Trends in surface temperature from new long-termhomogenised thermal data by applying remote
sensing techniques and its validation using in-situdata of five southern European lakes
Sajid Pareeth
Department of Biology, Chemistry and Pharmacy, Freie Universität BerlinCentre for Research and Innovation, Fondazione Edmund Mach
Supervisors: Dr. Markus Neteler, Dr. Nico Salmaso,Prof. Dr. Rita Adrian, Prof. Dr. Klement Tockner
Disputatio – 12 July 2016
Presentation outline
Rationale & Research questionsObjectives & BackgroundStudy area & DataMilestones
M1: Resolve the geometrical issues of earlier satellite dataM2: New Climate Data Record (CDR) – thirty years of daily lakesurface water temperature (1986 – 2015)M3: Results – validation of new LSWT and trend analysis
Discussion & Future workThesis outcome – PublicationsAcknowledgments
Introduction Outline 2 / 44
Rationale: Climate change perspective
Lake Surface Water Temperature (LSWT) - is a good indicator inunderstanding the changes in lake characteristicsLong-term changes in LSWT could give us a cue towardschanging climate and lake’s biological propertiesGlobally the summer mean LSWT are reported to be significantlyincreasingLakes’ response to long-term warming varies regionally
O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophysical Research Letters42, 10,773–10,781 (2015)Adrian, R. et al. Lakes as sentinels of climate change. Limnology and oceanography 54, 2283–2297 (2009)
Introduction Motivation 3 / 44
Rationale: Data perspective
Need high spatio-temporal resolution data to understand thelong-term dynamics of thermal variations over lake’s surfaceThe in-situ data from the study lakes are spatially and temporallycoarseSatellite data acquired at Thermal Infra-red Region(TIR) areconsidered as a good alternativeAvailability of daily satellite observations since 1980’s at 1 kmspatial resolutionSurface temperature is one of the accurate measurements usingremote sensing
Introduction Motivation 4 / 44
Research questions
How can we develop a reliable time series of daily LSWT from thehistorical discrete satellite data?What level of accuracy can be achieved with satellite derivedsurface temperature over large lakes in Northern Italy?Are the large lakes in the north of Italy warming due to climatechange and at what rates?
Introduction Research Questions 5 / 44
Objectives
To reconstruct thirty years (1986 – 2015) of homogenised timeseries of daily LSWT for five large Italian lakes by combiningthermal data from multiple satellitesTo assess the quality of the satellite derived LSWT usinglong-term in-situ dataTo report the summer and annual trends in LSWT using statisticaltests for last thirty years (1986 – 2015)
Introduction Objectives 6 / 44
Background (1/3)
Satellite sensors record the reflected energy in the optical range,while they record the emitted energy in the thermal regionAll objects with a temperature above absolute zero(0 K or -273.15 ◦C) emit electromagnetic radiation
Introduction Background 7 / 44
Background (1/3)
Satellite sensors record the reflected energy in the optical range,while they record the emitted energy in the thermal regionAll objects with a temperature above absolute zero(0 K or -273.15 ◦C) emit electromagnetic radiation
Introduction Background 7 / 44
Background (2/3)
Introduction Background 8 / 44
Background (3/3)
At-sensor radiance to Brightness Temperature(BT) using theinverse Planck’s equation.BT to surface temperature is calculated using Split-Window (SW)equation.
LSWT = T i + c1(T i − T j) + c2(T i − T j)2 + c0 (1)
c0 - c2: Satellite specific split-window coefficientsTi: Thermal data (BT) acquired from satellites at 10.5 - 11.5 µmTj: Thermal data (BT) acquired from satellites at 11.5 - 12.5 µm
Jimenez-Munoz, J.-C. & Sobrino, J. Split-Window Coefficients for Land Surface Temperature Retrieval From Low Resolution
Thermal Infrared Sensors. IEEE GREL 5, 806–809 (2008).
Introduction Background 9 / 44
Study lakes
Red rectangle: bounding box of processed satellite data
Introduction Study area 10 / 44
Thirty years of satellite data
NOAA9
NOAA11
NOAA12
ERS1
NOAA14
ERS2
NOAA16
NOAA17
Envisat
NOAA18
Terra
Aqua
NOAA19
1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
Year
Sat
ellit
e
Sensor
A(A)TSR
ATSR1
ATSR2
AVHRR
MODIS
Introduction Data 11 / 44
Data
Thermal data from 13 satellites (five sensors)Daily dual thermal channels
Ti [10.5 - 11.5 µm]Tj [11.5 - 12.5 µm]
Long-term in-situ data from study lakes obtained from thecollaborators
Lake Garda – Fondazione Edmund MachLake Iseo – Uni-Milano Bicocca (Dr. Barbara Leoni)Lake Como – ARPA (Dr. Fabio Buzzi)Lake Maggiore – ISE-CNR (Dr. Giuseppe Morabito)Lake Trasimeno – Uni-Perugia (Dr. Alessandro Ludovisi)
Introduction Data 12 / 44
Daily acquisition time of satellite data
1000
1100
1200
1300
1400
1500
1600
1700
1986 1993 1997 2000 2003 2005 2006 2008 2010 2012 2015
Year
Tim
e
Satellites
NOAA9
NOAA11
NOAA12
NOAA14
NOAA16
NOAA17
NOAA18
NOAA19
ERS1
ERS2
Envisat
Terra
Aqua
Introduction Data 13 / 44
Major milestones
M1: Resolve the geometrical issues of earlier AVHRR dataM2: New homogenisation method to develop daily LSWT timeseries for thirty years (1986 - 2015)M3: Validation of new LSWT and trend analysis
Miestone 1 Resolving AVHRR issues 14 / 44
Major milestones
M1: Resolve the geometrical issues of earlier AVHRR dataM2: New homogenisation method to develop daily LSWT timeseries for thirty years (1986 - 2015)M3: Validation of new LSWT and trend analysis
Miestone 1 Resolving AVHRR issues 15 / 44
Geometrical issues with AVHRR sensor data
Navigational discrepancies due to on board clock errors andsatellite orbital angular errorsOrbital drifts of the NOAA satellites
Miestone 1 Resolving AVHRR issues 16 / 44
Geometrical issues with AVHRR sensor data
Navigational discrepancies due to on board clock errors andsatellite orbital angular errorsOrbital drifts of the NOAA satellites
Miestone 1 Resolving AVHRR issues 16 / 44
Automated workflow for accurate AVHRR dataprocessing
New python readers: read raw data and calibrate, contributed tothe public repositoryFeature matching technique deployed to extract homologouspoints with respect to a reference imageGeo-rectification using the automatically extracted matching points
Miestone 1 Resolving AVHRR issues 17 / 44
Feature matching based geometric correction
Input – Date of acquisition - 09 Aug 1997Red and blue points: homologous point pairs
Miestone 1 Resolving AVHRR issues 18 / 44
Feature matching based geometric correction
Zoomed into study lakes
Miestone 1 Resolving AVHRR issues 19 / 44
Feature matching based geometric correction
Output – Date of acquisition - 09 Aug 1997Corrected image with aligned boundaries
Miestone 1 Resolving AVHRR issues 20 / 44
Feature matching based geometric correction
Zoomed into study lakes
Miestone 1 Resolving AVHRR issues 21 / 44
Validation: Geometric correction of AVHRRsensor data
Using 12,000 random pixels from 2000 imagesSub-pixel accuracy with an overall RMSE of 755.63 m (Nominalpixel size is 1 km)
Miestone 1 Resolving AVHRR issues 22 / 44
Major milestones
M1: Resolve the geometrical issues of earlier AVHRR dataM2: New homogenisation method to develop daily LSWT timeseries for thirty years (1986 - 2015)M3: Validation of new LSWT and trend analysis
Milestone 2 Homogenisation of LSWT 23 / 44
Work flow to homogenise LSWT time series
Milestone 2 Homogenisation of LSWT 24 / 44
Daily acquisition time of satellite data
1000
1100
1200
1300
1400
1500
1600
1700
1986 1993 1997 2000 2003 2005 2006 2008 2010 2012 2015
Year
Tim
e
Satellites
NOAA9
NOAA11
NOAA12
NOAA14
NOAA16
NOAA17
NOAA18
NOAA19
ERS1
ERS2
Envisat
Terra
Aqua
Milestone 2 Homogenisation of LSWT 25 / 44
Diurnal Temperature Cycle Model (DTC)
Homogenisation using the monthly diurnal cycles derived fromLSWT using DTC modelCorrection factor (cf) is estimated for each LSWT observationusing the respective monthly diurnal cycle
cf = abs(T s(t)− T s(12)) (2)
Original LSWT observation is then adjusted using the correctionfactor to standardise it to 12:00 UTCTime correction of an image taken in June at 14:00 UTC:
Milestone 2 Homogenisation of LSWT 26 / 44
Gap filling using harmonic analysis
Harmonic ANalysis of Time Series (HANTS) is applied to filteroutliers and fill the gaps due to cloud coverHANTS output for the year 2003 as an example for Lake Garda
Milestone 2 Homogenisation of LSWT 27 / 44
Seasonal and annual climatologies
Summer mean LSWT (June/July/August) for the year 2003Seasonal and annual mean LSWT were developed for all lakes
Milestone 2 Homogenisation of LSWT 28 / 44
Summer climatology of Lake Garda
Milestone 2 Homogenisation of LSWT 29 / 44
Major milestones
M1: Resolve the geometrical issues of earlier AVHRR dataM2: New homogenisation method to develop daily LSWT timeseries for thirty years (1986 - 2015)M3: Validation of new LSWT time series and trend analysis
Milestone 3 Validation and trends 30 / 44
Validation of new LSWT time series
Cross-platform LSWT’s between same day observations from asatellite pair reported an average RMSE of 0.88 ◦CFinal homogenised and gap-filled LSWT against in-situ datareported an average RMSE of 1.2 ◦CLake wise results using respective in-situ data are given below:
Name RMSE (◦C) MAE (◦C) R2 N SamplingLake Garda 1.06 0.83 0.98 217 MonthlyLake Iseo 1.08 0.95 0.97 129 MonthlyLake Como 1.14 0.96 0.96 83 MonthlyLake Maggiore 1.13 0.97 0.97 207 MonthlyLake Trasimeno 1.38 1.13 0.98 4392 Daily
Milestone 3 Validation and trends 31 / 44
Summer mean LSWT variation: 1986 – 2016
Milestone 3 Validation and trends 32 / 44
Summer and annual trends from 1986 to 2016
Trends estimated using Sen-slope and Mann Kendal test forsignificanceSignificant trends obtained for annual and summer meansAverage summer and annual trends at the rate of 0.032 ◦C yr-1
and 0.017 ◦C yr-1 respectively
Lake Summer Annual
Lake Garda 0.036*** 0.02*Lake Iseo 0.017 0.019*Lake Como 0.032* 0.012Lake Maggiore 0.033* 0.017Lake Trasimeno 0.044*** 0.017
***(P < 0.001), **(P < 0.01), *(P < 0.05)
Milestone 3 Validation and trends 33 / 44
Temporal coherence between summer meanLSWT
Lake Garda
21.0 22.0 23.0
0.76*** 0.83***21.5 22.5 23.5 24.5
0.76***
22.5
23.5
24.5
0.84***21
.022
.023
.0
Lake Iseo 0.87*** 0.73*** 0.64***
Lake Como 0.86***
2021
2223
0.62***
21.5
23.0
24.5
Lake Maggiore 0.64***
22.5 23.5 24.5 20 21 22 23 24.5 25.5 26.5
24.5
25.5
26.5
Lake Trasimeno
Scatter plot matrix showing temporal coherence between summermean LSWT of all the lakes
Milestone 3 Validation and trends 34 / 44
Applications of new LSWT time series
Inter-lake comparisons using data from same sourceHigh temporal frequency of new data helps to detect localminima/maxima of LSWT during a season/month.To study the ecological consequences due to warmingIt could help in understanding the variation in timing of thermalstratificationTo understand the disappearance of large lakes and its impact tosurrounding landscapesClimate Data Record (CDR) to various modelling frameworks
Discussion Applications 35 / 44
Discussion
The method is extensible and reproducible to other geographicallocationsThe new LSWT time series provides an opportunity to fill the gapdue to lack of high frequent in-situ dataGood alternative for lakes with difficult accessibilityThe satellites measure temperature over skinlayer (thin microlayer between lake surface and atmosphere), while in-situ datarepresents epilimnion layerThe skin layer explains the higher RMSE obtained in this study
Discussion Applications 36 / 44
Future work
To study the influence of larger climatic indices like North AtlanticOscillation (NAO) and Eastern Atlantic (EA) oscillations on thederived seasonal means from new LSWT seriesTo understand the ecological impacts of the reported warming onthe study lakes.
Discussion Future work 37 / 44
Conclusion
A total of 62,799 images (6 TB) were processed in highperformance computer using GRASS GIS and PythonNew method for developing homogenised time series of LSWTfrom multiple satellites is developedThe study lakes are reported to be warming at an average rate of0.032 ◦C yr-1 during summer and 0.017 ◦C yr-1 annuallyHigh coherence was reported between summer mean LSWT ofstudy lakesHighest coherence was reported between Lake Trasimeno andLake Garda during summer, depicting the higher influence ofMediterranean climate over Lake Garda.
Discussion Conclusion 38 / 44
Thesis manuscripts
M1: Pareeth, S., Delucchi, L., Metz, M., Rocchini, D., Devasthale, A., Raspaud, M.,Adrian, R., Salmaso, N., Neteler, M., 2016. New Automated method to developgeometrically corrected time series of brightness temperatures from historical AVHRR LACdata. Remote Sensing 8, 169.
M2: Pareeth, S., Salmaso, N., Adrian, R., Neteler, M. [In review]. Homogenized daily lakesurface water temperature data generated from multiple satellite sensors: A long-termcase study of a large sub-Alpine lake. Nature Scientific Reports.
M3: Pareeth, S., Bresciani, M., Buzzi, F., Leoni, B., Lepori, F., Ludovisi, A., Morabito, G.,Adrian, R., Neteler M., Salmaso N. [In review], Warming trends of perialpine lakes fromhomogenised time series of historical satellite and in-situ data, Science of the TotalEnvironment.
Discussion Publications 39 / 44
Conference contributionsPareeth, S., Salmaso, N., Adrian, R., Neteler, M. New homogenized daily lake surfacewater temperature data of three decades from multiple sensors confirm warming of largesub-alpine lake Garda (Poster). In: EGU 2016, Vienna, Austria, 17-22 April 2016Pareeth, S., Delucchi, L., Metz, M., Salmaso, N., Neteler, M. An open source frameworkfor processing daily satellite images (AVHRR) over last28 years. In: FOSS4G-Europe,Como, Milan, Italy, 14-17 July 2015Pareeth, S., Delucchi, L., Metz, M., Buzzi, Fabio., Leoni, B., Ludovisi, A., Morabito, G.,Salmaso, N., Neteler, M.. Inter-sensor comparison of lake surface temperatures derivedfrom MODIS, AVHRR and AATSR thermal bands. In: 35th EARSeL symposium 2015,Stockholm, Sweden, 15-19 June 2015Pareeth, S., Metz, M., Rocchini,D., Salmaso,N., Adrian,R., Neteler, M. (2014). Lakesurface temperature as a proxy to climate change: Satellite observations versus multiprobe data. Poster at Climate Symposium, Darmstadt, Germany, 13-17 October, 2014Pareeth, S., Metz, M., Neteler, M., Bresciani, M., Buzzi, F., Leoni, B., Ludovisi, A.,Morabito, G., Salmaso, N. (2014). Monitoring and retrieving historical daily surfacetemperature of sub-alpine Lakes from space. In: 15th World Lake Conference (WLC15),ISBN: 978-88-96504-05-5.Pareeth, S., Metz,M., Rocchini,D., Salmaso,N., Neteler,M. (2013). Warm Lakes: retrievalof lake surface water temperature (LSWT) for large sub-alpine lakes from multiple sensorsatellite imageries. In: XXI Congresso dell’AIOL, Lignano Sabbiadoro (Ud), 23-26September 2013: 9.Pareeth, S., Metz,M., Rocchini,D., Salmaso,N., Neteler,M. (2013). Lake surface watertemperature (LSWT) for large sub alpine lakes from satellite sensor derived surfacetemperature. In: EULAKES, Brescia, Italy, 30 May 2013
Discussion Publications 40 / 44
Acknowledgements
Discussion Acknowledgements 41 / 44
Acknowledgements
GIS and RS group members at FEMHydrobiology group members at FEMCollaborators - Mariano Bresciani (IREA-CNR); Barbara Leoni(Uni Milano Bicocca); Alessandro Ludovisi (Uni-Perugia); FabioLenti (SUPSI); Fabio Buzzi (ARPA); Giuseppe Morabito(ISE-CNR)Flavia Zanon, Elisabetta Perini, Alessandro GretterCRI PhD students and colleaguesData providers - NASA, ESA, NOAAGrant providers - FIRST-FEM, IRSAE, European Commission,AIOLOpen source projects - GRASS GIS, Pytroll and their wonderfulcommunities
Discussion Acknowledgements 42 / 44
THANK YOU !!!
Discussion Acknowledgements 43 / 44
References
Adrian, R. et al. Lakes as sentinels of climate change. Limnology and oceanography 54, 2283–2297 (2009).
O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophysical ResearchLetters 42, 10,773–10,781 (2015).
Jimenez-Munoz, J.-C. & Sobrino, J. Split-Window Coefficients for Land Surface Temperature Retrieval From Low-Resolution Thermal Infrared Sensors. IEEE Geoscience and Remote Sensing Letters 5, 806–809 (2008).
Riffler, M., Lieberherr, G. & Wunderle, S. Lake surface water temperatures of European Alpine lakes (1989–2013) basedon the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set. Earth System Science Data 7, 1–17 (2015)
Dokulil, M. T. et al. Twenty years of spatially coherent deepwater warming in lakes across Europe related to the NorthAtlantic Oscillation. Limnology and Oceanography 51, 2787–2793 (2006).
Pareeth, S. et al. New Automated Method to Develop Geometrically Corrected Time Series of Brightness Temperaturesfrom Historical AVHRR LAC Data. Remote Sensing 8, 169 (2016).
Jin, M. & Treadon, R. E. Correcting the orbit drift effect on AVHRR land surface skin temperature measurements.International Journal of Remote Sensing 24, 4543–4558 (2003
Discussion Acknowledgements 44 / 44