SENSITIVITY ANALYSIS OF COMPOSITING STRATEGIES : MODELLING AND EXPERIMENTAL INVESTIGATIONS

Transcription

1 Université Catholique de Louvain Department of Environmental Sciences Louvain-la-Neuve, BELGIUM Vlaamse Instelling voor Technologisch Onderzoek Centre for Teledetection and Atmospheric Processes Mol, BELGIUM CONVENTION N 95/CNES/0449 VEGETATION PREPARATORY PROGRAMME SENSITIVITY ANALYSIS OF COMPOSITING STRATEGIES : MODELLING AND EXPERIMENTAL INVESTIGATIONS Intermediate Report September 1996

2 TITLE : Sensitivity analysis of compositing strategies: modelling and experimental investigations TYPE OF INVESTIGATION: Improvement of products THEMATIC DOMAIN : Compositing procedures PRINCIPAL INVESTIGATOR: Title & Name: Prof. Pierre Defourny, Prof. Chargé de cours Research assistant: Dr Michel Schouppe / Dr Yves Govaerts Organisation: Department of Environmental Sciences and Land Use Planning Université Catholique de Louvain Address: Croix du Sud, 2 bte 9 B-1348 Louvain-la-Neuve Belgium Telephones: (32 10) (32 10) Fax: (32 10) s: defourny@mila.ucl.ac.be schouppe@mila.ucl.ac.be yves.govaerts@jrc.it CO-INVESTIGATOR: Title & Name: Dr Frank Veroustraete Research assistant: Ir. Els Brems Organisation: Flemish Institute for Technological Research Address: Boeretang, 200 B-2400 Mol Belgium Telephones: (32 14) (32 14) Fax: (32 14) s: veroustf@vito.be bremse@vito.be

3 1 TABLE OF CONTENTS SUMMARY 3 1 INTRODUCTION 4 2 OBJECTIVES 5 3 WORK PLAN 6 4 MODEL IMPLEMENTATION (TASK 1) S RADIATIVE TRANSFER CODE BI-DIRECTIONAL REFLECTANCE MODEL SATCO (SENSITIVITY ANALYSIS TOOL FOR COMPOSITING OPTIMISATION) ORBITAL MODELS SPECTRAL RESPONSE CURVES 12 5 SENSITIVITY ANALYSIS (TASK 2) INPUT PARAMETERS BRDF DOMAINS ATMOSPHERES GEOMETRIES RESULTS DISCUSSION ATMOSPHERIC EFFECTS BIOME INFLUENCE FUTURE STEPS OBJECTIVES VGT-DEDICATED SCENARIOS RESULTS AND DISCUSSION 25 6 RICH DATA SETS (TASK 3) INTRODUCTION HAPEX-FIFE AND HAPEX-SAHEL EXPERIMENTS HAPEX-FIFE HAPEX-SAHEL SELECTION OF ACTUAL SCENARIOS 32

4 HAPEX-FIFE HAPEX-SAHEL SIMULATION OF TOA REFLECTANCES RESULTS HAPEX-FIFE HAPEX-SAHEL QUALITY OF THE SIMULATED DATA EXPERIMENTAL 10-DAY TIME SERIES 46 7 FUTURE WORK 47 8 BIBLIOGRAPHY INTERNAL REPORTS SCIENTIFIC REFERENCES 48 9 LIST OF COMMONLY USED ACRONYMS 52 ANNEXE 1: SPECTRAL RESPONSE CURVES OF VEGETATION 53 ANNEXE 2: OZONE ABSORPTION WINDOWS 55 ANNEXE 3: ANALYSIS01 RESULTS 57 ANNEXE 4: ANALYSIS02 RESULTS 59 ANNEXE 5: FIFE ATMOSPHERIC DATA QUALITY 61 ANNEXE 6: FIFE AVHRR DATA QUALITY 62 ANNEXE 7: HAPEX-SAHEL ATMOSPHERIC AND AVHRR DATA QUALITY 63

5 3 SUMMARY The study, Sensitivity Analysis of Compositing Strategies, aims at investigating existing criteria and developing new ones for the production of composite imagery for the VEGETATION sensor onboard the SPOT4 platform. Based on criteria to be elaborated, it will be investigated whether a robust algorithm dedicated to the VEGETATION sensor, to be implemented in the VEGETATION ground segment operational environment, can be developed. Its purpose is the production of the daily (VGT-DS) as well as the 10 day synthesis product (VGT-PS). Specifically, the sensitivity of existing and new compositing procedures to the different perturbing factors of the atmosphere, as well as their biome dependency, both for a ten-day, as well as a one-day acquisition period, is investigated. The study consists of two steps. The assessment of the influence of perturbing factors on the temporal consistency of the VGT multi-spectral signal and a performance evaluation of compositing algorithms. In a first step the main issues which affect the consistency of temporal synthesis are addressed in a sensitivity analysis, based on a newly developed analysis tool (SATCO, i.e. Sensitivity Analysis Tool for Compositing Optimisation). Due attention has been given to the realism of the simulations obtained with SATCO, by incorporating state-of-the-art radiative transfer code for the Earth s atmosphere (6S) and its biomes (Rahman BRDF), specifically for the VEGETATION spectral bands. The orbital characteristics of the SPOT4 platform, have been taken into account by using an orbital model dedicated to the VEGETATION sensor. Coupled atmosphere-vegetation radiative transfer simulations, with realistic VEGETATION viewing and illumination geometries have been performed. Account has been taken of the bi-directional properties of the atmosphere and the surface cover. Furthermore, to verify the realism of the simulations with SATCO, use has been made of Rich data sets, which represent a unique set of NOAA-AVHRR acquisitions with simultaneous ground and atmospheric observations. The most affecting atmospheric parameter for the three channels (BLUE, RED and NIR) is aerosol optical thickness. Water vapour only has a low to medium influence in the RED and NIR channels respectively. Hence, it is proposed to consider as atmospheric perturbing factors, aerosol optical thickness in the BLUE, RED and NIR bands and water vapour content in the RED and NIR bands. It is confirmed that the RPV-6S model successfully simulates the geometric and atmospheric effects on the TOA reflectances. It has been checked that, for the considered acquisition conditions of the Hapex-Fife and Hapex-Sahel experiments, the BRDF effect on the simulated signal is equal or higher than the coupled atmospheric and geometric effects. It has been decided to favour model-based triplets for the simulations of TOA reflectances to benefit from the following major advantages: (1) better matching with the global scale of the study than field-based triplets that describe very local radiative properties, (2) generated scenes are fully documented, (3) angular sampling can be controlled at will, (4) diffuse radiation does not affect the signal values. An overview of the modelling approach, model implementation and pathfinder results is given in this report. A description and the rationale for use of a Rich data set also is presented. A short account of the status of our study will be given in connection with future research steps to be taken, to reach our research objectives. Essentially the second step of this study will focus on compositing criteria development as well as a comparative performance evaluation of existing compositing procedures and a newly developed one.

6 4 1 INTRODUCTION This study is jointly carried out by the Department of Environmental Sciences of the Université Catholique de Louvain (UCL, Louvain-la-Neuve, Belgium) and the Centre for Teledetection and Atmospheric Processes of the Vlaamse Instelling voor Technologisch Onderzoek (VITO, Mol, Belgium) in the framework of the VEGETATION PREPARATORY PROGRAMME. The research is supported by the Federal Office for Scientific, Technical and Cultural Affairs (OSTC, Belgium), as the main sponsor, and by the VEGETATION Preparatory Programme. This intermediate report concerns the first year of the project, officially started at the end of January 1996 for the VEGETATION Preparatory Programme. The pre-launch phase of the project is planned as a 2,5 year study. A conditional Post Launch phase should be completed within one year.

7 5 2 OBJECTIVES Earth observation with optical sensors based on satellite platforms is limited by the interference of clouds and especially atmospheric constituents like ozone, water vapour and aerosols. When several images are available over a short period of time, individual image pixels are composited to reduce these atmospheric perturbations. Algorithms to select the most suitable pixel out of a temporal series of measurements obtained at different dates, have been developed to create a mosaic of pixels or composite image. First used for NDVI composites, they are now used for compositing individual reflective channels. These algorithms have shown to produce radiometric artefacts in the spectral bands. This study aims at investigating systematically, the main issues related to the temporal synthesis production using VEGETATION simulated data during the pre-launch period and subsequently actual VEGETATION data after launch. A specific aim of this study is to investigate the sensitivity of existing and new compositing procedures to different perturbing factors not only on the NDVI composite but also for a reflective channel composite image, and this both for a ten-day as well as a one-day acquisition period. The performance of different compositing algorithms will be assessed using measured data for various environments as well. The findings of this research should be valid on a global scale and on a routine production basis hence leading to improvements of the quality of the VGT-DS and -PS products. The study combines a modelling approach and an experimental approach in order to achieve these objectives. The modelling approach allows a comprehensive sensitivity analysis and a preliminary assessment of the compositing strategies. The experimental analysis based on actual data will document the reliability of the simulation results and test the compositing strategies for different regions of the world.

8 6 3 WORK PLAN The work plan with regard to the sensitivity analysis of compositing strategies is illustrated in the flowchart of figure 1. Each of the tasks is shortly explained below. Input parameters (cover type, atmosphere,...) Feedback Task 1 Coupled vegetation/atmosphere model Task 8 Performance evaluation Iterative Task 2 Sensitivity analysis analysis01 analysis02 analysis03 Task 6 New and existing Compositing Algorithms Compositing Algorithm Output Task 7 Screening Task 4 Model generated VGT data Rich data set (HAPEX-FIFE,...) Task 3 VGT simulated data (AVHRR + TM) Task 5 Benchmark data sets Figure 1: Work plan s flowchart

9 7 The study consists of two main steps: (1) assessment of the influence of perturbing factors on the temporal consistency of the VGT multi-spectral signal and (2) performance evaluation of the compositing algorithms. The first step will be addressed by a modelling approach. This sensitivity analysis will elucidate the conditions where atmospheric and anisotropic effects are minimal and lead to propose new strategies for compositing. The second step will be addressed by both an experimental and a modelling approach using real AVHRR data as well as simulated and model generated VGT data to test the performance of proposed compositing algorithms. Task 1: Model implementation Simulation of the impact of scattering and absorption processes on the solar radiation during its double path between the sun, the target and the satellite sensor needs to be described by means of an atmospheric radiative transfer (RTF) model. In order to describe the bidirectionality (anisotropy) of the observed target (e.g. vegetated surface) and the atmosphere, this RTF model is coupled with a Bi-directional Reflectance Distribution Function (BRDF) model that describes the observed land cover. Furthermore, when simulating real observation geometries for a particular satellite sensor in view of compositing this RTF-BRDF code needs to be input with geometrical conditions calculated with an orbital model specific for the NOAA-AVHRR and VEGETATION sensors. Task 2: Sensitivity study A step-by-step strategy is the structure of the sensitivity analysis. The study will consist of different analyses which have a specific purpose and specific input parameters. The results of the first analysis, for instance, will allow to point out general trends and mechanisms of atmospheric surface cover radiative transfer. Definition of the input parameters for the second analysis has been based on the findings of the first analysis. The pathfinder analyses focus mainly on the general form of the BRDF curves, but the emphasis has been shifted towards compositing over time periods. Task 3: Rich data sets Rich data represent a unique set of NOAA-AVHRR acquisitions with synchronous ground and atmospheric observations. Investigation based on rich data sets is performed in order to: (1) test the coupled model (RTF and BRDF models) (2) simulate VEGETATION data during pre-launch, to build different multi-temporal data sets to test compositing algorithms. Task 4: Screening Screening represents a sequence of simple tests to discard derived or interpolated pixel values and particularly extreme observation and atmospheric conditions. This task also includes a review of existing literature, a confrontation with the results of the sensitivity analysis, the implementation of a screening procedure for clouds and their shadows, large optical depths and if possible hot spot and off-nadir view angles. It also includes the implementation of a quality flag documenting each pixel (screened or not screened). Ongoing studies requested by the VGT programme will also be considered as an additional source of information.

10 8 Task 5: VGT simulated data Since the aim of the research is to implement new compositing algorithms dedicated to VEGETATION. VGT data must be simulated during the pre-launch phase. Due to some of the technical similarities between VGT and the TM and AVHRR sensors, it is proposed to use TM and AVHRR data to simulate VGT data. Task 6: Compositing algorithm definition The results of the sensitivity analysis must allow the definition of Compatible Observation Conditions (COC) for a certain type of application. The COC s are the «ideal» or most suitable conditions of observation, e.g. those which meet closest the most ideal retrieval conditions for the physical parameter. Hence, the COC conditions are related to the final application type. They account for ideal conditions of viewing, illumination, or the most ideal or suitable atmospheric conditions within the temporal compositing image series. In that sense, the COC may be helpful to reduce the potential anisotropy effects incorporated in the composite imagery. Assessment of the different published compositing techniques will provide outcome on their relative performance in the light of the results of the sensitivity analysis. Any alternative strategy suggested by the modelling approach, by the experimental observations or by specific research on VEGETATION indices will also be considered. Task 7: Compositing output The goal of the compositing procedure is to approximate with the composite image, as much as possible, a single, composite, cloud free image that corresponds to one of the two following applications. Application 1: land cover discrimination applications. The aim of land cover applications is to identify the surface type, i.e. to minimize the instrumental, atmospheric and bi-directional perturbations. Qualitatively correct output is expected from compositing. As pixels representative for the same cover type must be similar, random errors must be avoided but systematic errors are workable. Application 2: applications oriented towards quantitative signal interpretations. For such applications, the expected information is typically a dedicated index (FPAR, NDVI, etc.) or a physical variable (reflectance, albedo)of which the value throughout the image must be correct. It is a quantitative approach where systematic errors must be taken into account and where possible, avoided, and where random errors are workable. Task 8: Performance of the compositing algorithm The performance of the compositing algorithm(s) can be assessed by its (their) efficiency to discriminate and retain pixels characterized by COC conditions within one-day or 10-day time series. Tools such as statistical comparisons, distribution analyses, image differencing with respect to site, date, spectral band and land cover types can be used for this purpose. Attention will be paid to avoid the speckle effect (i.e. strong local variations in reflectance values between neighbouring pixels) which is regularly introduced by the compositing procedures. This speckle effect is due to the selection of neighbour pixels from source images with a different viewing and illumination geometry.

11 The variation of the processing performances using pre-launch VGT simulated data and real VGT data (post-launch) will be documented. The results from newly proposed algorithm(s) should definitely be compared with the results of the MVC-NDVI algorithm and other algorithms available from the literature. Tests will be applied to check if the proposed compositing algorithm is robust enough, efficient enough, simple enough in order to be easily incorporated within the VEGETATION processing chain. In other words, the following question will be answered: will the VGT production chain use a fail-safe algorithm (as simple as possible), is it able to generate reproducible results (robustness) and does it assemble composite products meeting predefined compositing criteria (efficiency)?. 9

12 10 4 MODEL IMPLEMENTATION (TASK 1) 4.1 6S Radiative Transfer Code The atmospheric radiative transfer code that is used in the sensitivity analysis is the 6S code (Second Simulation of the Satellite Signal in the Solar Spectrum) from Vermote et al. (1994). The 6S model is a well recognized atmospheric radiation transfer model designed to simulate the influence on the signal of the presence of variable optically active elements of a cloud free atmosphere during the double path Sun - Target - Sensor. It mainly takes into account gaseous absorption, Rayleigh and aerosol scattering, and partly the non Lambertian, bidirectional and environmental effects of the reflecting surface. The atmospheric conditions can be modelled with different atmospheres (US62 standard atmosphere, Midlatitude Winter, Summer, etc.) or user-defined atmospheric profiles, standard or user-defined aerosol types and user-defined aerosol concentrations. 4.2 Bi-Directional Reflectance Model The Coupled Surface-Atmosphere Reflectance Model (CSAR) of Rahman H., Pinty, B., and Verstraete, M. (1993b), incorporated in 6S has been selected to take into account bidirectional effects of the surface. In the Rahman et al. model (further called the RPV model), more emphasis has been given to the representation of the bi-directional reflectance of arbitrary natural surfaces than to that of the atmosphere. This semi-empirical model presents mainly the following advantages. Firstly, it is a parametric and not a deterministic physically based model. It needs three input parameters which makes it more simple to use than a physical model. The three parameters describe efficiently the shape of the BRDF as a function of solar zenith and relative azimuth angles. These parameters do not have a strictly physical meaning but they are related to physical phenomena: ρ 0 is an arbitrary parameter characterizing the intensity of the reflectance of the surface cover, Θ is the phase function parameter or asymmetry factor resulting in the tilt angle of the distribution which controls the balance between forward and backward scattering and k is a measure of the degree of Lambertianity of the surface (related to the structural properties of the medium). They can be considered as a rather intuitive description of the BRDF. The RPV model describes the hot spot effect and has been tested in direct and inverse mode against several data sets (satellite images, airborne data, field and laboratory data) under a wide variety of conditions (personal communication of Pinty and Verstraete; Rahman, 1993a and 1993b; Engelsen et al., 1996). It is published and well understood and it is candidate for use in the standard surface BRF retrieval algorithm of the Multi-angle Imaging Spectro- Radiometer (MISR) data products (NASA EOS). The RPV model has been technically integrated (FORTRAN language) as an option of the 6S model.

13 SATCO (Sensitivity Analysis Tool for Compositing Optimisation) SATCO (Sensitivity Analysis Tool for Compositing Optimisation) is the name of the software package that is dedicated to sensitivity analyses for the optimisation of remotely sensed data temporal compositing. It was developed especially for this research project. SATCO allows to automatically simulate Top-Of-Atmosphere (TOA) and Top-Of-Canopy (TOC) reflectance factors for a large number of different surfaces and atmospheric conditions. TOA reflectance factors are computed with the 6S code (Vermote et al., 1995). The surface bi-directional reflectance (BRF) can be characterized with the RPV-model (Rahman et al., 1993b) or with the IAPI model (Iaquinta and Pinty, 1994). Realistic observation geometries (in view of compositing simulations) can be obtained for two sensors, namely the VEGETATION sensor and the NOAA AVHRR sensor, both platforms for which an orbital model has been implemented. The SATCO flowchart is composed of three main steps (figure 2): Step 1 biome.bid exper.tbl geom.obs satco.cnf Step 2 generation.pro expnam.res expnam.rep Step 3 plot_geom_hs.pro plot_geom_per.pro plot_2d.pro statist_outp.pro Y X Figure 2: The SATCO flow chart. (ovals are disk files, rectangles indicate IDL programs) Step 1) Restructuring of input data. This step consists in creating various input files which define the run scenarios for different atmospheric, surface and observation

14 12 geometrical conditions. The keystone of the SATCO tool is based on an ASCII table which contains the description of the scenarios. Step 2) Generation of run scenarios. This step consists in computing the TOA reflectance factors on the basis of the input files. An IDL programme reads and parses the scenario table and produces output files with TOA and TOC reflectances. Step 3) Result analysis. This step incorporates the analysis of result files. Several output analysis tools, written in IDL, have been created to facilitate the visualisation of the results. It has been made possible to produce a 3D graph of the BRDF calculated for a hemisphere, or a 2D representation of the BRDF calculated in the principal plane. It is also possible to calculate statistical parameters (mean, standard deviation, histograms, skewness, etc.) based on the obtained reflectance values. 4.4 Orbital Models To run 6S for the VEGETATION sensor, an orbital model of the appropriate satellite platform has to provide the viewing geometry as input for the 6S code. At present the first version (December 1995) of a VEGETATION orbital simulator developed by G. Saint (CNES) is used for this purpose. The principle is rather simple: a circular orbit on a spherical earth has been used, but with a constant rotation of the orbit plane to allow for sunsynchronous observations. A tentative launch date has been fixed (actually the first of January 1998) so the model can calculate for one specific geographical location the conditions under which a corresponding pixel is observed by the VEGETATION sensor at the moment of overpass. It also calculates the illumination angles for that specific location and time. These small changes make it possible to run SATCO for a specified time period, e.g. a 10-days compositing period. As soon as a better reference orbit is available for the orbital model, this part of the SATCO programme will have to be adjusted to improve the quality and realism of the VEGETATION viewing geometry simulations. A NOAA orbital model (EROS DATA Center, Sioux falls, USA) also has been implemented in the SATCO model to provide viewing geometry as an input for the 6S code in order to allow comparisons with NOAA, AVHRR data. 4.5 Spectral response curves Simulation of the four channels that will be available on the VEGETATION sensor has been based on measured values of the relative spectral sensitivity for the VGT cameras. These values have been obtained from G. Saint (CNES). Tables with the used values are listed in annexe 1. The representativeness of the modelled, simulated data depends for a great deal on the quality of the available spectral curves. On the other hand, representativeness of these data simulations for VGT observations also depends considerably on the realism of the simulated ground targets and atmospheres.

15 13 5 SENSITIVITY ANALYSIS (TASK 2) Pathfinder steps of sensitivity analysis (analyses 01 and 02) have focused on the simulation of Top of Canopy (TOC) and Top of Atmosphere (TOA) reflectances above different ground cover types. Viewing angles considered are located in the upper hemisphere, i.e. zenith angles vary between 0 to 90 and azimuth angles vary between 0 and 360. The shape of TOA BRDF curves depends on the parameters used to characterize the atmosphere, the observed target and the illumination geometry. Visual and quantitative comparisons between various BRDF curves allow to identify a perturbation hierarchy between the atmospheric factors. The influence of the illumination and observation geometries is assessed also, as well as the influence of the radiative properties of the observed biome. 5.1 Input parameters BRDF domains BRDF domains, as considered by the RPV model of Rahman et al. (1993), are defined on the basis of three parameters, namely ρ 0 (related to the intensity of the surface reflectance), Θ (asymmetry factor) and k (related to the structural properties of the reflecting biome type). The values of these three parameters (further called triplets) for typical surfaces can be obtained by inversion of the RPV model against the BRDF of these surface types. These BRDF values result from two different sources: (1) field measurements; (2) simulations of synthetic biomes with physically-based canopy reflectance models. In the sensitivity analyses, both kinds of triplets have been selected, in order to evaluate the advantages of favouring radiative properties of biomes measured in the field in comparison to radiative domains describing related synthetic biomes, or vice versa,. Tables 1 and 2 give an overview of the Rahman et al. triplets that have been used in sensitivity analyses 01 and 02. A more complete table with Rahman parameters available from the literature can be found in table 9 (section 6). Table 1: Results of the inversion of the RPV model against field measurements in the red and near-infrared spectral regions for five cover types. Red triplet NIR triplet Biome Data set Reference ρ 0 Θ k ρ 0 Θ k Pine BOREAS Deering et al. (1995) Grassland KIMES Kimes (1983; 1985; Corn KIMES 1986, 1987) Trop. forest AVHRR Engelsen (1995) Desert AVHRR Rahman et al. (1993)

16 14 Table 2: Inversion results of the RPV model against modelled BRF reflectances in the red and near-infrared spectral regions. The synthetic data set used has been generated with a 3D Discrete Ordinates radiative transfer Model (DOM) (Myneni and Asrar (1993)). Red triplet NIR triplet Biome Reference ρ 0 Θ k ρ 0 Θ k Grasses Shrubs Broad leaf crops Savannah Leaf forest Conifers Myneni and Asrar (1993) Myneni and Asrar (1993) Myneni and Asrar (1993) Myneni and Asrar (1993) Myneni and Asrar (1993) Myneni and Asrar (1993) The Rahman et al. triplets for the RED band can be used directly to simulate the BLUE band since the surface behaviour is similar in these bands. Simulation results for RED and BLUE bands differ because of the differences in magnitude of the atmospheric perturbation and the difference in spectral response of the VEGETATION sensor Atmospheres To grasp the effect of the atmosphere on TOA reflectance values, SATCO has been designed to allow atmospheric variations within one scenario. The possibility has been created to vary in each scenario one atmospheric parameter between user-defined thresholds and with a userdefined increment. The atmospheric parameters which are envisaged to be the most important ones, are aerosol optical thickness, integrated water vapour content and total ozone column. Table 3 describes three atmospheric scenarios designed for the pathfinder sensitivity analysis in order to cover a maximum of realistic atmospheric cases. Table 3: Atmospheric scenarios Scenarios Water vapour (g/cm 2 ) Ozone (cm.atm) Aerosol optical thickness ; 1.25; 2.5; 3.75; ; 0.2; 0.3; 0.4; ; 0.25; 0.5; 0.75; Geometries Since modelled BRDF s of isotropic surfaces are considered to be symmetrical with respect to the principal plane, calculation is done for eleven points situated on a half hemisphere of each BRDF curve. These points are located at: satellite zenith angles of 0 (nadir), 25 and 50 degrees; relative azimuth angles of 0, 45, 90, 135 and 180 degrees. The values of these eleven points have been calculated for three different sun zenith angle conditions: θ s = 0, 30 and 60 degrees.

17 15 This number of sampling points is a trade-off between calculation time on the one hand and the detail required to visualize the BRDF on the other hand. The choice of eleven points is satisfactory to demonstrate the general shape of the BRDF, although some phenomena like the hot spot are not calculated with high detail. For a quantitative assessment of these phenomena a finer geometric resolution is required. The selection of maximal solar and satellite zenith angles was determined by the range of validity of 6S: solar zenith angles must be smaller than 60, satellite zenith angles smaller than 50. The latter constraints is fully compatible with VEGETATION geometries (off nadir angles ranging between 0 and 50 ) sin θ v * sin φ sin θ v * cos φ Figure 3: Location of the eleven points chosen in the upper hemisphere. θ v is the view zenith angle and φ is the relative azimuth angle (view azimuth minus solar azimuth). 5.2 Results For the densely vegetated biomes, atmospheric effects (aerosol optical thickness, water vapour and ozone) tend to increase the reflectance (TOA > TOC) in the visible channels (BLUE and RED) (figures 4, 5 and 6). The only exception to this rule are the grasses of Kimes (1985) for which TOA < TOC in the visible channels. In the Near InfraRed channel (NIR), for all biomes, a decrease of reflectance (TOA < TOC) is observed, with increasing atmospheric perturbation (figures 7 and 8). On the contrary, highly reflecting targets (e.g. desert) are characterized by a decrease of reflectance values (TOA < TOC) in all channels with increasing atmospheric perturbation (not shown). The most affecting atmospheric factor in the three channels is aerosol optical thickness. The following hierarchy of perturbations can be observed (figures 4 to 8):

18 16 Table 4: Perturbing effects of the atmosphere in function of the VGT spectral bands. Band Aerosol perturbing effect Water vapour perturbing Ozone perturbing effect effect BLUE RED NIR Very high High High None Low Medium None Low None The maximum variance in reflectance being observed in the principal plane, this plane is chosen for 2D representations. The phenomenon of the hot spot (high reflectance in the backward scattering direction) can also be observed in the principal plane. Atmospheric effects (principally those related to the presence of aerosols) are maximum when the sun is close to the horizon. When the sun is close to the horizon, a progressive shift of the maximum reflectance from the backscatter direction (hot spot conditions), towards the forward scattering direction is observed, with increasing atmospheric optical depth (figures 9a and 9b). When vegetated biomes are considered in the NIR band, the progressive shift of the maximum reflectance towards the forward scattering direction under high solar zenith angles may induce TOA > TOC values for particularly high aerosol optical thickness (figures 10a and 10b). The sun being in nadir position (θ s = 0 ), the hot spot phenomenon can be observed at nadir and the shape of the BRDF is symmetrical with respect to the X and Y axes (figure 11). Changing the solar zenith angle up to 35, results in an asymmetric BRDF with a shift of the hot spot towards backscattering (figure 12). For higher solar zenith angles, up to 60, the hot spot is even more eccentric (figure 13). A comprehensive description of the outputs of the pathfinder sensitivity analyses (analysis01 and 02) can be found in annexes 3 and 4.

19 17 Figure 4: SATCO simulated BRDF in the principal plane, for grasses (Myneni et al., 1993), in the BLUE channel. Solar zenith angle is 0 and the variable atmospheric component is aerosol. grey = TOC red = TOA 1 (Aer opt.th.= 0) green = TOA 2 (Aer opt.th. = 0.25) blue = TOA 3 (Aer opt.th.= 0.5) yellow =TOA 4 (Aer opt.th.= 0.75) cyan = TOA 5 (Aer opt.th.= 1) Figure 5: SATCO simulated BRDF in the principal plane, for tropical forest (Myneni et al., 1993), in the RED channel. Solar zenith angle is 0 and the variable atmospheric component is ozone. grey = TOC red = TOA 1 (O 3 = 0.1 cm.atm) green = TOA 2 (O 3 = 0.2 cm.atm) blue = TOA 3 (O 3 = 0.3 cm.atm) yellow =TOA 4 (O 3 = 0.4 cm.atm) cyan = TOA 5 (O 3 = 0.5 cm.atm) Figure 6: SATCO simulated BRDF in the principal plane, for tropical forest (Myneni et al., 1993), in the RED channel. Solar zenith angle is 0 and the variable atmospheric component is water vapour. grey = TOC red = TOA 1 (Wat. vap. = 0 g/cm 2 ) green = TOA 2 (Wat. vap. = 1.25 g/cm 2 ) blue = TOA 3 (Wat. vap. = 2.5 g/cm 2 ) yellow =TOA 4 (Wat. vap. = 3.75 g/cm 2 ) cyan = TOA 5 (Wat. vap. = 5 g/cm 2 )

20 18 Figure 7: SATCO simulated BRDF in the principal plane, for grasses (Myneni et al., 1993), in the NIR channel. Solar zenith angle is 0 and the variable atmospheric component is aerosol. grey = TOC red = TOA 1 (Aer opt.th.= 0) green = TOA 2 (Aer opt.th. = 0.25) blue = TOA 3 (Aer opt.th.= 0.5) yellow= TOA 4 (Aer opt.th.= 0.75) cyan = TOA 5 (Aer opt.th.= 1) Figure 8: SATCO simulated BRDF in the principal plane, for grasses (Myneni et al., 1993), in the NIR channel. Solar zenith angle is 0 and the variable atmospheric component is water vapour. grey = TOC red = TOA 1 (Wat. vap. = 0 g/cm 2 ) green = TOA 2 (Wat. vap. = 1.25 g/cm 2 ) blue = TOA 3 (Wat. vap. = 2.5 g/cm 2 ) yellow= TOA 4 (Wat. vap. = 3.75 g/cm 2 ) cyan = TOA 5 (Wat. vap. = 5 g/cm 2 ) a) b) Figure 9: SATCO simulated BRDF for conifers (Myneni et al., 1993), in the RED channel. Solar zenith angle is 60 and the variable atmospheric component is aerosol. a) 3D representation, b) 2D representation in the principal plane grey = TOC red = TOA 1 (Aer opt.th.= 0) green = TOA 3 (Aer opt.th. = 0.5) blue = TOA 5 (Aer opt.th.= 1)

21 19 a) b) Figure 10: SATCO simulated BRDF for conifers (Myneni et al., 1993), in the NIR channel. Solar zenith angle is 60 and the variable atmospheric component is aerosol. a) 3D representation, b) 2D representation in the principal plane grey = TOC red = TOA 1 (Aer opt.th.= 0) green = TOA 3 (Aer opt.th. = 0.5) blue = TOA 5 (Aer opt.th.= 1) Figure 11: SATCO simulated BRDF in the principal plane, for conifers (Myneni et al., 1993), in the NIR channel. Solar zenith angle is 0 and the variable atmospheric component is water vapour. grey = TOC red = TOA 1 (Wat. vap. = 0 g/cm 2 ) green = TOA 3 (Wat. vap. = 2.5 g/cm 2 ) blue = TOA 5 (Wat. vap. = 5 g/cm 2 ) Figure 12: SATCO simulated BRDF in the principal plane, for conifers (Myneni et al., 1993), in the NIR channel. Solar zenith angle is 30 and the variable atmospheric component is water vapour. grey = TOC red = TOA 1 (Wat. vap. = 0 g/cm 2 ) green = TOA 3 (Wat. vap. = 2.5 g/cm 2 ) blue = TOA 5 (Wat. vap. = 5 g/cm 2 )

22 Figure 13: SATCO simulated BRDF in the principal plane, for conifers (Myneni et al., 1993), in the NIR channel. Solar zenith angle is 60 and the variable atmospheric component is water vapour. grey = TOC red = TOA 1 (Wat. vap. = 0 g/cm 2 ) green = TOA 3 (Wat. vap. = 2.5 g/cm 2 ) blue = TOA 5 (Wat. vap. = 5 g/cm 2 ) 20

23 5.3 Discussion Atmospheric effects Ozone induces no atmospheric perturbation in the BLUE and NIR bands and only a very limited perturbation in the RED band. Comparison of the ozone absorption windows (annexe 2) on the one hand and the spectral response curves of the four channels selected for the VEGETATION instrument (annexe 1) on the other hand, confirms that atmospheric ozone is a rather negligible atmospheric factor. VEGETATION senses at wavelengths not shorter than 430 nm, i.e. outside the Hartley band, which is located between 200 and 310 nm and outside the temperature dependent Huggins bands of ozone, ranging from 310 to 370 nm. The strongest ozone absorption occurs in those two bands. Only the RED band of VEGETATION samples in the Chappuis bands where absorption is fairly weak. As clearly shown in table 4, the most affecting atmospheric parameter for the three channels (BLUE, RED and NIR) is aerosol optical thickness. Water vapour only has a low to medium influence in the RED and NIR channels respectively. The above discussion elucidates the influence of ozone as a perturbing factor. Hence, it is proposed to consider as atmospheric perturbing factors: (1) the aerosol optical thickness in the BLUE, RED and NIR bands (2) the water vapour content in the RED and NIR bands Biome influence Although the number of geometric sampling intervals has to be increased for a better description of the observed phenomena, the 2D and 3D representations of the BRDF curves give a fair idea of the BRDF trends in function of the combined surface/atmospheric/geometric conditions. A quantitative assessment of these trends is possible on the basis of generated output tables and on the basis of 2D representations in the principal plane. The exception to the general TOA > TOC rule for vegetated biomes for the visible channels in the case of Kimes grassland, needs additional documentation on the choice of the BRDF triplets. Though Kimes grassland is expected to behave like a vegetated target, its behaviour is more similar to that observed for highly reflecting targets, like desert. This is not that surprising since the grasslands described and measured by Kimes (1985) are poorly vegetated and the radiative contribution of the soils is high. This observation stresses the local validity of the BRDF triplets extracted from field measurements (table 1) and especially the critical scrutiny needed to describe the natural variability of the radiative properties of a biome on the Earth. In addition field measurements are contaminated with the effects of incoming diffuse radiation due to atmospheric scattering (Engelsen, 1996). As a consequence, different biomes may be characterized by identical BRDF triplets or, inversely, different natural facies of a biome can be characterized by completely different BRDF triplets. On the other hand, the BRDF triplets of table 2 (Myneni et al., 1993) generate quite similar BRDF curves. This was expected since the triplet values are relatively close. The BRDF parameters derived from synthetic data sets are not masked with diffuse radiation and are assumed to be more generally valid on a global scale. But they describe only average synthetic biomes and hence, not very specific or extreme ones, such as Kimes grassland. Since compositing criteria have to be developed for applications at a global scale, it is preferable to use those parameters

24 22 which have a general global validity, and not parameter sets representing only a limited number of very specific biomes. The iteration that has been done related to the choice of the BRDF triplets can be clarified by means of figure 1. The output of the coupled model (model generated VGT data) serves as a feedback to the definition of the input parameters for a next sensitivity analysis. For a detailed description and discussion on the analysis results, we refer to annexe Future steps Objectives The main objective of the sensitivity analysis is the documentation of the effect of different perturbing factors on VEGETATION time series. Therefore, the next step, called analysis03, of the sensitivity analysis especially focuses on scenarios supposed to approach as close as possible the real cases of VGT compositing. Following aspects are emphasized: (1) the realism with respect to post-launch VGT data in terms of combined realistic atmospheres, realistic geometries and real biomes; (2) coverage of most of the main VGT post-launch cases (atmosphere, geometry and biome combinations). Results of analysis03 will be interpreted in view of the results obtained during the pathfinder analyses. New results will determine the basis on which compositing algorithm(s) will be developed VGT-dedicated scenarios A pragmatic approach has been adopted to meet criteria (1) and (2) of analysis03. In order to decrease the number of unrealistic scenarios, advantage has been taken of the latitudinal banding of biomes observed from the equatorial regions of the Earth to the polar zones. Based on global terrestrial biomes and climatic maps, simplified latitude range classes, characterized by one or different dominant associations of biomes and climates have been defined (table 5). Considering the global scale of the research and the Earth rotation around its axis and around the sun, longitudinal variations have not been taken into account. If necessary, more detailed Earth climate and biome distributions will be considered in the future. In total, 10 latitudinal zones have been differentiated depicting the main climate/biome associations of the globe (table 5). Analysis03 will not examine latitudes above 65 in the Northern hemisphere where tundra biome occurs. In all, 9 latitudinal zones were retained for analysis03. In a first step, realistic scenarios are computed for the central point of each latitudinal zone. The coupled viewing (VEGETATION) and solar zenith and azimuth angles that characterize the nine central points, are combined with the biomes (BRDF triplets) and atmospheric conditions that are latitudinally associated as shown in table 5. The output of each scenario is the temporal evolution of the simulated TOA reflectance of the most representative biome/climate associations acquired in realistic observation and illumination conditions. Additional realistic scenarios can be build if other points than the central ones are chosen within each latitudinal zone. Table 5: Latitudinal distribution of terrestrial biomes and climates. Authors interpretation of the world maps of Eyre, S.R. (1968), Matthews, E. (1983), Strahler, N. (1978), and Walter, H. (1977)

25 23 Latitude Biome BRDF biome Climate 80 N 65 N 55 N 40 N 30 N 23 N 15 N 8 N 5 S 20 S 40 S Tundra - Tundra climate Northern coniferous forest Myneni_conifers Boreal forest climate Midlatitude decideous forest Myneni_leaf_forest Tall grass prairie Myneni_grasses Moist continental climate Cultivated areas Myneni_crops Short grass prairie - Moist continental climate Steppe Kimes_steppe Mediterranean climate Desert Rahman_desert Dry tropical climate Scrub Myneni_shrubs Dry tropical climate Steppe Kimes_steppe Savannah Myneni_savannah Wet-dry tropical climate Low latitude rain forest Govaerts_tropical_forest Wet equatorial climate Savannah Myneni_savannah Wet-dry tropical climate Steppe Kimes_steppe Grassland Myneni_grasses Dry subtropical climate Total = 10 zones Total = 11 biomes Total = 9 BRDF biomes Total = 8 climates VEGETATION viewing and illumination geometries Realistic viewing geometries for VEGETATION are simulated on the basis of the SPOT-4 cycle of 26 days with the VEGETATION orbital model (courtesy of G. Saint - CNES, 1995) that has been implemented in SATCO. Illumination geometries are simulated identically for a half year period (183 days). The full set of potential viewing and illumination geometries corresponds theoretically to 26 x 183 combinations for each of the nine latitudinal reference points distributed along the Greenwich meridian. Nevertheless, tests over two subsequent years showed that some viewing conditions occur for both years, but on different dates with a maximal shift of a few days. Resulting angular differences between both years for viewing and illumination geometry never exceed 2 degrees. In consequence, a reduction of the simulation period to one year is justified. In addition, the combined viewing and illumination geometries for the Northern hemisphere during the first half of the year correspond to the coupled geometries valid in the Southern hemisphere during the second half of the year, and vice versa. Since two thirds of the continental zones are located in the Northern hemisphere, most cases are covered when a period of 365 days is considered for only the 6 latitudinal reference points situated in the Northern hemisphere. This strategy slightly overestimates the coupled viewing and illumination geometries in the Southern hemisphere since continental zones of the Northern hemisphere have Southern marine equivalents Atmospheres

26 To obtain realistic atmosphere simulations, the following steps were taken: 24 Documentation of the climates mentioned in table 5, including mean values and standard deviations of water vapour content, aerosol optical thickness and ozone mixing ratio on a monthly basis; Implementation in SATCO of atmospheric conditions (water vapour, aerosol optical thickness and vertical ozone profiles) compatible with 6S code; Integration of a code to randomly generate atmospheres differing in terms of water vapour content, ozone mixing ratio and aerosol optical thickness. Values will be fixed within a range determined by the respective mean and standard deviations ( x ± 3σ for instance) Biomes To simulate TOA reflectances for the biomes cited in table 5, it is necessary to relate these biomes with specific spectral bands characterized by BRDF triplets. Among the different triplets available in the literature, triplets describing synthetic biomes (table 2) have been preferably selected for the sensitivity analysis (see section 5.3.2). Lessons drawn from the simulation of AVHRR TOA reflectances on the basis of Fife and Hapex- Sahel rich data sets (section 6), show that Kimes grasses (1985) have geometric structures that are exceptional opposed to grassland reported in the literature. In particular, unexpected radiative properties as compared to those of «mean» grassland are explained by a local combination of soil and vegetation scattering components. To eliminate a mix of synthetic and observational biome definitions the use of the radiative properties of synthetic biomes was favoured, except when not available (steppe and desert biomes). A simplified approach to partly cover natural variability of biomes consists in considering standard deviations of the ρ 0 values for each BRDF triplet. Higher and lower levels of ρ 0 correspond to higher and lower absolute levels of BRDF. Practically, standard deviations per biome were chosen in such a way that the ρ 0 obtained includes most of the observed and modelled values (table 6). In a first step, the ρ 0 range defined in table 6 for each biome in function of the wavelength will be sampled using two limits and a mean value. Tests are envisaged to a posteriori evaluate for each biome, the impact on simulated data of the difference existing between the considered ranges of ρ 0 values and field based ρ 0 values. BRDF triplets describe a biome in a static situation. The biome s seasonality will change the BRDF parameters, but this change will not affect the signal over the time series to be composited since the seasonal cycle is much longer than the 10 day compositing period of VEGETATION. The change will only be sensitive if we compare different times series among themselves. Due to modelling considerations adopted in this project, changes in radiative properties are not directly in relation to biome specific phenology. Therefore, biomes will be considered independent from seasonality, in fully mature stage (peak of greenness) Results and discussion Analysis03 is in progress. Results and figures presented here are preliminary; they exemplify some types of sensitivity analysis tools that will be available from analysis03.

27 25 Histograms of VGT view angles/solar angles have been constructed to document the most frequently occurring geometries associated with VEGETATION (figures 14a and 14b). This documentation is relevant for compositing. If the sensitivity analysis identifies the need for a compositing algorithm producing as homogeneous geometrical conditions as possible, to reduce the BRDF effects in the composite, it is essential to know which are the most frequent VEGETATION observed geometries in function of latitude. A first evaluation of VEGETATION geometries for latitudes 35 N and 60 N shows that hot spot conditions are rarely encountered since VEGETATION view azimuth angles vary around an Eastern pole (105 ) and a Western pole (305 ) (figure 14a) while predicted solar azimuth angle conditions are situated between 110 and 185 with a maximum frequency between 140 and 180 (figure 14b). The viewing zenith angle at a given target varies from day to day due to a shift of the overpass location of VGT. The 5-day periodicity of the VEGETATION viewing geometry is illustrated in figure 14c. Sometimes, a cycle comprises up to 10 observations when VGT observes a ground target from two different orbits as it is the case for high latitudes (figure 14d). A zoom on the solar zenith angle variation over a period of one month (January for instance) is illustrated in figures 14e and 14f. The overall decrease of the solar zenith angle from the beginning to the end of the month expresses the progressive lengthening of the days which is observed in January. A 5-day periodicity is also observed due to the progressive VEGETATION orbital shift. Variation of VEGETATION view and illumination angles affects the reflectance measurements since land surfaces and atmospheres have non-lambertian radiative properties. Figure 15a shows simulated TOC and TOA reflectances for a steppe ground target situated at 35 N, 0 E during one month, assuming a constant atmosphere. The influence of observation geometry on modelled reflectances is clearly highlighted. The 5-day periodicity discussed earlier is eminently reflected in this temporal profile. Changes in latitude and in ground type (biome) generate considerable variations of the simulated reflectance values, as shown on figures 15a (steppe situated at 35 N, 0 E) and 15b (coniferous forest situated at 60 N, 0 E). In particular, the difference between TOC and TOA reflectance values is higher on figure 15b though a similar atmosphere has been used to generate TOA reflectance values of both figures. This fact justifies global sensitivity analyses based on latitudes and biomes differentiation.