An Improved Eutrophication Assessment Algorithm of Estuaries and Coastal Waters in Liaodong Bay

Eutrophication is considered to be a significant threat to estuaries and coastal waters. Various localized studies on the world’s oceans have recognized and confirmed that the Forel-Ule Color Index (FUI) or optical measurements are proportional to several water quality variables based on the relatively clear Chl-a-based waters. However, the application potential of FUI in the turbid estuary with complex optics has not been explored. In this study, we selected the coastal waters in the northern Liaodong Bay as the study area, using the field hyperspectral reflectances (Rrs) collected in 2018 to correct the hue angle and verify the Sentinel-2 images algorithm of FUI by in situ FUI in 2019–2020. The results show that there is a good agreement (R2 = 0.81, RMSE = 1.32, MAPE = 1.25%). Trophic Level Index (TLI) was used to evaluate the eutrophication status. The relationship between the in situ FUI and TLI collected in 2018 was discussed based on the difference in the dominant components of waters, while a number of non-algae suspended solids in the estuaries and coastal waters led to the overestimation of eutrophication based on FUI. The R(560)–R(704) (when FUI is between 11 and 15) and R(665)/R(704) (when FUI is between 19 and 21) was employed to distinguish total suspended matter (TSM)-dominated systems in the FUI-based eutrophication assessment. Based on the analysis, a new approach to assessing the eutrophication of coastal waters in Liaodong Bay was developed, which proved to have good accuracy by the field data in 2019 and 2020 (accuracy is 79%). Finally, we used Sentinel-2 images from Google Earth from 2019 to 2020 and locally processed data from 2018 to analyze the FUI spatial distribution and spatial and temporal statistics of the trophic status in the northern Liaodong Bay. The results show that the northern Liaodong Bay always presented the distribution characteristics of high inshore and low outside, high in the southeast and low in the northwest. The nutrient status is the worst in spring and summer.

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

Electronics ◽

10.3390/electronics10233004 ◽

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.

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Monitoring of water table dynamics in peatlands with OPTRAM: Towards globally applicable algorithms in Google Earth Engine using Landsat and Sentinel-2

10.5194/egusphere-egu21-4698 ◽

2021 ◽

Author(s):

Iuliia Burdun ◽

Michel Bechtold ◽

Viacheslav Komisarenko ◽

Annalea Lohila ◽

Elyn Humphreys ◽

...

Keyword(s):

Time Series ◽

Water Table ◽

Input Data ◽

Short Wave ◽

Google Earth ◽

Google Earth Engine ◽

Short Wave Infrared ◽

Northern Peatlands ◽

Sentinel 2

Fluctuations of water table depth (WTD) affect many processes in peatlands, such as vegetation development and emissions of greenhouse gases. Here, we present the OPtical TRApezoid Model (OPTRAM) as a new method for satellite-based monitoring of the temporal variation of WTD in peatlands. OPTRAM is based on the response of short-wave infrared reflectance to the vegetation water status. For five northern peatlands with long-term in-situ WTD records, and with diverse vegetation cover and hydrological regimes, we generate a suite of OPTRAM index time series using (a) different procedures to parametrise OPTRAM (peatland-specific manual vs. globally applicable automatic parametrisation in Google Earth Engine), and (b) different satellite input data (Landsat vs. Sentinel-2). The results based on the manual parametrisation of OPTRAM indicate a high correlation with in-situ WTD time-series for pixels with most suitable vegetation for OPTRAM application (mean Pearson correlation of 0.7 across sites), and we will present the performance differences when moving from a manual to an automatic procedure. Furthermore, for the overlap period of Landsat and Sentinel-2, which have different ranges and widths of short-wave infrared bands used for OPTRAM calculation, the impact of the satellite input data to OPTRAM will be analysed. Eventually, the challenge of merging different satellite missions in the derivation of OPTRAM time series will be explored as an important step towards a global application of OPTRAM for the monitoring of WTD dynamics in northern peatlands.

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Using Optical Water Types to Monitor Changes in Optically Complex Inland and Coastal Waters

Remote Sensing ◽

10.3390/rs11192297 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2297 ◽

Cited By ~ 6

Author(s):

Kristi Uudeberg ◽

Ilmar Ansko ◽

Getter Põru ◽

Ave Ansper ◽

Anu Reinart

Keyword(s):

Remote Sensing ◽

Coastal Waters ◽

Atmospheric Correction ◽

Suspended Sediments ◽

Reflectance Spectra ◽

Water Type ◽

Colored Dissolved Organic Matter ◽

Water Color ◽

Sentinel 2

The European Space Agency’s Copernicus satellites Sentinel-2 and Sentinel-3 provide observations with high spectral, spatial, and temporal resolution which can be used to monitor inland and coastal waters. Such waters are optically complex, and the water color may vary from completely clear to dark brown. The main factors influencing water color are colored dissolved organic matter, phytoplankton, and suspended sediments. Recently, there has been a growing interest in the use of the optical water type (OWT) classification in the remote sensing of ocean color. Such classification helps to clarify relationships between different properties inside a certain class and quantify variation between classes. In this study, we present a new OWT classification based on the in situ measurements of reflectance spectra for boreal region lakes and coastal areas without extreme optical conditions. This classification divides waters into five OWT (Clear, Moderate, Turbid, Very Turbid, and Brown) and shows that different OWTs have different remote sensing reflectance spectra and that each OWT is associated with a specific bio-optical condition. Developed OWTs are distinguishable by both the MultiSpectral Instrument (MSI) and the Ocean and Land Color Instrument (OLCI) sensors, and the accuracy of the OWT assignment was 95% for both the MSI and OLCI bands. To determine OWT from MSI images, we tested different atmospheric correction (AC) processors, namely ACOLITE, C2RCC, POLYMER, and Sen2Cor and for OLCI images, we tested AC processors ALTNNA, C2RCC, and L2. The C2RCC AC processor was the most accurate and reliable for use with MSI and OLCI images to estimate OWTs.

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

Remote Sensing ◽

10.3390/rs11192191 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2191 ◽

Cited By ~ 7

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.

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

10.5194/egusphere-egu2020-7133 ◽

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

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.

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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 ◽

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.

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BURNT AREA DETECTION USING MEDIUM RESOLUTION SENTINEL 2 AND LANDSAT 8 SATELLITES

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

10.5194/isprs-archives-xliii-b5-2020-131-2020 ◽

2020 ◽

Vol XLIII-B5-2020 ◽

pp. 131-137

Author(s):

D. Ongeri ◽

B. K. Kenduiywo

Keyword(s):

Forest Disturbance ◽

Hot Spot ◽

Google Earth ◽

Landsat 8 ◽

Burnt Area ◽

Resource Management System ◽

Medium Resolution ◽

The Difference ◽

Fire Information ◽

Sentinel 2

Abstract. Forest fire is one of the most serious environmental problems in Kenya that influences human activities, climate change and biodiversity. The main goal of this study is to apply medium resolution sensors (Landsat 8 OLI and Sentinel 2 MSI) to produce burnt area severity maps that will include small fires (< 100 ha) in order to improve burnt area detection and mapping in Kenya. Normalized burnt area indices were generated for specified pre- and post-fire periods. The difference between pre- and post-fire Normalized Burnt Ration (NBR) was used to compute δNBR index depicting forest disturbance by fire events. Thresholded classes were derived from the computed δNBR indices to obtain burnt severity maps. The spatial and temporal agreements of the Burnt area detection dates were validated by comparing against the MODIS MCD641 500 m products and MODIS Fire Information for Resource Management System (FIRMS) 1 km daily product hot-spot acquisition dates. This approach was implemented on Google Earth Engine (GEE) platform with a simple user interface that allows users to auto-generate burnt area maps and statistics. The operational GEE application developed can be used to obtain burnt area severity maps and statistics that allow for initial accurate approximation of fire damage.

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River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data

Remote Sensing ◽

10.3390/rs13142650 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2650

Author(s):

Dan Li ◽

Ge Wang ◽

Chao Qin ◽

Baosheng Wu

Keyword(s):

River Discharge ◽

Google Earth ◽

Image Resolution ◽

Mean Bias Error ◽

Main Stream ◽

Discharge Data ◽

Bankfull Discharge ◽

The Mean ◽

Sentinel 2

River discharge and width, as essential hydraulic variables and hydrological data, play a vital role in influencing the water cycle, driving the resulting river topography and supporting ecological functioning. Insights into bankfull river discharge and bankfull width at fine spatial resolutions are essential. In this study, 10-m Sentinel-2 multispectral instrument (MSI) imagery and digital elevation model (DEM) data, as well as in situ discharge and sediment data, are fused to extract bankfull river widths on the upper Yellow River. Using in situ cross-section morphology data and flood frequency estimations to calculate the bankfull discharge of 22 hydrological stations, the one-to-one correspondence relationship between the bankfull discharge data and the image cover data was determined. The machine learning (ML) method is used to extract water bodies from the Sentinel-2 images in the Google Earth Engine (GEE). The mean overall accuracy was above 0.87, and the mean kappa value was above 0.75. The research results show that (1) for rivers with high suspended sediment concentrations, the water quality index (SRMIR-Red) constitutes a higher contribution; the infrared band performs better in areas with greater amounts of vegetation coverage; and for rivers in general, the water indices perform best. (2) The effective river width of the extracted connected rivers is 30 m, which is 3 times the image resolution. The R2, root mean square error (RMSE), and mean bias error (MBE) of the estimated river width values are 0.991, 7.455 m, and −0.232 m, respectively. (3) The average river widths of the single-thread sections show linear increases along the main stream, and the R2 value is 0.801. The river width has a power function relationship with bankfull discharge and the contributing area, i.e., the downstream hydraulic geometry, with R2 values of 0.782 and 0.630, respectively. More importantly, the extracted river widths provide basic data to analyze the spatial distribution of bankfull widths along river networks and other applications in hydrology, fluvial geomorphology, and stream ecology.

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

Remote Sensing ◽

10.3390/rs11060617 ◽

2019 ◽

Vol 11 (6) ◽

pp. 617 ◽

Cited By ~ 6

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.

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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 ◽

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

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