Retrieval Of Water Quality Parameters Research Articles

GF5-Based Water Quality Monitoring of East Dongting Lake, China

Water quality monitoring is not only the basis for effective comprehensive management of water environment and control of water pollution, but also an important basis for risk assessment of algal blooms and construction of ecological safety. In this study, we used GF5-Advanced Hyperspectral Imager (AHSI) products to monitor the water quality of East Dongting Lake in winter. Through analyzing the obtained spectral reflectance, we calibrated our empirical models for retrieving the water quality parameters including chlorophyll a (Chl-a) concentration, total suspended matter (TSM) concentration and transparency (Secchi disk depth: SDD). This study preliminarily confirmed the potential of GF5-AHSI images in retrieving water quality parameters and could provide some useful information for the local environmental protection departments and Dongting Lake researchers.

Evaluation of the Crosta method for the retrieval of water quality parameters from remote sensing data in the Pearl River estuary

Abstract In recent decades, many algorithms have been developed for the retrieval of water quality parameters using remotely sensed data. However, these algorithms are specific to a certain geographical area and cannot be applied to other areas. In this study, feature-orientated principal component (PC) selection, based on the Crosta method and using Landsat Thematic Mapper (TM) for the retrieval of water quality parameters (i.e., total suspended sediment concentration (TSM) and chlorophyll a (Chla)), was carried out. The results show that feature-orientated PC TSM, based on the Crosta method, obtained a good agreement with the MERIS-based TSM product for eight Landsat TM images. However, the Chla information, selected using the feature-orientated PC, has a poor agreement with the MERIS-based Chla product. The accuracy of the atmospheric correction method and MERIS product may be the main factors influencing the accuracy of the TSM and Chla information identified by the Landsat TM images using the Crosta method. The findings of this study would be helpful in the retrieval of spatial distribution information on TSM from the long-term historical Landsat image archive, without using coincident ground measurements. This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www.cawq.ca).

Novel Spectra-Derived Features for Empirical Retrieval of Water Quality Parameters: Demonstrations for OLI, MSI, and OLCI Sensors

The empirical (regression-based) methods for estimation of water quality parameters are mostly built upon the features derived from the original feature space of optical imagery (e.g., band ratios). This article aims at examining novel features to retrieve in-water constituents including chlorophyll-a (Chl-a), total suspended solids (TSS), and colored dissolved organic matter (CDOM). In this article, direction cosines and transformation of either color space or the coordinate system are applied to the original feature space in order to derive new features. A full-search approach is exploited to identify the optimal band combination for a given type of feature. The proposed analysis seeks for a band combination among all the possible ones that yield the strongest correlation through regressing a given feature against the concentration of the constituent of interest. The effectiveness of the proposed features is examined against standard ones using radiative transfer simulations, in situ measurements, and satellite imagery in a wide range of in-water optical conditions. The simulated and in situ data enabled in-depth analyses on the efficacy of recent satellite sensors with the primary focus of the aquatic science community [Operational Land Imager (OLI), MutiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI)] for retrieval of in-water constituents. TSS and Chl-a concentration of two alpine lakes (Lake Constance and Lake Lucerne) are also mapped using a real OLI image. The results suggest the effectiveness of the proposed features that can be leveraged to estimate the constituents in inland/coastal waters. OLI-based retrievals of in-water constituents proved difficulties in optically complex waters, whereas enhanced spectral resolution of MSI and OLCI permitted accurate estimates.

Monitoring water quality in Singapore reservoirs with hyperspectral remote sensing technology

Abstract In this work, algorithms were developed for retrieving water quality parameters related to Singapore's reservoirs. The main constituents that affect the water reflectance (WR) – i.e. proportion of incident light reflected from the surface of water bodies after removing the surface glint component – are the suspended particles, phytoplankton and coloured dissolved organic matter (CDOM). The existing absorption spectrum model for phytoplankton in seawater is not accurate for the phytoplankton types that exist in the fresh water environment. The phytoplankton absorption spectrum was modelled by a series of Gaussian peaks from 400 to 750 nm. The peak strengths were dynamically derived from the WR measured. The phytoplankton absorption model is incorporated into a semi-empirical WR model for retrieving the absorption and backscattering coefficients of water components such as suspended sediments, CDOM and phytoplankton. The chlorophyll-a concentration and water turbidity estimated using this model correlate well with field sampling measurements, with coefficients of determination (R2) exceeding 0.8.

Retrieval of water quality parameters of South Andaman coral Islands using remotely operated underwater vehicle

ABSTRACT Remotely operated underwater vehicle (ROV)-based spectral irradiance measurements at South Andaman coral reef islands are used to derive water quality parameters (chlorophyll-a, total-suspended sediments, turbidity, salinity, Secchi disk depth, diffuse attenuation coefficient) at different depths of the water column. Different combination of standard formula is used to extract the water quality information from the sub-surface reflectance measurement and compared with the data derived from satellite images and conventional water sample analyses. Water quality data derived from ROV-based irradiance measurement and satellite sensor (Landsat data) correlate well to the extent of 0.85 (R2). Results from spectral irradiance measurements up to 20 m water depth using the ROV along with high-definition images of coral reef biodiversity can be effectively used to understand the habitats' diversity and also to derive different water quality parameters in spatio-temporal scale.

Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI

The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been a short time since L2 data became accessible, therefore validation of these products from different aquatic environments are required. In this work we study the possibility to use S3 OLCI L2 products to monitor an optically highly complex shallow lake. We test S3 OLCI-derived Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) for complex waters against in situ measurements over Lake Balaton in 2017. In addition, we tested the machine learning Gaussian process regression model, trained locally as a potential candidate to retrieve water quality parameters. We applied the automatic model selection algorithm to select the combination and number of spectral bands for the given water quality parameter to train the Gaussian Process Regression model. Lake Balaton represents different types of aquatic environments (eutrophic, mesotrophic and oligotrophic), hence being able to establish a model to monitor water quality by using S3 OLCI products might allow the generalization of the methodology.

Near real time water quality monitoring of Chivero and Manyame lakes of Zimbabwe

Abstract. Zimbabwe's water resources are under pressure from both point and non-point sources of pollution hence the need for regular and synoptic assessment. In-situ and laboratory based methods of water quality monitoring are point based and do not provide a synoptic coverage of the lakes. This paper presents novel methods for retrieving water quality parameters in Chivero and Manyame lakes, Zimbabwe, from remotely sensed imagery. Remotely sensed derived water quality parameters are further validated using in-situ data. It also presents an application for automated retrieval of those parameters developed in VB6, as well as a web portal for disseminating the water quality information to relevant stakeholders. The web portal is developed, using Geoserver, open layers and HTML. Results show the spatial variation of water quality and an automated remote sensing and GIS system with a web front end to disseminate water quality information.

Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval

Ocean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality parameters of the world’s oceans and inland waters. This is done by using various regression algorithms to retrieve water quality parameters from remotely sensed multi-spectral data for the given sensor and environment. There is a great number of such algorithms for estimating water quality parameters with different performances. Hence, choosing the most suitable model for a given purpose can be challenging. This is especially the fact for optically complex aquatic environments. In this paper, we present a concept to an Automatic Model Selection Algorithm (AMSA) aiming at determining the best model for a given matchup dataset. AMSA automatically chooses between regression models to estimate the parameter in interest. AMSA also determines the number and combination of features to use in order to obtain the best model. We show how AMSA can be built for a certain application. The example AMSA we present here is designed to estimate oceanic Chlorophyll-a for global and optically complex waters by using four Machine Learning (ML) feature ranking methods and three ML regression models. We use a synthetic and two real matchup datasets to find the best models. Finally, we use two images from optically complex waters to illustrate the predictive power of the best models. Our results indicate that AMSA has a great potential to be used for operational purposes. It can be a useful objective tool for finding the most suitable model for a given sensor, water quality parameter and environment.

MODIS –AQUA AND SENTINEL-2 DATA FUSION: APPLICATION TO OPTICALLY SHALLOW WATERS OF LAKE MICHIGAN

Subsumed under the category of ocean colour (OC) data fusion tools, a new approach is developed to efficiently use the merits of two OC satellite sensors differing in their spatial and spectral resolution characteristics. The tool permits to combine high spectral but lower spatial resolution optical data from one satellite sensor with higher spatial resolution but lower spectral resolution data from the other one into an image possessing simultaneously both high spectral and high spatial resolution qualities. The developed algorithm employs the artificial intelligence tool: emulated/artificial neuron networks (AANs). The developed ANN algorithm performance and efficiency are demonstrated for Lake Michigan. The fusion was effected making use of multiband data from Sentinel-2 Multispectral Instrument (MSI) and MODIS-Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. In this version MODIS-Aqua sensor is chosen as an analog of the Sentinel 3 OLCI, whose spectrometric and atmospheric corrected data are yet unavailable. The multi-sensor (MS) optical-optical fusion results have persuasively demonstrated the efficiency of the approach and its applicability to studies of natural water bodies of different optical complexity. It can be utilized in combination with any biogeochemical retrieval algorithms. In the case of retrieving water quality parameters (WQP) in optically shallow aquatic environments, the employment of the fusion tool developed is particularly promising as the bottom reflectance properties are frequently highly heterogeneous. Indeed, in such cases, remote sensing optical data acquired at simultaneously high spatial and spectral resolution are certainly more advantageous as compared to that acquired separately by two different sensors operating either at high spatial (but low spectral) or high spectral (but low spatial) resolution. For the retrieval of WQP in optically shallow waters (OSW) a special algorithm called Biooptical Retrieval Algorithm (BOREALI) - OSW was applied to study the eastern coastal zone of Lake Michigan. The application of both the OC fusion tool and our BOREALI-OSW algorithm permitted to document both interannual dynamics in WQPs as well as bottom substrate spatial heterogeneity in the target OSW area of Lake Michigan.

In Situ and Satellite Observation of CDOM and Chlorophyll-a Dynamics in Small Water Surface Reservoirs in the Brazilian Semiarid Region

We analyzed chlorophyll-a and Colored Dissolved Organic Matter (CDOM) dynamics from field measurements and assessed the potential of multispectral satellite data for retrieving water-quality parameters in three small surface reservoirs in the Brazilian semiarid region. More specifically, this work is comprised of: (i) analysis of Chl-a and trophic dynamics; (ii) characterization of CDOM; (iii) estimation of Chl-a and CDOM from OLI/Landsat-8 and RapidEye imagery. The monitoring lasted 20 months within a multi-year drought, which contributed to water-quality deterioration. Chl-a and trophic state analysis showed a highly eutrophic status for the perennial reservoir during the entire study period, while the non-perennial reservoirs ranged from oligotrophic to eutrophic, with changes associated with the first events of the rainy season. CDOM characterization suggests that the perennial reservoir is mostly influenced by autochthonous sources, while allochthonous sources dominate the non-perennial ones. Spectral-group classification assigned the perennial reservoir as a CDOM-moderate and highly eutrophic reservoir, whereas the non-perennial ones were assigned as CDOM-rich and oligotrophic-dystrophic reservoirs. The remote sensing initiative was partially successful: the Chl-a was best modelled using RapidEye for the perennial one; whereas CDOM performed best with Landsat-8 for non-perennial reservoirs. This investigation showed potential for retrieving water quality parameters in dry areas with small reservoirs.

Improved water quality retrieval by identifying optically unique water classes

Accurate remote sensing retrieval of water quality parameters in complex coastal environments is challenging due to variability of the coastal environment. For example, in the coastal waters of Hong Kong water quality varies from east to west. The currently existing water zones, defined by the Hong Kong Environmental Protection Department (EPD) are based on ease of access to sampling locations rather than on water quality alone. In this study an archive of fifty-seven Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and HJ-1 A/B Charged Couple Device (CCD) images over a 13-year period (January 2000–December 2012) was used to define optically distinct water classes by Fuzzy c-Means (FCM) clustering. The clustering was applied by combining the Surface Reflectance (SR) derived from the first four bands of Landsat and HJ-1 scenes with 240 insitu samples of Chlorophyll-a (Chl-a) and Suspended Solid (SS) concentrations collected within 2h of image acquisition. The FCM clustering suggested the existence of five optically different water classes in the region. The significance of the defined water classes was tested in terms of the water SR behaviour in each band. The SR for Classes 1 and 2 in bands 1–3 was lower than in other classes, and band 4 showed the lowest reflectance, indicating that these classes represent a clearer type of water. Class 3 showed intermediate reflectance in all bands, while Classes 4 and 5 showed overall higher reflectance indicating high sediment contribution from the Pearl River Delta. Application of water quality retrievals within individual classes showed much greater confidence with Root Mean Square Error (RMSE) of 1.32μg/l (1.21mg/l) for Chl-a (SS) concentrations, compared with 5.97μg/l (2.98mg/l) when applied to the whole spectrum of different water types across the region.

Developing a Comprehensive Spectral-Biogeochemical Database of Midwestern Rivers for Water Quality Retrieval Using Remote Sensing Data: A Case Study of the Wabash River and Its Tributary, Indiana

A comprehensive spectral-biogeochemical database was developed for the Wabash River and the Tippecanoe River in Indiana, United States. This database includes spectral measurements of river water, coincident in situ measurements of water quality parameters (chlorophyll (chl), non-algal particles (NAP), and colored dissolved organic matter (CDOM)), nutrients (total nitrogen (TN), total phosphorus (TP), and dissolved organic carbon (DOC)), water-column inherent optical properties (IOPs), water depths, substrate types, and bottom reflectance spectra collected in summer 2014. With this dataset, the temporal variability of water quality observations was first analyzed and studied. Second, radiative transfer models were inverted to retrieve water quality parameters using a look-up table (LUT) based spectrum matching methodology. Results found that the temporal variability of water quality parameters and nutrients in the Wabash River was closely associated with hydrologic conditions. Meanwhile, there were no significant correlations found between these parameters and streamflow for the Tippecanoe River, due to the two upstream reservoirs, which increase the settling of sediment and uptake of nutrients. The poor relationship between CDOM and DOC indicates that most DOC in the rivers was from human sources such as wastewater. It was also found that the source of water (surface runoff or combined sewer overflow (CSO)), water temperature, and nutrients were important factors controlling instream concentrations of phytoplankton. The LUT retrieved NAP concentrations were in good agreement with field measurements with slope close to 1.0 and the average estimation error was 4.1% of independently obtained lab measurements. The error for chl estimation was larger (37.7%), which is attributed to the fact that the specific absorption spectrum of chl was not well represented in this study. The LUT retrievals for CDOM experienced large variability, probably due to the small data range collected in this study and the insensitivity of Rrs to CDOM change. It is concluded that the success of the LUT method requires accurate spectral measurements and enough a priori information of the environment to construct a representative database for water quality retrieval. Therefore, future work will focus on continuing data collection in other seasons of the year and better characterization of the study area.

Spatial and temporal variations of the inherent optical properties in a tropical cascading reservoir system

In order to verify the effect of a cascade system in the water quality of two tropical reservoirs in Brazil, this work proposed to make use of the inherent optical properties (IOPs) as well as the specific inherent optical properties (SIOPs). From upstream to downstream, Barra Bonita (BB) is the first in a set of six reservoirs along Tiete River and Nova Avanhandava (Nav) is the fifth reservoir. BB is eutrophic whilst Nav is an oligotrophic environment. According to the IOP and SIOP analysis, it was possible to attest that BB was organic matter dominated and Nav was inorganic matter. These differences are noticeable in bio-optical modeling, must be considered different approaches to retrieve water quality parameters in both sites, once, the models take into account the specific information about the IOP and their relation with the optically active components (OACs) concentration. In addition, the results of this paper can provide comparable data to other water systems and improve the comprehension about the optical properties of complex water bodies.

Remote estimation of water quality parameters of Himalayan lake (Kashmir) using Landsat 8 OLI imagery

The overarching aim of this study was to derive simple and accurate algorithms for the retrieval of water quality parameters for the Wular Lake using Landsat 8 OLI satellite data. The water quality parameters include pH, COD, DO, alkalinity, hardness, chloride, TDS, total suspended solids (TSS), turbidity, electric conductivity and phosphate. Regression analysis was performed using atmospherically corrected true reflectance values of original OLI bands, images after applying enhancement techniques (NDVI, principal components) and the values of the water quality parameters at different sample locations to obtain the empirical relationship. Most of the parameters were well correlated with single OLI bands with R2 greater than 0.5, whereas phosphate showed a good correlation with NDVI image. The parameters like pH and DO showed a good relation with the principal component I and IV, respectively. The high concentration of pH, COD, turbidity and TSS and low concentration of DO infers the anthropogenic impact on lake.

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelf–sea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river–sea systems such as the Danube–Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river–sea systems including the Danube–Black Sea is considerable.

CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

Abstract. The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive intercomparison due to the availability of quality checked in situ databases. The CoastColour Round Robin (CCRR) project, funded by the European Space Agency (ESA), was designed to bring together three reference data sets using these to test algorithms and to assess their accuracy for retrieving water quality parameters. This paper provides a detailed description of these reference data sets, which include the Medium Resolution Imaging Spectrometer (MERIS) level 2 match-ups, in situ reflectance measurements, and synthetic data generated by a radiative transfer model (HydroLight). These data sets, representing mainly coastal waters, are available from doi:10.1594/PANGAEA.841950. The data sets mainly consist of 6484 marine reflectance (either multispectral or hyperspectral) associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: total suspended matter (TSM) and chlorophyll a (CHL) concentrations, and the absorption of coloured dissolved organic matter (CDOM). Inherent optical properties are also provided in the simulated data sets (5000 simulations) and from 3054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three data sets are compared. Match-up and in situ sites where deviations occur are identified. The distributions of the three reflectance data sets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.

MODIS-Based Radiometric Color Extraction and Classification of Inland Water With the Forel-Ule Scale: A Case Study of Lake Taihu

Serious difficulties are present in the application of remote sensing techniques for optically complex waters, as retrieval of water quality parameters is generally based on detailed local knowledge of optical properties of water bodies for specific areas and at specific times. Water color is measured in traditional water quality observations and characterized by the Forel-Ule scale, as it is intimately related to water compositions. In this paper, a Moderate Resolution Imaging Spectroradiometer (MODIS) based water color extraction and classification approach is developed and applied to Lake Taihu. By using MODIS data together with field data, we attempted to 1) retrieve the dominant wavelength of water color and classify water color into FU-classes; 2) analyze the relationship between water color dominant wavelength and the abundance of optically active component (OACs) in water; and 3) discover seasonal variations of water color based on Lake Taihu. Our results show that the dominant wavelength exhibits some relationship with the three types of OAC concentrations under certain conditions, particularly TSM and Chl-a; inorganic suspended matter (ISM) can be retrieved by using MODIS derived dominant wavelength in appropriate water body. Moreover, differences in water quality for different seasons can be detected by dominant wavelength and FU-class with some prior knowledge of the studied water. Therefore, dominant wavelength may be used as a comprehensive and promising indicator of water quality situation even though much work has to be done in the future to optimize the analyses and verify it on diverse sites.

Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment

Precise atmospheric correction is important for applications where small differences in surface reflectance (SR) are significant, such as biomass estimation, crop phenology, and retrieval of water quality parameters. It also enables direct comparison between different image dates and different sensors. As a precursor to monitoring different parameters of water quality around the coastline of Hong Kong using medium-resolution sensors Landsat TM/ETM, and HJ-1A/B, this study evaluated the performance of five atmospheric correction methods. The estimated SR of the first four reflective bands of Landsat 7 ETM+ and of the identical bands of the HJ-1A/B satellites was compared with in situ multispectral radiometer (MSR) SR measurements over sand, artificial turf, grass, and water surfaces for the five atmospheric correction methods – second simulation of the satellite signal in the solar spectrum (6S), fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH), atmospheric correction (ATCOR), dark object subtraction (DOS), and the empirical line method (ELM). Among the five methods, 6S was observed to be consistently more precise for SR estimation, with significantly less difference from the in-situ-measured SR, especially over lower reflective water surfaces. Of the two image-based methods, DOS performed well over the darker surfaces of water and artificial turf, although still inferior to 6S, while ELM worked well for grass sites as compared to the DOS and equalled the good performance of 6S over the high reflective sand surfaces.The study also evaluated the new standard Landsat SR product Landsat ecosystem disturbance adaptive processing system (LEDAPS) using the in situ measured SR data for the three land surface types – sand, artificial turf, and grass. For the highly and moderately reflecting bright sand and artificial turf, LEDAPS performed poorly, while for the darker grass site it performed better, although still inferior to 6S and ELM methods. This is probably due to the variable aerosol types and atmospheric conditions of Hong Kong, as LEDAPS was mainly compiled with reference to larger continental landmass areas.

SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters

The launch of several new satellites such as Sentinel-2, Sentinel-3, HyspIRI, EnMAP and PRISMA in the very near future, opens new perspectives for the inland and coastal water community. The monitoring of the water quality closer to the coast, within estuaries or small lakes with satellite data will become feasible. However for these inland and nearshore coastal waters, adjacency effects may hamper the correct retrieval of water quality parameters from remotely sensed imagery. Here, we present a sensor-generic adjacency pre-processing method, SIMilarity Environment Correction (SIMEC). The correction algorithm estimates the contribution of the background radiance based on the correspondence with the Near-INfrared (NIR) similarity spectrum. The performance of SIMEC was tested on MERIS FR images both above highly reflecting waters with high SPM loads, as well as dark lake waters with high CDOM absorption. The results show that SIMEC has a positive or neutral effect on the normalized remote sensing reflectance above optically-complex waters, retrieved with the MERIS MEGS or C2R processor.

Application of Landsat 5 and Landsat 7 images data for water quality mapping in Mosul Dam Lake, Northern Iraq

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 p 0.9 and p 0.9, 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.