2010 0 Downloaded from http://iwaponline.com/hr/article-pdf/46/5/661/369199/nh0460661.pdf by guest that the null hypothesis is rejected and the regression Time spectral band or band ratio most correlated with in situ SDT, which were used to generate a model to estimate water clarity for all lakes within the region. In this case, the multiple linear regression model used was 665 M. Bonansea et al. | Hydrology Research Monitoring of regional water clarity by Landsat | 46.5 | 2015 defined as Yi ¼ β 0 þ β 1 X1 þ β 2 X2


þ β v Xv þ ε (1) where Yi refers to the response of the variable SDT, Xn are the explanatory variables of each Landsat spectral bands, βn are the regression coefficients, and ε is the random error. Simple regression analysis was made to evaluate the correlation between estimated versus observed SDT data. The RMSE of predicted SDT, which gives an estimate of the Figure 2 | Pearson correlation coefficients between SDT versus Landsat spectral bands and band ratios. Asterisks represent the spectral bands retained by the stepwise multiple regression analysis (p < 0.05). error associated with the estimations (Matthews et al. ), was calculated according to sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn XÞ2 i¼1 ðXi RMSE ¼ n ), showed a low association with measured SDT (r ¼ 0.39). Landsat band 2 is centred on an algal reflectance peak (Brezonik et al. ; Domínguez Gómez (2) et al. ). This spectral band, which was widely used for estimating chlorophyll-a concentration (Domínguez where Xi and X are the in situ and satellite-derived SDT and Gómez et al. ; Kulkarni ), showed a low relation n is the sampling size. with measured SDT (r ¼ 0.39). A low negative associ- Finally, as a demonstration of the real potential of ation was found between SDT and band 4 (r ¼ 0.47); remote sensing, the validated algorithm was applied to the this could be explained because absorbance by water pre-processed November 10, 2010 ETMþ image, obtaining increases sharply in this band (Brezonik et al. ). the spatial distribution of simulated errors, calculated as There was no association between measured SDT and the difference between simulated and observed SDT data, Landsat bands 5 and 7 (r ¼ 0.02 and 0.10, respect- and a map that characterizes water clarity of reservoirs in ively). Sriwongsitanon et al. () suggest that in the the central region of Argentina. infrared regions (band 5 and 7), water increasingly absorbs the light making it darker so these bands are useful for vegetation and soil moisture studies and for discriminating between rock and mineral types. Therefore, RESULTS AND DISCUSSION we have not analyzed the band ratios of these bands. Estimation of water clarity (band 6) was not used in the analysis because this band, The thermal infrared band of TM and ETMþ sensors which is based on the reflective properties of the Earth’s Remote sensing of water quality parameters is dependent surface in the short-wave part of the electromagnetic spec- upon how parameter variations alter the optical proper- trum, is used to estimate surface temperature (Giardino ties of the water column (Pavelsky & Smith ). et al. ; Chao Rodríguez et al. ). Our results Figure 2 shows the results of Pearson correlation coeffi- demonstrated that Landsat band 3 (ρ3) and band ratio cient and step-wise regression analyses between SDT 1/3 (ρ1/ρ3) can be used to investigate the most suitable versus Landsat spectral bands. According to Brezonik relationships for SDT monitoring as evidenced by high et al. (), suspended particles cause and increase in Pearson correlation coefficients (r ¼ 0.78 and 0.79, the measured response for Landsat bands 1–4. Landsat respectively). These results were confirmed in the step- band 1, which can be used to measure the irradiance wise multiple regression analysis where only band 3 and attenuation due to the absorption of aquatic humus and ratio band 1/3 were retained (R 2 ¼ 0.80). According to phytoplankton pigment concentration (Giardino et al. Matthews (), the negative correlation with band 3 Downloaded from http://iwaponline.com/hr/article-pdf/46/5/661/369199/nh0460661.pdf by guest 666 M. Bonansea et al. | Hydrology Research Monitoring of regional water clarity by Landsat | 46.5 | 2015 may be explained by the direct positive correlation combinations of Landsat bands 1 to 4 (Doña et al. ). between reflectance in the red and gross particulate load Some studies use Landsat band 2 (green band) or 4 (NIR inducing particulate scattering. Therefore, as SDT band) to estimate SDT (Lathrop & Lillesand , ; Doña et al. ), although there are few recent examples decreases, brightness in the red usually increases. Thus, the estimated response between in situ SDT and of this (Matthews ). Domínguez Gómez et al. (), the atmospherically corrected reflectance values in ρ3 and studying the trophic status of lakes located to the south of 2 ρ1/ρ3, was finally formulated as (R ¼ 0.80) Madrid, Spain, found that SDT, which is affected by phytoplankton and total suspended solids concentration, could be b ¼ 3:22 SDT 1:66ρ3 þ 0:64ρ1 =ρ3 (3) The 95% confidence intervals for the parameters of the model (Equation (1)) were calculated as 2.35 < β0 < 4.09; 2.32 < β1 < 1.00; and 0.41 < β2 < 0.88. Values of estimated and observed SDT were correlated applying a simple regression model. The good fit between observed and estimated SDT indicated the high predictive capacity of this model (R 2 ¼ 0.81). The error associated with the estimations (RMSE ¼ 0.64 m) was also reasonable and lower than the RMSE in SDT measured in McCullough et al. (a, b). Figure 3 also confirms the robustness of this algorithm as giving a good agreement between the gradient and intercept of the regression line. Therefore, the methodology used was considered to be adequate to study water clarity assessment in different water bodies of the region. According to Matthews (), there are a large number of studies using Landsat to retrieve SDT, and most of these use linear regressions of single bands or band ratios. Different studies suggest that SDT can be estimated from different associated with Landsat band 2, which shows the highest light penetration. However, in our study, Landsat band 3 plus the ratio 1/3 provided strong predictive relationship with SDT in reservoirs of Córdoba province. Several investigators had success with similar relationship. The same band combination was used by Lavery et al. () studying an estuarine system in western Australia. Hellweger et al. () found that TM band 3 provided a strong relationship to SDT. McCollough et al. (a) used TM bands 1 and 3 to predict SDT for Maine lakes, United States. According to Matthews (), the ratio between TM bands 1 and 3 is particularly common to estimate lake water clarity. Lathrop () and Cox et al. () suggest that ratio 1/3 is a strong predictor of SDT. Kloiber et al. (b) and Brezonik et al. () used Landsat band 1 plus ratio 1/3 to predict SDT with high accuracy. Similar results were found by Olmanson et al. () studying a series of lakes in Minnesota, United States and Zhao et al. () in Taihu lake, China. Map generation The analysis of the spatial distribution of simulation errors of the November 10, 2010 ETMþ image indicated that the central region of the reservoirs showed a lower difference between simulated and observed data (Figure 4). Both reservoirs showed that higher simulation errors, which were located near the shores and tributaries, could be related with the effect of the bottom or with tributaries inflow which generate an important change in SDT (Bonansea & Fernandez ). Although the method lost accuracy, the trend curve continues to be coherent. Although the validated algorithm was based on ground observations from only two reservoirs of the region, we used it to estimate SDT for all lakes within the study area. Figure 3 | Scatter plot of Landsat-estimated and observed SDT with 1:1 fit line. Downloaded from http://iwaponline.com/hr/article-pdf/46/5/661/369199/nh0460661.pdf by guest This is in agreement with McCullough et al. (a) who 667 Figure 4 M. Bonansea et al. | | Hydrology Research Monitoring of regional water clarity by Landsat | 46.5 | 2015 Spatial distribution of simulated errors and scatter plot of Landsat-estimated versus observed SDT from November, 10 2010 ETMþ image. demonstrated that remote sensing is useful in regions con- rainfall, as the case of this date. Dividing the reservoirs taining a large number of lakes that are cost prohibitive to into classes based on 1.5 m SDT intervals, we found that monitor regularly using traditional field methods. Thus, we the most common clarity class is 3.0 to 4.5 m. Mean water obtained a complete regional spatial sampling of water clarity remained stable between the reservoirs, with the clarity, allowing the mapping and analysis of spatial pat- exception of Arroyo Corto. terns. Figure 5 shows the spatial distribution of SDT in Córdoba reservoirs applying the validated algorithm to the Limitations pre-processed November 10, 2010 ETMþ image. Satellite estimated SDT ranged from <0.5 to about 6.7 m, with a Landsat sensors are a powerful tool that can provide sys- mean value of 3.5 m. Lower SDT values were registered in tematic and periodic information of water clarity in San Roque reservoir, coinciding with Amé et al. () reservoirs. However, there are limitations to monitoring and Galanti et al. (), who suggest that this reservoir is water quality with Landsat imagery. Over the past decade, classified as eutrophic to hypereutrophic with elevated con- TM and ETMþ imagery availability decreased over time centrations of nutrients. Lower SDT were also registered in due to different problems or anomalies (Wulder et al. ; Arroyo Corto reservoir whose waters are pumped to Cerro Marx & Loboda ). The deteriorated image quality result- Pelado reservoir and reused to generate energy by the ing from SLC failure, which was mentioned before, has Cerro Pelado Hydroelectric Complex in times of low become a major obstacle for Landsat 7 data applications Downloaded from http://iwaponline.com/hr/article-pdf/46/5/661/369199/nh0460661.pdf by guest 668 M. Bonansea et al. | Hydrology Research Monitoring of regional water clarity by Landsat | 46.5 | 2015 Landsat sensors. Thus, other satellite remote sensors, such as SPOT, ASTER, MODIS, MERIS, may be useful alternatives for lake water quality (McCullough et al. b; Chawira et al. ). However, a careful comparison of remote sensing reflectance data between sensors would be required beforehand and specific user needs should guide the selection of alternate data source. CONCLUSIONS Remote sensing provides suitable information concerning water quality and aquatic systems management. In this study, we demonstrated that water clarity of Córdoba reservoirs can be estimated by Landsat imagery. Pearson correlation coefficients and step-wise multiple regression analyses were used to investigate the relationship between SDT versus Landsat bands and band ratios. Band 3 and the ratio 1/3 proved to be consistent predictors of water clarity. The obtained algorithm was used as a standardized procedure for regional-scale lake Figure 5 | Estimated water clarity map obtained from November, 10 2010 ETMþ image. clarity assessment in the central region of Argentina. Rather than using regressions equations where the independent variables and coefficients are different for each (Chen et al. ). Since 2005, Landsat 5 has had problems Landsat image, we examined the feasibility of using a consist- with its solar array drive which has affected data availability ent water clarity equation form to relate ground observation (Wulder et al. ) and in mid-2013 this satellite was offi- and satellite data. Use of a consistent equation form is prefer- cially decommissioned. However, Landsat 5 is the longest- able because it allows for easier comparison of the results operating Earth-observing satellite mission in history, trans- from different images. Thus, the procedure presented here mitting over 2.5 million images of land surface conditions could become an independent, low additional training and around the world and resulting in a unique, long-term, sys- low cost measurement tool for water management authorities tematic collection of moderate resolution imagery (Wulder and decision-makers, obtaining simpler and practical results et al. ). As we have included a TM image, we could per- for regional water clarity monitoring. However, the form a retrospective analysis of water clarity back to the implementation and continuation of field-based reservoir early 1980s, since Landsat 5 was launched, and surface water quality monitoring in Córdoba reservoirs is essential data were obtained by TM sensor. for better calibration and validation of future remote clarity The scope of our study may be expanded with the estimation models. Finally, the inclusion of the new LDCM inclusion of the new Landsat 8 LDCM which was launched or other potential satellite sensors (e.g. SPOT, ASTER, on January 2013 and is the follow-on mission to Landsat 7, MODIS, and MERIS) could be useful to extend our study. presenting a higher imaging capacity than previous Landsat satellites (Loveland & Dwyer ; Wulder et al. ). Although there are no other missions analogous to Landsat ACKNOWLEDGEMENTS with global observation capabilities or accumulated global archives, Wulder et al. () suggest that several programs The authors thank the editor and reviewers for their helpful and sensors are identified as having the potential to emulate comments on this manuscript. This work was supported by Downloaded from http://iwaponline.com/hr/article-pdf/46/5/661/369199/nh0460661.pdf by guest 669 M. Bonansea et al. | Monitoring of regional water clarity by Landsat SECyT-UNRC (Secretaría de Ciencia y Técnica, UNRC) and SECyT-UNC. 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