CHRIS / PROBA Data Analysis at the Swiss Midlands Testsite

Transcription

1 CHRIS / PROBA Data Analysis at the Swiss Midlands Testsite 4th CHRIS / PROBA Workshop Frascati September 2006 Mathias Kneubühler, RSL, Univ. Zürich, CH Benjamin Koetz, Silvia Huber, Juerg Schopfer, Rolf Richter, Klaus Itten

2 Outline Description of test site and field data Research goals at the Swiss midlands test site Anisotropy in multiangular vegetation data Available CHRIS data sets Data processing Geometric and atmospheric correction Data analysis Spectrodirectional variation in vegetation over time (phenology) Conclusions and outlook

3 Swiss Midlands Testsite Site Description Hilly area dominated by agricultural fields in the lower parts and mixed forests mainly on the hilltops Two main test sites within the scene Agricultural test site (barley, maize, wheat, sugar beet) Forest test site (core test site for the Long-term Forest Ecosystem Research Programme (LWF) of the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) Various data sets since 2004: HyMap, CHRIS, SPOT-5, ADS-40, Lidar Forest test site Agricultural test site

4 Research Goals at the Swiss Midlands Test Site Agriculture Assess the spectro-directional information contained in a multitemporal set of CHRIS data over an agricultural area. The spectro-directional behaviour of crops varies over time as a function of vegetation stages (phenology). Forest Investigate if the added information in multi-angular data improves biochemistry retrieval Investigate if certain view angles emerge to be superior for forest biochemical retrieval (Inter-) Calibration / Validation of Leica Geosystems ADS-40 Airborne Digital Sensor (nadir, forward and backward looking RGB and NIR channels) with CHRIS.

5 Anisotropy in Multiangular Data Reflectance of a vegetation canopy is primarily a function of foliage optical properties, canopy structure, illumination conditions and viewing geometry. Multiangular measurements have shown the potential to distinguish different land cover and surface types of different structural characteristics [Barnsley et al., IEEE TGARS 42(7), 2004]. Multiangular measurements in agriculture contain crop specific information that varies over time as a function of phenology. In the red, the directional pattern of solar radiation scattered by vegetation is largely controlled by the plants physical properties and geometrical arrangements [Pinty et al.,ieee TGARS 40(7), 2002], [Widlowski et al., Climatic Change 76(2-3), 2004]. The degree of anisotropy can be described by e.g. the Minnaert function parameter k in the parametric RPV model [Rahman et al., JGR 98, 1993] Heterogeneous canopies of medium density over a bright background show a bell-shaped Bidirectional Reflectance Factor in the red domain.

6 CHRIS Acquisitions 2005 CHRIS Acquisition Mode 5 26 May June 2005 Quality 5 angles, -55 mispointing 5 angles, -55, +55 mispointing 21 June angles, haze 15 July July angles, partly cloudy, +55 cloud covered 3 angles, -36 and -55 missing, partly cloudy 17. August angles 22. September angles Used scenes (agriculture) +55, +36, 0, , 0, , 0, -36, , +36, 0, -36, , +36, 0, -36, -55 Corresponding Ground Truth - Spectroradiometric measurements - LAI (maize, winter barley, forest stands) - Leaf water content - Absolute chl.. content - Relative chl. content (SPAD) - Phenological characterization - Land cover mapping

7 Agricultural Test Site: Maize 26 May June July August 2005 *15 July data processed, but not yet included in this presentation

8 Agricultural Test Site: Winter Barley 26 May June July 2005 *15 July data processed, but not yet included in this presentation

9 Geometric Processing Parametric Geocoding Use of a 3D physical model (parametric approach) [Toutin 1985, 1992] as implemented in PCI / Geomatica A physical model can mathematically describe all distortions of the platform (position, velocity, orientation) the sensor (view angles, IFOV, panoramic effect) the Earth (ellipsoid, relief) the cartographic projection Needs orbit and sensor information and a small number of GCP s to compute / refine the parameters of the mathematical model Number of GCP s depend on orbit and sensor information availability, GCP accuracy and final expected accuracy (iterative least-square method)

10 Parametric Geocoding Information Typical information required for parametric geocoding approach: Orbit and sensor information [Barnsley et al. IEEE TGARS Vol. 42(7), 2004] or TLE Data from e.g., Sensor altitude Orbital period Eccentricity Actual inclination Across and along track angle (HDF info) IFOV Imge information Pixel spacing Approx. scene centre Ellipsoid (DEM)

11 Geometric Processing Mean rms errors of all CHRIS scenes per acquisition date CHRIS scene RMS X RMS Y RMS mean 26 May June July August September A: maize B: winter barley Accurate geolocation is a prerequisite for multitemporal studies on small-scale scale testsites. Geocorrected subset of the 26 May 2005 CHRIS nadir scene with overlying pixel map (1:25 000, swisstopo)

12 CHRIS Image Acquisition Geometry CHRIS Mode 5 FZA +55 FZA +36 FZA 0 FZA -36 FZA -55 Solar zen./az. 26 May June July August September 2005 Obs. zen ; Obs. az.* ; Obs. zen ; Obs. az.* ; Obs. zen Obs. az.* Obs. zen Obs. az.* Obs. zen Obs. az.* / / / / / * sensor to target viewing direction, negative observation zenith is a sensor position in the south, ; agricultural test sites not covered

13 CHRIS Image Acquisition Geometry 17 August 2005 QuickTime and a decompressor are needed to see this picture. Hot-spot constellation

14 Atmospheric Processing Atmospheric correction of the CHRIS radiance data is performed using ATCOR2/3 [Richter, Applied Optics 37, 1998], which is based on MODTRAN-4 Different CHRIS modes are implemented in ATCOR2/3 ATCOR3 is a radiative transfer code for atmospheric correction of optical spaceborne sensors, including the option to process tilted sensors (accounts for varying path length and transmittance) ATCOR3 supports atmospheric correction over rugged terrain by including digital elevation models (elevation, slope, aspect, sky view factor, cast shadow)

15 Nadir scene, 26 May 2005 Geocorrected CHRIS Subset

16 Nadir scene, 22 June 2005 Geocorrected CHRIS Subset

17 Nadir scene, 15 July 2005 Geocorrected CHRIS Subset

18 Nadir scene, 17 August 2005 Geocorrected CHRIS Subset

19 Nadir scene, 22 September 2005 Geocorrected CHRIS Subset

20 Results - Temporal Changes in Canopy Reflectance CHRIS nadir reflectances for maize (HDRF Hemispherical Directional Reflectance Factor) 22/09/ /06/ /08/ /08/ /05/ /06/ /05/2005 *15 July data not yet included

21 Results - Temporal Changes in Canopy Reflectance CHRIS nadir reflectances for winter barley (HDRF Hemispherical Directional Reflectance Factor) 22/09/2005 Grass 26/05/ /05/ /06/ /08/ /06/2005 Bare soil after harvest *15 July data not yet included

22 Results - Multiangular Behaviour of Canopy Reflectance HDRF data for maize, 17 August 2005 FZA -36 FZA -55 FZA +55 FZA 0 FZA +36 FZA = -36 shows highest reflectance values (closest to hotspot) FZA = +36 and FZA = +55 (forward scatter direction) are darkest in VIS [Bach et al., 3rd CHRIS / PROBA WS, 2005] *15 July data not yet included

23 Results - Multiangular Behaviour of Canopy Reflectance HDRF data for winter barley, 26 May 2005 FZA -36 FZA +55 FZA +36 FZA 0 FZA = -36 shows highest reflectance values (closest to hotspot) FZA = +36 and FZA = +55 (forward scatter direction) are darker than backward scatter angles In the NIR region, increased multipe scattering within the canopy for larger FZA may increase HDRF in forward scatter direction (e.g. FZA = +55 ) *15 July data not yet included

24 Results - Multiangular Behaviour of Canopy Reflectance Multiangular and multitemporal HDRF patterns measured with CHRIS at 672 nm (red domain) for maize. Actual view zenith angles used instead of FZA 26/05/2005 Bare soil shows a linear increase in reflectance from forward scatter directions (+) to backscatter geometries (-)( FZA=0 20/06/2005 Trend in HDRF patterns visible, but difficult to interpret (change in zenit / azimuth angles and sun illumination geometry hotspot 17/08/ /09/2005 Hotspot situations and deviations from nadir view for FZA = 0 0 disturb this pattern *15 July data not yet included

25 Results - Multiangular Behaviour of Canopy Reflectance Multiangular and multitemporal HDRF patterns measured with CHRIS at 672 nm (red domain) for winter barley. Actual view zenith angles used instead of FZA hotspot bare soil 17/08/2005 Large effect of hotspot visible (bare soil) Trend in HDRF patterns visible, however no data for early phenological stages 26/05/2005 Influence of maturing ears? young grass 22/09/ /06/2005 The spectral contribution of maturing ears becomes more dominant for large FZA (e.g., 20 June 2006 [Bach[ et al., 3rd CHRIS / PROBA WS, 2005] *15 July data not yet included

26 Results - Multiangular Behaviour of Canopy Reflectance Multiangular and multitemporal HDRF patterns measured with CHRIS at 672 nm (red domain) for sugar beet. Actual view zenith angles used instead of FZA hotspot 26/05/ /06/2005 Bare soil shows a linear increase in reflectance from forward scatter directions (+) to backscatter geometries (-)( Trend in HDRF patterns visible, however no data for intermediate phenological stages 22/09/ /08/2005 *15 July data not yet included

27 Conclusions Multitemporal CHRIS data show the spectro-directional variation of crops in relation to phenology Preprocessing of CHRIS data includes geometric (3D physical model) and atmospheric correction (physically based on MODTRAN) Multiangular and multitemporal HDRF patterns of crops show trends in HDRF anisotropy in the red domain with phenological development (bare soil, early and late phenological stages). These patterns may be disturbed by Close-to-hotspot constellations Deviations from nadir view for FZA = 0 Individual plant parts (e.g., ears) Understanding of the HDRF anisotropy effect may improve agricultural monitoring and crop classification

28 3-D Outlook Study the temporal evolution of the Minnaert function parameter k, retrievable by the RPV model Observe more phenological stages to further study the effects of heterogeneity on HDRF bowl/bell shape (CHRIS 2006 data) Biophysical/chemical parameter retrieval of forests and agricultural fields Based on Radiative Transfer Models (SAIL, Prospect) Coupling of an RTM and a canopy structure dynamic model (CSDM) to exploit the complementary content in the spectral and temporal information dimensions for LAI Exploitation and integration of the directional information content Verification with available ground truth data Pinty et al (Inter-) Calibration / Validation of Leica Geosystems ADS-40 Airborne Digital Sensor (nadir looking RGB and NIR channels) with CHRIS k=1.18 Bell-shape

29 Outlook Estimation of Nitrogen Content from HyMap Data 11 species Abies, Picea, Pinus HyMap 2004 Data RMSE: RMSE: Future work Apply methods to multi-angular CHRIS data Assess the up-scaling issue (GeoSAIL( GeoSAIL) Compare biochemistry products from different sensors, spatial and spectral scales European Beech RMSE: 0.095

30 Outlook ADS-40 Airborne Digital Sensor 17 August 2005: Nearly simultaneous data take of CHRIS and Leica ADS-40 Airborne Digital Sensor (Inter-) ) Calibration / Validation

31 Outlook ADS-40 Airborne Digital Sensor (Inter-) Cal / Val Source: Leica Geosystems ADS-40 Airborne Digital Sensor Three-line PAN scanner for photogrammetric imaging (+26,, ) Red: nm Green nm Blue nm NIR nm NIR nm (optional) RGB and NIR lines: pixels GSD multispectral: : 16 cm (3000 m asl) GSD PAN: 5 cm (3000 m asl) ADS-40 mainly used for photogrammetric purposes so far Swisstopo interested in increasingly serving the remote sensing community (e.g. vegetation products (LAI, NDVI etc.) Radiometric calibration (RGB, NIR) needed Cal / Val relative to CHRIS data planned

32 ADS-40 (Inter-) Cal / Val 17 August 2005

33 Acknowledgments We would like to thank The CHRIS / PROBA team for data acquisition Rolf Richter (DLR) for implementing CHRIS into ATCOR-3 You, for your attention!