Presented at EARSEL 2015, Stockholm

1. Inter-sensor comparison of lake surface
temperatures derived from MODIS, AVHRR and
AATSR thermal bands
S. Pareeth 1,2,3, L. Delluchi 1, M. Metz 1, F. Buzzi 4, B. Leoni 5, A. Ludovisi 6, G. Morabito 7 ,
N. Salmaso 2
and M. Neteler 1
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. ARPA Lombardia, via I Maggio 21/B Oggiono (Lc), Italy
5. Department of Earth and Environmental Sciences, University of Milan-Bicocca, Milan, Italy.
6. Dipartimento di Chimica, Biologia e Biotecnologie, Università degli Studi di Perugia, Via Elce di Sotto – 06124 - Perugia, Italy
7. CNR - Istituto per lo Studio degli Ecosistemi, Largo Tonolli 50, 28922 Pallanza (VB)- Italy
EARSEL SYMPOSIUM, JUNE 2015

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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
Presentation mainly focusing on methods

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Collaborations
IGB Berlin,
working on Lake Müggelsee

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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 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
Offers dual thermal bands in the spectral range of 10 – 12 micro meters

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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)
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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Processing A(A)TSR
BEAM software to read and calibrate the thermal data and angles

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PYTROLL
● Package of multiple python libraries to read, calibrate,
correct, visualize meteorological and polar orbiting
satellite images at L1B level
● mpop – to read and process polar orbiting satellite
images, incl. L1B formats
● pygac – to calibrate and apply corrections on AVHRR
L1B images
● pyresample - Different resample algorithms satellite data
and tie-point data
● www.pytroll.org

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Processing MODIS Swath data
● Used pytroll libs to read thermal bands b31 and b32 , convert
to Brightness Temperature
● Products – MYD021KM and MOD021KM
● Applied geolocation using the associated MYD03/MOD03 files
● Cloud detection using SPARC algorithm – Khlopenkov et.al
(2007)

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The curious case of AVHRR
● Local Area Coverage (LAC) in 1.1 km resolution
● Longest historical high resolution data
● Acquired by multiple NOAA satellites – NOAA-7,9,11,14,16,18,19
● Difficulty in achieving precise geometric correction
● Orbital drifts
● Clock error
● Attitude errors
● Lack of readers to process L1B level data

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number of image distribution

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number of image distribution

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AVHRR processing workflow

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Before geometric correction using sift
NOAA 9
14/07/1985

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Automated feature
matching process
Extract tie-points
Scale Invariant Feature
Transform algorithm

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results
For the day, 01/04/2008
T(K) AATSR AVHRR
(NOAA18)
MODIS-Terra Field
Brenzone 284.58
(0.11)
286.4
(-1.71)
285.05
(-0.36)
284.69
Bardolino 283.68
(-0.34)
285.01
(-1.67)
284.7
(-1.34)
283.34
Acquisition times:
NOAA 18 – 11:35 AM
AATSR – 09:40 AM
MODIS – 09:15 AM

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LSWT – from multiple sensors, single day
283 K
297 K
AATSR (9:40) MODIS (9:15 AM) AVHRR(11:35 AM)

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Histogram AVHRR

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BT channel 4 comparison
270
275
280
285
290
295
300
avhrr_b4
aatsr_b4
modis_b4

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BT channel 5 comparison
274
276
278
280
282
284
286
288
290
292
294
avhrr_b5
aatsr_b5
modis_b5

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Conclusion
Thermal images from sensors on-board satellites are good in
measuring lake surface temperature
Good alternative to in-situ data
Gives seamless spatial coverage and daily data sets
Unified dataset combining sensors, still need good inter sensor
calibration
Optimization in terms of algorithms, statistical reconstructions,
observation timings are required
For AVHRR, the observation timings with respect to orbital drift has to
be considered and corrected

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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/