Monitoring and retrieving historical daily surface temperature of sub-alpine Lakes from space
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The document describes a study that aims to monitor and retrieve historical daily surface temperature data of sub-alpine lakes from satellite imagery over the past two decades. The study will leverage daily thermal imagery from multiple satellite sensors to develop daily homogenized lake surface water temperature time series for each lake. Statistical methods will be used to reconstruct temperature time series with gaps filled in. The reconstructed time series will then be analyzed to study long term warming trends and their links to climatic factors. Preliminary results show good agreement between reconstructed lake surface temperatures and in situ measurements.
![09/04/14 WLC15, 1 - 5 Sept 2014 11
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.](https://image.slidesharecdn.com/wlc2014sajidpareeth-160107150826/85/Monitoring-and-retrieving-historical-daily-surface-temperature-of-sub-alpine-Lakes-from-space-11-320.jpg)
Monitoring and retrieving historical daily surface temperature of sub-alpine Lakes from space
- 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
- 3. 09/04/14 WLC15, 1 - 5 Sept 2014 3 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
- 5. 09/04/14 WLC15, 1 - 5 Sept 2014 5 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
- 6. 09/04/14 WLC15, 1 - 5 Sept 2014 6 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
- 7. 09/04/14 WLC15, 1 - 5 Sept 2014 7 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
- 8. 09/04/14 WLC15, 1 - 5 Sept 2014 8 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
- 9. 09/04/14 WLC15, 1 - 5 Sept 2014 9 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
- 10. 09/04/14 WLC15, 1 - 5 Sept 2014 10 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
- 11. 09/04/14 WLC15, 1 - 5 Sept 2014 11 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.
- 12. 09/04/14 WLC15, 1 - 5 Sept 2014 12 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
- 13. 09/04/14 WLC15, 1 - 5 Sept 2014 13 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
- 14. 09/04/14 WLC15, 1 - 5 Sept 2014 14 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
- 15. 09/04/14 WLC15, 1 - 5 Sept 2014 15 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
- 16. 09/04/14 WLC15, 1 - 5 Sept 2014 16 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
- 17. 09/04/14 WLC15, 1 - 5 Sept 2014 17 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
- 18. 09/04/14 WLC15, 1 - 5 Sept 2014 18 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
- 19. 09/04/14 WLC15, 1 - 5 Sept 2014 19 -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
- 20. 09/04/14 WLC15, 1 - 5 Sept 2014 20 -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
- 21. 09/04/14 WLC15, 1 - 5 Sept 2014 21 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
- 22. 09/04/14 WLC15, 1 - 5 Sept 2014 22 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/