SENSITIVITY ANALYSIS OF COMPOSITING STRATEGIES: MODELLING AND EXPERIMENTAL INVESTIGATIONS

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1 SENSITIVITY ANALYSIS OF COMPOSITING STRATEGIES: MODELLING AND EXPERIMENTAL INVESTIGATIONS de Wasseige Carlos, Vancutsem Christelle and Defourny Pierre Department of Environmental Sciences - Université catholique de Louvain Croix du Sud, bte B- Louvain-la-Neuve Abstract High temporal resolution satellites, such as VEGETATION provide multiple images of the same site over short periods of time. Time series constituted of these individual images are characterised by a lack of signal consistency since measured radiance generally result from various cloudiness, atmospheric and geometric conditions. To reduce the related noise, various compositing techniques are available. The pre-launch phase went into a systematic investigation of the main issues related to the temporal synthesis production using a one-year time series simulated for the VEGETATION sensor spectral and geometric configuration. The aim of that investigation was to test globally the sensitivity of the compositing process to different factors that perturb the signal. Those factors have been ranked according to their impact on the sensor signal. This sensitivity analysis highlighted the large effect of the viewing angle as opposed to atmosphere variability with regard to day-to-day variations. However, the perturbing factors were always manifested as a coupled effect on the sensor signal. The analysis of the one-year simulated time series showed three nested scales of variation. A five-day cycle related to the viewing angle and due to the wide swath of the sensor. A -day cycle corresponding to the satellite orbit revisit time, and the sun annual cycle changing according to latitude. The conclusions drawn from the pre-launch phase of the VEGETATION programme have resulted in a proposal for two new image compositing strategies. The approach pursued in the pre-launch phase was repeated using actual VEGETATION data in the postlaunch phase. Two decades of global daily VGT-P (June and October 99) and three decades of northern Africa from the st October to the 0 th November 999 of VGT-S were used. A sampling approach based on the global data set of VGT-P segments was designed with 0 x 0 km chips to assess the performances of the existing compositing strategies for the various sun-target-sensor geometries and the different surfaces of the main terrestrial biomes. The spatial and temporal variability of the signal was first analysed for the various chips with regard to the simulation results. The current compositing technique for VEGETATION data (VGT-S product) shows radiometric artefacts in the reflective bands that may cause a significant noise for subsequent retrievals of surface parameters. The performances of various compositing strategies are assessed as well for the reflective bands as for the NDVI composites. Dedicated indicators and statistical analysis are computed to provide quantitative results by zone and by band. The results obtained using actual VEGETATION data are compared to the current MVC-NDVI approach and to other documented alternatives. As for the simulated time-series, the analysis of the data set shows that the Min Red criteria is the best candidate to replace the current MVC-NDVI. A discussion of the results will provide suggestions for possible improvements in the VEGETATION processing chain compositing algorithms. As the tested compositing criteria can not remove the BRDF effect in the synthesis, a new angular normalisation has been proposed to reduce these effects before the compositing. The preliminary results are very promising but need to be tested over the whole set of samples.. Introduction Earth observation with optical sensors based on satellite platforms is known to be limited by the interference of clouds and atmospheric constituents like ozone, water vapour and aerosols. Any quantitative use of composite image for land applications relies on the ability to accurately correct these noise-like variations of the signal. High temporal resolution satellites provide multiple images of the same site over short periods of time. When several images from the same sensor are available for an area, individual images can be merged to create a temporal composite image in order to reduce the atmospheric perturbations and produce image products devoted to land applications. However, time-series constituted of these individual images are

2 characterised by a lack of signal consistency since measured radiance result generally from various cloudiness, atmospheric and geometric conditions (Cihlar & al, 99). The compositing consist in selecting the best pixel on a pixel to pixel basis within a time series in order to create a new image. The compositing algorithm currently implemented for the VEGETATION S products, i.e. the Maximum Value Composite NDVI (Holben, 9), should minimise cloud and BRDF effects in the composites. While this procedure tends to produce visually satisfactory NDVI composites, the composited channel data may exhibit substantial radiometric variations (D Ioro and al., 99). The MVC works nicely over near- Lambertian surfaces where the primary source of pixel variations within a composite period is associated with atmosphere contamination and path length. However, the MVC becomes inconsistent and unpredictable due to anisotropic properties of vegetate canopies and varying atmospheric conditions (van Leeuwen & al, 999). Moreover the MVC NDVI amplifies the greenness level (Gutman and al., 99). Therefore, spatial structures characterised by weak NDVI values such as rivers tend to disappear in the composites, whereas structures characterised by high NDVI values such as densely vegetated alluvial plains tend to be overestimated (Bartholomé, 99). In order to improve the compositing strategy, existing criteria used for the compositing of AVHRR such as the Maximum Difference (MaD), the Minimum RED and the Minimum VZN in two stages have been tested and assessed in this study. Other strategies like the Min VZN in one stage and the Maximum MirVI have been assessed too.. Objectives This study aim at investigating systematically issues related to the temporal synthesis production of VEGETATION data. The project focuses on the performance assessment of existing and newly developed compositing algorithms, keeping in mind that the findings of the research should be valid on a global scale and in the long run. The newly proposed algorithm should lead to improvements of the quality of the VGT-S products on a production routine basis.. Data The data set consists of two global decades of the VEGETATION satellite (P products) and three decades over the North African region of S products. Because there are no overlaps between the VEGETATION tracks at the Latitude of the North African Region (mostly under of North Latitude), the S products have not been composited yet. Therefore, they are similar to the P products but the atmospheric correction. Coverage Number of chips Decade VGT Products Atmosphere correction North Africa rd of October 999 S SMAC North Africa st of November 999 S SMAC North Africa nd of November 999 S SMAC Global nd of June 99 P none Global nd of October 99 P none Table : Vegetation Data set The original P products received from the CTIV, contained the four reflectance channels, the four angle channels and the Status map (cloud screening). The S product had the same composition plus an NDVI channel. According to the IGBP Land Cover Classification (Belward, 99) homogeneous chips of 00 km² (0*0 km) have been extracted from the original data. From the P products, chips, distributed around the world at different latitudes (table ) were analysed for two decades (from the th to the st June 9 and from the th to the st October 9). The sample set represents the global variability of the observation conditions. Biome (country) UL longitude UL latitude LR longitude LR latitude Boreal forest/taiga (Russia), E, N 9 E N Agriculture (France), E, N,0 E, N Mediterranean steppe (Spain), W, N W N Desert (Algeria) 0, W, N 0, W, N Herbaceous savannah (Senegal),0 W, N, W, N Tropical rain forest (RCA), E, N, E, N Pampas (Argentina), W, S W, S Paddy fields (Vietnam), E, N, E 0, N Table : Location of P-products

3 Concerning the S products, six different sites, distributed in various biome-climate systems in Northern Africa were analysed, i.e. desert, savannah, two types of tropical forest, agriculture and mangrove for three decades (from the st October to the 0 th November 999). This huge data set consist of a total of different cases ( areas * decades (P products) + areas* decades (S products)) widely distributed in space and time.. Method The figure summarises the different steps for the processing chain of the various S products. P and S VGT products from CTIV Homogeneous sites extraction 0 X 0 km chips P and S products - reflectance channels - angle channels Calcul of other parameters : NDVI, MIRVI, DVI Phi and Gfa P products (0 X 0 km) - reflectance channels - angle channels - NDVI, MIRVI, DVI, Phi and Gfa P products (0 X 0 km) - reflectance channels - angle channels - NDVI, MIRVI, DVI, Phi and Gfa S products (0 X 0 km) - reflectance channels - angle channels - NDVI, MIRVI, DVI, Phi and Gfa Compositing - criteria : MVC NDVI, MVC MIRVI, Min RED, Min VZN () and Min VZN () S products (0 X 0 km) Figure : Different stages of the method This processing chain includes the computation of a set of parameters defined as follows: The Phi The Phi parameter is defined by the difference between the solar (SZA) and the viewing azimuth angles (VZA). The G factor The geometry of observation, is estimated by the geometric factor G defined by Rahman & al (99): G = (tan² SZA + tan² VZA - * tan SZA * tan VZA * cos (Phi)) ½ The DVI The difference between NIR and RED is calculated in order to choose the maximum value in compositing strategy (MaD): DVI= NIR RED The MIRVI This Vegetation Index is used to create a new criteria in the compositing method (max MIRVI). MIR VI = (NIR MIR) / (NIR + MIR)

4 .. The compositing strategies The goal of the compositing procedure is to approximate with the composite image, as much as possible, a single, composite, cloud free image devoted to one of the two following applications. Fife criteria were used and compared to the MVC NDVI existing criteria:... MaD MaD retains the pixel that shows the maximum value for the DVI. It is based on the shape of the spectral signature of the vegetation, with a particularly low signal level in the red band and high signal level in the NIR band. This criterion has been used in the past for the compositing of AVHRR Global Area Coverage product (Tarpley, 99 in: Cihlar, 99).... Min RED When single-criterion compositing algorithms such as MVC NDVI or MaD have been judged too weak for eliminating externals factor-related noises, two criteria compositing algorithms have been implemented that involve two-step pixel selection. The first step of the pixel selection is carried out with the maximum value of the NDVI to retain a range of original NDVI values (%) within each composite period. Then, the second step of the pixel selection is performed by picking out the pixel with the minimum value in the red band from the pixels retained after the first stage of the selection (D Iorio and al., 990, 99). Min RED is based on the observation that clouds show much higher reflectance values in the visible bands than the other terrestrial surfaces. Main objective is thus the selection of the clearest, cloud-free atmospheric conditions.... Min VZN () The second example of two-criteria compositing algorithm, Minimum View Zenith Angle (Cihlar and al., 99; Qi and Kerr, 99) selects the maximum NDVI as a first criteria (such as the Min Red) and the lowest view zenith angle value among the retained pixels in the first step (%). The main objective is the consistent selection of the pixels that are closest to nadir in view to approximate a single-date, near-nadir image, obtained under cloud-free conditions.... Min VZN () The Min VZN in one stage just selects the lowest view zenith angle value. Nadir view angles are supposed to reduce atmospheric path and perturbations as well as the bi-directional effects. This criteria is only sensible if an efficient cloud screening is used before the compositing.... MVC MIR VI The new algorithm MVC MIRVI aims at producing a composite that selects the greenest pixel. Within each compositing period, the pixel with the maximum MIRVI value is selected. Considering the characteristics of the MIRVI, such pixel is supposed to encounter optimal measurement conditions by eliminating cloudaffected, atmosphere-contaminated and large off-nadir view angle pixels since these conditions are assumed to depress the MIRVI value... Performance assessment The performance of a compositing algorithm can be assessed by its efficiency to discriminate and retain pixels that best describe the surface within one-day or -day time series. Several indicators were used to assess the performance of the compositing criteria. First, statistics, such as the mean and the standard deviation, were calculated on the entire surface of the area (approximately 0*0 pixels) for each day of the decade and for each compositing criteria. For each channel, the mean reflectance and the average texture of the composed images were analysed and compared with that of the daily images. Comparisons have been made essentially for the Blue, Red, and NIR mean reflectances and for the NDVI mean values. Secondly, attention is paid to the speckle effect (strong local variations in reflectance values between neighbouring pixels) which is regularly introduced by the compositing procedures. The local variance indices have been calculated on a bases of a moving window with a size of * pixels. Then these texture indices were averaged over the whole chips for each compositing criteria and for each day of the decades. As for the first analysis, mean texture values of the criteria have been compared to that of each day. Third, the VZN distributions of the different composite strategies were compared.

5 Fourth, the quality of the composited images has been visually assessed with regards of the best cloud free images of the decades (when it was possible). Finally, some small maps representing the compositing of the VZN, days, SZN, G factor, etc. served for visual quality assessment and mutual comparison of the compositing strategies. Compositing results obtained from newly proposed criteria are compared with the results obtained with the MVC-NDVI algorithm.. Results and analysis Because the current Status Map is not efficient enough to remove all cloudy perturbed pixels, it has not been used in our composite processing chain. Therefore, and because the Minimum VZN () is only meaningful with an efficient cloud screening, this criteria is not relevant for the current study... Atmospheric perturbations Because the blue spectral band is sensitive to the cloudy atmosphere, it is a good indicator to assess compositing strategies whom the goal is to produce cloud free images. For all cover types and decades, the blue reflectance of the MVC-MIRVI is in 0% of the cases greater or equal than the maximum blue reflectance value observed during the decade. An example of these observations is illustrated in the figure for the paddy fields cover. Day number in the compositing period 9 Figure : Comparison of mean reflectances in Blue channels (Paddy fields, June 99) The figure shows a typical example where MaD composite is second worst for atmospheric effects with regards to the other composites. The MaD composites exhibit large spatial patterns made of cloud (Cihlar et al., 99). On the other hand, MVC NDVI, Minimum RED and Minimum VZN() are atmospheric resistant thanks to the NDVI sensitivity to atmospheric effects (in 0 % of the time-series, composite reflectance is lower or equal than the daily minimum). Furthermore the Minimum RED reflectance is lower or equal ( %) than the MVC NDVI reflectance... Indicators of vegetation (NIR and NDVI) With regards to the three best criteria we notice that the NIR reflectance for the Minimum Red composite is always lower or equal (0 %) that of the MVC NDVI composite. The Minimum VZN () reflectance is similar to the MVC NDVI reflectance (figure ). Reflectance (%) MaD MVC MirVI MVC NDVI Min RED Min VZN() Min VZN() 9 Reflectance (%) MaD MVC MirVI Figure : Comparison of mean reflectance in NIR channels (Boreal forest, June 99) MVC NDVI Min RED Min VZN() Min VZN()

6 As illustrated in the figure, the NDVI mean values for Minimum Red criteria are always lower or equal than those one for MVC NDVI criteria (0%) while the NDVI values for Minimum VZN () are often (9 %) lower or equal than those in minimum RED. 9 Reflectance (%) 0, 0, 0, 0, 0, 0, 0,0 0,0 0,0 0,0 0 MaD MVC MirVI MVC NDVI Min RED Min VZN() Min VZN() Figure : Comparison of NDVI means values (Mediterranean steppe, October 99).. Image texture For the three still relevant composite the texture is always (0%) greater than any daily image texture (figure ).,0 Texture value,,0,,0 0, 9 0,0 MaD MVC MirVI MVC NDVI min RED min VZA () Figure : Comparison of texture values in NIR channel (Tropical forest, November 999) This statistically confirms the importance of the speckle induced by the compositing process min VZA ().. Bi-directional effects The VZN distribution of the Min VZN() criteria is obviously concentrated around the nadir view and presents a mono-modal distribution. On the contrary, the MVC NDVI and the Min Red present a bi-modal VZN distribution and likely select data with high VZN. The MVC NDVI composite favours off-nadir pixels in the forward direction (D Iorio et al., 990, 99). Nevertheless, the Minimum Red has a higher distortion also in the forward scattering. MVC RED VZN() Figure : Comparison of VZN distribution of the S composites obtained using the compositing strategies i.e. the MVC Ndvi, the Min Red and the Min VZN in two steps

7 .. Visual performance assessment The visual comparison between composites and daily images give further information about the texture and the atmospheric effects. The figure show that Minimum Red strategy clearly select some cloud shadows. Figure : Comparison of daily data with the Min RED composite (Desert, June 99). Discussion The tested compositing criteria can not remove the BRDF effect in the synthesis. A new angular normalisation has been proposed to reduce the BRDF effect before the compositing. The surface signal is simulated by choosing the adequate CSAR triplet of the model of Rahman and al (99). Since every angular configuration can be simulated, the simulated reflectance corresponding to the real image geometry is compared to the reflectance of the nadir view. The difference is used to correct and normalise the measured reflectance. The temporal signal variation of the time series is thus reduced to produce a signal more related to a given illumination and viewing angles condition. (figure ). 0 Evolution of mean's reflectance Simulated signal Measured time series Corrected time series Reflectance (%) //99 //99 //99 //99 //99 //99 9//99 Figure : Angular normalisation of a Desert time series (VEGETATION images of Oct-Nov 999) using the model of Rahman and al (99). Conclusion The systematic and global approach used shows the following points: - The MVC MirVI and MaD strategies generate major atmospheric impacts - The Minimum VZN () strategy is relevant only if atmospheric issues are solved - The Minimum VZN () strategy is not really better than MVC NDVI - The Minimum Red strategy is the most atmospheric resistant 0//99 //99 0//99 It appears that the Minimum Red strategy seems to be the best candidate to replace the MVC NDVI in the current pre-processing chain however the problem related to cloud shadows still exists. The comparison of composites in different regions shows that the efficiency of the two-steps criteria could be tuned by changing the number of pixels retained after the first step of compositing. It has been observed that the number of pixel retained depends on the level of the mean NDVI value of the decade. Generally, for high mean NDVI values, the range of NDVI values in the decade is high and vice versa. Consequently, for high mean NDVI value, the amount of pixel retained is low. On the opposite, a lower level of NDVI values will exhibit a greater number of pixels retained for the second step. Therefore, the threshold chosen for the 0//99 0//99 0//99 0//99 0//99 0//99 0//99 09//99 //99 Days

8 first criteria (MVC NDVI) must be adjusted according to the NDVI level and consequently to the region of interest. It s important to recall that the results of the cloud screening, of the angular normalisation and of the compositing performances are interdependent. It is therefore necessary to analyse the complementarily performances between the various steps. The compositing strategy is highly dependent on the cloud screening and atmospheric correction applied up hill in the processing chain. An efficient cloud screening and atmospheric correction must reduce the temporal signal variation. Nevertheless, for atmospherically corrected time series, a temporal signal variation is still observed. The geometry of observation and illumination is partly responsible for this variation. In order to reduce this, a method to correct the geometrical effects such as an angular normalisation is a necessary step to improve the compositing strategy. The method proposed normalises the reflectances of the decade at the nadir geometry taking into account the surface type and the geometry of illumination. The preliminary results of the angular normalisation are very promising but need to be tested over the whole set of samples.. References - Belward, A., Loveland, T., (99) The DIS km Land Cover data set, Global Change NewsLetter n ( - Cihlar, J., Manak, D., and D Iorio, M. (99), An evaluation of compositing algorithms for AVHRR data over Canada, Remote Sens. - Cihlar, J., Manak, D., and Voisin, N. (99), AVHRR Bidirectional Reflectance Effects and compositing, Remote Sens. - Cihlar, J., C. Ly, H., Li, Z., Chen, J., Pokrant, H., and Huang, F. (99), Multitemporal, multichannel AVHRR data sets for land biosphere studies-artefacts and corrections. Remote Sens. Environ. 0:-. - Defourny, P., Schouppe, M., (99), Sensitivity Analysis of Compositing Strategies: Modelling and Experimental Investigations, Pre-launch Phase Final report. - D Iorio, M., A., Cihlar, J., and Morasse, C. R., (99), Effect of the calibration of AVHRR data on the normalised difference vegetation index and compositing, Can. J. Remote Sens. (): -. - Gutman, G. G. (99), Vegetation indices from AVHRR: an update and future prospect. Remote Sens. Environ. :-. - Holben, B. N., 9, Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing. : -. - Jiaguo, Qi, Compositing multitemporal remote sensing data, the Qi and Kerr, (99), On current Compositing Algorithms, Remote Sensing Reviews. - Rahman H., Verstraete M.M. and Pinty B., (99), coupled Surface-Atmosphere Reflectance (CSAR) Model.. Model description and Inversion on Synthetic Data. Journal of Geophysical Research - Wim J. D. van Leeuwen, Alfredo R. Huete, and Trevor W. Laing. (999), MODIS Vegetation Index Compositing Approach : A Prototype with AVHRR Data, Remote Sens.