scholarly journals ESTIMATION OF WATER QUALITY IN A RESERVOIR FROM SENTINEL-2 MSI AND LANDSAT-8 OLI SENSORS

2020 ◽  
Vol V-3-2020 ◽  
pp. 401-408
Author(s):  
F. M. C. Pizani ◽  
P. Maillard ◽  
A. F. F. Ferreira ◽  
C. C. de Amorim
Keyword(s):  
Water Quality ◽  
Multiple Regression ◽  
Regression Models ◽  
Quality Parameters ◽  
Optically Active ◽  
Water Quality Parameters ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Multiple Regression Models ◽  
Sentinel 2

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).

A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery

2020 ◽  
Vol 42 (5) ◽  
pp. 1841-1866
Author(s):  
Hongwei Guo ◽  
Jinhui Jeanne Huang ◽  
Bowen Chen ◽  
Xiaolong Guo ◽  
Vijay P. Singh
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Quality Parameters ◽  
Optically Active ◽  
Water Quality Parameters ◽  
Sentinel 2

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

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.

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

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.

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

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.

SentiLake - Sentinel-2 satellite data-based service for monitoring of Latvian lakes

2020 ◽  
Author(s):  
Dainis Jakovels ◽  
Agris Brauns ◽  
Jevgenijs Filipovs ◽  
Tuuli Soomets
Keyword(s):  
Water Quality ◽  
Satellite Data ◽  
Ecological Status ◽  
Quality Parameters ◽  
Water Bodies ◽  
Water Quality Parameters ◽  
Chl A ◽  
Regular Monitoring ◽  
Sentinel 2

&lt;p&gt;Lakes and water reservoirs are important ecosystems providing such services as drinking water, recreation, support for biodiversity as well as regulation of carbon cycling and climate. There are about 117 million lakes worldwide and a high need for regular monitoring of their water quality. European Union Water Framework Directive (WFD) stipulates that member states shall establish a programme for monitoring the ecological status of all water bodies larger than 50 ha, in order to ensure future quality and quantity of inland waters. But only a fraction of lakes is included in in-situ monitoring networks due to limited resources. In Latvia, there are 2256 lakes larger than 1 ha covering 1.5% of Latvian territory, and approximately 300 lakes are larger than 50 ha, but only 180 are included in Inland water monitoring program, in addition, most of them are monitored once in three to six years. Besides, local municipalities are responsible for the management of lakes, and they are also interested in the assessment of ecological status and regular monitoring of these valuable assets.&amp;#160;&lt;/p&gt;&lt;p&gt;Satellite data is a feasible way to monitor lakes over a large region with reasonable frequency and support the WFD status assessment process. There are several satellite-based sensors (eg. MERIS, MODIS, OLCI) available specially designed for monitoring of water quality parameters, however, they are limited only to use for large water bodies due to a coarse spatial resolution (250...1000 m/pix). Sentinel-2 MSI is a space-borne instrument providing 10...20 m/pix multispectral data on a regular basis (every 5 days at the equator and 2..3 days in Latvia), thus making it attractive for monitoring of inland water bodies, especially the small ones (&lt;1 km&lt;sup&gt;2&lt;/sup&gt;).&amp;#160;&lt;/p&gt;&lt;p&gt;Development of Sentinel-2 satellite data-based service (SentiLake) for monitoring of Latvian lakes is being implemented within the ESA PECS for Latvia program. The pilot territory covers two regions in Latvia and includes more than 100 lakes larger than 50 ha. Automated workflow for selecting and processing of available Sentinel-2 data scenes for extracting of water quality parameters (chlorophyll-a and TSM concentrations) for each target water body has been developed. Latvia is a northern country with a frequently cloudy sky, therefore, optical remote sensing is challenging in or region. However, our results show that 1...4 low cloud cover Sentinel-2 data acquisitions per month could be expected due to high revisit frequency of Sentinel-2 satellites. Combination of C2X and C2RCC processors was chosen for the assessment of chl-a concentration showing the satisfactory performance - R&lt;sup&gt;2&lt;/sup&gt; = 0,82 and RMSE = 21,2 &amp;#181;g/l. Chl-a assessment result is further converted and presented as a lake quality class. It is expected that SentiLake will provide supplementary data to limited in situ data for filling gaps and retrospective studies, as well as a visual tool for communication with the target audience.&lt;/p&gt;

scholarly journals Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2

2020 ◽  
Vol 12 (23) ◽  
pp. 3984
Author(s):  
Milad Niroumand-Jadidi ◽  
Francesca Bovolo ◽  
Lorenzo Bruzzone
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Chl A ◽  
Aquatic Science ◽  
Turbid Lake ◽  
The Impact ◽  
Sentinel 2

A new era of spaceborne hyperspectral imaging has just begun with the recent availability of data from PRISMA (PRecursore IperSpettrale della Missione Applicativa) launched by the Italian space agency (ASI). There has been pre-launch optimism that the wealth of spectral information offered by PRISMA can contribute to a variety of aquatic science and management applications. Here, we examine the potential of PRISMA level 2D images in retrieving standard water quality parameters, including total suspended matter (TSM), chlorophyll-a (Chl-a), and colored dissolved organic matter (CDOM) in a turbid lake (Lake Trasimeno, Italy). We perform consistency analyses among the aquatic products (remote sensing reflectance (Rrs) and constituents) derived from PRISMA and those from Sentinel-2. The consistency analyses are expanded to synthesized Sentinel-2 data as well. By spectral downsampling of the PRISMA images, we better isolate the impact of spectral resolution in retrieving the constituents. The retrieval of constituents from both PRISMA and Sentinel-2 images is built upon inverting the radiative transfer model implemented in the Water Color Simulator (WASI) processor. The inversion involves a parameter (gdd) to compensate for atmospheric and sun-glint artifacts. A strong agreement is indicated for the cross-sensor comparison of Rrs products at different wavelengths (average R ≈ 0.87). However, the Rrs of PRISMA at shorter wavelengths (<500 nm) is slightly overestimated with respect to Sentinel-2. This is in line with the estimates of gdd through the inversion that suggests an underestimated atmospheric path radiance of PRISMA level 2D products compared to the atmospherically corrected Sentinel-2 data. The results indicate the high potential of PRISMA level 2D imagery in mapping water quality parameters in Lake Trasimeno. The PRISMA-based retrievals agree well with those of Sentinel-2, particularly for TSM.

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

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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