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Estimates of Water-Quality in Coastal Waters Using Multi-Date Landsat Thematic Mapper Data
International Journal of Remote Sensing
Abstract
An evaluation of Landsat Thematic Mapper (TM) data for the routine monitoring of surface chlorophyll concentration (C) and Secchi disk depth (SOD, a measure of the water clarity) used Cockburn Sound as a study area. Multi-temporal empirical algorithms to predict these parameters have been developedfrom the atmospherically-corrected satellite-received radiance and field data collected at the time of the satellite overpass. Highly significant, predictive algorithms for the surface C (range: 0·2–2·7 μg 1) and SOD (range: 4–15m) were obtained using bands I and 3 of the Thematic mapper. It is shown that a high confidence may be placed on the predictions using these algorithms and therefore it offers a cost-effective tool for complementing regular monitoring programmes.
... It has also been calculated that every year, between 4.8 and 12.7 million tons of plastic find their way into the ocean from coastal populations worldwide [16], while the Ellen Macarthur sensing methods to identify plastics in the sea [29,[68][69][70][71][72][73]. Satellite images can be used to identify plastics in the water, such as Sentinel-1A and COSMO-Sky-Med Sar images [74], C-Band Radarsat-1 SAR images [75] as well as Landsat TM and EMT+ satellite images [76][77][78]. ...
... Spectral signatures show high reflectance in plastics and no reflectance for water in the nearinfrared (NIR) domain [29,84,96]. NIR spectroscopy is currently used in related applications, including the sorting of plastic debris in recycling facilities [65,66,77,83,97]. After placing the plastic litter target in the water, the spectral signatures of the sea water and the plastic bottles were taken and plotted according to the different channels of the Sentinel-2 satellite. ...
... Right: target as evident at 850nm (Sony Exmor camera).Spectral signatures show high reflectance in plastics and no reflectance for water in the nearinfrared (NIR) domain[29,84,96]. NIR spectroscopy is currently used in related applications, including the sorting of plastic debris in recycling facilities[65,66,77,83,97]. After placing the plastic litter target in the water, the spectral signatures of the sea water and the plastic bottles were taken and plotted according to the different channels of the Sentinel-2 satellite. ...
Plastic litter floating in the ocean is a significant problem on a global scale. This study examines whether Sentinel-2 satellite images can be used to identify plastic litter on the sea surface for monitoring, collection and disposal. A pilot study was conducted to determine if plastic targets on the sea surface can be detected using remote sensing techniques with Sentinel-2 data. A target made up of plastic water bottles with a surface measuring 3 m × 10 m was created, which was subsequently placed in the sea near the Old Port in Limassol, Cyprus. An unmanned aerial vehicle (UAV) was used to acquire multispectral aerial images of the area of interest during the same time as the Sentinel-2 satellite overpass. Spectral signatures of the water and the plastic litter after it was placed in the water were taken with an SVC HR1024 spectroradiometer. The study found that the plastic litter target was easiest to detect in the NIR wavelengths. Seven established indices for satellite image processing were examined to determine whether they can identify plastic litter in the water. Further, the authors examined two new indices, the Plastics Index (PI) and the Reversed Normalized Difference Vegetation Index (RNDVI) to be used in the processing of the satellite image. The newly developed Plastic Index (PI) was able to identify plastic objects floating on the water surface and was the most effective index in identifying the plastic litter target in the sea.
... The estimation of OAC with remote sensing has been addressed extensively in research for least two decades [12,19,22,60,82,[91][92][93][94][95][96][97][98][99]. Particularly, parameters such as SDD, turbidity or Chl-a and TSM have been studied with great detail, and their estimation has been the target of different modeling approaches, from empirical to semi-analytical models [26,83,95,[100][101][102][103][104][105][106][107][108][109][110]. ...
Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R2 = 0.68) but lower performances for TSM and NO3-N (R2 = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring.
... Hence, these restraints and drawbacks make the conventional methods challenging for continuous water quality prediction at spatial scales (Panwar et al., 2015;Chabuk et al., 2017). For observing and analyzing water quality parameters, such as turbidity, chlorophyll, temperature, and suspended inorganic materials, techniques, such as optical remote sensing, are being used (Pattiaratchi et al., 1994;Fraser, 1998;Kondratyev et al., 1998). To calculate the measure of solar irradiance at varied wavelength bands reflected by the surface water, remote sensing satellite sensors are used (Zhang et al., 2003;Dwivedi and Pathak, 2007;Girgin et al., 2010;Ronghang et al., 2019). ...
The Yamuna river has become one of the most polluted rivers in India as well as in the world because of the high-density population growth and speedy industrialization. The Yamuna river is severely polluted and needs urgent revival. The Yamuna river in Dehradun is polluted due to exceptional tourist activity, poor sewage facilities, and insufficient wastewater management amenities. The measurement of the quality can be done by water quality assessment. In this study, the water quality index has been calculated for the Yamuna river at Dehradun using monthly measurements of 12 physicochemical parameters. Trend forecasting for river water pollution has been performed using different parameters for the years 2020–2024 at Dehradun. The study shows that the values of four parameters namely, Temperature, Total Coliform, TDS, and Hardness are increasing yearly, whereas the values of pH and DO are not rising heavily. The considered physicochemical parameters for the study are TDS, Chlorides, Alkalinity, DO, Temperature, COD, BOD, pH, Magnesium, Hardness, Total Coliform, and Calcium. As per the results and trend analysis, the value of total coliform, temperature, and hardness are rising year by year, which is a matter of concern. The values of the considered physicochemical parameters have been monitored using various monitoring stations installed by the Central Pollution Control Board (CPCB), India.
... Therefore, the remote sensing technique proved its ability as an economic and useful source of data in large areas (Erener and Yakar 2012). Several studies confirmed the ability of remote sensing in retrieving the water quality variables from satellite data (Allan et al. 2007;Alparslan et al. 2007;Baban 1993;Dekker et al. 1996;Dewidar and Khedr 2005;El-Din et al. 2013;El-Masri and Rahman 2006;Farag and El-Gamal 2011;Hellweger et al. 2004;Hereher et al. 2010;Isenstein and Park 2014;Lillesand 2002;Moore 1980;Nelson et al. 2003;Olet 2010;Olmanson et al. 2002;Palacios et al. 1995;Pattiaratchi et al. 1994;Ritchie et al. 2003;Rogers et al. 1976;Usali and Ismail 2010;Waxter 2014;Wen and Yang 2011). Water quality of water bodies can be monitored using remote sensing instruments, as it relies on the spectral properties of waterleaving radiance (Hinton 1991). ...
Lake Burullus is one of the most important coastal lakes in Egypt, as it participates with a considerable amount of fish yield in Egypt. Despite its importance, it is considered as a vulnerable lake, since it is subjected to significant environmental changes caused by various anthropogenic activities. Severe deterioration of its water quality status, as well as a decrease in its water area, has been noticed throughout the last four decades. The main objective of this study is to evaluate the ability of remote sensing for assessing the water quality status of the lake for the period from August 2010 to August 2013. Remote sensing technique was used to retrieve and predict the lake water quality parameter records through the assessment period (August 2010 to August 2013). Stepwise multi-linear regression technique was used to correlate between the field measurements of water quality parameters and the reflectance of remote sensing imageries, and then the derived models were validated. The results revealed the critical status of water quality conditions of the lake, particularly of its southern and southeastern parts. The results showed also that some water quality parameters (Chl-a, TSS, pH, Fe, Zn, Cr, and NH4) can be retrieved from remote sensing imageries with reasonable accuracy (R² = 0.86, 0.67, 0.65, 0.63, 0.62, 0.61, and 0.6, respectively), while the water quality parameters, which can be predicted, based on calibrated and validated regression models are TSS and Chl-a, with acceptable accuracy (R² = 0.6 and 0.43, respectively). Lake Burullus needs urgent plans and strategies to protect its water quality from the potential hazards of human activities. The study proved the ability of remote sensing as an effective technique to monitor the changes in water quality conditions of shallow coastal lagoons and to predict with some water quality parameters without field measurements; therefore, it is highly recommended to be used by decision makers.
... This study aims to estimate TC and PHC using MODIS sensor along the south west coast of India by formulating an algorithm using multiple linear regression. There are many studies which state the relation of reflectance and their ratios on water quality parameters [17]. The algorithm formulated in this study is showing such a relationship and has been validated for its future application. ...
Coastal waters are showing deteriorating trend in its quality. This leads to the damage of marine ecosystems and interferes in its normal use. In order to tackle this issue, it is important to know about the extent of pollution. Conventional method of water quality estimation includes analysis of water samples from various locations. This is a tiresome and costly process limiting its application to small scales and accessible sampling sites. In this paper, an attempt has been made to quickly estimate the concentration of Petroleum Hydrocarbons (PHC) and counts of Total Coliform (TC) which are important water quality parameters, along the south west coast of India. This study formulated satellite data-based multiple regression equations for determining the count of total coliform bacteria and concentration of petroleum hydrocarbons. The sea surface temperature and remote sensing reflectance values of different bands of MODIS sensor along with field values were used in the process. The developed algorithm is validated for future use. Maps are created using these equations, showing the distribution of these parameters along the coast using Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) data. Hence, the feasibility of VIIRS for determination of these parameters with the same algorithm is examined. Thus, based on the results, areas of high PHC and TC could be identified and necessary control measures could be adopted.
Lake Taihu, located in a densely populated and highly industrialized area in eastern China, has experienced dramatic changes in water quality since the reform and opening-up in the 1980s. Landsat data can be used to trace water quality changes over approximately 40 years. However, chlorophyll-a (Chla) estimation, which characterizes the trophic status, has not been thoroughly explored (especially in turbid water using wide bandwidth Landsat) due to the interference of suspended particulate matter (SPM) to Chla. In this study, we used Landsat TM/OLI for turbid water Chla inversion and to analyze the spatiotemporal variation of Chla in Lake Taihu for 38 years and its influencing factors. An optical classification algorithm based on Rrs(green)/Rrs(red) was used to exclude highly turbid waters dominated by SPM; Chla was estimated only in waters with low SPM. We constructed an exponential estimation model based on Rrs(NIR)/Rrs(red), and verified the accuracy of the model using the measured Chla synchronized with satellite data. The model was applied to Landsat images to calculate the Chla concentration in Lake Taihu during 1984-2021, and its spatiotemporal distribution patterns were further analyzed. Spatially, the Chla concentrations in the western and northern regions of Lake Taihu were higher than those in other regions, probably because these areas are estuaries with large exogenous pollutant discharge and more nutrients are imported from exogenous sources. Chla showed an overall significant upward trend from 1984 to 2021 probably because of temperature rise, wind speed reduction, and nutrient increase. The results of the spatial and temporal variation of Chla and the influencing factors in this study provide supporting data for eutrophication monitoring and management in Lake Taihu. The proposed Chla estimation method can be extended to assess the spatial and temporal distribution of eutrophication in other inland waters with similar optical properties.
The major lockdown due to the COVID-19 pandemic has affected the socio-economic development of the world. On the other hand, there are also reports of reduced pollution levels. In this study, an indicative analysis is adopted to understand the effect of lockdown on the changes in the water quality parameters for Lake Hussain Sagar using two remote sensing techniques: (i) spectral reflectance (SR) and (ii) chromaticity analysis (Forel-Ule color Index (FUI) and Excitation Purity). The empirical relationships from earlier studies imply that (i) increase in SR values (band B2) indicates a reduction in Chlorophyll-a (Chl-a) and Colored Dissolved Organic Matter (CDOM) concentrations, and (ii) increase in FUI indicates an increase in Total Suspended Solids (TSS). The Landsat 8 OLI satellite images are adopted for comparison between (i) January to May of year 2020: the effect of lockdown on water quality, and (ii) March and April for years 2015 to 2020: historical variations in water quality. The results show notable changes in SR values and FUI due to lockdown compared to before lockdown and after unlock suggesting a significant reduction in lake water pollution. In addition, the historical variations within April suggest that the pollution levels are least in the year 2020.
A R T I C L E I N F O Keywords: Long-term water quality monitoring Remote sensing Integration Multi-sensor satellites In-situ measurements Hyperspectral observations MERIS MSI OLCI ENVISAT Sentinel-2 Sentinel-3 2SeaColor MODTRAN Radiative Transfer modeling Coastal waters The Wadden Sea A B S T R A C T Recently, there have been significant efforts in the integration of in-situ and satellite observations for effective monitoring of coastal areas (e.g., the Copernicus program of the European Space Agency). In this study, a 15-year diurnal variation of Water Constituent Concentrations (WCCs) was retrieved from multi-sensor satellite images and in-situ hyperspectral measurements using Radiative Transfer (RT) modeling in the Dutch Wadden Sea. The existing RT model 2SeaColor was inverted against time series of in-situ hyperspectral measurements of water leaving reflectances (R rs [sr −1 ]) for the simultaneous retrieval of WCCs (i.e., Chlorophyll-a (Chla), Suspended Particulate Matter (SPM), Dissolved Organic Matter (CDOM)) on a daily basis between 2003 and 2018 at the NIOZ jetty station (the NJS) located in the Dutch part of the Wadden Sea. At the same time, the existing coupled atmosphere-hydro-optical RT model MOD2SEA was used for the simultaneous retrieval of WCCs from time series of multi-sensor satellite images of the MEdium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT, Multispectral Instrument (MSI) onboard Sentinel-2 and Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3 between 2003 and 2018 over the Dutch Wadden Sea. At the NJS, a direct comparison (Taylor diagram and statistical analysis) showed strong agreement between in-situ and satellite-derived WCC values (Chla: R 2 ≥ 0.70, RMSE ≤7.5 [mg m −3 ]; SPM: R 2 ≥ 0.72, RMSE ≤5.5 [g m −3 ]; CDOM absorption at 440 nm: R 2 ≥ 0.67, RMSE ≤1.7 [m −1 ]). Next, the plausibility of the spatial variation of retrieved WCCs over the study area was evaluated by generating maps of Chla [mg m −3 ], SPM [g m −3 ], and CDOM absorption at 440 nm [m −1 ] from MERIS and OLCI images using the MOD2SEA model. The integration of the spatio-temporal WCC data obtained from in-situ measurements and satellite images in this study finds applications for the detection of anomaly events and serves as a warning for management actions in the complex coastal waters of the Wadden Sea.
Airborne Thematic Mapper data from the NERC MSS-82 campaign in Swansea Bay, Bristol Channel, have been correlated, in linear and multiple regression analyses, with measured water quality parameters: suspended sediment concentration ( SSC) mean grain size of material in suspension, surface salinity and chlorophyll-a concentrations. The investigation is an extension of that carried out by Collins and Pattiaratchi (1984). The spectral wavelengths providing maximum information on the distribution and concentrations of water quality parameters are determined. The conclusions reached are compared with those of other investigators. It is shown that particle size influences the spectral signature of suspended sediment, and that there is a masking effect of high SSCs on the spectral reflectance of chlorophyll. Multiple regression equations are derived and applied to the digital remotely-sensed data, to produce thematic maps of water quality. The maps are compared to the sea-truth data. It is concluded that airborne remote sensing provides a useful means of mapping surface water quality in coaslal regions.
A multi-temporal study of chlorophyll-a concentration, has been made by means of Landsat-5 Thematic Mapper (TM) band 1 (0·45-0·52μm) in the Albufera of Valencia, a hypertrophic lagoon of shallow depth. The relationship between this parameter and satellite data was set up from in situ measurements and concurrent satellite data obtained in a campaign from 22 July 1985. This relation has been applied to images of different dates by using a simple procedure of correction, which is based on the use of selected surfaces whose ground reflectance is assumed to be constant with time. Good results were obtained and chlorophyll-a concentrations have been determined with an error of 20 per cent.
Due to the impacts of rural and urban development on southwestern Australian estuaries, and the general isolation of these water bodies, there is a need to develop water quality monitoring systems that are both repetitive and cost-effective. The literature suggests that Landsat Thematic Mapper (TM) has spectral and spatial characteristics that are suited to monitoring small coastal water bodies. This study examined the potential for the satellite-based TM sensor to serve as a regulator monitoring tool. Atmospherically corrected TM digital data acquired on four dates over summer 1990/91 and concurrent field measurements collected at the time of the satellite over-pass over the Peel-Harvey Estuarine System were used to obtain multitemporal, empirical algorithms for predicting pigment concentration, Secchi disk depth (SDD), and salinity. Highly significant, predictive algorithms were developed for these parameters. It is concluded that Landsat TM has the resolution and accuracy to be a potentially very useful monitoring tool. However, cloud cover and delays in data acquisition seriously diminish its usefulness for monitoring on anything less than a seasonal basis. Laboratory-based radiometric studies also indicated that Landsat TM was unlikely to be useful in determining the taxonomic composition of phytoplankton blooms in coastal waters.
Studies that use multitemporal images require the conversion of original digital data into the corresponding physical magnitudes. Atmospheric correction is one of the most important steps in this process, which is usually undertaken using atmospheric radiative transfer models. The main difficulty in these models is the need of atmospheric input data which are not usually available. An alternative approach to atmospheric correction is proposed in this Letter. It is based on the idea that the atmospheric effects over two or more dates can be determined in a relative way, by using the apparent reflectance values of surfaces whose ground reflectance can be considered unchangeable with time. This method has been applied to correct Landsat-5 Thematic Mapper images and satisfactory results have been obtained in comparison with those obtained by a traditional model.
New equations are presented for spectrophotometric determination of chlorophylls, based on revised extinction coefficients of chlorophylls a, b, c1 and c2. These equations may be used for determining chlorophylls a and b in higher plants and green algae, chlorophylls a and c1 + c2 in brown algae, diatoms and chrysomonads, chlorophylls a and c2 in dinoflagellates and cryptomonads, and chlorophylls a, b, and c1 + c2 in natural phytoplankton.
Eutrophication, or enrichment, of the natural water in New Jersey's coastal waters has an adverse impact on the economic welfare of the state. Since shipboard sampling is not cost-effective for providing data on coastal'estuarine water processes and materials, satellite remote sensing, with its synoptic and repetitive coverage, is used to obtain some of the data for eutrophication-related water quality analysis. For the purpose of this study, existing Landsat-5 Thematic Mapper digital data, acquired on 1 September 1985, along with the sea truth observations, were obtained for quantitative analysis, The goal was to establish a correlation between total plankton content and remote sensing signals indicating the relative degree of eutrophy and productivity in New Jersey's coastal waters.
The potential of the Thematic Mapper (TM) on board Landsal-5 for marine applications has been investigated with reference to the well-known performance of the Coastal Zone Color Scanner of Nimbus-7. The study consisted of (a) a sensitivity analysis, considering such fundamental error sources as retrieval algorithm sensitivity, atmospheric correclion, instrument noise and signal digitization and (b) the interpretation of a TM scene by the procedure suggested in (a). The evidence provided by the experimental test validated the positive conclusions of the theoretical work, indicating that the analysis of TM bands 1 to 4 data should be capable of yielding quantitative information of satisfactory quality on fundamental water quality parameters, such as chlorophyll (i.e. phytoplankion) and suspended sediment concentrations.
An atmospheric correction scheme using Landsat Thematic Mapper (TM) bands 1, 2 and 4 was developed and the data corrected for atmospheric effects due to Rayleigh scattering and aerosols. Secondly phytoplankton pigment mapping was achieved through substitution of corrected radiances in TM bands 1 and 2 into a bio-optical algorithm developed for the study area off Azhikal in the Arabian Sea. A C map showing near-surface concentrations of phytoplankton pigments (mgm−3)and a K map showing the pigment distribution (m−1) in one attenuation length were generated.
This study reports on an investigation of multi-date water-quality calibration algorithms under turbid inland water conditions using Landsat Thematic Mapper (TM) multispectral digital data. TM data and water-quality observations were obtained near-simultaneously in Green Bay, an embayment of Lake Michigan, and related using linear regression techniques. The high concentrations of terrestrially-derived suspended solids appear to be controlling the water volume reflectance. A simple exponential model using the band 3/band 1 (0-660μm/0-485μm) ratio was found to be a useful index for estimating total suspended solids and water transparency (i.e. Secchi disk depth) with an accuracy of ± 25 per cent over the calibration data range.