Modelling diverse soil parameters with visible to longwave infrared spectroscopy using PLSR employed by an automatic modelling engine

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00025798%3A_____%2F17%3A00000002" target="_blank" >RIV/00025798:_____/17:00000002 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3390/rs9020134" target="_blank" >http://dx.doi.org/10.3390/rs9020134</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs9020134" target="_blank" >10.3390/rs9020134</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modelling diverse soil parameters with visible to longwave infrared spectroscopy using PLSR employed by an automatic modelling engine

Popis výsledku v původním jazyce

The study tested a data mining engine (PARCUDA®) to predict various soil attributes (BC, CEC, BS, pH, Corg, Pb, Hg, As, Zn and Cu) using reflectance data acquired for both optical and thermal infrared regions. The PARCUDA® was designed to utilize large data in parallel and automatic processing to build and process hundreds of diverse models in a unified manner while avoiding bias and deviations caused by the operator(s). The system is able to systematically assess the effect of diverse preprocessing techniques; additionally it analyses other parameters, such as different spectral resolutions and spectral coverages, that affect soil properties. Accordingly, the system was used to extract models across both optical and thermal infrared spectral regions, which holds significant chromophores. In total, 2880 models were evaluated where each model was generated with a different preprocessing scheme of the input spectral data. The models were assessed using statistical parameters such as R2 SEP, RPD and by physical explanation (spectral assignments). It was found that the smoothing procedure is the most beneficial preprocessing stage, especially when combined with spectral derivation (1st or 2nd derivatives). Automatically and without any operator intervention the PARACUDA® engine enabled the best prediction models to be found out of all the combinations tested. Furthermore, the PARACUDA® engine and the presented processing scheme proved to be efficient tools for getting a better understanding of the geochemical properties of the samples studied (e.g., mineral associations).

Název v anglickém jazyce

Modelling diverse soil parameters with visible to longwave infrared spectroscopy using PLSR employed by an automatic modelling engine

Popis výsledku anglicky

The study tested a data mining engine (PARCUDA®) to predict various soil attributes (BC, CEC, BS, pH, Corg, Pb, Hg, As, Zn and Cu) using reflectance data acquired for both optical and thermal infrared regions. The PARCUDA® was designed to utilize large data in parallel and automatic processing to build and process hundreds of diverse models in a unified manner while avoiding bias and deviations caused by the operator(s). The system is able to systematically assess the effect of diverse preprocessing techniques; additionally it analyses other parameters, such as different spectral resolutions and spectral coverages, that affect soil properties. Accordingly, the system was used to extract models across both optical and thermal infrared spectral regions, which holds significant chromophores. In total, 2880 models were evaluated where each model was generated with a different preprocessing scheme of the input spectral data. The models were assessed using statistical parameters such as R2 SEP, RPD and by physical explanation (spectral assignments). It was found that the smoothing procedure is the most beneficial preprocessing stage, especially when combined with spectral derivation (1st or 2nd derivatives). Automatically and without any operator intervention the PARACUDA® engine enabled the best prediction models to be found out of all the combinations tested. Furthermore, the PARACUDA® engine and the presented processing scheme proved to be efficient tools for getting a better understanding of the geochemical properties of the samples studied (e.g., mineral associations).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

<a href="/cs/project/8G15004" target="_blank" >8G15004: Nový přístup pro modelování degradace půd s využitím superspektrálních dat</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Remote Sensing

ISSN

1424-8220

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

22

Strana od-do

Article n. 134

Kód UT WoS článku

000397013700036

EID výsledku v databázi Scopus

2-s2.0-85013676577