PHENOLOGY ESTIMATION FROM METEOSAT SECOND GENERATION DATA

Yves Julien, José A. Sobrino, Guillem Sòria

Global Change Unit, Image Processing Laboratory, University of Valencia, P.O. Box 22085, E-46071 Valencia, Spain (phone: +34 96 354 4054; e-mail: yves.julien@uv.es)

ABSTRACT

Many studies have focused on land surface phenology as a means to characterize global climate. The Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor onboard Meteosat Second Generation (MSG) geostationary satellite can also contribute to this task thanks to its adequate spatial and temporal resolutions. Here, four years of MSG-SEVIRI Normalized Difference Vegetation Index (NDVI) daily time series have been retrieved, which were then gap-filled with the help of an algorithm based on the iterative Interpolation for Data Reconstruction [Julien and Sobrino, 2010]. Finally, phenological parameters have been retrieved from the reconstructed time series, and compared with independent MODIS (Moderate resolution Imaging Spectrometer) data, showing differences for specific land covers although the stability of the retrieved phenophases over the year is surprisingly good for MSG data. This approach can be applied to other geostationary satellites worldwide to obtain quick remotely sensed estimates of vegetation phenology at global scale.

Index Terms— Phenology, NDVI, Meteosat Second Generation, vegetation.

1. INTRODUCTION

Under the concern for climate change and its effect on ecosystem, the scientific community has focused on phenology as a mean to evidence changes in vegetation response or composition to climate change [1]. From remote sensing, efforts have been concentrated on retrieving land surface phenology from polar orbiting platforms [2], although geostationary satellites also allow for land surface phenology retrieval. Retrieved satellite time series usually suffer from gap presence due to both cloud cover and atmospheric contamination, for which several methods have been developed in the literature to reconstruct the contaminated time series (see [4] and references therein). Here, we use Meteosat Second Generation (MSG) data acquired from SEVIRI (Spinning Enhanced Visible and InfraRed Imager) sensor to estimate phenology parameters from space for years 2008 to 2011, through a modification of the iterative Interpolation for Data Reconstruction method [4].

2. DATA

In this work, MSG data retrieved at the Global Change Unit of the University of Valencia have been used. These data are received every 15 min from a MSG direct broadcast HRPT (High Resolution Picture Transmissions) system, using a parabolic dish and hardware to decode L-band data, which are then processed automatically to obtain several land surface parameters such as NDVI (Normalized Difference Vegetation Index [3]), which we use here to estimate yearly phenology characteristics from 2008 to 2011. The resulting dataset amounts to more than 40 TB of data, which were processed in near-real time to retrieve NDVI parameters.

3. METHODS

Each year of MSG NDVI data have been processed separately, starting on January 1st and ending on December 31st. In a first step, for each day, we selected the 15-minute acquisition closer to local noon, in order to minimize illumination effects. The corresponding daily composites were assembled into a 365 (or 366) element time series.

Since cloud contamination affects most pixels of the image, data reconstruction techniques have to be applied to obtain a coherent time series. To that end, we used the IDR technique [4], adapted to daily data. This technique consists in identifying and reconstructing atmospherically contaminated observations pixel by pixel from iterative linear interpolation from its two closest neighbors in time, although the noise present in daily time series imposes an adaptation of this technique. Here, we used the maximum value over subsequent 8-days period to reconstruct the data with the IDR technique, and the reconstructed 8-day composite time series has then been transformed back to daily resolution through linear interpolation. This modified IDR approach (M-IDR) leads to similar results than the classic IDR approach while dealing with daily resolution more efficiently. Since the timing of middle amplitude crossing has been evidenced as an adequate estimate of start and end of season in vegetation index time series [5], we used this method here to estimate the timing of start (green-up) and end (brown-down) for the longest growing period of the year (in order to take into account vegetation with one or more

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growth cycles per year), as well as the maximum and minimum NDVI values reached during this growth cycle.

4. RESULTS AND DISCUSSION

Figure 1 presents green-up and brown-down dates as well as NDVI maximum and minimum values for the

longest growth cycle of 2010. Since the algorithm does not distinguish between areas with low yearly NDVI amplitude, homogeneous values of start and end of season are obtained for evergreen (in central Africa) and arid areas, which are probably due to sun-target-sensor geometric considerations. Very short growing seasons (less than a month) can be observed in Sahel and southern Europe. Longer growing

a) green-up date b) brown-down date

1 50 100 150 200 250 300 350 DOY c) min NDVI d) max NDVI

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 NDVI units Figure 1. Retrieved NDVI parameters for year 2010 based on MSG-SEVIRI NDVI data: a) green-up date (NDVI increase), b) brown-down date (NDVI decrease), c) minimum NDVI value, and d) maximum NDVI value.

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seasons are observed in northern temperate areas. As regards NDVI optimum values, low values are obtained in areas, while central Africa (evergreen tropical forest) and most of Europe (crops and deciduous forests) show high maximum NDVI values.

We estimated green-up and brown-down dates for years 2008, 2009, 2010 and 2011 (not shown). These phenophases show little variation between years (figure 2), with differences mainly concentrated in specific areas for which a climate variation has occurred between 2008 and 2011.

To validate the estimated start and end dates presented in figure 1, we compared MODIS MCD12Q2 data (Global Vegetation Phenology product [6]) to the phenology dates obtained from MSG data for year 2008 and 2009, which are the most recent MODIS phenology product available to date. The comparison for vegetated pixels between both approaches led to RMSE of up to 60 days depending on the land cover. Although these errors seem important, these are not uncommon and have been reported in the literature ([7], [8]), even in homogeneous areas observed under the same conditions ([8]). Moreover, the difference between the vegetation indices used (Enhanced Vegetation Index for MODIS phenology; NDVI for MSG-SEVIRI phenology) has been shown to lead to differences in phenology estimation for specific land covers ([9]), although the SEVIRI bands do not allow for the estimation of more advanced vegetation indices than the NDVI.

5. CONCLUSION

In this paper, a modified method (M-IDR), based on the IDR approach, has been presented to reconstruct time series affected by both data gaps and atmospheric and cloud contamination, and has been shown to provide temporal profiles with similar characteristics to the original method, while dealing efficiently with higher temporal resolution (daily versus bi-monthly). This M-IDR methodology has been applied to MSG NDVI time series for estimation of phenological phases of vegetation for years 2008 to 2011. When compared with MODIS-retrieved phenology, observed differences can be explained both by methodology differences (different vegetation indices are used) and the heterogeneity of the vegetation, which results in different landscape composition within each pixel. Through the 4 year of the study, a higher stability of the retrieved phases for MSG phases is observed for all vegetation covers with marked seasonality, except for areas observed under high viewing angles for which retrieved NDVI values are flawed. Since a key point for phenology studies is the rapid availability of observations for the scientific community, processing times between complete year of data retrieval and phenological phases availability should be minimized. At the moment this paper was redacted, the latest phenological retrievals from MODIS data were 2009, with a more than two year lag between complete year data acquisition and phenology parameter extraction. As shown in this paper, MSG-retrieved phenology is available within weeks of yearly data completion. Since most geostationary

a) 2008-2011 MSG green-up standard deviation b) 2008-2011 MSG Brown-down standard deviation

0 10 20 30 40 50 60 daysFigure 2. Four year standard deviation for MSG-retrieved green-up (left) and brown-down (right) dates. MSG standard deviations have been estimated from 2008-2011 phenology.

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satellites spectral characteristics include red and near- infrared bands, such an approach could also be applied to data provided by these satellites, leading to quicker observation of land surface phenology phases than what is available at the moment.

6. ACKNOWLEDGEMENT

The authors wish to thank the NASA for making the MODIS phenology product publicly available. The authors are grateful to all the members of the Global Change Unit of the University of Valencia for their dedication in keeping the antennas running over the years. The authors also wish to thank the European Union (CEOP-AEGIS, project FP7-ENV-2007-1 Proposal No. 212921) and the Spanish Ministerio de Ciencia y Tecnología (EODIX, project AYA2008-0595-C04-01) for their financial support.

7. REFERENCES

[1] Körner, C. & Basler, D., “Phenology under global warming”, Science, Vol. 327, 19 March 2010, 1461-1462, 2010.

[2] Jeong, S.-J., Ho, C.-H., Gim, H.-J. & Brown, M. E., “Phenology shifts at start vs. End of growing season in temperate vegetation over the Northern Hemisphere for the period 1982-2008”, Global Change Biology, (2011) 17, 2385-2399, 2011.

[3] Tucker, C. J., “Red and photographic infrared linear combinations for monitoring vegetation”, Remote Sensing of Environments, 8:127-150, 1979.

[4] Julien, Y. & Sobrino, J. A., “Comparison of cloud-reconstruction methods for time series of composite NDVI data”, Remote Sensing of Environment, 114 (2010) 618–625, 2010.

[5] White, M. A., De Beurs, K. M., Didan, K., Inouye, D. W., Richardson, A. D., Jensen, O. P., O'Keefe, J., Zhang, G., Nemani, R. R., Van Leeuwen, W. J. D., Brown, J. F., De Wit, A., Schaepman, M., Lin, X., Dettinger, M., Bailey, A. S., Kimball, J., Schwartz, M. D., Baldocchi, D. D., Lee, J. T. & Lauenroth, W. K., “Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006”, Global Change Biology, (2009) 15, 2335-2359, 2009.

[6] Ganguly, S., Friedl, M. A., Tan, B., Zhang, X. & Verma, M., “Land surface phenology from MODIS: characterization of the collection 5 global land cover dynamics product”, Remote Sensing of Environment, 114 (2010) 1805-1816, 2010.

[7] White, M. A., De Beurs, K. M., Didan, K., Inouye, D. W., Richardson, A. D., Jensen, O. P., O'Keefe, J., Zhang, G., Nemani, R. R., Van Leeuwen, W. J. D., Brown, J. F., De Wit, A., Schaepman, M., Lin, X., Dettinger, M., Bailey, A. S., Kimball, J., Schwartz, M. D., Baldocchi, D. D., Lee, J. T. and Lauenroth, W. K. (2009). Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006, Global Change Biology, (2009) 15, 2335-2359.

[8] Richardson, A. D., Braswell, B. H., Hollinger, D. Y., Jenkins, J. P. and Ollinger, S. V. (2009). Near-surface remote sensing of spatial and temporal variation in canopy phenology, Ecological Applications, 19(6), 2009, 1417–1428.

[9] Garrity, S. R., Bohrer, G., Maurer, K. D., Mueller, K. L., Vogel, C. S. and Curtis, P. S. (2011). A comparison of multiple phenology data sources for estimating seasonal transitions in deciduous forest carbon exchange, Agricultural and Forest Meteorology, 151 (2011) 1741-1752.

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