Combining reflectance spectroscopy and the digital elevation model for soil oxidizable carbon estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F17%3A73539" target="_blank" >RIV/60460709:41210/17:73539 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.geoderma.2017.05.018" target="_blank" >http://dx.doi.org/10.1016/j.geoderma.2017.05.018</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.geoderma.2017.05.018" target="_blank" >10.1016/j.geoderma.2017.05.018</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Combining reflectance spectroscopy and the digital elevation model for soil oxidizable carbon estimation

Popis výsledku v původním jazyce

As soil oxidizable carbon has several different absorptions in the visible and near infrared region due to its complex composition, multivariate calibration techniques such as multiple linear regression, partial least squares regression, support vector machines or random forest can be advantageously used to obtain a good prediction. Besides that, the content of Cox is often affected by the character of the terrain, mainly due to prevailing water regime with associated transport and sedimentation processes. Therefore, the question arises, if predictive models calibrated by combining vis-NIR diffuse reflectance spectroscopy and the digital elevation model derivatives will provide a more accurate estimate of Cox. Focused on a sloping arable land affected by distinct water erosion, we tested for this purpose two conceptually different predictive approaches that differ in the nature of the spectroscopic predictor variables.

Název v anglickém jazyce

Combining reflectance spectroscopy and the digital elevation model for soil oxidizable carbon estimation

Popis výsledku anglicky

As soil oxidizable carbon has several different absorptions in the visible and near infrared region due to its complex composition, multivariate calibration techniques such as multiple linear regression, partial least squares regression, support vector machines or random forest can be advantageously used to obtain a good prediction. Besides that, the content of Cox is often affected by the character of the terrain, mainly due to prevailing water regime with associated transport and sedimentation processes. Therefore, the question arises, if predictive models calibrated by combining vis-NIR diffuse reflectance spectroscopy and the digital elevation model derivatives will provide a more accurate estimate of Cox. Focused on a sloping arable land affected by distinct water erosion, we tested for this purpose two conceptually different predictive approaches that differ in the nature of the spectroscopic predictor variables.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

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

2017

Kód důvěrnosti údajů

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

Název periodika

Geoderma

ISSN

0016-7061

e-ISSN

—

Svazek periodika

303

Číslo periodika v rámci svazku

N

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

133-142

Kód UT WoS článku

000404496200016

EID výsledku v databázi Scopus

2-s2.0-85019444237