Digital soil mapping using machine learning-based methods to predict soil organic carbon in two different districts in the Czech Republic

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F24%3A100048" target="_blank" >RIV/60460709:41210/24:100048 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.17221/119/2023-SWR" target="_blank" >https://doi.org/10.17221/119/2023-SWR</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17221/119/2023-SWR" target="_blank" >10.17221/119/2023-SWR</a>

Result language

angličtina

Original language name

Digital soil mapping using machine learning-based methods to predict soil organic carbon in two different districts in the Czech Republic

Original language description

Soil organic carbon (SOC) is an important soil characteristic as well as a way how to mitigate climate change. Information on its content and spatial distribution is thus crucial. Digital soil mapping (DSM) is a suitable way to evaluate spatial distribution of soil properties thanks to its ability to obtain accurate information about soil. This research aims to apply machine learning algorithms using various environmental covariates to generate digital SOC maps for mineral top-soils in the Liberec and Domažlice districts, located in the Czech Republic. The soil class, land cover, and geology maps as well as terrain covariates extracted from the digital elevation model and remote sensing data were used as covariates in modelling. The spatial distribution of SOC was predicted based on its relationships with covariates using random forest (RF), cubist, and quantile random forest (QRF) models. Results of the RF model showed that land cover (vegetation) and elevation were the most important environmental variables in the SOC prediction in both districts. The RF had better efficiency and accuracy than the cubist and QRF to predict SOC in both districts. The greatest R2 value (0.63) was observed in the Domažlice district using the RF model. However, cubist and QRF showed appropriate performance in both districts, too.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

40104 - Soil science

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2024

Confidentiality

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

Name of the periodical

Soil and Water Research

ISSN

1801-5395

e-ISSN

1801-5395

Volume of the periodical

19

Issue of the periodical within the volume

1

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

18

Pages from-to

32-49

UT code for WoS article

001148213900001

EID of the result in the Scopus database

2-s2.0-85185780012