Land surface phenology from SPOT VEGETATION time series

A. Verger, I. Filella, F. Baret, J. Peñuelas


Land surface phenology from time series of satellite data are expected to contribute to improve the representation of vegetation phenology in earth system models. We characterized the baseline phenology of the vegetation at the global scale from GEOCLIM-LAI, a global climatology of leaf area index (LAI) derived from 1-km SPOT VEGETATION time series for 1999-2010. The calibration with ground measurements showed that the start and end of season were best identified using respectively 30% and 40% threshold of LAI amplitude values. The satellite-derived phenology was spatially consistent with the global distributions of climatic drivers and biome land cover. The accuracy of the derived phenological metrics, evaluated using available ground observations for birch forests in Europe, cherry in Asia and lilac shrubs in North America showed an overall root mean square error lower than 19 days for the start, end and length of season, and good agreement between the latitudinal gradients of VEGETATION LAI phenology and ground data.


global phenology; VEGETATION; leaf area index; climatic drivers; ground data

Full Text:



Atkinson, P.M., Jeganathan, C., Dash, J., Atzberger, C. 2012. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sensing of Environment, 123, 400-417.

Atzberger, C., Klisch, A., Mattiuzzi, M., Vuolo, F. 2013. Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series. Remote Sensing, 6(2), 257-284. https://doi. org/10.3390/rs6010257

Baret, F., Guyon, G. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35(2-3), 161-173.

Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P., Smets, B. 2013. GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment, 137, 299-309.

Camacho, F., Cernicharo, J., Lacaze, R., Baret, F., Weiss, M. 2013. GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. Remote Sensing of Environment, 137, 310-329. https://doi. org/10.1016/j.rse.2013.02.030

Cesaraccio, C., Spano, D., Snyder, R.L., Duce, P. 2004. Chilling and forcing model to predict bud-burst of crop and forest species. Agricultural and Forest Meteorology, 126(1-2), 1-13. https://doi. org/10.1016/j.agrformet.2004.03.002

De Beurs, K.M., Henebry, G.M. 2010. Spatio-Temporal Statistical Methods for Modelling Land Surface Phenology. I.L. Hudson, M.R. Keatley (Eds.), Phenological Research: Methods for Environmental and Climate Change Analysis. London: Springer.

Jönsson, P., Eklundh, L. 2002. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE transactions on Geoscience and Remote Sensing, 40, 1824-1832. TGRS.2002.802519

Kandasamy, S., Baret, F., Verger, A., Neveux, P., Weiss, M. 2013. A comparison of methods for smoothing and gap filling time series of remote sensing observations: application to MODIS LAI products. Biogeosciences, 10, 4055-4071. https://

Liang, L., Schwartz, M.D., Fei, S. 2011. Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sensing of Environment, 115(1), 143-157. rse.2010.08.013

Myneni, R.B., Williams, D.L. 1994. On the relationship between FAPAR and NDVI. Remote sensing of the environment, 49(3), 200-211. https://doi. org/10.1016/0034-4257(94)90016-7

Nagai, S., Nasahara, K., Inoue, T., Saitoh, T., Suzuki, R. 2015. Review: advances in in situ and satellite phenological observations in Japan. International Journal of Biometeorology, 1-13.

Pouliot, D., Latifovic, R., Fernandes, R., Olthof, I. 2011. Evaluation of compositing period and AVHRR and MERIS combination for improvement of spring phenology detection in deciduous forests. Remote Sensing of Environment, 115(1), 158-166. https://

Reed, B.C., Brown, J.F., VanderZee, D., Loveland, T.R., Merchant, J.W., Ohlen, D.O., 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 5(5), 703-714. https://

Rodriguez-Galiano, V.F., Dash, J., Atkinson, P.M. 2015. Intercomparison of satellite sensor land surface phenology and ground phenology in Europe. Geophysical Research Letters, 42(7), 2015GL063586.

Verger, A., Baret, F., Weiss, M. 2011. A multisensor fusion approach to improve LAI time series. Remote Sensing of Enviroment, 115(10), 2460-2470. https://

Verger, A., Baret, F., Weiss, M., Filella, I., Peñuelas, J. 2015. GEOCLIM: A global climatology of LAI, FAPAR, and FCOVER from VEGETATION observations for 1999–2010. Remote Sensing of Environment, 166, 126-137. https://doi. org/10.1016/j.rse.2015.05.027

Verger, A., Baret , F., Weiss, M., Kandasamy, S., Vermote, E., 2013. The CACAO method for smoothing, gap filling and characterizing seasonal anomalies in satellite time series. IEEE transactions on Geoscience and Remote Sensing, 51(4), 1963- 1972.

Verger, A., Filella, I., Baret, F., Peñuelas, J. 2016. Vegetation baseline phenology from kilometric global LAI satellite products. Remote Sensing of Environment, 178, 1-14. rse.2016.02.057

Weedon, G.P., Balsamo, G., Bellouin, N., Gomes, S., Best, M.J., Viterbo, P. 2014. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resources Research, 50, 7505-7514.

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., Kimbal, J., Schwartz, M.D., Baldocchi, D.D., Lee, J.T., Lauenroth, W.K. 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Global Change Biology, 15(10), 2335-2359. j.1365-2486.2009.01910.x

Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C., Huete, A. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3), 471-475.

Abstract Views

Metrics Loading ...

Metrics powered by PLOS ALM


Cited-By (articles included in Crossref)

This journal is a Crossref Cited-by Linking member. This list shows the references that citing the article automatically, if there are. For more information about the system please visit Crossref site

1. A Bayesian model to estimate land surface phenology parameters with harmonized Landsat 8 and Sentinel-2 images
Chad Babcock, Andrew O. Finley, Nathaniel Looker
Remote Sensing of Environment  vol: 261  first page: 112471  year: 2021  
doi: 10.1016/j.rse.2021.112471


This journal is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Universitat Politècnica de València

Official Journal of the Spanish Association of Remote Sensing

e-ISSN: 1988-8740    ISSN: 1133-0953