Optical/Thermal: Principles & Applications

Transcription

1 Optical/Thermal: Principles & Applications Jose F. Moreno University of Valencia, Spain Lecture D1T2 1 July /07/ OPTICAL PRINCIPLES AND APPLICATIONS Information content of optical data: retrievable information Forward modelling of surface reflectance: soil, leaf and canopy models Pre-processing aspects Information retrieval techniques, validation and scaling issues Data usage as inputs to models and applications Perspectives 1

2 1 Understanding the actual information content of the optical data: forward modelling of the signal 2

3 OPTICAL SYSTEMS: - Panchromatic: - very high spatial resolution (broadband) - Multispectral: - colour imaging - Hyperspectral: - chemical composition - Multi-angular: - structure Many systems available as a function of spatial, temporal and spectral resolutions 3

4 Solar Radiance (µw/cm 2 /nm/sr) Available Signal Atmosphere Solar 1.0 Reflectance Earth 300 K, 1.0 Emisivity Earth Radiance (µw/cm 2 /nm/sr) Wavelength (nm) Signatures of natural targets: - Spectral signatures What we measure is always radiance, either reflected and / or emitted by the land surface, which variations depend on the optical properties of land targets (and illumination conditions) - Angular signatures - Spatial signatures - Temporal signatures - Other signatures (i.e., fluorescence, polarization, etc.) 4

5 TM 6 Real (n) 10 0 TM 1 3 TM C L P Imag (n) TM 7 5 C L P WATER SCATTERING WATER ABSORPTION ( m) ( m) Optical properties of elementary constituents determine the spectral reflectance of land elements 5

6 SCATTERING BY VEGETATION MATERIAL Key issues: - existing model parameterisations do not account for the observed variability - high variability set limits to the possible decomposition of effects due to different pigments High modelling variability! Are all pigments separable in the signal? 6

7 Senescent / dry leaves 2-1 specific absorption coefficient (cm g ) PROTEIN ABSORPTION 80 CELLULOSE+LIGNIN ABSORPTION wavelength (nm) 7

8 Non photosynthetic elements: spectral variability green vegetation (alfalfa) senescent vegetation (barley) 45 reflectance wavelength ( m) 8

9 violaxanthin zeaxanthin absorption coefficient wavelength (nm) zeaxanthin-violaxanthin absorption difference wavelength (nm) 9

10 canopy reflectance wavelength (nm) Chlorophyll fluorescence as a new tool for remote sensing of vegetation activity. energy levels S 2 S 1 absorption S 0 internal energy conversions fluorescence absorption (a.u.) light absorption lower energy fluorescence emission wavelength (nm) fluorescence (a.u.) 10

11 Chlorophyll fluorescence as an indicator of usage of the absorbed light by vegetation Energy budget at the leaf level De-excitation path ways POLARIZATION OF TOA RADIANCES - Mostly due to atmosphere - Surface also introduces polarization effects Degree of polarization: forward backward 11

12 CHANGES IN CANOPY HEIGHT = 675 nm Sun canopy height values (m) CHANGES IN LEAF SIZE = 675 nm Sun leaf size values (m) reflectance reflectance view angle view angle POLDER noon flight POLDER morning flight 12

13 POLDER data Channel 3 (550 nm) specular Irrigated alfalfa field Rare events sometimes observed in real data Not always predicted by the models! 13

14 reflectance reflectance ALFALFA wavelength (nm) SUGAR BEET V16 - C01 UTM-X: UTM-Y: LAI = 1.84 fcover = 0.95 SV6 - C03 UTM-X: UTM-Y: LAI = 1.71 fcover = 0.61 Line 1, noon Line 2, noon Line 1, morning Line 2, morning Line 1, af t ernoon Line 2, af t e rnoon Line 1, noon Line 2, noon Line 1, morning Line 2, morning Line 1, af t ernoon Line 2, af t e rnoon Spectral versus angular information A rather Lambertian surface wavelength (nm) A highly anysotropic surface Spatial information in the images -Textures - Higher order statistics 14

15 TEMPORAL SIGNATURES Time series and data assimilation will be common strategies with future GMES Sentinel data How well the spectral reflectance signal is understood? 15

16 CHRIS HyMap MERIS Landsat TM Planck s Law 16

17 THERMAL INFRARED 17

18 THERMAL INFRARED energy balance Kirchoff s law ~ 0 THERMAL INFRARED: Temperature versus emissivity effects 18

19 2 Data processing methods for optical remote sensing data Pre-processing steps: - Radiometric calibration - Noise removal - Cloud screening - Geometric correction - Atmospheric correction - Data integration 19

20 CLOUD SCREENING - Very dependent on the available spectral information - Many different algorithms (from simple thresholds up to sophisticate techniques) Atmospheric effects 20

21 TOA BOA SATELLITE SIGNAL MODELLING 21

22 Measured Top-Of-Atmosphere signal Sentinel-2 SENTINEL-2 22

23 OUTPUT OF THE ATMOSPHERIC/ TOPOGRAPHIC CORRECTION FOR QUANTITATIVE COMPARISONS IN MULTITEMPORAL STUDIES CHRIS/PROBA DATA 62 spectral bands 34 m resolution 5 view angles 23

24 Effects introduced by topography: A - Vertical geometric distorsion (horizontal displacement due to relief) B - Variation of atmospheric (optical) properties with height C - Relative changes in slope and orientation of surface introduce variations in illumination conditions: Direct irradiance: - illuminated areas - self-shadowed areas - cast-shadowed areas Diffuse irradiance: - directional distribution - modeling of sky view factors Surface reflectance model: - non-lambertian effects - modeling of direct/diffuse components D - Adjacency effects (additional contributions) E - Additional multiple reflections simulation & inversion software tools 24

25 1nm Sentinel 2 Sentinel 3 Atmospheric effects in the thermal domain atmosphere surface 25

26 - Monochanel equation - Split-window equation - Dual-ange equation 26

27 3 Exploitation of optical remote sensing data for land science and applications SPECTRAL INDICES AS PROXIES EMPIRICAL APPROACHES 27

28 Visible Atmospherically Resistant Index 28

29 1,0 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 leaf reflectance leaf t ransmit t ance leaf absor ptance soil reflectance 0,40 0,35 0,30 Signal composed by multiple contributions (soil+vegetation) soil direct canopy direct multiple scattering total reflectance 0,1 0, wavelength (nm) Multiple scattering effects play a major role reflectance 0,25 0,20 0,15 0,10 0,05 0, wavelength (nm) SURFACE MODEL PARAMETERISATION: (a) Leaf inputs: - Leaf effective thickness - Leaf water content - Total leaf chlorophyll (a+b) - Specific leaf weight - Ratio Ca/Cb - Leaf cellulose content - Fraction of Ca in LHCP - Leaf lignin content - Leaf carotenes content (b) Canopy inputs: - LAI - fcover - Clumping parameter (H/D) (c) Soil inputs: - Soil wetness parameter 29

30 MODEL INVERSION STRATEGIES The problem of model inversion can be considered from different perspectives: (a) Root finding of a given function (b) Solving non-linear set of equations (c) Function minimisation (d) Non-linear least-squares modeling of data Root finding and solving non-linear set of equations would require that the function is exact, and for this reason function minimisation is normally preferred. Merit function: Incorporation of the uncertainties in the inversion process 2 t 1 t 1 R R ( V ) W R R ( V ) V V C V V mes mod a priori Covariance Matrix mes mod a priori Covariance Matrix p p Residuals Observations Residuals Residuals Residuals Model variables Use of constrained minimization procedures that guarantee the minimal variation of model variables to produce the same output, and a robust initialization procedure of such variables (consistency even if model has global bias). 30

31 THE SHAPE OF THE MERIT FUNCTION Y max f (X) non valid solutions maximum range of probable values valid solution relative minimum Absolute minimum at first guess (most probable value) (location of minimum is variable) Y ref absolute minimum Y min X min ref X min X 0 ref X max X max maximum range of possible values Neural network methods Training becomes the critical issue 31

32 Spectral fitting methods are especially useful because we can use the well-known shape of spectral features. Requires rather high spectral resolution LAI=4 Leaf Water Content (cm ) (n m)

33 0.6 LAI = 1 LAI = (nm) (nm) Decoupling of atmospheric effects when retrieving leaf / canopy information transmittance & reflectance (relative units) surface liquid water atmospheric water vapour wavelength ( m)

34 FIRST STEP spectral channels multi-resolution spatial classification homogeneity test MULTI-STEP PROCEDURES A general multi-step procedure - Explicit separation of almost pure pixels from spectral mixtures homogeneous pixels: reflectance model inversion heterogeneous pixels: mixture model inversion - Use of several retrieval techniques for each step SECOND STEP preliminary values from empirical relationships N=0 Nth iteration parameters N a i { } i = 1,..., P end-members definition (soil, vegetation, shade...) end-members parametric characterisation - Produce different adequate outputs for each retrieval procedure unmixing procedure THIRD STEP N N+1 convergence test generation of a complete output map for each variable (merge homogeneous + heterogeneous pixel outputs) heterogeneous pixel masking additional outputs Such methods are used in practice for real images SCALING ASPECTS Comparison among different sensor products MERIS FR CHRIS/PROBA 34

35 4,0 3,5 3,0 2,5 2,0 1,5 1,0 0,5 0,0 5 4,5 4 3,5 3 2,5 2 1,5 1 0,5 0 V2 9 V20 V14B V1 Intensive field campaigns Ground validation data: - vegetation properties - soil properties - solar radiation - atmospheric status - surface fluxes Mean values Individual samples spatial variability 35

36 Usage of the derived information: - Tendency: from proxies to quantitative information - Multi-resolution spatial inputs and time series - First approach: Land cover mapping, classification and tables of biophysical variables assigned to each class - Second approach: Retrievals of biophysical variables as direct inputs to models - Third approach: direct assimilation of radiances/reflectances into models REMOTE SENSING OF LAND SURFACE PROCESSES - Mapping Applications - cartography - thematic mapping - Monitoring Applications - ecosystems dynamics - natural hazards (fires, floods, desertification) - Research about Land Surface Processes - heat and mass exchange at Land/Atmosphere interface - photosynthesis and net primary production - hydrologic processes - Land/A tmosphere exchange of biochemicals } 36

37 MOSAICS SPOT-5 /

38 INTEGRATION IN A GIS ENVIRONMENT 38

39 2003 growing Season Barrax ALFALFA MULTITEMPORAL SERIES CORN Validation 15/07/2003 LANDSAT LAI Ground measurements ALFALFA CORN PAPAVER POTATO SUGARBEET GARLIC ONION LANDSAT retrieved Optical signature of water targets CASI Reflectancia (Rrs, sr-1) Reflectance (%) MERIS TM Longitud de onda (nm) Wavelength (nm)

40 Visible bands CHRIS/PROBA IMAGERY OVER ALBUFERA DE VALENCIA LAKE CHRIS / PROBA Chl-a March 1 st 2007 PC March 1 st >250 mg m -3 40

41 Hot spots from 21 to 26 August

42 New capabilities for fires monitoring with upcoming sensors Change detection applications Bi-temporal and multitemporal detectors 42

43 Las Vegas urban development Athens, PROBA image 43

44 THERMAL INFRARED APPLICATIONS carbon cycle water cycle 44

45 <1-300 meters Local site RESOLVED SPATIAL SCALES Global sampling global few days RESOLVED TIME SCALES RESOLVED several years Requires very large time series 45

46 from local measurements to global models NEW GENERATION OF SENSORS - Well calibrated (more suitable for multitemporal studies) - Increased spatial resolution (0.5 m PAN now available) - Increased spatial coverage (global mapping in high spatial resolution (as ESA GMES/Sentinel-2) - New type of information (i.e., vegetation fluorescence) - Time series: gap filling using multi-sensor data, better temporal resolution with high spatial resolution - Integration of multi-resolution data with diverse spectral information in common temporal databases 46

47 SENTINEL-3 Sentinel-3 Sentinel-2 SENTINEL-2 PERSPECTIVES IN DATA EXPLOITATION - Adequate exploitation of the different data sources: multi-source (multi- resolution data integration). - Focus on systematic data assimilation approaches exploiting the time-series concept and synergy among simultaneously available satellite systems - Consistent incorporation in the modelling approaches of processes covering time scales from weeks to decades and exploiting spatially distributed inputs - Accounting for spatial variability and temporal dynamics as the main contributions 47

48 EO Applications Methods Calibration & Validation Vegetation monitoring Agriculture Forestry Water quality Climate change Damage Assessment Cartography Physical models Instrument design Image processing and analysis Automatic classification Analysis of time series Biophysical parameter estimation Data fusion The near future GMES / Sentinels 48