Sensitivity of TOA spectral radiance to variability in terrestrial biosphere properties

Transcription

1 Sensitivity of TOA spectral radiance to variability in terrestrial biosphere properties Michael Schaepman, Daniel Schläpfer, and Roland Meynart Wageningen University and Research Centre Centre for Geo-Information Wageningen, The Netherlands University of Zürich Remote Sensing Laboratories Zürich, Switzerland ESA/ESTEC Noordwijk, The Netherlands

2 APEX CDR

3 APEX CDR

4 APEX CDR

5 Outline Problem Description Approach Results Conclusions/Outlook

6 Problem Description Translation of scientific requirements into engineering specifications covering all relevant terrestrial biosphere properties (e.g., LAI SNR) Definition of scientific requirements and constraints E.g., snow grain size retrieval on Mt. Kenya Definition of engineering requirements NedL, SNR, min./max. Radiance

7 Building Scientific Requirements Approach Selection of various reference biosphere properties Collect expert opinions for each property Provide a model scenario to constrain uncertainties Model spectral reflectances and sensitivities thereof for relevant geophysical/-chemical variables (using typical atmospheric situations) Forward model all combinations to at-sensor-radiances using a (simple) sensor model Extract the relevant TOA radiance changes for each variable Constrain the system in critical wavelength ranges Derive system relevant parameters for engineering specifications (SNR, NedL, min./max. radiance)

8 Compilation of Parameters and Properties Atmosphere Daniel Schläpfer, Univ. Zurich, RSL Calibration Jens Nieke, NASDA Vegetation Massimo Menenti, and Antonio Leone, Univ. Strasbourg Frédéric Baret, INRA Soils, Rocks, and Minerals Hermann Kaufmann, Jo Bind, Dany Schmalz, and Karl Segl, GFZ Potsdam Joachim Hill, Univ. Trier Roger Clark, USGS Limnology Snow Urban Peter Keller, MeteoSchweiz Thomas Painter, Univ. Calif. Santa Barbara Ludovic Basly and Lucien Wald, Ecole des Mines Continuity requirements/wavelength ranges/system parameters Michael Schaepman, Univ. Zurich, RSL

9 Contribution: Limnology Example Parameter Requirement Season spring to fall Chlorophyll a µg l -1 ± 10% Inorganic particulate matter mg l -1 ± 20% Gelbstoff absorption at 450 nm m -1 ± 30% Geographical altitude 0-70 Sun zenith angle 0-70 Ground sampling distance m Repetition rate 2 scenes per month Time series years Flight altitude (not relevant) Weather condition clear atmosphere Wind condition from weak to strong Heading in the solar principal plane Data delivery yearly composite Costs Euro/km 2 Wavelength range nm Relevant variables Chlorophyll a, particulate matter, Gelbstoff

10 Limnology: Chlorophyll a / Particulate Matter Median and standard deviation of delta limnology signals ( dynamic range ) for Chlorophyll a, and particulate matter.

11 Variables considered in Model Approach Cal/Val NedL [W/(m2 sr µm)] Lmax [W/(m2 sr µm)] Dynamic range [bit] Polarization for the complete FOV Linearity Accuracy of the absolute rad. Calibration Accuracy of the relative rad. Calibration Ground resolution Swath width Flight altitude H Tilting (pointing) possibility Spectral range [nm] Number of spectral channels Spectral bands width Atmospheric Signatures Aerosol (total amount) Water vapor (column) Aerosol characteristics Oxygen Ozone and Methane Rocks / Minerals 10 x 1000 (depending on spectral feature) Wavelength range 400 nm - 10 µm Vegetation (Processes) FWHM << 25 nm (for most of the features), important ones require < 15 nm Limnology Leaf Area Index (LAI) Chlorophyll a Leaf orientation Inorganic particulate matter Leaf size and shape Gelbstoff Canopy height Snow and Ice Canopy water mass Grain size Chlorophyll content Impurities / Optical depth Water content Surface liquid water Temperature Seasonal snow cover Surface soil moisture Urban (Air Quality) Roughness Nitrogen Oxide (NO2) Residues Ozone (O3) Organic matter Continuity / Wavelength ranges / System parameters Soil type 7 contiguous spectral bands per feature fcover Required wavelength range nm fapar (optional readout up to 2500 nm) Albedo #Bands, FWHM, Center Wavelength Minerals / Soils Iron (Fe2+,3+) Al-OH-, Mg-OH- Carbonates Organic carbon Clay minerals Soil color, moisture, and roughness

12 Conclusions on Variable Selection A broad choice of variables relevant for imaging spectroscopy has been evaluated and finally selected for further sensitivity modeling Minimizing the permutations (Atmosphere/Parameter/Values) performed to constrain to 3 parameters per application Potential or not well explained applications are not included (e.g., Urban)

13 The Model Approach Application requirements translated into surface reflectance (model or spectral libraries) Definition of at-sensor radiance levels (using constraints (atmosphere/illumination angles) Modeling TOA (at-sensor) radiance in pairs of relevant change at given radiance Spectral convolution / spatial noise added for realistic instrument

14 Forward Modeling Variable surface reflectance data Forward modelling using MODTRAN4 Convolve to 5nm response (7.5nm FWHM) Calculate generic radiance levels Calculate at-sensor delta radiance Retrieve NedL values from parametrization Wavelength dependent NedL and L and SNR values

15 Spectral Reflectance Variation of Vegetation

16 Spectral Reflectance Variation of Vegetation

17 Vegetation Signals for Leaf Chlorophyll SVAT modelled reflectance signatures (top left) and forward-modelled at-sensor radiances (bottom left) for vegetation parameters. The respective difference levels of reflectance and radiance are shown in the right figures.

18 Publications/Products MODO The MODTRAN/IDL user interface ( Brazile, J., Schaepman, M.E., Richter, R., & Itten, K.I. (2005 (submitted)) Cluster versus Grid for Operational Generation of ATCOR's MODTRANbased Look Up Tables. IEEE Parallel Computing. Brazile, J., Schaepman, M.E., Schlaepfer, D., Kaiser, J.W., Nieke, J., & Itten, K.I. (2004) Cluster versus grid for large-volume hyperspectral image preprocessing. In Proceedings of SPIE Vol. 5548, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to- End System Perspective (eds H.A. Huang & H.J. Bloom), Vol. 5548, pp Boerner, A., Wiest, L., Keller, P., Reulke, R., Richter, R., Schaepman, M., & Schlaepfer, D. (2001) SENSOR: A tool for the simulation of hyperspectral remote sensing systems. ISPRS Journal of Photogrammetry and Remote Sensing, 55,

19 Modelled Difference/Radiance Pairs (LAI)

20 Normalization of Vegetation Variables to Median Radiance Normalization of the vegetation variables Chlorophyll, LAI, and leaf water to median radiance levels at two exemplary wavelengths. The square-root normalization function is fitted to the lower boundary of the radiances in the L/ L space (shot noise limited system). Fitting points are indicated by gray squares on the curves.

21 Exclusion Process (Tweak & Tune) Normalisation process with defined cut-off level

22 Noise Equivalent Radiance for Typical Levels

23 SNR for 4 typical radiance levels

24 Application Radiance Levels

25 Minimum, Maximum and Forest Radiance Levels

26 Updated Min./Max. Radiance Levels

27 Driver Applications for given Reflectances: 0%

28 Driver Applications for given Reflectances: 20%

29 Driver Applications for given Reflectances: Forest

30 Driver Applications Listing of driving application per wavelength (without calibration requirements).

31 SWIR detector array for air- and space-borne applications A real product: SWIR Detector Specifications 1000x256 HgCdTe on CMOS read-out circuit, modular in 500x256 blocks Pitch: 30 µm, charge handling capacity: 0.5 Me- or 2 Me- (selectable) λc = 2.4 µm, adjustable Hyperspectral functionality : full staring, high linearity, operation at K, fast operation, # outputs selectable, row skipping, Space-compatible, including dewar-cooler assembly Commercial product (through SOFRADIR (F)) Data/Photo courtesy R. Meynart (ESA)

32 Conclusions A forward model approach based on scientific requirements is developed to derive engineering specifications Almost no assumption on technical specifications in the model (except shot-noise limited system) Analysis limited to variation in illumination angle (no FOV effects or directionality included) Computationally not the cheapest model (Space) hardware was successfully developed and tested based on the above

33 Comments Arbitrary complexity and detail could be added (e.g., considerations to specify required detectable changes at any lookangle) Trade-off s must be made between level of detail, reproducibility, and overly demanding requirements Users usually cannot play a role in this exercise (experts well), since they do not know what to ask for (SNR magic number = 1000) Significant confusion still exists when comparing user s expectations, specifications (scientific, engineering), in-orbit performance, and product promises.

34 Publications Schaepman, M.E., Schlaepfer, D., & Mueller, A. (2002). Performance requirements for airborne imaging spectrometers. In Imaging Spectrometry VII (eds M.R. Descour & S.S. Shen), Vol. 4480, pp SPIE, San Diego. Schlaepfer, D. & Schaepman, M. (2002). Modeling the noise equivalent radiance requirements of imaging spectrometers based on scientific applications. Applied Optics, 41, Schaepman, M.E., Schläpfer, D., Kaiser, J., Brazile, J., & Itten, K.I. (2003). Land Application Driven Performance Requirements for Airborne Imaging Spectroscopy. In Geophysical Research Abstracts, Vol. 5, pp EGU, Nice. Rast, M., Baret, F., van de Hurk, B., Knorr, W., Mauser, W., Menenti, M., Miller, J., Moreno, J., Schaepman, M.E., & Verstraete, M. (2004). SPECTRA - Surface Processes and Ecosystem Changes Through Response Analysis. ESA Publications Division, ESA SP- 1279(2), Noordwijk. Schaepman, M.E., Itten, K.I., Schläpfer, D., Kaiser, J.W., Brazile, J., Debruyn, W., Neukom, A., Feusi, H., Adolph, P., Moser, R., Schilliger, T., de Vos, L., Brandt, G.M., Kohler, P., Meng, M., Piesbergen, J., Strobl, P., Gavira, J., Ulbrich, G.J., & Meynart, R. (2004) APEX: current status of the airborne dispersive pushbroom imaging spectrometer. In Proceedings of SPIE: Sensors, Systems, and Next-Generation Satellites VII (ed R. Meynart), Vol. 5234, pp

35 Thank you for your attention! Wageningen UR