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

Chl A ◽

Regular Monitoring ◽

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.&#160;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 (<1 km2).&#160;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 - R2 = 0,82 and RMSE = 21,2 &#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.

Remote Sensing of Coastal Water-quality Parameters from Sentinel-2 Satellite Data in the Tyrrhenian and Adriatic Seas

2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) ◽

10.1109/piers-spring46901.2019.9017293 ◽

2019 ◽

Author(s):

Michele Iacobolli ◽

Massimo Orlandi ◽

Domenico Cimini ◽

Frank S. Marzano

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Coastal Water ◽

Satellite Data ◽

Quality Parameters ◽

Coastal Water Quality ◽

Chlorophyll and Suspended Solids Estimation in Portuguese Reservoirs (Aguieira and Alqueva) from Sentinel-2 Imagery

Water ◽

10.3390/w13182479 ◽

2021 ◽

Vol 13 (18) ◽

pp. 2479

Author(s):

Vítor Hugo Neves ◽

Giorgio Pace ◽

Jesús Delegido ◽

Sara C. Antunes

Keyword(s):

Water Quality ◽

Satellite Data ◽

Suspended Solids ◽

Low Cost ◽

Water Quality Assessment ◽

Fine Tuning ◽

Future Research ◽

Chl A ◽

Reservoirs have been subject to anthropogenic stressors, becoming increasingly degraded. The evaluation of ecological potential in reservoirs is remarkably challenging, and consistent and regular monitoring using the traditional in situ methods defined in the WFD is often time- and money-consuming. Alternatively, remote sensing offers a low-cost, high frequency, and practical complement to these methods. This paper proposes a novel approach, using a C2RCC processor to analyze Sentinel-2 imagery data to retrieve information on water quality in two reservoirs of Portugal, Aguieira and Alqueva. We evaluate the temporal and spatial evolution of Chl a and total suspended solids (TSS), between 2018 and 2020, comparing in situ and satellite data. Generally, Alqueva reservoir allowed lower relative (NRMSE = 8.9% for Chl a and NRMSE = 21.9% for TSS) and systematic (NMBE = 1.7% for Chl a and NMBE = 2.0% for TSS) errors than Aguieira, where some fine-tuning would be required. Our paper shows how satellite data can be fundamental for water-quality assessment to support the effective and sustainable management of inland waters. In addition, it proposes solutions for future research in order to improve upon the methods used and solve the challenges faced in this study.

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

Remote Sensing ◽

10.3390/rs12233984 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3984

Author(s):

Milad Niroumand-Jadidi ◽

Francesca Bovolo ◽

Lorenzo Bruzzone

Keyword(s):

Water Quality ◽

Quality Parameters ◽

Radiative Transfer Model ◽

Transfer Model ◽

Chl A ◽

Aquatic Science ◽

Turbid Lake ◽

The Impact ◽

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.

Surface Water Pollution Risk From Vietnam Water Quality Index (VN-WQI) in the Ca Mau City, Mekong Delta

Nature Environment and Pollution Technology ◽

10.46488/nept.2021.v20i04.007 ◽

2021 ◽

Vol 20 (4) ◽

Author(s):

Nguyen Ngan Ha ◽

Tran Thi Thu Huong ◽

Pham The Vinh ◽

Tran Thi Van

Keyword(s):

Water Quality ◽

Surface Water ◽

Water Quality Index ◽

Quality Index ◽

Surface Water Quality ◽

Quality Parameters ◽

This paper presents the study of integrating the remote sensing technology with in-situ ground observation for assessing the status of water quality in Ca Mau city through the Vietnam Water Quality Index (VN-WQI). The Sentinel-2 image and in-situ surface water samples were collected on 20 February 2020 for this study. The sample results were then specified by samples’ coordination. Besides, Sentinel-2 imaging was processed by radiometric and atmospheric correction, geometric registration, and extracted pixel spectral values from the sample locations. The multiple linear regressions of seven water quality parameters including BOD5, COD, NH4, PO4, TSS, pH, Coliform with surface water’s pixel spectral values from the satellite images were calculated and used to simulate water quality parameters on the satellite image. They were integrated into the VN-WQI to estimate, classify, and evaluate the general surface water quality of the Ca Mau city. The results show that there is a regressive correlation between measured data and image spectral values, and the simulation also well fits with the data with an acceptable error. The surface water quality of Ca Mau city is heavily polluted with almost all water quality parameters recognized at B1 to above B2 level according to the QCVN08-MT:2015/BTNMT. In terms of VN-WQI, the results also illustrate the low quality of surface water and heavy pollution only used for water transportation, not for domestic use. This approach can be a powerful method in spatially monitoring water quality and supporting environment management.

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

Remote Sensing ◽

10.3390/rs12060931 ◽

2020 ◽

Vol 12 (6) ◽

pp. 931 ◽

Cited By ~ 1

Author(s):

Kristi Uudeberg ◽

Age Aavaste ◽

Kerttu-Liis Kõks ◽

Ave Ansper ◽

Mirjam Uusõue ◽

...

Keyword(s):

Water Quality ◽

Quality Parameters ◽

Water Quality Parameter ◽

Water Type ◽

Secchi Disk ◽

Secchi Disk Depth ◽

Chl A ◽

Monitoring Programs

Currently, water monitoring programs are mainly based on in situ measurements; however, this approach is time-consuming, expensive, and may not reflect the status of the whole water body. The availability of Multispectral Imager (MSI) and Ocean and Land Colour Instrument (OLCI) free data with high spectral, spatial, and temporal resolution has increased the potential of adding remote sensing techniques into monitoring programs, leading to improvement of the quality of monitoring water. This study introduced an optical water type guided approach for boreal regions inland and coastal waters to estimate optical water quality parameters, such as the concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM), the absorption coefficient of coloured dissolved organic matter at a wavelength of 442 nm (aCDOM(442)), and the Secchi disk depth, from hyperspectral, OLCI, and MSI reflectance data. This study was based on data from 51 Estonian and Finnish lakes and from the Baltic Sea coastal area, which altogether were used in 415 in situ measurement stations and covered a wide range of optical water quality parameters (Chl-a: 0.5–215.2 mg·m−3; TSM: 0.6–46.0 mg·L−1; aCDOM(442): 0.4–43.7 m−1; and Secchi disk depth: 0.2–12.2 m). For retrieving optical water quality parameters from reflectance spectra, we tested 132 empirical algorithms. The study results describe the best algorithm for each optical water type for each spectral range and for each optical water quality parameter. The correlation was high, from 0.87 up to 0.93, between the in situ measured optical water quality parameters and the parameters predicted by the optical water type guided approach.

Estimation of water quality parameters from space images

10.47350/aicts.2020.17 ◽

2021 ◽

Author(s):

A.K. Popova

Keyword(s):

Water Quality ◽

Lake Baikal ◽

Spatial Dynamics ◽

Quality Parameters ◽

Pollution Level ◽

Space Images ◽

Chl A ◽

In Situ Data ◽

Water quality affects many human activities. Remote sensing is efficient and economical instrument for water monitoring. The paper investigates the problem of choosing an algorithm for Chl-a concentration determination. In this study, we made calculations for Multispectral Instrument (MSI) on Sentinel-2 for Lake Baikal by different empirical algorithms and C2RCC tool. We choose 3 band combination that have high correlation with in situ data of Chl-a. Resultant distribution map display spatial dynamics of Chl-a in the lake. Our research is intended to help environmental scientist to assess pollution level of the Lake Baikal and interpret the ecological meaning of results

Adaptive ML-Models for analysis of TSS, Chlorophyll-a, in mixed water type scenarios in Ca Mau peninsula, Vietnam

10.5194/egusphere-egu21-15059 ◽

2021 ◽

Author(s):

Long Vu Huu ◽

Andreas Schenk ◽

Stefan Hinz

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Surface Water ◽

Remote Sensing Data ◽

Quality Parameters ◽

Water Type ◽

Water Types ◽

Chl A ◽

The multispectral mission of Sentinel-2 enables reliable, affordable and continuous environmental monitoring systems in fields like agriculture, biodiversity, environmental hazards and surface water. Several studies have proven that main water quality parameters like total suspended solids (TSS) and chlorophyll (Chl-a) can be estimated from multispectral data using different methods and algorithms. However, independently of the specific approach, these algorithms are selected and optimized to work primarily for one of the main water types i.e. open water, coastal water or inland water. This is also shown by the fact that there is not a single universal algorithm, which can be applied to all water types with consistent and reliable performance at the same time.Ca Mau peninsula is a spacious area located in the southern part of the Mekong Delta, with an area of around 1.6 million hectares. This area has high growth rates of agricultural and aquaculture production, hence diverse water demands and water use types. In this study we use Sentinel-2 remote sensing data to monitor surface water quality using adaptive ML models to account for the different surface water types which occur in this area. Through using remote sensing data, we can provide a synoptic and sufficient view in spatial aspects about water quality parameters in the Ca Mau peninsula. Adapting the ML model will address the bio-optical model for a mixed water scenario.The study is based on Sentinel-2 satellite images acquired in 2019 and 2020, supplemented by field data, i.e. hyperspectral measurements using close range observations, in-situ measurements and water samples, with the aim to collect a comprehensive reference data set as biophysical parameters are closely connected with spectral parameters at close range as well as at high spectral resolution. Therefore, surface hyperspectral measurement has been used to simulate Sentinel 2 multispectral image data at the respective bands.We automatically assign the water type classes to observed surface water by integrating GIS data and remote sensing as the pre-processing step. For each class, the ML models are trained based on the experimental measurements with the multispectral and the simulated multispectral images on the respective water types. We devote special attention to water type boundaries to provide a smooth transition of estimated parameters.The outputs of this model are surface water quality distribution maps with turbidity, TSS, and Chl-a parameters for all areas in Ca Mau peninsula, independent of the actual water type. Through the acceptable accuracy of model testing, the consolidation model will contribute water quality parameters that are crucial and meaningful to the planning and use of water for domestic use and production, besides, it also supports the decision-making of sustainable water use.

Deriving Water Quality Parameters Using Sentinel-2 Imagery: A Case Study in the Sado Estuary, Portugal

Remote Sensing ◽

10.3390/rs13051043 ◽

2021 ◽

Vol 13 (5) ◽

pp. 1043

Author(s):

Giulia Sent ◽

Beatriz Biguino ◽

Luciane Favareto ◽

Joana Cruz ◽

Carolina Sá ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Water Quality Monitoring ◽

Quality Parameters ◽

Quality Monitoring ◽

Chl A ◽

Area Of Interest ◽

Sado Estuary ◽

Monitoring water quality parameters and their ecological effects in transitional waters is usually performed through in situ sampling programs. These are expensive and time-consuming, and often do not represent the total area of interest. Remote sensing techniques offer enormous advantages by providing cost-effective systematic observations of a large water system. This study evaluates the potential of water quality monitoring using Sentinel-2 observations for the period 2018–2020 for the Sado estuary (Portugal), through an algorithm intercomparison exercise and time-series analysis of different water quality parameters (i.e., colored dissolved organic matter (CDOM), chlorophyll-a (Chl-a), suspended particulate matter (SPM), and turbidity). Results suggest that Sentinel-2 is useful for monitoring these parameters in a highly dynamic system, however, with challenges in retrieving accurate data for some of the variables, such as Chl-a. Spatio-temporal variability results were consistent with historical data, presenting the highest values of CDOM, Chl-a, SPM and turbidity during Spring and Summer. This work is the first study providing annual and seasonal coverage with high spatial resolution (10 m) for the Sado estuary, being a key contribution for the definition of effective monitoring programs. Moreover, the potential of remote sensing methodologies for continuous water quality monitoring in transitional systems under the scope of the European Water Framework Directive is briefly discussed.