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

Abstract. The low operational cost of using freely available remote sensing data is a strong incentive for water agencies to complement their field campaigns and produce spatially distributed maps of some water quality parameters. The objective of this study is to compare the performance of Sentinel-2 MSI and Landsat-8 OLI sensors to produce multiple regression models of water quality parameters in a hydroelectric reservoir in Brazil. Physical-chemistry water quality parameters were measured in loco using sensors and also analysed in laboratory from water samples collected simultaneously. The date of sampling corresponded to the almost simultaneous overflight of Sentinel-2B and Landsat-8 satellites which provided a means to perform a fair comparison of the two sensors. Four optically active parameters were considered: chlorophyll-a, Secchi disk depth, turbidity and temperature (the latter using Landsat-8 TIR sensor). Other six optically non-active parameters were also considered. The multiple regression models used the spectral reflectance bands from both sensors (separately) as predictors. The reflectance values were based on averaging kernels of 30 m and 90 m. Stepwise variable selection combined with a priori knowledge based on other studies were used to optimize the choice of predictors. With the exception of temperature, the other optically active parameters yielded strong regression models from both the Sentinel and Landsat sensors, all with r2 > 0.75. The models for the optically non-active parameters produced less striking results with r2 as low as 0.03 (temperature) and as high or better than > 0.8 (pH and Dissolved oxygen).

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Estimation of both optical and nonoptical surface water quality parameters using Landsat 8 OLI imagery and statistical techniques

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.11.046008 ◽

2017 ◽

Vol 11 (04) ◽

pp. 1 ◽

Cited By ~ 2

Author(s):

Essam Sharaf El Din ◽

Yun Zhang

Keyword(s):

Water Quality ◽

Surface Water ◽

Surface Water Quality ◽

Quality Parameters ◽

Water Quality Parameters ◽

Statistical Techniques ◽

Landsat 8 ◽

Landsat 8 Oli

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Modelling Reservoir Chlorophyll-a, TSS, and Turbidity Using Sentinel-2A MSI and Landsat-8 OLI Satellite Sensors with Empirical Multivariate Regression

Journal of Sensors ◽

10.1155/2020/8858408 ◽

2020 ◽

Vol 2020 ◽

pp. 1-21

Author(s):

Yashon O. Ouma ◽

Kimutai Noor ◽

Kipkemoi Herbert

Keyword(s):

Water Quality ◽

Optical Sensors ◽

Multivariate Regression ◽

Atmospheric Correction ◽

Quality Parameters ◽

Water Quality Parameters ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Band Adjustment ◽

Sentinel 2A

Sentinel-2A/MSI (S2A) and Landsat-8/OLI (L8) data products present a new frontier for the assessment and retrieval of optically active water quality parameters including chlorophyll-a (Chl-a), suspended particulate matter (TSS), and turbidity in reservoirs. However, because of their differences in spatial and spectral samplings, it is critical to evaluate how well the sensors are suited for the seamless generation of the water quality parameters (WQPs). This study presents results from the retrieval of the WQP in a reservoir from L8 and S2A optical sensors, after atmospheric correction and standardization through band adjustment. An empirical multivariate regression model (EMRM) algorithmic approach is proposed for the estimation of the water quality parameters in correlation with in situ laboratory measurements. From the results, both sensors estimated Chl-a concentrations with R2 of greater than 70% from the visible green band for L8 and a combination of green and SWIR-1 bands for S2A. While the NMSE% was nearly the same for both sensors in Chl-a estimation, the RMSE was <10 μg/L and >10 μg/L for L8 and S2A estimations of Chl-a, respectively. For TSS retrieval, L8 outperformed S2A by 31% in accuracy with R2>0.9 from L8’s red, blue, and green bands, as compared to 0.47≤R2≥0.61 from S2A’s red and NIR bands. The RMSE were the same as for Chl-a, and the NMSE% were both in the same range. Both sensors retrieved turbidity with high and nearly equal accuracy of R2>70% from the visible and NIR bands, with equal RMSE at <10% NTU and NMAE% from S2A being higher by more than 30% as compared to L8’s NMAE% at 15%. The study concluded that the higher performance accuracy of L8 is attributed to its higher SNR and spectral bandwidth placement as compared to S2A bands. Comparatively, S2A overestimated Chl-a and turbidity but performed equally well compared to OLI in the estimation of TSS. The results show that while absolute accuracy of retrieval of the WQPs still requires improvements, the developed algorithms are broadly able to discern the biooptical water quality in reservoirs.

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Detecting spatiotemporal variability in the physicochemical properties of water in the Lower Mearim using remote sensing data

Ciência e Natura ◽

10.5902/2179460x40984 ◽

2020 ◽

Vol 42 ◽

pp. e32

Author(s):

George Colares Silva Filho ◽

Juliana Martins dos Santos ◽

Paulo Cesar Mendes Villis ◽

Ingrid Santos Gonçalves ◽

Isael Coelho Correia ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Physicochemical Properties ◽

Monitoring Program ◽

Remote Sensing Data ◽

Quality Parameters ◽

Water Quality Parameters ◽

Spatiotemporal Variability ◽

Landsat 8 ◽

Solids Content

Natural or anthropogenic chemical compounds of different origins often accumulate in estuarine regions. These compounds may alter the water quality. Therefore, It is important to constantly monitor the quality of estuarine regions. A combination of remote sensing and traditional sampling can lead to a better monitoring program for water quality parameters. The objective of this work is to assess the spatiotemporal variability of the physicochemical properties of water in the lower region of the Mearim River and estimate water quality parameters via remote sensing. Samples were collected at 16 points, from Baixo Arari to the mouth of the watershed, using a multiparameter meter and Landsat 8 satellite images. The physicochemical parameters of the water had high salinity levels, between 2.30 and 20.10 parts per trillion; a high total dissolved solids content, between 2.77 and 19.70 g/L; and minimum dissolved oxygen values. Estimating the physicochemical properties of the water via remote sensing proved feasible, particularly in the dry season when there is less cloud cover.

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Determination of water quality parameters in the Tan Rai exploiting area (Lam Dong province) using Sentinel-2 msi and Landsat 8 data

Journal of Mining and Earth Sciences ◽

10.46326/jmes.2020.61(2).14 ◽

2020 ◽

Vol 61 (2) ◽

pp. 126-134

Author(s):

Nghia Viet Nguyen ◽

Hung Le Trinh ◽

Keyword(s):

Water Quality ◽

Surface Water ◽

Secchi Depth ◽

Great Influence ◽

Surface Water Quality ◽

Mining Area ◽

Quality Parameters ◽

Water Quality Parameters ◽

Landsat 8 ◽

Optical Remote Sensing

Despite high profits, the mining process often leads to negative effects on the quality of groundwater around the mining site. Due to the close relationship between the concentration of water quality parameters and spectral reflectance values of surface watẻ, optical remote sensing image has been used effectively in the world in assessing and monitoring surface water quality. This paper presents the results of determining some surface water quality parameters in the Tan Rai bauxite mining area (Lam Dong province) such as turbidity, water-transparency (Secchi depth), and surface temperature from Sentinel-2A and Landsat 8 images taken on January 29, 2019. The results obtained in this study show that the mining process has a great influence on the surface water quality in Tan Rai (Lam Dong), reflected in all three water quality parameters such as turbidity, Secchi depth, and water temperature.

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Assessment of Water Quality Parameters for Some Inland Water Bodies of Western India Using Landsat 8 Data

World Environmental and Water Resources Congress 2019 ◽

10.1061/9780784482346.010 ◽

2019 ◽

Author(s):

Atri Dave ◽

Nehal Chaplot ◽

Sharad Chander ◽

Ashwin Gujarati ◽

R. P. Singh ◽

...

Keyword(s):

Water Quality ◽

Quality Parameters ◽

Water Bodies ◽

Water Quality Parameters ◽

Western India ◽

Landsat 8 ◽

Inland Water ◽

Inland Water Bodies

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Comparing PlanetScope to Landsat-8 and Sentinel-2 for Sensing Water Quality in Reservoirs in Agricultural Watersheds

Remote Sensing ◽

10.3390/rs13091847 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1847

Author(s):

Abubakarr S. Mansaray ◽

Andrew R. Dzialowski ◽

Meghan E. Martin ◽

Kevin L. Wagner ◽

Hamed Gholizadeh ◽

...

Keyword(s):

Water Quality ◽

Chlorophyll A ◽

Water Quality Monitoring ◽

Agricultural Runoff ◽

Quality Parameters ◽

Water Quality Parameters ◽

Quality Monitoring ◽

Landsat 8 ◽

Spectral Bands ◽

Sentinel 2

Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may help improve water quality monitoring compared to coarser-resolution satellites. This work compared PlanetScope to Landsat-8 and Sentinel-2 in their ability to detect key water quality parameters. Spectral bands of each satellite were regressed against chlorophyll a, turbidity, and Secchi depth data from 13 reservoirs in Oklahoma over three years (2017–2020). We developed significant regression models for each satellite. Landsat-8 and Sentinel-2 explained more variation in chlorophyll a than PlanetScope, likely because they have more spectral bands. PlanetScope and Sentinel-2 explained relatively similar amounts of variations in turbidity and Secchi Disk data, while Landsat-8 explained less variation in these parameters. Since PlanetScope is a commercial satellite, its application may be limited to cases where the application of coarser-resolution satellites is not feasible. We identified scenarios where PS may be more beneficial than Landsat-8 and Sentinel-2. These include measuring water quality parameters that vary daily, in small ponds and narrow coves of reservoirs, and at reservoir edges.

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Assessment of biological and physic chemical water quality parameters using Landsat 8 time series

Remote Sensing for Agriculture, Ecosystems, and Hydrology XX ◽

10.1117/12.2513277 ◽

2018 ◽

Cited By ~ 1

Author(s):

Gordana Jakovljevic ◽

Miro Govedarica ◽

Flor Álvarez Taboada

Keyword(s):

Water Quality ◽

Time Series ◽

Quality Parameters ◽

Water Quality Parameters ◽

Landsat 8 ◽

Chemical Water

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

10.20944/preprints201711.0075.v1 ◽

2017 ◽

Author(s):

Christine Coelho ◽

Birgit Heim ◽

Saskia Förster ◽

Arlena Brosinsky ◽

José Carlos De Araújo

Keyword(s):

Water Quality ◽

Chlorophyll A ◽

Quality Parameters ◽

Water Quality Parameters ◽

Semiarid Region ◽

Landsat 8 ◽

Small Surface ◽

Entire Study ◽

Chl A ◽

Brazilian Semiarid

We aimed at analyzing Chlorophyll-a and CDOM dynamics from field measurements and at assessing 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 comprises 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 as 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; whereas CDOM performed best with Landsat-8 for non-perennial reservoirs. This investigation showed high potential for retrieving water quality parameters in dry areas with small reservoirs.

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