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Statements

Subject Item
n2:RIV%2F60460709%3A41210%2F13%3A60047%21RIV14-MSM-41210___
rdf:type
skos:Concept n6:Vysledek
dcterms:description
Visible and near infrared (VNIR) diffuse reflectance spectroscopy (DRS) offers high potential as a fast and accurate proximal soil sensing technique for soil carbon estimation. The objective of this study is to evaluate the use of VNIR soil spectroscopy for mapping soil organic carbon (SOC) spatial distribution on a 100 ha arable field strongly affected by erosion. The analysis was performed in two main steps: firstly, we focused on the uncertainty in the VNIR spectroscopy regression model (PLSR) under varying number and locations of training samples from which an optimal number of input samples were selected; secondly, we analysed uncertainty propagation in the coupled PLSR and spatial prediction for the selected optimal number of training samples. The PLSR quality parameters are changing exponentially with increasing number of input training samples. The PLSR model constructed using only 37 samples provided a good predictive capability with R2 over 0.7 and RPD over 1.5. The uncertainty of Visible and near infrared (VNIR) diffuse reflectance spectroscopy (DRS) offers high potential as a fast and accurate proximal soil sensing technique for soil carbon estimation. The objective of this study is to evaluate the use of VNIR soil spectroscopy for mapping soil organic carbon (SOC) spatial distribution on a 100 ha arable field strongly affected by erosion. The analysis was performed in two main steps: firstly, we focused on the uncertainty in the VNIR spectroscopy regression model (PLSR) under varying number and locations of training samples from which an optimal number of input samples were selected; secondly, we analysed uncertainty propagation in the coupled PLSR and spatial prediction for the selected optimal number of training samples. The PLSR quality parameters are changing exponentially with increasing number of input training samples. The PLSR model constructed using only 37 samples provided a good predictive capability with R2 over 0.7 and RPD over 1.5. The uncertainty of
dcterms:title
Uncertainty propagation in VNIR reflectance spectroscopy soil organic carbon mapping Uncertainty propagation in VNIR reflectance spectroscopy soil organic carbon mapping
skos:prefLabel
Uncertainty propagation in VNIR reflectance spectroscopy soil organic carbon mapping Uncertainty propagation in VNIR reflectance spectroscopy soil organic carbon mapping
skos:notation
RIV/60460709:41210/13:60047!RIV14-MSM-41210___
n6:predkladatel
n7:orjk%3A41210
n4:aktivita
n12:Z n12:P
n4:aktivity
P(GA526/08/0434), P(GA526/09/1762), Z(MSM6046070901)
n4:cisloPeriodika
SI
n4:dodaniDat
n15:2014
n4:domaciTvurceVysledku
n11:7619332 n11:9947922 n11:9023208 n11:9655743 n11:9918477
n4:druhVysledku
n18:J
n4:duvernostUdaju
n16:S
n4:entitaPredkladatele
n19:predkladatel
n4:idSjednocenehoVysledku
112556
n4:idVysledku
RIV/60460709:41210/13:60047
n4:jazykVysledku
n20:eng
n4:klicovaSlova
Soil organic carbon, VNIR spectroscopy, Partial least squares regression, Digital soil mapping, Uncertainty
n4:klicoveSlovo
n10:VNIR%20spectroscopy n10:Partial%20least%20squares%20regression n10:Digital%20soil%20mapping n10:Uncertainty n10:Soil%20organic%20carbon
n4:kodStatuVydavatele
NL - Nizozemsko
n4:kontrolniKodProRIV
[9482BB69C0A8]
n4:nazevZdroje
Geoderma
n4:obor
n14:DF
n4:pocetDomacichTvurcuVysledku
5
n4:pocetTvurcuVysledku
5
n4:projekt
n8:GA526%2F08%2F0434 n8:GA526%2F09%2F1762
n4:rokUplatneniVysledku
n15:2013
n4:svazekPeriodika
199
n4:tvurceVysledku
Jakšík, Ondřej Klement, Aleš Vašát, Radim Brodský, Lukáš Zádorová, Tereza
n4:wos
000317882900008
n4:zamer
n5:MSM6046070901
s:issn
0016-7061
s:numberOfPages
10
n9:organizacniJednotka
41210