scholarly journals Monitoring cyanoHABs and water quality in Laguna Lake (Philippines) with Sentinel-2 satellites during the 2020 Pacific typhoon season

2021 ◽  
pp. 147700
Author(s):  
Isabel Caballero ◽  
Gabriel Navarro
Keyword(s):  
Water Quality ◽  
Typhoon Season ◽  
Laguna Lake ◽  
Sentinel 2

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

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.

scholarly journals MANAGEMENT EFFECTS ON GROUND COVER <q>CLUMPINESS</q>: SCALING FROM FIELD TO SENTINEL-2 COVER ESTIMATES

2017 ◽  
Vol XLII-3/W2 ◽  
pp. 183-188 ◽  
Author(s):  
P. Scarth ◽  
R. Trevithick
Keyword(s):  
Water Quality ◽  
Spatial Resolution ◽  
Field Data ◽  
Temporal Dynamics ◽  
Management Practice ◽  
Spatial Arrangement ◽  
Practice Change ◽  
Grazing Land ◽  
Land Condition ◽  
Sentinel 2

Significant progress has been made in the development of cover data and derived products based on remotely sensed fractional cover information and field data across Australia, and these cover data sets are now used for quantifying and monitoring grazing land condition. The availability of a dense time-series of nearly 30 years of cover data to describe the spatial and temporal patterns in landscape changes over time can help with monitoring the effectiveness of grazing land management practice change. With the advent of higher spatial resolution data, such as that provided by the Copernicus Sentinel 2 series of satellites, we can look beyond reporting purely on cover amount and more closely at the operational monitoring and reporting on spatial arrangement of cover and its links with land condition. We collected high spatial resolution cover transects at 20&amp;thinsp;cm intervals over the Wambiana grazing trials in the Burdekin catchment in Queensland, Australia. Spatial variance analysis was used to determine the cover autocorrelation at various support intervals. Coincident Sentinel-2 imagery was collected and processed over all the sites providing imagery to link with the field data. We show that the spatial arrangement and temporal dynamics of cover are important indicators of grazing land condition for both productivity and water quality outcomes. The metrics and products derived from this research will assist land managers to prioritize investment and practice change strategies for long term sustainability and improved water quality, particularly in the Great Barrier Reef catchments.

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

In-Situ Validation of Water Quality Algorithms and Monitoring of Irish Lakes using Sentinel 2 Imagery

2020 ◽  
Author(s):  
Sita Karki ◽  
Kevin French ◽  
Valerie McCarthy ◽  
Jennifer Hanafin ◽  
Eleanor Jennings ◽  
...  
Keyword(s):  
Water Quality ◽  
Time Window ◽  
Atmospheric Correction ◽  
European Space Agency ◽  
Weather Condition ◽  
Water Bodies ◽  
Observation Data ◽  
Field Validation ◽  
Field Samples ◽  
Sentinel 2

&lt;p&gt;Through Remote Sensing of Irish Surface Water (INFER) project, we are validating the algorithms to measure the &amp;#160;water quality using Sentinel 2 imagery, which comprises of two European Space Agency (ESA) terrestrial satellites with combined temporal resolution of 5 days. The project is focused on selection of optimal algorithms that will be applicable in Irish context in relation to the high cloud cover and relatively small sizes of the water bodies. The current procedure entails collection of reflectance data from the lakes during the Sentinel overpass as it helps to identify the correct atmospheric correction algorithm. Field radiometry tasks were carried out using TRIOS RAMSES radiometers. Standard field procedures were employed for acquiring glint free reflectance from the water bodies.&lt;/p&gt;&lt;p&gt;Historical data collected from the 11 lakes, which had field bathymetry survey data, were analysed in order to determine the influence of environmental conditions on the quality of samples. Based on the analysis, recommendations to collect field samples from areas deeper than 10 m and 30 m away from the shoreline were provided in order to avoid the reflectance from the bottom and the surrounding topography. A site selection process was undertaken during the spring of 2019 to shortlist appropriate sites for field validation of satellite-derived products. A total of fifteen lakes were identified for field validation based on several criteria so as to ensure lakes with varying size, depth, trophic status and Water Framework Directive (WFD) status . In addition, a timetable for proposed sampling was established by drawing up a timetable of satellite passes starting from summer of 2019. C2RCC and Acolite processors are being used to compute the chlorophyll and turbidity from identified lakes. Considering the fast changing weather condition of Ireland, it was difficult to obtain the exact overlap between the sentinel overpass and the field sampling. In order to address this issue, the field samples collected within 10 days from the sensor overpass were considered for the field validation. Study of the satellite derived water chemistry data showed that the data collected outside of that time window may not represent the natural fluctuation that occurs in the water bodies.&lt;/p&gt;&lt;p&gt;The end product of this project is the web platform with the access to Sentinel 2 MSI data products where users can visualize the water quality products for Ireland. This platform will promote the use of earth observation data for inland water quality monitoring and would enable sustainable utilization of the water resources.&lt;/p&gt;

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 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>

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.

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).

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