Presented at World Lake Conference, Perugia, Italy

1. Monitoring and retrieving historical daily surface
temperature of sub-alpine Lakes from space
S. Pareeth 1,2,3, M. Metz 1, M. Neteler 1, M. Bresciani 4, F. Buzzi 5, B. Leoni 6, A. Ludovisi 7, G.
Morabito 8 andN.Salmaso2,
1. GIS and Remote Sensing unit, Department of Biodiversity and Molecular Ecology, The Research and Innovation centre (CRI), Fondazione Edmund
Mach (FEM), Trento, Italy
2. Limnology and River Ecology unit, Department of Sustainable Agro-Ecosystems and Bioresources, The Research and Innovation centre (CRI),
Fondazione Edmund Mach (FEM), Trento, Italy
3. Department of Biology, Chemistry and Pharmacy, Freie Universität, Berlin, Germany
4. National Research Council, Institute for Electromagnetic Sensing of the Environment, CNR-IREA, Milano, Italy,
5. ARPA Lombardia, via I Maggio 21/B Oggiono (Lc), Italy
6. Department of Earth and Environmental Sciences, University of Milan-Bicocca, Milan, Italy.
7. Dipartimento di Chimica, Biologia e Biotecnologie, Università degli Studi di Perugia, Via Elce di Sotto – 06124 - Perugia, Italy
8. CNR - Istituto per lo Studio degli Ecosistemi, Largo Tonolli 50, 28922 Pallanza (VB)- Italy

2. Introduction
WarmLakes – Study the long term warming trends of sub-alpine
lakes using temperature derived from satellite data
Leveraging the availability of daily thermal imageries for last 2
decades from multiple sensors aboard satellites
Lake specific validation and model development using field data
Develop daily homogenized Lake Surface Water Temperature
(LSWT) for last 2 decades.
Time series analysis linking the trend with climatic tele - connection
indices like NAO, EA and EMP
Preliminary results

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Collaborations
IGB Berlin,
working on Lake Müggelsee

4. 09/04/14 WLC15, 1 - 5 Sept 2014 4
Scope
● Lakes as sentinels of climate
change
● Reported warming at major lakes
resulting in ecological
consequences
● Difficulties in acquiring high
temporal resolution field data from
lakes
● Seasonal thermal variations versus
teleconnection (oscillation patterns)
● Thermal image processing –
temperature measurement from
space
● Availability of daily thermal data
from multiple satellite sensors
● Combining different sensors
different time frames, temporally
and daily
● Available from early 1980's
Ecological/Climatic perspective Data perspective

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Remote sensing
Collection and interpretation of Earth objects without
being in physical contact, unlike in-situ measurements
Main sources – Images from aircrafts and satellites
Using sensors to detect the variation in energy reflected
and emitted
– Human eyes – Only visible spectrum (Red,
Green, Blue)
– Sensors/detectors – whole range of electro
magnetic spectrum
Key principle of Remote sensing
– Spectral response of the objects in different
wavelengths gives valuable information on its
properties
Source: http://waves.marine.usf.edu/oceans_menu/scope/sidebars.htm

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Remote sensing of water
Source : http://www.intechopen.com/books/topics-in-oceanography/challenges-
and-new-advances-in-ocean-color-remote-sensing-of-coastal-waters
Spatial resolution
0.5 m
15 m
30 m
250 m
> 1000 m
Very High Resolution
Worldview
Ikonos
Geoeye
High Resolution
Aster
Landsat TM, ETM
SPOT
IRS
Medium Resolution
MODIS
Landsat MSS
EOS
Course Resolution
MODIS
ATSR/AATSR
AVHRR
Water quality,
Extent of algal blooms,
Detection of species
Local level, expensive
Water quality,
Extent of algal blooms,
Surface temperature
Local level, Lake wise
Extent of algal blooms,
Surface temperature daily
National level studies
Suitable for very large lakes....
Surface temperature daily
Global level studies

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Sensors
MODIS - Moderate Resolution Imaging Spectroradiometer, NASA
A(A)TSR - Advanced Along-Track Scanning Radiometer, ESA
AVHRR - Advanced Very High Resolution Radiometer, NOAA
~
2014
AVHRR
June 1991 April 2012
2000 2014
~
ATSR/A(A)TSR
MODIS
4:36 and 16:36 local solar time~
~
0130 and 1330 , 10:30 and 22:30 local solar time
Launched Sentinel3 as a successor to Envisat
June 1995
10:00 and 22:00 local solar time
~
1980,s 1998
Geocoding issues
Usable data

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MODIS Land Surface Temperature products (LST)
–MOD11A1, MYD11A1 @ 1km , daily 2 observations, from 2002
–Covers all the lakes globally
–1km spatial resolution
–https://lpdaac.usgs.gov/products/modis_products_table
MODIS Sea Surface Temperature (SST) products
–4 km spatial resolution, daily 2 observations, from 2002
–few lakes
– http://oceancolor.gsfc.nasa.gov/
AVHRR pathfinder SST products
–4 km spatial resolution, daily
–few lakes are covered
–longest time series (from January 1985)
ArcLakes – Lake Surface Water Temperature(LSWT) from ATSR/AATSR
– 0.05 degrees, 1995 – 2012, daily
–developed by School of Geosciences, University of Edinburg
–daily recostructed data, day and night
–covers1600 lakes globally
–http://www.geos.ed.ac.uk/arclake/
Global products for surface temperature
from satellite imageries

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Shortcomings
LST – algorithm using land specific
emissivities
Gaps in time series due to clouds and
bad raw data
Coarse spatial resolution of the
available products
Scope of using lake/sensor specific
coefficients to derive Lake Surface
Water Temperature (LSWT)
7 9 11 13 15 17 19 21 23 25
5
10
15
20
25
f(x) = 1.05x - 0.32
R² = 0.98
Field data
MODISSST
5
7
9
11
13
15
17
19
21
23
25
f(x) = 0.94x + 0.36
R² = 0.95
MODISLST
LST vs SST
G.C. Hulley et al. / Remote Sensing of
Environment 115 (2011) 3758–3769

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Work flow
Raw thermal data from
MODIS;A(A)TSR;AVHRR
Brightness temperatures
Global LST/SST
products
Optimized split window
SST algorithm
for Lakes
Lake Surface
Water Temperature
(LSWT)
Level 1
(Calibration)
G.C. Hulley et al. / Remote Sensing of
Environment 115 (2011) 3758–3769
Lake/Sensor
specific
coefficients(clear sky)
Level 2
Cloud mask
/QC layers
Statistical
Reconstruction
Methods
Gap filled seamless
Time series
data set
Level 3
Validation/Model
development
using field data
Modeled
Time series
of LSWT
Level 4
Cloud mask
/QC layers

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Statistical reconstruction of gaps in
time series data
Mutiple Spatio-temporal regression approach using secondary datasets (PGIS – FEM)
Metz et.al, Remote Sensing. 2014, 6(5): 3822-3840
– Daily four observations of MODIS LST at 250m for entire Europe
– Using hierarchial temporal and spatial interpolation
– Regression model using climatology parameters, DEM etc
Harmonic ANalysis of Time series (HANTS)
Roerink et.al, International Journal of Remote Sensing, 21:9, 1911-1917
– Fourier Analysis
– Temporal interpolation
– Implemented in GRASS – “r.hants” (http://grass.osgeo.org/grass70/manuals/addons/r.hants.html)
Data Interpolating Empirical Orthogonal Functions (DINEOF)
Alvera-Azcárate et.al Journal of Geophysical Research, 112:C03008, 2007. doi:10.1029/2006JC003660.
– ArcLakes global database is using this approach
– Spatio-Temporal interpolation
– Use MODIS SST climatology to initialize the settings
– Hook, S., R. C. Wilson, S. MacCallum and C. J. Merchant (2012), [Global Climate] Lake Surface Temperature [in
"State of the Climate in 2011], Bull. Amer. Meteorol. Soc., 93 (7), S18-S19.

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Reconstructed LST for Europe
Image taken on October 27 2006, MODIS Aqua – 13:30
http://gis.cri.fmach.eu/modis-lst
Before (raw data) After (reconstructed data)
GRASS
Metz, M.; Rocchini, D.; Neteler, M. 2014: Surface temperatures at the continental scale: Tracking
changes with remote sensing at unprecedented detail. Remote Sensing. 2014, 6(5): 3822-3840

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03/12/09 22/01/10 13/03/10 02/05/10 21/06/10 10/08/10 29/09/10 18/11/10 07/01/11
0
5
10
15
20
25
9.81
20.91
21.99
19.17
14.03
11.69
9.51
Field data_surface
FromLSTAqua(13:30)-
brenzone
LST_raw
Missing data in raw LST
The graph shows raw LST data, reconstructed LST data versus field data
collected from Brenzone point in the year 2010
Missing values in raw LST is reconstructed with acceptable accuracy

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Summer mean comparison
MODIS reconstructed
15
17
20
22
25 °C
15
17
20
22
25 °C
0.5
1.2
1.8
2.4
3.0 °C
2003 Summer Mean 2008 Summer Mean 2003-2008 Summer Mean difference
Metz, M.; Rocchini, D.; Neteler, M. 2014: Surface temperatures at the continental scale: Tracking
changes with remote sensing at unprecedented detail. Remote Sensing. 2014, 6(5): 3822-3840

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Results – clear sky observations
Lake Garda
- 2003 – 2013
- MODIS SST
- Monthly field data
Lake Trasimeno
- 1996 - 2002
- ATSR/AATSR
- Arc-lakes product
- Daily field data
0 5 10 15 20 25 30 35
0
5
10
15
20
25
30
f(x) = 0.96x + 0.22
R² = 0.99
1996 - 2002
Field data
ATSRLSWT
6 8 10 12 14 16 18 20 22 24 26
0
5
10
15
20
25
30
f(x) = 1.05x - 0.32
R² = 0.98
2003 - 2013
Field data
MODISLSWT

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Results – clear sky observations
6 11 16 21 26
5
10
15
20
25
30
f(x) = 1.04x - 0.22
R² = 0.98
2003 - 2012
Field data
MODISLSWT
5 10 15 20 25
0
5
10
15
20
25
30
f(x) = 1x + 1.04
R² = 0.97
2003 - 2012
Field data
MODISLSWT
Lake Maggiore
- 2003 – 2012
- MODIS SST
- Monthly field data
Lake Como
- 2003 - 2012
- MODIS SST
- Monthly field data

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Results – Reconstructed Time series
7 9 11 13 15 17 19 21 23 25
5
10
15
20
25
f(x) = 1.03x - 0.09
R² = 0.96
2003 - 2013 monthly
Field data
MODISLSWT
0 5 10 15 20 25 30 35
0
5
10
15
20
25
30
f(x) = 0.92x + 0.36
R² = 0.96
1996 - 2006 Daily
Field data
ATSRLSWT
Lake Garda
- 2003 – 2013
- MODIS SST
- Reconstructed using
HANTS
Lake Trasimeno
- 1996 - 2006
- ATSR/AATSR
- Arc-lakes product
- Reconstructed using
DINEOF
- Daily field data

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5 10 15 20 25
0
5
10
15
20
25
30
f(x) = 1.05x + 0.58
R² = 0.96
2003 - 2013 Monthly
Field data
MODISLSWT
5 10 15 20 25
0
5
10
15
20
25
30
f(x) = 1.04x + 0.52
R² = 0.94
2003-2013 Monthly
Field data
MODISLSWT
Lake Maggiore
- 2003 – 2012
- MODIS SST
- Reconstructed using
HANTS
- Monthly field data
Lake Como
- 2003 – 2012
- MODIS SST
- Reconstructed using
HANTS
- Monthly field data
Results – Reconstructed Time series

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-2
-1.5
-1
-0.5
0
0.5
1
1.5
Garda Maggiore
MODIS LSWT, 2003 – 2013 daily
p = 0.002 0.01
Sen slope = 0.06 0.06
Cor = 0.84
Trend of daily mean deviations

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-1.5
-1
-0.5
0
0.5
1
1.5
Trasimeno Garda
ATSR LSWT, 1996 - 2011 daily
p = 0.02 0.13
Sen slope = 0.04 0.02
Cor = 0.65
Trend of daily mean deviations

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Conclusion
Thermal images from sensors on-board satellites are very effective in
measuring LSWT
Good alternative to in-situ data
Gives seamless spatial coverage and daily data sets
Enormous value to research as surface temperature being important
indicator of climate change
Optimization in terms of algorithms, statistical reconstructions,
observation timings are required

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sajid.pareeth(at)fmach.it
http://gis.cri.fmach.it/pareeth/
Fondazione Edmund Mach- Research and Innovation Centre
Limnology and River ecology/GIS and Remote Sensing Unit
Via Mach 1, 38010 San Michele all'Adige (TN) - Italy
Thank you,
GRASS
http://grass.osgeo.org/ http://r-project.org/