First Results of Phytoplankton Spatial Dynamics in Two NW-Mediterranean Bays from Chlorophyll-a Estimates Using Sentinel 2: Potential Implications for Aquaculture

Shellfish aquaculture has a major socioeconomic impact on coastal areas, thus it is necessary to develop support tools for its management. In this sense, phytoplankton monitoring is crucial, as it is the main source of food for shellfish farming. The aim of this study was to assess the applicability of Sentinel 2 multispectral imagery (MSI) to monitor the phytoplankton biomass at Ebro Delta bays and to assess its potential as a tool for shellfish management. In situ chlorophyll-a data from Ebro Delta bays (NE Spain) were coupled with several band combination and band ratio spectral indices derived from Sentinel 2A levels 1C and 2A for time-series mapping. The best results (AIC = 72.17, APD < 10%, and MAE < 0.7 mg/m3) were obtained with a simple blue-to-green ratio applied over Rayleigh corrected images. Sentinel 2–derived maps provided coverage of the farm sites at both bays allowing relating the spatiotemporal distribution of phytoplankton with the environmental forcing under different states of the bays. The applied methodology will be further improved but the results show the potential of using Sentinel 2 MSI imagery as a tool for assessing phytoplankton spatiotemporal dynamics and to encourage better future practices in the management of the aquaculture in Ebro Delta bays.

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Chlorophyll-a concentration retrieval in eutrophic lakes in Lithuania from Sentinel-2 data

Geologija Geografija ◽

10.6001/geol-geogr.v4i1.3720 ◽

2018 ◽

Vol 4 (1) ◽

Cited By ~ 6

Author(s):

Dalia Grendaitė ◽

Edvinas Stonevičius ◽

Jūratė Karosienė ◽

Ksenija Savadova ◽

Jūratė Kasperovičienė

Keyword(s):

Water Quality ◽

Chlorophyll A ◽

Remote Sensing Data ◽

Empirical Equations ◽

Active Constituent ◽

Eutrophic Lakes ◽

Band Ratio ◽

Local Conditions ◽

Chlorophyll A Concentration ◽

Sentinel 2

Inland waters are an important habitat for flora and fauna and are also used for aesthetic, recreational, and industrial needs; therefore, monitoring the current state of freshwaters and applying measures to improve water quality are of high importance. To have an efficient monitoring system that could cover large areas, the use of remote sensing data is crucial. In this study the suitability of the Sentinel-2 Multispectral Imager data is tested for observing cyanobacteria bloom events in the eutrophic lakes and retrieving the chlorophyll-a concentration – an indicator of phytoplankton biomass. The analysis is carried out using data from four lakes in Lithuania – two eutrophic blooming lakes and two oligo-mesotrophic non-blooming lakes. The results showed that reflectances are higher in the eutrophic lakes than in the oligo-mesotrophic lakes due to the presence of an optically active constituent, namely, chlorophyll-a pigment. We tested empirical equations for chlorophyll-a concentration retrieval in eutrophic lakes derived in other studies to check whether they could be used without adaptation to local conditions. Most of the equations performed well (R2 = 0.5–0.8); however, they had high RMSEs = 17–53 μg L–1. The equation used with the bottom of atmosphere data CHL8_L2A (R2 = 0.76) had the lowest RMSE = 9 μg L–1. In addition, we derived empirical equations for eutrophic lakes in Lithuania. The equations that were based on the Sentinel-2 band ratio B5/B4 and the three band (B4, B5, and B8A) expression performed the best (R2 = 0.77–0.79) and had lower RMSE = 7 μg L–1 than empirical equations from other studies. A larger in situ dataset could improve the algorithm performance in retrieving the chlorophyll-a concentration. The first attempts to map water quality parameters in eutrophic lakes in Lithuania using the data received from the Sentinel-2 MSI sensor show good results, as the changes in reflectance, caused by the changes in chlorophyll-a concentration, can be seen from satellite images.

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Retrieval and mapping of chlorophyll-a concentration from Sentinel-2 images in an urban river in the semiarid region of Brazil

Ambiente e Agua - An Interdisciplinary Journal of Applied Science ◽

10.4136/ambi-agua.2488 ◽

2020 ◽

Vol 15 (2) ◽

pp. 1

Author(s):

Alessandro Rhadamek Alves Pereira ◽

João Batista Lopes ◽

Giovana Mira de Espindola ◽

Carlos Ernando da Silva

Keyword(s):

Chlorophyll A ◽

River Mouth ◽

Water Quality Monitoring ◽

Semiarid Region ◽

Surface Reflectance ◽

Chlorophyll A Concentration ◽

In Situ Data ◽

Sentinel 2A ◽

Sentinel 2

Recently, the Poti river mouth region has experienced environmental impacts that resulted in a change of landscape in its dry season, highlighting the eutrophication and proliferation of phytoplankton, algae, cyanobacteria and aquatic plants. Considering the aspects related to water-quality monitoring in the semiarid region of Brazil from remote sensing, this study aimed to evaluate the performance of Sentinel-2A satellite data in the retrieval of chlorophyll-a concentration in Poti River in Teresina, Piaui, Brazil. The chlorophyll-a concentration retrieval and mapping methodology involved the study of the water surface reflectance in Sentinel-2A images and their correlation with the chlorophyll-a data collected in situ during the years 2016 and 2017. The results generated by the Chl-1, Ha et al. (2017), Chl-2, Page et al. (2018), and Chl-3, Kuhn et al. (2019) equations show the need for calibrating the algorithms used for the Poti River water components. However, the empirical algorithm Chl-2 shows a correlation has been established to identify the spatiotemporal variation of chlorophyll-a concentration along the Poti River broadly and not punctually. The spatial distribution of this pigment in maps derived from Sentinel-2A is consistent with the pattern of occurrence determined by the in situ data. Therefore, the MSI sensor proved to be a tool suitable for the retrieval and monitoring of chlorophyll-a concentration along the Poti River.

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Using Sentinel-2 images to implement Precision Agriculture techniques in large arable fields: First results of a case study

Advances in Animal Biosciences ◽

10.1017/s2040470017000784 ◽

2017 ◽

Vol 8 (2) ◽

pp. 377-382 ◽

Cited By ~ 5

Author(s):

A. Escolà ◽

N. Badia ◽

J. Arnó ◽

J. A. Martínez-Casasnovas

Keyword(s):

Precision Agriculture ◽

Vegetation Indices ◽

Linear Regression Models ◽

Yield Data ◽

Promising Tool ◽

Arable Fields ◽

First Results ◽

Sentinel 2A ◽

Sentinel 2

This work assesses the potential of Sentinel-2A images in precision agriculture for Barley production in a case study. Two workflows are proposed: 1) images were acquired with a relatively simple methodology to follow the crop development; 2) two images around harvest time were downloaded and processed using a more complex and accurate methodology to calculate four vegetation indices (NDVI, WDRVI, GRVI and GNDVI) to be correlated to yield with linear regression models. Yield data were acquired with a yield monitor installed in a combine harvester. Green-based vegetation indices performed slightly better. However, the highest correlation coefficient was 0.48. Better results may be achieved with earlier imagery and other vegetation indices. Sentinel-2 is a promising tool for precision agriculture in large arable crop fields.

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The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany

Remote Sensing ◽

10.3390/rs13081542 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1542

Author(s):

Igor Ogashawara ◽

Christine Kiel ◽

Andreas Jechow ◽

Katrin Kohnert ◽

Thomas Ruhtz ◽

...

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Spectral Band ◽

Satellite Image ◽

Atmospheric Correction ◽

Spatial Dynamics ◽

Eutrophic Lakes ◽

Chl A ◽

Spatial Coverage ◽

Sentinel 2

Eutrophication of inland waters is an environmental issue that is becoming more common with climatic variability. Monitoring of this aquatic problem is commonly based on the chlorophyll-a concentration monitored by routine sampling with limited temporal and spatial coverage. Remote sensing data can be used to improve monitoring, especially after the launch of the MultiSpectral Instrument (MSI) on Sentinel-2. In this study, we compared the estimation of chlorophyll-a (chl-a) from different bio-optical algorithms using hyperspectral proximal remote sensing measurements, from simulated MSI responses and from an MSI image. For the satellite image, we also compare different atmospheric corrections routines before the comparison of different bio-optical algorithms. We used in situ data collected in 2019 from 97 sampling points across 19 different lakes. The atmospheric correction assessment showed that the performances of the routines varied for each spectral band. Therefore, we selected C2X, which performed best for bands 4 (root mean square error—RMSE = 0.003), 5 (RMSE = 0.004) and 6 (RMSE = 0.002), which are usually used for the estimation of chl-a. Considering all samples from the 19 lakes, the best performing chl-a algorithm and calibration achieved a RMSE of 16.97 mg/m3. When we consider only one lake chain composed of meso-to-eutrophic lakes, the performance improved (RMSE: 10.97 mg/m3). This shows that for the studied meso-to-eutrophic waters, we can reliably estimate chl-a concentration, whereas for oligotrophic waters, further research is needed. The assessment of chl-a from space allows us to assess spatial dynamics of the environment, which can be important for the management of water resources. However, to have an accurate product, similar optical water types are important for the overall performance of the bio-optical algorithm.

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