Chlorophyll-a and total suspended solids retrieval and mapping using Sentinel-2A and machine learning for inland waters

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12220%2F20%3A43900848" target="_blank" >RIV/60076658:12220/20:43900848 - isvavai.cz</a>

Alternative codes found

RIV/60076658:12520/20:43900848

Result on the web

<a href="https://doi.org/10.1016/j.ecolind.2020.106236" target="_blank" >https://doi.org/10.1016/j.ecolind.2020.106236</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecolind.2020.106236" target="_blank" >10.1016/j.ecolind.2020.106236</a>

Result language

angličtina

Original language name

Chlorophyll-a and total suspended solids retrieval and mapping using Sentinel-2A and machine learning for inland waters

Original language description

Chlorophyll-a (Chl-a) and Total Suspended Solids (TSS) are both key indicators of the biophysical status of inland waters, and their continued monitoring is essential. Existing conventional methods (e.g., in situ monitoring) have shown that they are impractical due to their time and space limitations. The recently operated Sentinel-2A satellite offers the potential to have higher temporal, spatial, and spectral resolution images with no cost for monitoring water quality parameters of inland waters. The main aim of this study was to develop a semi-empirical model for predicting water quality parameters by combining Sentinel-2A data and machine learning methods using samples collected from several water reservoirs within the southern part of the Czech Republic, Central Europe. A combination of 10 spectral bands of the Sentinel-2A and 19 spectral indices, as independent variables, were used to train prediction models (i.e., Cubist) and then produce spatial distribution maps for both Chl-a and TSS. The results showed that the prediction accuracy based on Sentinel-2A was adequate for both Chl-a (R-2 = 0.85, RMSEp = 48.572) and TSS (R-2 = 0.80, RMSEp = 19.55). The spatial distribution maps derived from Sentinel-2A performed well where Chl-a and TSS were relatively high. The temporal changes in both Chl-a and TSS could be seen in the distribution maps. The temporal changes are showing that The values of TSS dramatically changed in fishponds compared to sand lakes over time which might be due to indifferent management practices. Overall, it can be concluded that Sentinel-2A, when coupled with machine learning algorithms, could be employed as a reliable, inexpensive, and accurate instrument for monitoring the biophysical status of small inland waters like fishponds and sandpit lakes.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20705 - Remote sensing

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Ecological Indicators

ISSN

1470-160X

e-ISSN

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Volume of the periodical

113

Issue of the periodical within the volume

neuveden

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

11

Pages from-to

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UT code for WoS article

000523335900024

EID of the result in the Scopus database

2-s2.0-85080126885