Examining the sensitivity of simulated EnMAP data for estimating chlorophyll-a and total suspended solids in inland waters.

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12220%2F23%3A43907895" target="_blank" >RIV/60076658:12220/23:43907895 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41210/23:94516

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1574954123000870?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1574954123000870?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecoinf.2023.102058" target="_blank" >10.1016/j.ecoinf.2023.102058</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Examining the sensitivity of simulated EnMAP data for estimating chlorophyll-a and total suspended solids in inland waters.

Popis výsledku v původním jazyce

Our study investigates the capability of the environmental mapping and analysis program (EnMAP) scenes simulated using the EnMAP end-to-end simulator software (EeteS) based on the AISA Eagle airborne data to predict chlorophyll-a (Chl-a) and total suspended solids (TSS) as two of the most crucial water quality indicators. Three machine learning (ML) approaches (principal component regression(PCR), partial least square regression (PLSR) and random forest (RF)) were employed to establish links between the simulated image spectra and the above-mentioned water attributes of the samples collected from several inland water reservoirs within the southern part of the Czech Republic. Airborne hyperspectral images were also used to develop a model to compare its performance with models developed based on the simulated EnMAP data. Adequate prediction accuracy was obtained for both Chl-a (R2 = 0.89, RMSE = 43.06 g/L, and Lin’s concordance correlation coefficient (LCCC) = 0.91) and TSS (R2 = 0.91, RMSE = 17.53 mg/L, and LCCC = 0.94), which were close enough to those obtained from the airborne hyperspectral images. Chl-a and TSS correlated with the wavelengths around 550 nm and 700 to 750 nm of the red and near-infrared (NIR) regions. In addition, the spatial distribution maps derived from the simulated EnMAP were comparable to those obtained from the AISA Eagle airborne data. Overall, it can be concluded that the simulated EnMAP image successfully and reliably predicted and spatially mapped the selected biophysical properties of the small inland water bodies.

Název v anglickém jazyce

Examining the sensitivity of simulated EnMAP data for estimating chlorophyll-a and total suspended solids in inland waters.

Popis výsledku anglicky

Our study investigates the capability of the environmental mapping and analysis program (EnMAP) scenes simulated using the EnMAP end-to-end simulator software (EeteS) based on the AISA Eagle airborne data to predict chlorophyll-a (Chl-a) and total suspended solids (TSS) as two of the most crucial water quality indicators. Three machine learning (ML) approaches (principal component regression(PCR), partial least square regression (PLSR) and random forest (RF)) were employed to establish links between the simulated image spectra and the above-mentioned water attributes of the samples collected from several inland water reservoirs within the southern part of the Czech Republic. Airborne hyperspectral images were also used to develop a model to compare its performance with models developed based on the simulated EnMAP data. Adequate prediction accuracy was obtained for both Chl-a (R2 = 0.89, RMSE = 43.06 g/L, and Lin’s concordance correlation coefficient (LCCC) = 0.91) and TSS (R2 = 0.91, RMSE = 17.53 mg/L, and LCCC = 0.94), which were close enough to those obtained from the airborne hyperspectral images. Chl-a and TSS correlated with the wavelengths around 550 nm and 700 to 750 nm of the red and near-infrared (NIR) regions. In addition, the spatial distribution maps derived from the simulated EnMAP were comparable to those obtained from the AISA Eagle airborne data. Overall, it can be concluded that the simulated EnMAP image successfully and reliably predicted and spatially mapped the selected biophysical properties of the small inland water bodies.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10617 - Marine biology, freshwater biology, limnology

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Ecological Informatics

ISSN

1574-9541

e-ISSN

1878-0512

Svazek periodika

75

Číslo periodika v rámci svazku

neuveden

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

102058

Kód UT WoS článku

000958030600001

EID výsledku v databázi Scopus

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