Testing utility of Landsat 8 for remote assessment of water quality in two subtropical African reservoirs with contrasting trophic states

Masocha, Mhosisi; Dube, Timothy; Nhiwatiwa, Tamuka; Choruma, Dennis Junior

doi:10.1080/10106049.2017.1289561

Cited by 27 publications

(28 citation statements)

References 27 publications

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“…The results of the study show that the NDTI provided strong positive correlation with turbidity in Borabey Lake (R 2 = 0.84). The results are similar to other investigations where, Masocha et al [4], found that blue/red ratio provided strong positive relation between measured and retrieved turbidity in two different lakes (R 2 = 0.81; R 2 = 0.65). Different turbidity retrieval approach showed a good correlation for two different study areas (R 2 = 0.87; R 2 = 0.66) [12].…”

Section: Discussionsupporting

confidence: 92%

“…It has been concluded that remote sensing is an effective tool for synoptic soil moisture assessment, water extends and level monitoring, water demand modeling, groundwater management, flood mapping, and water quality monitoring [1]. Thus, Sentinel-1 has been used for water resource management applications [2], Landsat-8 [3,4], and Sentinel-2 [5] have been used for water bodies extraction, MODIS (Moderate Resolution Imaging Spectroradiometer) has been used for water quality assessment [6]. Alos, Unmanned Aerial Vehicle (UAV) data have been used for water quality measurements.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of RapidEye-3 Satellite Data for Assessing Water Turbidity of Lake Borabey

Kaplan

Avdan

Göncü

et al. 2019

The 4th International Electronic Conference on Water Sciences

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In water resources management, remote sensing data and techniques are essential in watershed characterization and monitoring, especially when no data are available. Water quality is usually assessed through in-situ measurements which require high cost and time. Water quality parameters help in decision making regarding the further use of water-based on its quality. Turbidity is an important water quality parameter and an indicator of water pollution. In the past few decades, remote sensing has been widely used in water quality research. In this study, we compare turbidity parameters retrieved from a high-resolution image with in-situ measurements collected from Borabey Lake, Turkey. Here the use of RapidEye-3 images (5 m-resolution) allows for detailed assessment of spatio-temporal evaluation of turbidity, through the Normalized Difference Turbidity Index (NDTI). The turbidity results were then compared with data from twenty-one in-situ measurements collected in the same period. The actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R 2 of 0.84. The research findings support the use of remote sensing data of RadipEye-3 to estimate water quality parameters in small water areas. For future studies, we recommend investigating different water quality parameters using high-resolution remote sensing data.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Evaluation of RapidEye-3 Satellite Data for Assessing Water Turbidity of Lake Borabey

Kaplan

Avdan

Göncü

et al. 2019

The 4th International Electronic Conference on Water Sciences

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“…The results achieved for both TSS and chlorophyll-a predictions reached a higher R 2 coefficient than research studies for the same region [20,27,48], as well as other similar studies over different areas [1,26]. Since the two studied regions present different environmental characteristics, these results indicated the soundness of the proposed method for water quality monitoring through remote sensing and ML techniques.…”

Section: Discussionsupporting

confidence: 67%

“…The preservation of water resources creates many global-scale challenges. Constant and dynamic monitoring of these environments is only viable through extensive use of technologies that allow inexpensive and effective monitoring [1]. Considering that the prediction of water quality parameters is a critical aspect in any aquatic system, the research on methods that allow this estimate in lakes and reservoirs has significant value [2].…”

Section: Introductionmentioning

confidence: 99%

A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning

Kupssinskü

Guimarães

Souza

et al. 2020

Sensors

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† This paper is an extended version of our paper published in Kupssinskü, L.; Guimarães, T.; Freitas, R.; de Souza, E.; Rossa, P.; Ademir Marques, J.; Veronez, M.; Junior, L.G.; Mauad, F.; Cazarin, C. Prediction of chlorophyll-a and suspended solids through remote sensing and artificial neural networks.Abstract: Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS and chlorophyll-a are optically active components, therefore enabling measurement by remote sensing. Two study cases in distinct water bodies are performed, and those cases use different spatial resolution data from Sentinel-2 spectral images and unmanned aerial vehicles together with laboratory analysis data. In consonance with the methodology, supervised ML algorithms are trained to predict the concentration of TSS and chlorophyll-a. The predictions are evaluated separately in both study areas, where both TSS and chlorophyll-a models achieved R-squared values above 0.8.Sensors 2020, 20, 2125 2 of 18 bodies [3]. Moreover, the suspended solids may indicate erosive processes within the watershed and additional water pollution since it can carry or store pollutants [4].Generally, once the samples get to a laboratory, their concentrations are analyzed, and manual measurements are taken to extract these parameters. The involved processes are costly, time-consuming, and require skilled personnel [5]. Although there are accurate evaluations, they do not indicate the individual spatio-temporal variation of water quality [6].In this sense, applications involving remote sensing can overcome the drawbacks of conventional monitoring since they allow real-time, spatial, continuous, and long-term monitoring in large areas [7,8]. The presence of components alters the spectral characteristics of the pure water, called Optically Active Components (OAC). The main OACs in water bodies are chlorophyll, suspended solids, and organic material [4]. The possibility of monitoring non-accessible areas and composing time series of water quality from historical data of remote sensing [9] are also factors that make this tool essential and its use promising.Among the most common satellites in OAC remote sensing studies are Landsat 8 and Sentinel-2. Although Landsat is more widely used, the European Sentinel-2 satellite has promising characteristics: it has higher spatial resolution than Landsat 8 and also spectral bands in the region called the red edge, which is of great interest for water and vegetation studies.Remote sensing serves as a powerful technique for monitoring environmental and seasonal changes, and its ability to remotely monitor water resources has increased in recent decades because of the quality and availability of satellite imagery data [10]. Even so, the analysis of small water bodies may not be adequ...

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“…With the development of high spatial and temporal resolutions of satellite sensors, remote sensing has proven to be one of the most comprehensive tools for observing the temporal and spatial variations of the water turbidity (Dogliotti et al, 2015;Randolph et al, 2008). Over the past decade, satellite remote sensing has been as an important tool for monitoring sediment transport and analyzing the spatial distribution of total suspended matter (TSM; Wang et al, 2012;Zhang, Lin, et al, 2010;Zhang, Tang, et al, 2010), although some satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS; Brezonik et al, 2005;Kim et al, 2017;Robert et al, 2016;Wu et al, 2009), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS; Blakey et al, 2016;D'sa et al, 2002;Pozdnyakov et al, 2003;Tassan, 1994), and Landsat Thematic Mapper (Barnes et al, 2014;Barrett & Frazier, 2016;Bonansea et al, 2015;Masocha et al, 2017;Pattiaratchi et al, 1994;Torbick et al, 2013) have been used to monitor water quality in lakes or rivers. However, for long-term turbidity remote sensing monitoring, although some previous researchers have developed some empirical models for estimating the concentrations of total suspended matter with satellite data (Shi et al, 2015;Shi et al, 2018), due to the large amount of data processing, how to quickly obtain water quality information from long-term remote sensing images is still very difficult.…”

Section: Introductionmentioning

confidence: 99%

Application of Remote Sensing to Identify and Monitor Seasonal and Interannual Changes of Water Turbidity in Yellow River Estuary, China

Wang

Shen

et al. 2019

JGR Oceans

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Water turbidity is an important indicator for water security and environmental security in the Yellow River estuary. However, due to the complex terrain and harsh climatic environment, it is difficult to monitor the water turbidity over the complex surface of the estuary. In this study we applied a self‐organizing map clustering method, an artificial neural network clustering method, to extract turbidity patterns from the long‐term remote sensing data sets in the Yellow River estuary. Based on the Moderate Resolution Imaging Spectroradiometer data from 2000 to 2015, six turbidity patterns were identified by using the self‐organizing map clustering method: high turbidity pattern, moderate turbidity pattern, low turbidity pattern, very low turbidity pattern, extreme high turbidity pattern and sea ice pattern, and the first four patterns appear every year. All patterns have significant seasonal characteristics, and monthly turbidity is dominated by one of these turbidity patterns. The water turbidity in the estuary has decreased in the past 16 years, and the interannual variation of the turbidity pattern is the result of the combination of the sediment transported into the sea by the Yellow River and the wind and waves on the sea surface.

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Testing utility of Landsat 8 for remote assessment of water quality in two subtropical African reservoirs with contrasting trophic states

Cited by 27 publications

References 27 publications

Evaluation of RapidEye-3 Satellite Data for Assessing Water Turbidity of Lake Borabey

Evaluation of RapidEye-3 Satellite Data for Assessing Water Turbidity of Lake Borabey

A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning

Application of Remote Sensing to Identify and Monitor Seasonal and Interannual Changes of Water Turbidity in Yellow River Estuary, China

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