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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/60076658:12520/20:43900848

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Ecological Indicators

ISSN

1470-160X

e-ISSN

—

Svazek periodika

113

Číslo periodika v rámci svazku

neuveden

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

—

Kód UT WoS článku

000523335900024

EID výsledku v databázi Scopus

2-s2.0-85080126885