Improved water quality mapping based on cross-fusion of Sentinel-2 and Landsat-8 imageries

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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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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RADIOMETRIC CALIBRATION OF LANDSAT-8 OLI AND SENTINEL-2 MSI IMAGES FOR WATER QUALITY MODELLING OF LAGUNA LAKE, PHILIPPINES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w19-229-2019 ◽

2019 ◽

Vol XLII-4/W19 ◽

pp. 229-232

Author(s):

E. V. Gubatanga Jr ◽

A. C. Blanco ◽

C. H. Lin ◽

B. Y. Lin

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Reference Image ◽

Time Interval ◽

Landsat 8 ◽

Radiometric Calibration ◽

Landsat 8 Oli ◽

Laguna Lake ◽

Over Time ◽

Sentinel 2

Abstract. Regular monitoring of water quality in Laguna Lake is important for it supports aquaculture and provides water supply for Metro Manila. Remote sensing makes it possible to monitor the spectral conditions of the lake on a regular time interval and with complete coverage except for the areas with cloud and shadow cover. Along with in-situ water quality measurements, bio-optical models can be developed to determine the relationship between spectral and bio-optical properties of the lake water and consequently enables the estimation of water quality through remote sensing. However, radiometric calibration is needed to minimize the effects of the changing atmospheric conditions over time and to account for the difference in sensors (e.g., Landsat-8 OLI, Sentinel-2 MSI) used for water quality assessment. Canonical correlation analysis is used to detect pseudo-invariant features (PIFs), which are ground objects that do not dramatically vary in spectral properties over time. Road surface and other large man-made infrastructures are the commonly detected PIFs. These PIFs are used to compute for the parameters used to normalize reflectance values of remotely-sensed images obtained on different dates and using different sensors. The normalization resulted to a reduction of difference in reflectance values between the reference image and the adjusted image, though not marginal. This is due to the use of a linear equation to adjust the image, which limits the ability of the reflectance values of the image to fit to the values of the reference image.

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The validation of water quality parameter algorithm using Landsat 8 and Sentinel-2 image in Palabuhanratu Bay

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/846/1/012022 ◽

2021 ◽

Vol 846 (1) ◽

pp. 012022

Author(s):

M K Mukhtar ◽

Supriatna ◽

M D M Manessa

Keyword(s):

Water Quality ◽

Quality Parameter ◽

Water Quality Parameter ◽

Landsat 8 ◽

Sentinel 2

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River Color Monitoring Using Optical Satellite Data

Proceedings ◽

10.3390/iecg_2018-05336 ◽

2018 ◽

Vol 2 (10) ◽

pp. 569

Author(s):

Federico Filipponi

Keyword(s):

Water Quality ◽

Satellite Data ◽

Landsat 8 ◽

Inland Water ◽

River Monitoring ◽

Processing Chain ◽

High Resolution Satellite Images ◽

Water Constituents ◽

Solid Transport ◽

Sentinel 2

Knowledge of inland water quality and riverine inputs to oceans is fundamental for water management, environmental monitoring, and the definition of policies and planning strategies related to the sustainable use of rivers. While European Union directives aim at the conservation of inland water resources, the ground operational monitoring network is often inadequate. River monitoring using Remote Sensing may complement in-situ measurements, supplying continuous, spatially explicit representation of parameters related to water quality and solid transport, even if the high-frequency dynamics of water parameters may not be caught due to limited satellite revisit time. Sentinel-2 and Landsat-8 satellites, equipped with MSI and OLI optical sensors whose spectral bands perform a more accurate atmospheric correction, allow for the development of methodologies for monitoring river color from space, thanks to high spatial resolution and short revisit times. This study presents a processing chain, developed to monitor water constituents in rivers using high-resolution satellite images. Multi-temporal analysis of chlorophyll-a (Chl-a) and total suspended matter (TSM) bio-geophysical variables was performed for the case study of the Po River (Italy) for the year 2017. Quantitative estimations of water constituents were retrieved from Sentinel-2 optical multispectral satellite data using the C2RCC algorithm, and the main outcomes are discussed. The developed processing chain can be used to create operational services for river monitoring, and represent a major improvement in the identification of spatio-temporal dynamics (like solid transport) in riverine systems.

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Near-Real Time Water Quality Monitoring with Sentinel-2 and Landsat-8 Satellites During the 2021 Volcanic Eruption in La Palma (Canary Islands)

SSRN Electronic Journal ◽

10.2139/ssrn.3980077 ◽

2021 ◽

Author(s):

Isabel Caballero ◽

Alejandro Román ◽

Antonio Tovar-Sánchez ◽

Gabriel Navarro

Keyword(s):

Water Quality ◽

Real Time ◽

Canary Islands ◽

Volcanic Eruption ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Landsat 8 ◽

La Palma ◽

Sentinel 2

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Prediction of some physico-chemical parameters of water in Alton Reservoir, Suffolk, England

WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT ◽

10.37394/232015.2020.16.12 ◽

2020 ◽

Vol 16 ◽

Keyword(s):

Neural Networks ◽

Water Quality ◽

Spatial Resolution ◽

Fuzzy Neural Networks ◽

Freshwater Ecosystems ◽

Landsat 8 ◽

Inference System ◽

Physico Chemical ◽

Animal Diversity ◽

Sentinel 2

Predict water quality variables such as Chlorophyll-a (CHL), Algae, Turbidity and Total Suspended Solids (TSS) are important for the analysis of freshwater ecosystems, that are significant not only for human populations but also essential for plant and animal diversity. However, monitoring all these variables from space is a very challenging task, which becomes particularly difficult when dealing with cyanobacteria blooms, because in high concentrations, they form scum on the water surface, which is a concern for public health due to the production of toxins. This article describes empirical algorithms to estimate these variables using LandSat-8 and Sentinel-2 images, multi-spectral instrument data, the Landsat spatial resolution (30 m) and imagery from the Sentinel-2 sensor, with a resampled 10 m spatial resolution can be used for environmental monitoring. These images, analyzed by Wavelets Neural Networks can be very useful to estimate physico-chemical and biological parameters of water. This approach is applied in Alton water reservoir, Suffolk, UK using spatial and temporal scales. The Alton Reservoir is the largest in Suffolk, with a perimeter of over 8 miles (13 km). This article presents techniques based on wavelets neural networks and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and Least Square Estimat, which are well suited to predict data sequences stemming from real-world applications techniques. The prediction behavior shows good forecasts as (NMSE = 0.00004; MARE = 0.00078, MSE =0.00013) for test data, results showed that the predicted values have good accurate. This article contributes to improving efficiency to monitor water quality parameters and confirm the reliability and accuracy of the approaches proposed for monitoring water reservoirs.

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