Absorption Features in Soil Spectra Assessment

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F15%3A68911" target="_blank" >RIV/60460709:41210/15:68911 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1366/14-07800" target="_blank" >http://dx.doi.org/10.1366/14-07800</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1366/14-07800" target="_blank" >10.1366/14-07800</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Absorption Features in Soil Spectra Assessment

Popis výsledku v původním jazyce

From a wide range of techniques appropriate to relate spectra measurements with soil properties, partial least squares (PLS) regression and support vector machines (SVM) are most commonly used. This is due to their predictive power and the availability of software tools. Both represent exclusively statistically based approaches and, as such, benefit from multiple responses of soil material in the spectrum. However, physical-based approaches that focus only on a single spectral feature, such as simple linear regression using selected continuum-removed spectra values as a predictor variable, often provide accurate estimates. Furthermore, if this approach extends to multiple cases by taking into account three basic absorption feature parameters (area, width, and depth) of all occurring features as predictors and subjecting them to best subset selection, one can achieve even higher prediction accuracy compared with PLS regression. Here, we attempt to further extend this approach by adding two additional

Název v anglickém jazyce

Absorption Features in Soil Spectra Assessment

Popis výsledku anglicky

From a wide range of techniques appropriate to relate spectra measurements with soil properties, partial least squares (PLS) regression and support vector machines (SVM) are most commonly used. This is due to their predictive power and the availability of software tools. Both represent exclusively statistically based approaches and, as such, benefit from multiple responses of soil material in the spectrum. However, physical-based approaches that focus only on a single spectral feature, such as simple linear regression using selected continuum-removed spectra values as a predictor variable, often provide accurate estimates. Furthermore, if this approach extends to multiple cases by taking into account three basic absorption feature parameters (area, width, and depth) of all occurring features as predictors and subjecting them to best subset selection, one can achieve even higher prediction accuracy compared with PLS regression. Here, we attempt to further extend this approach by adding two additional

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

DF - Pedologie

OECD FORD obor

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Projekt

<a href="/cs/project/QJ1230319" target="_blank" >QJ1230319: Vodní režim půd na svažitém zemědělsky využívaném území</a><br>

Návaznosti

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

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

APPLIED SPECTROSCOPY

ISSN

0003-7028

e-ISSN

—

Svazek periodika

69

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

7

Strana od-do

1425-1431

Kód UT WoS článku

000366146300009

EID výsledku v databázi Scopus

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