NOTE

Inferring organic content of sediments by scanningreflectance spectroscopy (380–730 nm): applying a novelmethodology in a case study from proglacial lakesin Norway

Mathias Trachsel • Bjørn Christian Kvisvik •

Pal Ringkjøb Nielsen • Jostein Bakke •

Atle Nesje

Received: 18 May 2012 / Accepted: 11 July 2013 / Published online: 17 September 2013

� Springer Science+Business Media Dordrecht 2013

Abstract Reflectance spectroscopy in the visible

spectrum (VIS-RS) is a method that has been

successfully applied for inferring organic content of

sediments. In this study, we test the applicability of

VIS-RS to lake sediments in Norway. On the one hand

we use conventional, established algorithms for infer-

ring organic content of sediments, on the other hand

we test the potential of multivariate calibration

techniques to infer organic content. For absolute

quantification of organic content, conventional Corg

measurements are needed when using conventional

algorithms as well as when employing multivariate

calibration techniques. Both, conventional algorithms

and multivariate calibrations, result in estimates of

organic content closely mirroring loss-on-ignition

measurements. When using multivariate calibration

techniques, a conventional Corg measurement every

5 cm is sufficient to obtain estimates of organic matter

that are more accurate than those obtained by means of

conventional algorithms. Therefore, the potential of

multivariate calibration techniques and VIS-RS to

substitute measurements of more time consuming and

costly sediment parameters (e.g. clay minerals) should

be tested.

Keywords Reflectance spectroscopy �Multivariate calibration � LOI � Organic carbon

Introduction

Loss-on-ignition at 550 �C (LOI550) is a method

commonly used to determine the organic content of

sediments (Heiri et al. 2001; Nesje et al. 2004) and is

one of the most widely measured sediment parameters

(Heiri et al. 2001). Organic matter in a sediment is

composed of autochthonous organic production in a

lake, and allochthonous input of terrestrial organic

matter. Additionally, organics in sediments are subject

to microbial decomposition and alteration (Janbu et al.

2011). The concentration of organic matter in

M. Trachsel (&) � J. Bakke � A. Nesje

Department of Earth Science, University of Bergen,

Allegaten 41, Bergen, Norway

e-mail: mathias.trachsel@bio.uib.no

J. Bakke

e-mail: jostein.bakke@geo.uib.no

A. Nesje

e-mail: atle.nesje@geo.uib.no

M. Trachsel � B. C. Kvisvik � J. Bakke � A. Nesje

Bjerknes Centre for Climate Research, University of

Bergen, Allegaten 55, Bergen, Norway

e-mail: bjorn.kvisvik@uni.no

Present Address:

M. Trachsel

Department of Biology, University of Bergen,

Thormøhlensgate 53 A, Bergen, Norway

B. C. Kvisvik � P. R. Nielsen

Department of Geography, University of Bergen,

Fosswinckelsgate 6, Bergen, Norway

e-mail: Pal.R.Nielsen@geog.uib.no

123

J Paleolimnol (2013) 50:583–592

DOI 10.1007/s10933-013-9739-1

sediments is influenced by the amount of allochtho-

nous clastic sediment input (Nesje et al. 2004). LOI550

of sediments of proglacial lakes is therefore inversely

related to glacier activity (Dahl and Nesje 1996; Nesje

et al. 2001). Scanning in situ reflectance spectroscopy

in the visible spectrum (380—730 nm; VIS-RS) is a

non-destructive sediment scanning technique. VIS-RS

has been applied successfully in many studies to infer

the amount of organic material (Rein and Sirocko

2002; Wolfe et al. 2006; Michelutti et al. 2010), the

amount of clastic material (Rein et al. 2005), for

developing climate reconstructions based on calibra-

tion in time (von Gunten et al. 2009; Trachsel et al.

2010), and for general characterisations of sediments

(Debret et al. 2011). Specific compounds (e.g. pig-

ments, clay minerals) show distinct absorption/reflec-

tion characteristics, and three different algorithm types

for extracting reflectance characteristics were pro-

posed (Rein and Sirocko 2002; Wolfe et al. 2006).

These algorithms are based on relative absorption

band depths and relative absorption band areas

respectively (i.e. the absorption change compared to

a theoretical continuum), and on ratios of reflectances

in two wavelengths.

Rein and Sirocko (2002) and Debret et al. (2011)

additionally demonstrated different reflectance prop-

erties of constituents of organic matter [e.g. carote-

noids, chlorins (chlorophyll a derivatives)] or altered

organic matter.

Algorithms used in these studies were specifically

developed for the sediment type analysed. For instance

the relative absorption band depth at 670 nm was used

for sediments rich in organics while the ratio between

reflectances at 570 and 630 nm was preferred for

exclusively clastic sediments. The three lakes included

in the present study experienced massive changes in

sedimentation regime throughout the sediment

sequences considered (presence and absence of gla-

ciers). The lakes have therefore undergone changes

from organic rich lakes to lakes dominated by clastic

sedimentation. Because of these factors, it is not a

priori clear which of the algorithms proposed is best

suited for inferring the amount of organic matter.

Relative absorption band areas, relative absorption

band depths, and ratios of reflectances in two specific

wave-length have in common that they only use a part

of the reflectance spectrum for inference, but do not

use data from other parts of the spectrum that might be

useful as well. In contrast to VIS-RS, in Fourier

transform infrared spectroscopy, the entire measured

spectrum is calibrated to specific compounds (e.g.

Corg, biogenic silica) by means of multivariate

calibration (Rosen et al. 2010).

In this study, we first tested the applicability of VIS-

RS to sediments in Northern Europe using two

conventional algorithms (min670 and d590d690, see

‘‘Materials and methods’’ section) that previously have

been successfully applied. We then tested the potential

of two multivariate calibration techniques partial least

squares regression (PLS) and random forests for

inferring LOI550. Preliminary tests with nine lakes

showed that calibrations based on training sets were

not successful. In this study, we therefore concentrated

on internal calibrations of individual lakes, i.e. cali-

brating on a part of the data and predicting the data not

used for calibration (Vogel et al. 2008). After first

successful tests of this approach, we assessed how

much data was needed for calibration to accurately

infer LOI550 values. At last we compared the accuracy

of LOI550 inferred by multivariate calibration and by

conventional algorithms.

Study sites

The study sites (lakes) are all situated along the western

coast of Norway (Fig. 1), and are all receiving (or

received) glacial melt water. Lake Goddalsvatnet

(60�1005600N, 6�2002800E) is situated to the North-East

of Maurangerfjorden, a branch of Hardangerfjorden, at an

altitude of 532 m a.s.l. Glaciers from the western side of

the Folgefonna icecap drain melt water into Goddalsvat-

net. The catchment area has a total of 65.5 km2. The

bedrock consists of gneiss, migmatite, andesite, dacite

and meta-basalt (Bryhni et al. 2007). The sediment core,

GOP, was retrieved in a distal position of the lake and

spans a late Holocene time interval. LOI550 values vary

between 0.3 and 20 % (Table 1).

The lake Lower Sørsendalsvatnet (61�6703500N,

6�2808500E) is located at 918 m a.s.l. to the south of

Gloppefjorden, a south-easterly trending branch of

Nordfjorden. The glacier Blabreen covers one quarter

of 8.5 km2 catchment area. The bedrock in the area

consists of migmatite, granite, feldspar and augen-

gneiss. The core (SOP) spans the entire Holocene

(Bakke et al. 2013) and a LOI550 range from 0.5 to

17.5 % (Table 1).

Lake Nattmalsvatn (N69�1303900, E15�5901400,160 m a.s.l.) is situated at Andøya, an archipelago of

584 J Paleolimnol (2013) 50:583–592

123

the Vesteralen Island in northern Norway. The bed-

rock consists of banded (amphibole, gneissic horn-

blende, gneissic mica) and migmatic light grey

gneisses (feldspar, biotite and hornblende) and have

some intrusions of quartzite. LOI550 values vary

between 3 and 22.5 % (Table 1). The sediment core

(NAP) spans a Lateglacial—early Holocene interval.

Two cores were retrieved from a raft (GOP and

SOP) while NAP was retrieved from lake ice using a

110-mm diameter piston corer (Nesje 1992). The cores

have lengths of 140 cm (GOP), 158 cm (SOP) and

94 cm (NAP).

Materials and methods

We measured LOI550 and in situ reflectance spectros-

copy on the three cores SOP-205 (n = 316), GOP-310

(n = 280) and NAP-109 (n = 188). LOI550 was

measured following standard procedures with ignition

at 550 �C for 1 h. Samples were retrieved at contig-

uous 5-mm intervals in the cores.

In situ reflectance spectroscopy was measured with

a Gretag-Spectrolino (Gretag Macbeth) at a contigu-

ous measurement interval of 2 mm on the polyethyl-

ene-covered surface of split cores. Reflectance spectra

were recorded at a spectral resolution of 3 nm

integrated into 10-nm intervals between 380 and

Fig. 1 Overview map of Northern Europe and composite core pictures. Study sites are indicated with blue dots. Composite core

pictures of GOP-310, SOP-205 and NAP-109 are shown. (Color figure online)

Table 1 Basic properties of the three sediment cores pre-

sented in this study

GOP SOP NAP

n 280 316 188

Min LOI (%) 0.3 0.5 3

Max LOI (%) 20.5 17.5 22.5

Cv r2 0.63 0.92 0.86

Cv MAD (%) 1.34 0.94 1.5

n = number of LOI550 samples measured; min and max LOI:

lowest and highest LOI (%) value measured, cv r2 = mean of

cross validated squared correlations (coefficient of

determination) between LOI550 and VIS-RS inferred Corg.

For cross validation the calibration data set consisted of every

10th sample, sampled at a regular interval, correlations were

calculated on the verification data set (data not used for

calibration). cv MAD: cross validated mean absolute deviation

for the same data as the coefficient of determination

J Paleolimnol (2013) 50:583–592 585

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730 nm. Each spectrum was corrected accounting for

illumination and transparency effects by dividing it by

the spectrum of a transparency-covered white standard

(BaSO4). Data was subsequently integrated to inter-

vals of 5 mm for comparison with LOI550.

In the literature, many algorithms have been

proposed to extract information from reflectance

spectra (Rein and Sirocko 2002; Wolfe et al. 2006;

von Gunten et al. 2009; Trachsel et al. 2010; Debret

et al. 2011). They are all based on relative absorption

band depths and relative absorption band areas,

respectively (i.e. the absorption change compared to

a theoretical continuum), and on ratios of reflectances

in two wavelengths. These algorithms are reducing the

effects of water content and changing grain-sizes on

results extracted from reflectance spectra (Rein and

Sirocko 2002). In this study, we used the algorithms

relative absorption band depth at 670 nm (hereafter

referred to as min670, calculated as ((6 9 R590 ?

7 9 R730)/13)/R670, where Rx indicates reflectance

at x nm, indicative of chlorins (Rein and Sirocko 2002)

and the ratio between the reflectances at 590 and

690 nm. (590d690, calculated as R590/R690) indica-

tive of terrigenous mineroclastics (Rein and Sirocko

2002; Trachsel et al. 2010). We chose an algorithm

indicative of chlorins as a proxy for organic matter

since first derivatives of spectra did not indicate the

presence of altered organic matter in the sediments

(Debret et al. 2011). The reflectance change between

590 and 690 nm indicative of terrigenous mineroclas-

tics is mainly caused by clay minerals (chlorite, illite

and biotite, USGS 2010), whereas most other minerals

have no diagnostic reflectance features in the visible

spectrum. The algorithm derived, dimensionless data

series were re-sampled to an interval of 5 mm. We then

used standard major axis regression (Legendre and

Legendre 1998, a type II regression also referred to as

scaling) to calibrate re-sampled VIS-RS data to

LOI550. The accuracy of these LOI550 estimates was

assessed calculating mean absolute deviations

(MAD ¼ 1=nR LOIi � VISij j where n is the number

of samples, VISi is the ith VIS-RS inferred LOI

concentration and LOIi is the ith LOI sample).

Debret et al. (2011) used first derivatives to describe

reflectance spectra. Following this idea, we created a

data set consisting of first derivatives and ratios among

reflectances in adjacent wavelengths [i.e. (R440–R430)/

10, (R450–R440)/10 … (R730-R720)/10 and R430/

R440, R440/R450 … R720/R730] for multivariate

calibration. Measurements in the wave-length between

380 and 420 nm were not included in the calibration

data set because of low signal to noise ratios in these

wavelengths (Rein 2003). Prior to multivariate calibra-

tion, we tested to which extent the new data set was

related to LOI550 by means of redundancy analysis

(RDA, van den Wollenberg 1977).

To calibrate the aforementioned data set to LOI550,

we used the two multivariate techniques of random

forests (Breiman 2001) and PLS (Martens and Naes

1989). Random forests are computational learning

algorithms consisting of a set of regression trees

(Breiman et al. 1984; De’ath and Fabricius 2000). We

used 500 trees in which 21 variables (i.e. 1/3 of the 62

available variables) were randomly sampled at each

nod. PLS is a dimension reduction technique that

reduces a large number of predictors to a small number

of components (latent variables) that are then used in

place of the original predictors. Unlike similar tech-

niques, such as principal component regression,

components are chosen to provide maximum correla-

tion with the dependent variable (Martens and Naes

1989; de Jong 1993). The number of components

included in the model was assessed by tenfold cross-

validation: Only components leading to a reduction of

the root mean square error of prediction of more than

5 % were retained in the model (Birks 1998).

Preliminary tests including nine lakes showed that

calibrations based on training sets (using data from

several lakes to predict LOI550 of sediments not used

for calibration) were not successful. We therefore

developed VIS-RS based LOI inference models for

each lake individually (Vogel et al. 2008). To test the

performance of the three calibration approaches (i.e.

conventional univariate and two multivariate), we

divided the measurements obtained on one core into a

calibration and a verification data set. We additionally

wanted to assess how much data was required in the

calibration to obtain meaningful estimates of LOI550.

We therefore varied the amount of data used for

calibration between 3 and 50 % of the total data, and

applied the models to a verification data set consisting

of the data not used for calibration (i.e. between 97 and

50 % of the data). Samples used for calibration were

randomly chosen among all samples (sampling with-

out replacement). For a given amount of data (e.g.

50 %) this procedure was repeated 1,000 times.

Performance was assessed calculating mean absolute

deviation (MAD) for the verification data set.

586 J Paleolimnol (2013) 50:583–592

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We then tested two additional, realistic ways of

constructing calibration and verification data sets. We

used data sampled at regular intervals of 5 cm (i.e.

every 10th measurement) as calibration data set and

used the remaining data for verification. In total, this

approach resulted in 10 calibration and verification

possibilities. In a realistic study, we would have had

10 % of the data measured conventionally (i.e. a

sample every 5 cm) and would have had to decide

based on this constrained data set which method to use

for inferring LOI throughout the core. We therefore

split the data set consisting of a sample every 5 cm in

two equal parts consisting of estimates of LOI550 at

10 cm intervals. We thereby built new calibration and

verification data sets (twofold cross-validation, result-

ing in 20 calibration and verification possibilities) that

were used to select the best model (i.e. lowest MAD)

for the entire core. All numerical analyses were carried

out using R (R Development Core Team 2011) and its

add-on packages PLS (Wehrens and Mevik 2007),

random forest (Liaw and Wiener 2011), boot (Canty

and Ripley 2011) and lmodel2 (Legendre 2013).

Results

The reflectance spectra of the sediments from the three

studied lakes all show distinct characteristics (Fig. 2a–

c). In core GOP reflectance increases between 380 and

590 nm (Fig. 2a). In the clastic part of the sediment

(LOI550 \ 5 %) the reflectance only shows moderate

increase after 590 nm (Fig. 2a), whereas reflectance

continues to increase in the more organic part

(LOI550 [ 8 %, Fig. 2b). In the clastic part of core

SOP (LOI550 \ 5 %), reflectance increases to 590 nm,

subsequently decreases to 690 nm and increases again

towards 730 nm (Fig. 2c). The reflectances measured

at 590 and 730 nm are at about the same level. In the

organic part (LOI550 [ 8 %), reflectances increase to

630 nm, decrease to a pronounced minimum at 670 nm

and show a sharp increase to 730 nm (Fig. 2d). In core

NAP, spectra in the first organic section (LOI550 [10 %) are similar to the organic spectra in SOP

(Fig. 2e). Clastic spectra (LOI550 \ 5 %) are as well

similar to clastic spectra from SOP, but reflectances at

730 nm are lower than reflectances at 590 nm

(Fig. 2f). In the second organic part (LOI550 8–10 %,

Fig. 2g), reflectances increase to 590 nm and show a

long lasting decrease to 670 nm that is accelerated

around 640 nm. The minimum at 670 nm is followed

by a sharp increase in reflectance to 730 nm. For more

details about the three cores, readers are referred to

Table 1.

The two algorithms min670 and 590d690, indicative

of organic content and terrigenous input, respectively,

yield values very close to LOI550 for cores GOP (min670

with lowest MAD, Fig. 3a) and SOP (590d690 with

lowest MAD, Fig. 3b). 590d690 completely fails to

track LOI550 in core NAP and min670 is only able to

depict changes in LOI550 for the upper part of NAP (i.e.

the first organic and the clastic part, Fig. 3c). For the

second organic part, the values inferred are too high. A

ratio between the reflectance at 730 and 590 nm is able

to capture fluctuation in LOI550 for the entire core

(Fig. 3c). Hence, three different algorithms best repre-

sent LOI550 in the three cores investigated. Thus a

certain amount of conventional test measurements of

LOI550 is needed for each core to decide which

algorithm is best in detecting LOI550. We therefore

investigated the possibility of using the entire spectrum

and multivariate calibration techniques to infer LOI550.

RDA indicates that reflectance spectra, their first

derivatives, and the ratios between adjacent reflec-

tance bands are strongly related to LOI550. For SOP,

LOI550 explains 80 % of the variance of the raw

spectra and 63 % of the combined first derivatives and

ratio data set. For GOP the explained variance

amounts to 46 and 41 %, respectively, for NAP to

61 and 53 % respectively. Correlations between

individual series of the calibration (VIS-RS) data set

and LOI550 range from low (r = 0.03, p [ 0.05) to

very high values (r = 0.92, p \ 0.05).

Results of multivariate calibrations are shown in

Fig. 4a–c. Mean absolute deviations for multivariate

calibration approaches are very low and range

between 0.5 and 2 % (LOI550) for all three cores.

Generally, mean absolute deviations of multivariate

calibration approaches are lower than MAD obtained

from conventional algorithms. The results for the three

lakes are presented individually.

For core GOP (Fig. 4a) random forests calibration

technique shows excellent performance and constantly

results in lower mean absolute deviations than the best

conventional algorithm (min670). The performance of

PLS calibration technique, however, is more ambig-

uous. Mean absolute deviations increase rapidly when

reducing calibration data and median mean absolute

deviations of PLS are higher than mean absolute

J Paleolimnol (2013) 50:583–592 587

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deviations of min670 when\21 % of the data are used

for calibration.

For core SOP (Fig. 4b) both PLS and random

forests have a performance superior to the perfor-

mance of the best conventional algorithm (590d690).

Notably, the 90 % quantile mean absolute deviations

of PLS and random forests are lower than the median

mean absolute deviations of 590d690 as soon as more

than 12 % of the data are used for calibration.

Comparing random forests and PLS, PLS results in

lower mean absolute deviations when including few

(\16 %) data for calibration, whereas random forests

result in lower mean absolute deviations when

including[16 % of the data for calibration.

For core NAP (Fig. 4c) both random forests and

PLS result in low and comparable mean absolute

Fig. 2 Reflectance spectra of a clastic, and b organic parts of GOP, c clastic and d organic parts of SOP, e the first organic part, f the

clastic part and g the second organic part of NAP

588 J Paleolimnol (2013) 50:583–592

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deviations that are constantly lower than mean abso-

lute deviations of min670. Mean absolute deviations

obtained with random forests are slightly lower than

those of PLS when [10 % of the data is used for

calibration but are larger than mean absolute devia-

tions of PLS when\10 % of the data are considered.

The 90 % quantile of the mean absolute deviations is

always lower when using random forests.

When following the regular sampling including

10 % of the data for calibration (i.e. a sample every

5 cm), mean absolute deviations of random forests and

PLS are further reduced (dots in Fig. 4a–c). For NAP

and GOP the lowest mean absolute deviation (median

of the 10 MAD) is found for random forests whereas

for SOP PLS performs best. Following the calibration

approach where 5 % of the data are used for calibra-

tion and 5 % for verification, we find the lowest mean

absolute deviations using random forests for GOP and

using PLS for NAP and SOP. We would therefore

choose a calibration based on these two methods for

the corresponding cores.

Applying these models to the entire core resulted in

estimates of LOI550 closely following the measured

LOI550 values (Fig. 5a, b, c; Table 1). For GOP, we

find highest mean absolute deviations amounting to

7.5 % of the total amplitude, minimum MAD amount-

ing to 6.5 % of the amplitude and median mean

absolute deviations amounting to 6.75 % of the

amplitude of LOI550. For SOP, these numbers amount

to 9, 5.5 and 7 % of the amplitude, respectively. For

Fig. 3 Comparison of LOI550 and conventional calibrations of VIS-RS data. a GOP: black: LOI550, red: d590d690, blue: min670.

b SOP: black: LOI550, red: d590d690, blue: min670. c NAP: black: LOI550, orange: d730d590, blue: min670. (Color figure online)

Fig. 4 Comparison of multivariate and univariate calibration

approaches. Mean absolute deviations as a function of the amount

of data used for calibration (median and 90 % quantile values for

1,000 bootstrap replicates, respectively). Partial least squares

regression: Red and orange diamonds, random forests: blue and

green diamonds, min670 black and grey diamonds, for a GOP,

b SOP c NAP. Red and blue circles: median MAD for PLS and RF,

respectively when using every tenth sample for calibration. Pink

and coral diamonds in b: median and 90 % quantile values for

1,000 bootstrap replicates of d590d690. (Color figure online)

J Paleolimnol (2013) 50:583–592 589

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NAP they amount to 11, 5 and 6 % of the amplitude,

respectively.

Discussion

In this study, we confirmed the high potential of using

VIS-RS to infer organic content of lake sediments. For

cores SOP and GOP, two published algorithms,

indicative of clastic input and Corg, respectively,

showed high skill for predicting LOI550. For core

NAP, only a hitherto unpublished algorithm d730d590

mirrored the LOI550 curve. In contrast to min670 this

algorithm only considered the reflectance at 730 and

590 nm, respectively, but did not account for the

reduction of reflectance caused by chlorins at 670 nm.

When comparing the first derivatives of the spectra of

NAP to the first derivative of spectra for different

types of organic matter (mainly chlorins and altered

organic matter) presented by Debret et al. (2011) both

spectra in NAP seemed indicative of chlorins (or

chlorophyll a and its by-products). Hence the change

in the relation between LOI550 and the amount of

chlorins was not caused by a change in organic matter

from chlorins to altered organic matter.

The fact that we needed a new, hitherto unknown

algorithm for NAP, pointed to two weaknesses of

conventional algorithms: (1) the choice of algorithm

was not unequivocal and we therefore (2) could not be

sure about the performance of a specific algorithm in a

specific lake without conventional measurements.

These weaknesses of conventional algorithms and

the failure of calibration by means of training sets

including more than one lake have several reasons: (1)

the conversion factor of weight loss-on-ignition to

total organic carbon is depending on the type of

organic matter and therefore differing between lakes

and possibly within one sediment sequence [see

comparison of LOI550 and TOC in Dean (1974) and

Janbu et al. (2011), or the comparison for soils in de

Vos et al. (2005)] (2) the composition of total organic

carbon varies between different lakes [e.g. chlorins

and altered organic matter, or more generally the

amount of terrestrial and aquatic organic matter (Janbu

et al. 2011)] in turn affecting the results obtained by

algorithms indicative of chlorins that are more abun-

dant in aquatic organic matter (3) different types of

bedrock that result in different basic reflectance

properties (see ‘‘Study site’’ section) (4) LOI550 in

cores with low organic content might partly reflect loss

of lattice water in clays (Dean 1974).

It was remarkable how long we found reasonable

mean absolute deviations when applying multivariate

calibration techniques. Using 10 % of the data for

calibration still results in reasonable mean absolute

deviations (maximum of 11 % of the gradient/ampli-

tude) and we have virtually no information gain when

using more than 20 % of the data for calibration. Using

multivariate calibration techniques seems especially

promising when trying to substitute conventional

measurement techniques by VIS-RS. We have two

gains compared to the use of conventional algorithms

using multivariate calibration techniques: (1) we can

avoid choosing an algorithm (2) a very low number of

Fig. 5 LOI550 values inferred applying multivariate calibration

techniques. Every tenth LOI550 and VIS-RS sample were used

for calibration (i.e. sampling at regular interval of 5 cm)

resulting in 10 calibration (and prediction) possibilities (black

lines). Blue line indicates measured LOI550 values. a GOP,

b SOP, c NAP. Data for GOP were calibrated using random

forests, cores SOP and NAP were calibrated using partial least

squares regression. (Color figure online)

590 J Paleolimnol (2013) 50:583–592

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conventional measurements (10 % of the data, a

sample every 5 cm) enables us to obtain more accurate

estimates of LOI550 than obtained with any conven-

tional algorithm. Hence, the data we additionally

include in multivariate calibration have the potential

to improve our inference of a specific chemical

compound found in the sediments. We, however,

move away from a (geo)chemical and physical

understanding of reflectance spectra to a purely

numerical approach. This is a major weakness of our

approach. We further have to apply complex numer-

ical methods to calibrate reflectance measurements to

LOI550. In light of available software packages this is,

however, not a major problem.

Conclusions

In this study, we tested the potential of using VIS-RS

for inferring organic content of proglacial lake sedi-

ments. We confirmed the high potential of using

published algorithms for inferring organic content of

sediment cores, but found a few shortcomings of these

algorithms, mainly the ambiguous choice of algo-

rithm. We therefore tested the ability of two multi-

variate calibration techniques, random forests and

PLS, for inferring LOI550 of sediment cores. The two

techniques generally result in estimates of LOI550 that

are closer to the measured values (i.e. mean absolute

deviations lower) than conventional algorithms. Using

a sample every 5 cm for calibration seems sufficient to

obtain low mean absolute deviations (lower than 10 %

of the amplitude). Furthermore these mean absolute

deviations are lower than mean absolute deviations of

conventional algorithms. The same amount of data is

needed to decide which conventional algorithm to use.

In this study we tested the potential for substituting

LOI550 by VIS-RS. In future studies, the potential of

multivariate calibration techniques and VIS-RS to

substitute measurements of more time consuming and

costly sediment parameters should be tested. These

tests could comprise two directions: (1) testing if the

determination of the amount of clay minerals (mainly

chlorite and illite) in clastic sediments are possible.

Clay minerals are usually measured by sediment

destructive and time consuming X-ray diffraction; (2)

as done by Rein and Sirocko (2002) and Michelutti

et al. (2010) the possibility to determine sedimentary

pigments (e.g. chlorins) as conventionally measured

by means of high-performance liquid chromatography

should be pursued. Additionally, applying the methods

used in this study to more sophisticated reflectance

scanning techniques like hyper spectral sediment core

scanners that measure at higher spatial and spectral

resolution should be tested.

Acknowledgments We would like to thank Richard J. Telford

and H. John B. Birks for discussions on multivariate calibration

techniques. We thank two anonymous reviewers and Oliver

Heiri for comments that greatly improved the clarity of this

manuscript. Funding was provided by the Swiss National

Science Foundation through a personal grant to MT and the

Bjerknes Centre for Climate Research. This is publication no.

A430 from the Bjerknes Centre for Climate Research.

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