Introduction to teledection

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1 Introduction to teledection Formation Sébastien Clerc, ACRI-ST ACRI-ST

2 Earth Observation Actors and Markets 2

3 Earth Observation economic importance Earth Observation is one of the major application of space in terms of business Satellite TV Basic Activities 5% Telecommunications 9% Human Spaceflight 10% Space Situational Awareness Robotic Exploration 3% Technology 0% 3% General Budget 5% Others 0% Earth Observation 21% Navigation 17% Meteosat GPS/ Galileo ISS Science 12% Launchers 15% ESA Budget (2011) Rosetta Planck VEGA Source: wikipedia 3

4 Why Observe Earth from space? Positive points A space observation system provides images continuously, on a worlwide scale One to 5 satellites needed for a global observation system Observation obtained in very remote places (Pacific, high-latitudes...) Same sensor gives consistent data over time and space: enable reliable long term series and global maps The satellite and launch cost is relatively high, but once the satellite is in orbit, it operates almost at no cost No refuelling needed, no maintenance Negative points Temporal coverage can be a problem Clouds reduces visbility Altitude is not favourable for spatial resolution and signal accuracy 4

5 Who are the actors of space observation? Institutions Type Examples Mission example National agencies CNES, DLR MERLIN: methane concentration European Defence DGA, ASI HELIOS: military high resolution imaging, COSMO: dual-use radar imaging Extra-European Defence European institutions Kazakhstan, EAU EU, ESA, Eumetsat Dual-use medium/high-resolution imaging Copernicus program: multi-satellite global observation system for environment monitoring and security 5

6 Who are the actors of space observation? Commercial Image providers Type Examples Services Private companies Data processing and distribution DigitalGlobe, Airbus Defense and Space, Skybox Imaging, BlackBridge Commercial medium to high resolution on-demand imaging Type Examples Services SMEs ACRI-ST, Elecnor, CS Processing, storage, quality control, value-added services 6

7 Earth Observation Applications Earth Observation Exploring Monitoring Forecasting Earth Observation provides new geophysical data Earth Observation detects changes Assimilation of Earth Observation data in geophysical models 7

8 Example Public Service Applications Earth Observation Exploring Monitoring Forecasting Sea Surface Elevation: El Nino Ozone hole monitoring Climate Change: Surface temperature Image NASA/CNES Image ESA/Eumetsat/DLR Image NOAA 8

9 Example Public Service Applications Earth Observation Exploring Monitoring Forecasting Bathymetry Flood monitoring Wind velocity map Image Digital Globe Image ESA Image Eumetsat 9

10 Example Commercial Service Applications Earth Observation Exploring Monitoring Forecasting 3D Elevation map for oleoduc installation Counting the cars Predicting crop maturity Image Airbus Image Digital Globe Image Digital Globe 10

11 The Copernicus Program ACRI-ST role in Copernicus program Leader of Sentinel-3 Mission Performance Center Responsible of Sentinel-3 Processing and Archiving Center for SLSTR and Synergy products (S3) Contributes to Sentinel-2 Processing and Archiving Center 11

12 Earth Observation Missions 12

13 What can we see from space? What can we observe? Solar light reflected by the Earth / atmosphere Earth /atmosphere Infrared radiation Linked to temperature Active sensors: radar, lidar Works day and night Even with clouds (in some cases) Need very high power Others: gravity field What information can we obtain? Reflectance of the ground: identification of objects, materials, relief / texture / altitude Absorption / reflection of the atmosphere: clouds, aerosols, gases 13

14 Sensor types Electro-magnetic Field Gravity Field Active sensor Passive sensor Radar altimeter Imaging Radar Lidar Imager Sounder Gravimeter Jason (CNES/NASA) Sea surface height Sentinel-1 (ESA) Radar images CALIPSO (CNES/NASA) Aerosol and cloud profile SEVIRI on METEOSAT-2 (EumetSat) clouds, temperature GOME on METOP (EumetSat) Ozone concentration GOCE (ESA) Earth and Ocean gravity field 14

15 Mission types: Satellites Micro-mini satellite Single small sensor Short lifetime ENVISAT 9 sensors Mass kg Proba V 1 sensor: VEGETATION Mass 140 kg Medium-large satellites Several sensors or one heavy sensor (radar) Long lifetime 15

16 Mission types: orbits Low-Earth Orbit km Most observation satellites Low altitude: Favourable for launcher Favourable for sensor design (better resolution, higher signal) Sun-synchronous orbit observation at fixed hour (e.g.10h00) Each point of the globe observed every N>3 days Geostationary km A few observation satellites (Meteorology) High altitude: Heavy launcher needed (Ariane / Soyouz) Low signal, low resolution Geo-synchronous orbit Continuous observation of 1/3 of the globe 16

17 How does an Earth Observation sensor work? Image strip acquired after one day Clouds Satellite Ground Track 17

18 How does an Earth Observation sensor work? After one week, using 2 satellites Clouds 18

19 Earth Observation Sensors 19

20 Detector How does an Earth Observation sensor work? Imaging sensor: a (large) digital camera in space Photons Photons Electrons Optics Mass Memory RF signal Telecommunication Electrons 20

21 How does an Earth Observation sensor work? Example: PLEIADES High Resolution instrument Secondary mirror M2 (with thermal refocusing device) Highly Integrated Detection Unit with its radiators Ring and Spider Blades Shutter mechanism Primary mirror M1 (Zerodur) Carbon-Carbon cylinder Bus interface (with launcher interface cone) Folding mirror Tertiary mirror M3 Optical bench Detector Optics 21

22 How does an Earth Observation sensor work? Seeing «colours»: spectral imaging 4 colours (R/G/B + black and white) => Filters 100 and more spectral bands => Spectrometer (gratings, prisms) XS1 XS2 XS product (sampling 4 m) PAN product (sampling 1 m) XS3 XS4 Colored product (sampling 1 m) XS bands (sampling 4 m) 22

23 How does an Earth Observation sensor work? LEO missions: the pushbroom concept The satellite captures the image of a band on the ground with a single line of pixels The velocity of the image band is approximately 7 km/s The detector needs to be refreshed fast to acquire the next image band 23

24 Sensor Characteristics Geometric characteristics: swath width and spatial sampling 10 m resolution: Roads visible Swath width impacts the revisit time m resolution: cars 2 m resolution: houses Images: DigitalGlobe 24

25 Sensor Characteristics Image noise Characterised by Signal to Noise Ratio (SNR) Scene Loss of contrast Characterized by the FTM Image of the scene 25

26 Detector Sensor errors Imaging sensor: a (large) digital camera in space Electronic noise Non-linearity Quantization error From this point, errors should be negligible Poisson noise Photons Photons Electrons Optics Mass Memory Aberrations (MTF) straylight Radiometric and spectral response errors RF signal Telecommunication Electrons 26

27 Sensor Error sources Error source Cause Impacting parameters Poisson noise Intrinsic noise = sqrt(signal) Signal level: instrument diameter, exposure time, spectral bandwidth MTF diffraction Instrument diameter, spectral wavelength Aberrations Spectral and radiometric response error Misalignment/deformation of optical elements, staylight Imperfect optical elements, straylight Alignment procedure, choice of materials, thermal control Manufacturing and material selection, calibration Detector noise Electronic noise Detector technology, ageing & radiation, operating temperature Quantization noise Quantization error Number of bits (bit depth) 27

28 Satellite error sources Satellite pointing error (control error) The image on ground is shifted with respect to target position Satellite pointing stability error Image degradation (motion blur) Satellite position and pointing knowledge error Image geolocation error 28

29 Earth Observation Data Processing 29

30 Data processing Instrument Level 0 Data Ancillary Data: Time, Attitude, Position Calibration Lookup Tables Conversion Instrument count => physical fields Instrument Error Corrections Geolocation, rectification Level 1 images = Perfect Sensor 30

31 Data processing Level 1 images = Perfect Sensor Meteorological data Atmosphere correction Pixel characterisation: cloud, land, ocean Level 2 images = Bottom of Atmosphere 31

32 Data processing Atmospheric correction 32

33 Data processing Level 2 images = Bottom of Atmosphere Level 2 images = Bottom of Atmosphere Regridding, Averaging, etc. Data bases Geophysical algorithms Level 3 products = Geophysical fields 33

34 Example Geophysical Algorithm: Chlorophyll retrieval Reflected signal at water surface is the sum of the contribution of: Water Plankton (Chlorophyll pigment) Coloured Dissolved Matter Air bubbles Each consistuent is characterized by its: Absorption coefficient Backscattering coefficient At different wavelengths 34

35 Example Geophysical Algorithm: Chlorophyll retrieval Question: Can we retrieve the chlorophyll concentration from the measured reflectances? 35

36 Example Geophysical Algorithm: Chlorophyll retrieval The GSM algorithm Geometric parameters Total reflectance Absorption coefficient Backscattering coefficient water phytoplankton Dissolved matter water Particles 36

37 Example Geophysical Algorithm: Chlorophyll retrieval The GSM algorithm (semi-analytic) Assumptions: Water properties a w (l) and b w (l) are know functions Chlorophyll absorption: a ph (l) = Chl. a*(l), a* is a known function Chl concentration unknown Detrital Matter absorption a dg (l) = a(l 0 ) exp[-s(l-l 0 )] Spectral coefficient S is known a(l 0 ) is unknown Particle Back-scattering b bp (l) = b bp (l 0 ) (l/l 0 ) -h Spectral slope h is known b bp (l 0 ) is unknown 37

38 Example Geophysical Algorithm: Chlorophyll retrieval Summary: 3 scalar unknowns Chl concentration a(l 0 ) b bp (l 0 ) With reflectance measured at > 3 reflectances, we can retrieve the three unknowns Non-linear best fit algorithm of Newton type (Levenberg-Marquat) 38

39 Example Geophysical Algorithm: Chlorophyll retrieval Estimated Chlorophyll concentration from satellite images vs. In-situ measurements 39

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