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

Water ◽  
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 ◽  
Sentinel 2

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.

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

<p>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. </p><p>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 km<sup>2</sup>). </p><p>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<sup>2</sup> = 0,82 and RMSE = 21,2 µg/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.</p>

scholarly journals Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

2019 ◽  
Vol 11 (6) ◽  
pp. 617 ◽  
Author(s):  
Sidrah Hafeez ◽  
Man Wong ◽  
Hung Ho ◽  
Majid Nazeer ◽  
Janet Nichol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Satellite Data ◽  
Coastal Waters ◽  
Machine Learning Techniques ◽  
Blue Band ◽  
Chl A ◽  
Learning Techniques ◽  
Reflectance Data

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.

scholarly journals Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3004
Author(s):  
Antonia Ivanda ◽  
Ljiljana Šerić ◽  
Marin Bugarić ◽  
Maja Braović
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Coastal Waters ◽  
Optical Parameter ◽  
Data Set ◽  
Chl A ◽  
Sentinel 2

In this paper, we describe a method for the prediction of concentration of chlorophyll-a (Chl-a) from satellite data in the coastal waters of Kaštela Bay and the Brač Channel (our case study areas) in the Republic of Croatia. Chl-a is one of the parameters that indicates water quality and that can be measured by in situ measurements or approximated as an optical parameter with remote sensing. Remote sensing products for monitoring Chl-a are mostly based on the ocean and open sea monitoring and are not accurate for coastal waters. In this paper, we propose a method for remote sensing monitoring that is locally tailored to suit the focused area. This method is based on a data set constructed by merging Sentinel 2 Level-2A satellite data with in situ Chl-a measurements. We augmented the data set horizontally by transforming the original feature set, and vertically by adding synthesized zero measurements for locations without Chl-a. By transforming features, we were able to achieve a sophisticated model that predicts Chl-a from combinations of features representing transformed bands. Multiple Linear Regression equation was derived to calculate Chl-a concentration and evaluated quantitatively and qualitatively. Quantitative evaluation resulted in R2 scores 0.685 and 0.659 for train and test part of data set, respectively. A map of Chl-a of the case study area was generated with our model for the dates of the known incidents of algae blooms. The results that we obtained are discussed in this paper.

scholarly journals Estimation of water quality parameters from space images

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 ◽  
Sentinel 2

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

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

scholarly journals Water Quality Assessment of Tributaries of Batang Baleh in Sarawak Using Cluster Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Teck-Yee Ling ◽  
Chen-Lin Soo ◽  
Teresa-Lee-Eng Heng ◽  
Lee Nyanti ◽  
Siong-Fong Sim ◽  
...  
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Total Phosphorus ◽  
Organic Nitrogen ◽  
Suspended Solids ◽  
Oxygen Demand ◽  
Aquatic Organisms ◽  
Water Quality Assessment ◽  
Upstream Region ◽  
Nitrite Nitrate

Assessment of river water quality is essential as it provides the knowledge required to make informed decisions. Therefore, water quality was determined at 15 tributary stations located along the Batang Baleh, Sarawak. Results of the study indicate that all tributaries were well-aerated (≈ 7.7 mg/L) with pH (≈ 7.3) and conductivity (≈ 37.3 μS/cm) values falling within acceptable ranges. However, there were tributaries that showed very high turbidity (> 1000 NTU) and suspended solids (> 800 mg/L) which were contributed by the soil erosion from logging activities in the watershed. Tributary stations associated with logging activities also showed significantly higher total phosphorus and organic nitrogen. Cluster analysis demonstrated that water quality at tributary stations along the Batang Baleh exhibited a longitudinal variation from upstream to downstream regions, particularly, dissolved oxygen, five-day biochemical oxygen demand, and nitrite-nitrate nitrogen, which were found higher in upstream region and steadily decreased towards the downstream region. Two stations located at Sg. Serani and Sg. Melatai were distinct from the other stations with the highest concentrations of turbidity, total suspended solids, organic nitrogen, and total phosphorus. Thus, there is an urgent need to reduce the pollutants in the tributaries of Batang Baleh for the health of the sensitive aquatic organisms.

scholarly journals High-Resolution Sea Surface Temperature and Salinity in the Global Ocean from Raw Satellite Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2191 ◽  
Author(s):  
Encarni Medina-Lopez ◽  
Leonardo Ureña-Fuentes
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Data ◽  
Google Earth ◽  
Sea Surface ◽  
Global Ocean ◽  
The Neural Network ◽  
Wide Range ◽  
Sentinel 2

The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82 % and 84 % for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 ∘ C and 0 . 4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

scholarly journals Estudio multitemporal de calidad del agua del embalse de Sitjar (Castelló, España) utilizando imágenes Sentinel-2

2020 ◽  
pp. 117
Author(s):  
C. Radin ◽  
X. Sòria-Perpinyà ◽  
J. Delegido
Keyword(s):  
Water Quality ◽  
Atmospheric Correction ◽  
Ecological Status ◽  
Water Structure ◽  
Chl A ◽  
Reservoir Conditions ◽  
Parameter Values ◽  
Reflectance Data ◽  
Control Water ◽  
Sentinel 2

<p class="p1">Water quality is a subject of intense scientific inquiry because of its repercussion in human’s life, agriculture or even energy generation. Remote sensing can be used to control water masses by analyzing biophysical variables. Chlorophyll-a (Chl-a) and Total Suspended Solids (SS) are a well-known feature of water quality. These variables have been measured in Sitjar reservoir (Castelló, Spain) as a part of the project Ecological Status of Aquatic Systems with Sentinel Satellites (ESAQS), in order to compare the results with satellite reflectance data. Two processes were compared to correct atmospherically the level 1C Sentinel 2 (S2) images. The results show that Case 2 Regional Coast Colour (C2RCC) method, with a Root Mean Square Error of 2.4 mg/m<span class="s1">3 </span>(Chl-a) and 3.9 g/m<span class="s1">3 </span>(SS) is a better tool for atmospheric correction in this scenario due to the low turbidity levels of water. Besides, in this paper we study the Chl-a and SS variability through April 2017 to March 2019 with fourteen S2 images with the automatic products from C2RCC correction, finding correlations between them and the climate and reservoir conditions. Chl-a increase from 0.4 mg/m<span class="s1">3 </span>to 9.5 mg/m<span class="s1">3 </span>while SS rise 18 g/m<span class="s1">3 </span>in this period, which makes Sitjar as an oligotrophic-mesotrophic system. The correlation results demonstrate an excellent correspondence between them (R<span class="s1">2</span>=0.9). Sitjar reservoir lost almost 40 hm<span class="s1">3 </span>at the beginning of the study, which it had a possible relationship with the increasing parameter values. Also discussed was the role played by the climatology in the reservoir conditions due to the changes in the water structure with seasons, which explains the ariability through the year.</p><p class="p1"> </p>

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