Assessing the impact of sampling strategy in random forest-based predicting of soil nutrients: a study case from northern Morocco

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F22%3A92304" target="_blank" >RIV/60460709:41210/22:92304 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/10106049.2022.2048091" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/10106049.2022.2048091</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/10106049.2022.2048091" target="_blank" >10.1080/10106049.2022.2048091</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Assessing the impact of sampling strategy in random forest-based predicting of soil nutrients: a study case from northern Morocco

Popis výsledku v původním jazyce

In this work, we tested different combinations of sampling strategies, random sampling and conditioned Latin Hypercube sampling and sample ratios to predict soil phosporus and potassium contents, previously estimated using dtandard laboratory protocols. Other environmental covariates, used as input data for prediction, were obtained from different sources (multispectral Landsat-OLI 8 image, WorldClim database, ISRIC soil database, and ASTER-GDEM). Our findings showed that random sampling was suitable for predicting phosphorus, while the conditioned Latin Hypercube sampling was suitable for predicting potassium. Furthermore, we observed that when the sample ratio increased from 10 to 25%, model accuracy improved in random sampling and cLHS for phosphorus and potassium prediction. However, before generalizing these findings, we recommend that further studies be conducted under different conditions (climate, soil types and parent materials) and testing other sample ratios to determine the best sampling strategy with the optimum ratio to predict soil nutrients better.

Název v anglickém jazyce

Assessing the impact of sampling strategy in random forest-based predicting of soil nutrients: a study case from northern Morocco

Popis výsledku anglicky

In this work, we tested different combinations of sampling strategies, random sampling and conditioned Latin Hypercube sampling and sample ratios to predict soil phosporus and potassium contents, previously estimated using dtandard laboratory protocols. Other environmental covariates, used as input data for prediction, were obtained from different sources (multispectral Landsat-OLI 8 image, WorldClim database, ISRIC soil database, and ASTER-GDEM). Our findings showed that random sampling was suitable for predicting phosphorus, while the conditioned Latin Hypercube sampling was suitable for predicting potassium. Furthermore, we observed that when the sample ratio increased from 10 to 25%, model accuracy improved in random sampling and cLHS for phosphorus and potassium prediction. However, before generalizing these findings, we recommend that further studies be conducted under different conditions (climate, soil types and parent materials) and testing other sample ratios to determine the best sampling strategy with the optimum ratio to predict soil nutrients better.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Geocarto Imnternational

ISSN

1010-6049

e-ISSN

1010-6049

Svazek periodika

37

Číslo periodika v rámci svazku

26

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

14

Strana od-do

11209-11222

Kód UT WoS článku

000769901700001

EID výsledku v databázi Scopus

2-s2.0-85126724711