Can in situ spectral measurements under disturbance-reduced environmental conditions help improve soil organic carbon estimation?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027073%3A_____%2F22%3AN0000117" target="_blank" >RIV/00027073:_____/22:N0000117 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41210/22:91397 RIV/60460709:41330/22:91397

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0048969722034015" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0048969722034015</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.scitotenv.2022.156304" target="_blank" >10.1016/j.scitotenv.2022.156304</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Can in situ spectral measurements under disturbance-reduced environmental conditions help improve soil organic carbon estimation?

Popis výsledku v původním jazyce

In situ visible and near-infrared (Vis-NIR) spectroscopy has proven to be a reliable tool for determining soil organic carbon (SOC) content with a small loss of precision as compared to laboratory measurements. The loss of precision is a result of disturbing external environmental factors that disrupt spectral measurements. For example, roughness, changes in weather conditions, humidity, temperature, human factors, spectral noise and especially soil water. It has been assumed that, in situ predictive capability could be improved if some of these factors are either minimized or eliminated during the in situ measurement. For this study, the prediction of SOC was carried out under two different in situ measurement conditions; less favourable environmental conditions (with disturbances) and more favourable site-specific conditions (disturbance-reduced conditions). The primary goal is to determine whether the estimate of SOC can be improved under more favourable site-specific conditions, as well as the impact of pre-treatment algorithms on both less and more favourable disturbed conditions. The study employed a large range of pretreatment algorithms and their combinations. Three separate multivariate models were used to predict SOC, namely Cubist, support vector machine regression (SVMR), and partial least squares regression (PLSR). The result clearly shows that reduced disturbing factors (i.e., drier and unploughed soil as well as noise reduction) result in an improvement of SOC prediction with in situ Vis-NIR spectroscopy. The best overall result was achieved with SVMR (R2CV = 0.72, RMSEPcv = 0.21, RPIQ = 2.34). Although the combination of pre-treatment algorithms resulted in an improvement, overall, these pre-treatment algorithms could not compensate for the factors affecting the measured spectra with disturbance. Though the obtained result is promising, further study is still needed to disentangle the impacts and interactions of various disturbing factors for different soil types.

Název v anglickém jazyce

Can in situ spectral measurements under disturbance-reduced environmental conditions help improve soil organic carbon estimation?

Popis výsledku anglicky

In situ visible and near-infrared (Vis-NIR) spectroscopy has proven to be a reliable tool for determining soil organic carbon (SOC) content with a small loss of precision as compared to laboratory measurements. The loss of precision is a result of disturbing external environmental factors that disrupt spectral measurements. For example, roughness, changes in weather conditions, humidity, temperature, human factors, spectral noise and especially soil water. It has been assumed that, in situ predictive capability could be improved if some of these factors are either minimized or eliminated during the in situ measurement. For this study, the prediction of SOC was carried out under two different in situ measurement conditions; less favourable environmental conditions (with disturbances) and more favourable site-specific conditions (disturbance-reduced conditions). The primary goal is to determine whether the estimate of SOC can be improved under more favourable site-specific conditions, as well as the impact of pre-treatment algorithms on both less and more favourable disturbed conditions. The study employed a large range of pretreatment algorithms and their combinations. Three separate multivariate models were used to predict SOC, namely Cubist, support vector machine regression (SVMR), and partial least squares regression (PLSR). The result clearly shows that reduced disturbing factors (i.e., drier and unploughed soil as well as noise reduction) result in an improvement of SOC prediction with in situ Vis-NIR spectroscopy. The best overall result was achieved with SVMR (R2CV = 0.72, RMSEPcv = 0.21, RPIQ = 2.34). Although the combination of pre-treatment algorithms resulted in an improvement, overall, these pre-treatment algorithms could not compensate for the factors affecting the measured spectra with disturbance. Though the obtained result is promising, further study is still needed to disentangle the impacts and interactions of various disturbing factors for different soil types.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

<a href="/cs/project/SS02030018" target="_blank" >SS02030018: Centrum pro krajinu a biodiverzitu</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Science of The Total Environment

ISSN

0048-9697

e-ISSN

1879-1026

Svazek periodika

838

Číslo periodika v rámci svazku

10 September 2022

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

6

Strana od-do

156304

Kód UT WoS článku

000809761500013

EID výsledku v databázi Scopus

2-s2.0-85131398650