Supplementary Online MaterialTable of content:

- Glossary: Hyperspectral Imaging

- SOM_Table 1: metrics, formula, description and references for the parameters used in

the text

- SOM_Fig. 1: Sediment core of Lake Jaczno with stratigraphic cluster zones and zoom-

in locations of SOM_Fig. 2

- SOM_Fig. 2: Zoomed images of pigment distributions and classification results from

two selected locations in the sediment core

- SOM_Fig. 3: correlation matrix for the multiproxy dataset (Fig 4) showing the

individual scatterplots, the Pearson correlation and significance levels

- References

Glossary: Hyperspectral imaging analysis

The following section contains a short glossary and description of the methods used for

hyperspectral image analysis. Note that there is a detailed manual for hyperspectral imaging

of lake sediments in Butz (2016). The extension package for the ENVI program can be

obtained from the corresponding author.

Normalization (Butz et al. 2015)

Normalization describes the transformation of camera raw data (radiometric counts) into

reflectance values using white- and dark references. Reflectance is the fraction of light

reflected from a sample with respect to the light source.

The normalization is calculated as:

data cubenorm=

dcraw−df av

wf av−df av∗t∫(white)

t∫ (sample )

dcraw = Data cube of raw data

dfav =Dark reference averaged into one frame

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wfav = White reference averaged into one frame

tint = Integration time / exposure time

Median filter (Tukey 1977)

In a window of 5x5 spatial pixels the median value is calculated. Then the centre pixel is

replaced by the median value. Subsequently, the window is moved by one pixel and the

calculation starts again on the original dataset. This operation is performed in the spatial

dimension on all pixels in each spectral band. The result is a dataset where erroneously high or

low single pixels are smoothed. The first and the last two rows/columns of pixels located at

the edges of the dataset are kept in the original form.

Subsetting (Butz et al. 2015)

The image data is cut spatially to the extent of the sediment without the core liner or other

materials. Bad spectral bands (here: 396-499 nm) are removed.

Endmember detection (Butz et al. 2015; Kruse et al. 1999)

A spectral endmember is a single spectrum of a pure substance or a mixture of substances if

the pure form is not existent. Spectral endmembers are, therefore, the purest compounds in a

sample. All other spectra are combinations of the spectral endmembers.

We use the spectral hourglass wizard of the envi 5.03 software (Exelisvis ENVI, Boulder

Colorado) for endmember detection. This wizard performs a noise whitening of the

reflectance data by application of a principal component analysis (PCA). Then a second PCA

is applied to spectrally reduce the dataset. These two cascaded PCAs are called a Minimum

Noise Fraction (MNF). Then the dataset is spatially reduced by a pixel purity index (PPI).

Random vectors are projected on each combination of principal components. Pixels located at

extreme positions are counted until all extreme pixels are found. The remaining extreme pixels

are shown in an n-dimensional scatterplot and endmembers are selected from extreme

positions in the data cloud.

The figure shows endmember spectra from Lake Jaczno (Butz et al. 2015). Some spectra were

highlighted for better contrast. These endmembers are spectra of compound substances rather

than pure substances. Endmembers are derived in order to evaluate the range of spectra to be

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found in a sample. Based on spectral features such as chlorophyll absorption (green bar) and

bacteriopheophytin absorption (blue bar) spectral indices are chosen and calculated.

Endmember detection (continued)

Spectral endmembers from Lake Jaczno (after Butz et al. 2015)

Spectral index calculation (Butz et al. 2015; Rein and Sirocko 2002)

The endmembers are investigated for absorption bands or prominent spectral features.

Depending on the features found, spectral indices are calculated. In the case of Lake Jaczno

(this paper), there are absorption features with minima at R673 and at R845 nm (Figure above

and SOM_Fig. 2). The spectral indices for the relative absorption band depth (RABD) were

calculated after Rein and Sirocko (2002). This method was adapted to the hyperspectral

camera and the absorption features of the spectral endmembers.

Spectral indices were calculated as:

RAB D673=(36∗R589+54∗R729

90)/R673

RAB D845=(34∗R790+34∗R899

68)/ R845

Ri = Reflectance at wavelength i in nanometers

RMean=1n∑i=0

n−1

Ri

3

Ri = Reflectance at spectral band index

n = Number of spectral bands

Minimum distance (Richards and Jia 1999)

This classification method uses the Euclidian distance between a sample spectrum and a range

of reference spectra (classes) to determine to which class the sample is closest. With no

thresholds set, every sample pixel is assigned a class.

The minimum Euclidian distance is calculated as:

Di ( x )=√( x−mi )T− ( x−mi )

D = Euclidian distance

I = i-th class

X = number of dimensions (image bands)

Mi = mean spectrum of class i

For Lake Jaczno, eight training classes were chosen from specific sediment regions (figure:

class means of training areas). Training areas were selected manually after thin section

analysis for non-varved areas and calcite, charcoal, clay and organic varve layers. RABD673

and RABD845 classes were created based on the highest RABD values for each class.

4

Class means of training areas. Bad bands (396-500 nm) were removed before classification.

Spectral angle mapper (Kruse et al. 1993)

The spectral angle mapper compares the calculated angle between the vector of a target

spectrum and the vector of a reference spectrum. If the angle is below a specified threshold

(this paper: 0.04 rad) then the sample classification is positive. If the angle is higher than the

threshold, the classification is negative.

The spectral angle mapper is calculated as:

α=cos−1 { ∑i=1

n

ai ∙ bi

√∑i=1

n

ai2 ∙√∑i=1

n

b i2 }

α = angle [rad]

a = target vector/spectrum

b = reference vector/spectrum

n = number of spectral bands (dimensions)

5

i = i-th band

The classes from the spectral library (see minimum distance classification, figure above) were

used to perform the classification.

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SOM Table 1: metrics, formula, description and references for the parameters used in the

text.

Parameter Description References

Water content

[%]

w c=(mw−md

mw)∗100

wc = water content [%]

mw = mass of wet sediment [g]

md = mass of freeze-dried sediment [g]

(Menounos 1997)

Dry bulk density (DBD)

[g cm-3]

The DBD is the mass of the freeze-dried sediment

per volume.

Sedimentation rate (SR)

[cm yr-1]

The sedimentation rate is the varve thickness

(derived from varve counting).

Mass accumulation rate

(MAR)

[g c m−2 y r−1 ¿

MA R ( gcm−2 y r−1 )=DB D ( g c m−3 ) x S R (cm yr−1) (Zolitschka 1998)

Loss on ignition at 550°C

(LOI550) [%]

LOI550;FLUX [g cm-2 yr-1]

LO I 550 (% )=(md−md ( 550 ))/md ¿∗100

md = mass of freeze-dried sediment [g]

md(550) = mass after 4h @550°C

LO I 550; FLUX=(LO I550 (% )

100)x MA R( g c m−2 yr−1)

(Heiri et al. 2001)

(Zolitschka 1998)

Loss on ignition at 950°C

(LOI950) [%]

LOI950;FLUX [g cm-2 yr-1]

LO I 950 (% )=(md (950)−md ( 550 ))

md∗100

md = mass of freeze-dried sediment [g]

md(550) = mass after 4h @550°C

md(950) = mass after 2h @950°C

LO I 950 ;FLUX=(LO I 950 ( %)

100) x MA R ( g cm−2 y r−1 )

(Heiri et al. 2001)

(Zolitschka 1998)

Lithogenic flux (LF)

[g c m−2 y r−1 ¿ LF=(100−LO I 550 ( %)−LO I 950 (% ))

100∗MAR

Bacteriopheophytin a flux

[µg cm-2 yr-1]Bphe a( µgc m−2 yr−1)=Bphe a (µg g−1)∗MAR After Zolitschka

(1998)

7

Total chlorophylls flux

[µg cm-2 yr-1]Tchl( µg cm−2 y r−1 )=Tchl( µg g−1 )∗MAR After Zolitschka

(1998)

Supplementary Online Figures

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SOM_Fig. 1: Lake Jaczno sediment core. The figure shows the stratigraphic cluster zones and a more detailed view on

the sediments. Coloured boxes show sections enlarged in SOM_Fig. 2.

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SOM_Fig. 2: Section zooms from SOM_Fig. 1. A: Zoom-in to the top section of the sediment core showing the transition

from charcoal enriched layers to the non-charcoal state. The figure shows from the left to the right a true colour image,

the pigment distributions for Bphe a and total chlorophylls and the carbonate, the charcoal and the clay classifications.

Bphe a is completely absent while Tchls are weak. Carbonate layers are very thin in this section. The charcoal

classification shows a hard transition over one or two years. Clay classification shows ongoing erosion after the charcoal

was washed out from the catchment. The clay classification is spectrally more delicate than the pigment abundance.

Therefore, the clay classification is absent within the charcoal enriched layers because the charcoal signal is superseding

it. B: Zoom-in to the bottom section of the sediment core. The index setup is the same as in A, however, there were no

charcoal layers in the bottom section, and thus, the charcoal map was omitted. The figure shows the fine lamination of the

varves in the bottom section and the structure of the pigment distributions. A prominent clay layer can be observed at

~2130 mm sediment depth (greyish layer). The layer was well detected by the spectral angle mapper algorithm.

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SOM_Fig. 3: Correlation matrix for the multiproxy dataset (Fig 4) showing the individual scatterplots and the Pearson

correlation and significance levels (*p<0.05,**p< 0.01,***p< 0.001).

11

References:

Butz C (2016) Hyperspectral imaging of lake sediments: Methods and applications in a

meromict lake of NE Poland. Institute of Geography & Oeschger Centre For Climate Change

Research. University of Bern, Bern, p 225

Butz C, Grosjean M, Fischer D, Wunderle S, Tylmann W, Rein B (2015) Hyperspectral

imaging spectroscopy: a promising method for the biogeochemical analysis of lake sediments.

J Appl Remote Sens 9:096031-096031

Heiri O, Lotter AF, Lemcke G (2001) Loss on ignition as a method for estimating organic and

carbonate content in sediments: reproducibility and comparability of results. J Paleolimnol

25:101-110

Kruse F, Boardman J, Huntington J (1999) Fifteen years of hyperspectral data: northern

Grapevine Mountains, Nevada. Proceedings of the 8th JPL Airborne Earth Science

Workshop: Jet Propulsion Laboratory Publication, JPL Publication, pp 99-17

Kruse F, Lefkoff A, Boardman J, Heidebrecht K, Shapiro A, Barloon P, Goetz A (1993) The

spectral image processing system (SIPS)—interactive visualization and analysis of imaging

spectrometer data. Remote Sens Environ 44:145-163

Menounos B (1997) The water content of lake sediments and its relationship to other physical

parameters: an alpine case study. Holocene 7:207-212

Rein B, Sirocko F (2002) In-situ reflectance spectroscopy–analysing techniques for high-

resolution pigment logging in sediment cores. Int J Earth Sci 91:950-954

Richards JA, Jia X (1999) Remote sensing digital image analysis: An introduction. Springer,

Berlin

Tukey JW (1977) Exploratory data analysis. Addison-Wesley Publishing Company, Reading,

MA, 688 pp

Zolitschka B (1998) A 14,000 year sediment yield record from western Germany based on

annually laminated lake sediments. Geomorphology 22:1-1712