Vegetation phenology from satellite imagery: the case of the Iberian Peninsula and Balearic Islands (2001-2017)




spring, autumn, seasonality, MODIS, time series


Phenological dynamics of vegetation is considered as an important biological indicator for understanding the functioning of terrestrial ecosystems. Land surface phenology (LSP), the study of vegetation phenology from time series of vegetation indices (IV), has provided a comprehensive overview of ecosystem dynamics. Iberian Peninsula is one of the regions with the greatest diversity of ecosystems in European continent. It is therefore an excellent study area for monitoring phenological dynamics of vegetation. The aim of this study is to analyse the spatial variability of the phenology of the vegetation of the Iberian Peninsula and Balearic Islands for the period 2001-2017. NDVI (Normalized Difference Vegetation Index) time series were generated from the surface reflectance product MOD09Q1 at a spatial resolution of 250 meters and with a composite period of 8 days. Atmospheric disturbances and noise were reduced using a Savitzky-Golay smoothing filter. Different phenological metrics or phenometrics were extracted using a threshold-based method. Results showed the existence of a different behaviour between spring and autumn phenophases in the Atlantic and Mediterranean biogeographic regions. The Mediterranean mountainous areas showed a similar phenological behaviour to the Atlantic vegetation. Biogeographic regions showed an internal variability, which may be derived from the different behaviour of land covers (e.g., natural vegetation vs. crops).


Download data is not yet available.

Author Biographies

J.A. Caparros-Santiago, Universidad de Sevilla

Departamento de Geografía Física y Análisis Geográfico Regional

V.F. Rodríguez-Galiano, Universidad de Sevilla

Departamento de Geografía Física y Análisis Geográfico Regional


Adole, T., Dash, J., Atkinson, P.M., 2016. A systematic review of vegetation phenology in Africa. Ecological Informatics, 34, 117-128.

Adole, T., Dash, J., Rodriguez-Galiano, V., Atkinson, P.M., 2019. Photoperiod controls vegetation phenology across Africa. Communications Biology, 2(1), 391.

Ahas, R., Aasa, R., Menzel, A., Fedotova, V.G., Scheifinger, H., 2002. Changes in European spring phenology. International Journal of Climatology, 22(14), 1727-1738.

Aragones, D., Rodriguez-Galiano, V.F., Caparros-Santiago, J.A., Navarro-Cerrillo, R.M., 2019. Could land surface phenology be used to discriminate Mediterranean pine species? International Journal of Applied Earth Observation and Geoinformation, 78, 281-294.

Asam, S., Callegari, M., Matiu, M., Fiore, G., De Gregorio, L., Jacob, A., Menzel, A., Zebisch, M., Notarnicola, C., 2018. Relationship between spatiotemporal variations of climate, snow cover and plant phenology over the Alps-An Earth observation-based analysis. Remote Sensing, 10(11).

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(1), 257-284.

Catry, F.X., Moreira, F., Deus, E., Silva, J.S., Águas, A., 2015. Assessing the extent and the environmental drivers of Eucalyptus globulus wildling establishment in Portugal: results from a countrywide survey. Biological Invasions, 17(11), 3163-3181.

Chen, X., Wang, D., Chen, J., Wang, C., Shen, M., 2018. The mixed pixel effect in land surface phenology: A simulation study. Remote Sensing of Environment, 211, 338-344.

Chen, X., Yang, Y., 2020. Observed earlier start of the growing season from middle to high latitudes across the Northern Hemisphere snow-covered landmass for the period 2001-2014. Environmental Research Letters, 15(3).

de Beurs, K.M., Henebry, G.M., 2005. Land surface phenology and temperature variation in the International Geosphere-Biosphere Program high-latitude transects. Global Change Biology, 11(5), 779-790.

EEA, 2017. Climate Change, Impacts and Vulnerability in Europe 2016: An indicator-based report. Copenhagen: Environmental Science and Engineering - European Environment Agency (EEA).

Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., 2007. The shuttle radar topography mission. Reviews of Geophysics, 45(2), RG2004.

Friedl, M.A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., Huang, X., 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168-182.

Garonna, I., de Jong, R., Schaepman, M.E., 2016. Variability and evolution of global land surface phenology over the past three decades (1982-2012). Global Change Biology, 22(4), 1456-1468.

Gómez-Limón, J.A., Picazo-Tadeo, A.J., 2012. Irrigated agriculture in Spain: Diagnosis and Prescriptions for Improved governance. International Journal of Water Resources Development, 28(1), 57-72.

Gonsamo, A., Chen, J.M., David, T.P., Kurz, W.A., Wu, C., 2012. Land surface phenology from optical satellite measurement and CO2 eddy covariance technique. Journal of Geophysical Research: Biogeosciences, 117(3).

Helman, D., 2018. Land surface phenology: What do we really 'see' from space? Science of the Total Environment, 618, 665-673.

Hird, J.N., McDermid, G.J., 2009. Noise reduction of NDVI time series: An empirical comparison of selected techniques. Remote Sensing of Environment, 113(1), 248-258.

Holben, B.N., 1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7(11), 1417-1434.

Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213.

Ivits, E., Cherlet, M., Horion, S., Fensholt, R., 2013. Global biogeographical pattern of ecosystem functional types derived from earth observation data. Remote Sensing, 5(7), 3305-3330.

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(8), 1824-1832.

Jönsson, P., Eklundh, L., 2004. TIMESAT - A program for analyzing time-series of satellite sensor data. Computers and Geosciences, 30(8), 833-845.

Julien, Y., Sobrino, J.A., 2009. Global land surface phenology trends from GIMMS database. International Journal of Remote Sensing, 30(13), 3495-3513.

Keenan, T.F., Gray, J., Friedl, M.A., Toomey, M., Bohrer, G., Hollinger, D.Y., Munger, J.W., O'Keefe, J., Schmid, H.P., Wing, I.S., Yang, B., Richardson, A.D., 2014. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nature Climate Change, 4(7), 598-604.

Klosterman, S.T., Hufkens, K., Gray, J.M., Melaas, E., Sonnentag, O., Lavine, I., Mitchell, L., Norman, R., Friedl, M.A., Richardson, A.D., 2014. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences, 11(16), 4305-4320.

Menzel, A., 2000. Trends in phenological phases in Europe between 1951 and 1996. International Journal of Biometeorology, 44(2), 76-81.

Menzel, A., 2002. Phenology: Its importance to the global change community: An editorial comment. Climatic Change, 54(4), 379-385.

Pastor-Guzman, J., Dash, J., Atkinson, P.M., 2018. Remote sensing of mangrove forest phenology and its environmental drivers. Remote Sensing of Environment, 205, 71-84.

Peñuelas, J., Filella, I., 2001. Phenology: Responses to a warming world. Science, 294(5543), 793-795.

Peñuelas, J., Filella, I., Zhang, X., Llorens, L., Ogaya, R., Lloret, F., Comas, P., Estiarte, M., Terradas, J., 2004. Complex spatiotemporal phenological shifts as a response to rainfall changes. New Phytologist, 161(3), 837-846.

Piao, S., Liu, Q., Chen, A., Janssens, I.A., Fu, Y., Dai, J., Liu, L., Lian, X., Shen, M., Zhu, X., 2019. Plant phenology and global climate change: Current progresses and challenges. Global Change Biology, 25(6), 1922-1940.

Qader, S.H., Atkinson, P.M., Dash, J., 2015. Spatiotemporal variation in the terrestrial vegetation phenology of Iraq and its relation with elevation. International Journal of Applied Earth Observation and Geoinformation, 41, 107-117.

Ramos, A., Pereira, M.J., Soares, A., Rosário, L.D., Matos, P., Nunes, A., Branquinho, C., Pinho, P., 2015. Seasonal patterns of Mediterranean evergreen woodlands (Montado) are explained by long-term precipitation. Agricultural and Forest Meteorology, 202, 44-50.

Richardson, A.D., Keenan, T.F., Migliavacca, M., Ryu, Y., Sonnentag, O., Toomey, M., 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology, 169, 156-173.

Rivas-Martínez, S., 1987. Memoria del mapa de series de vegetación de España. Madrid: Ministerio de Agricultura, Pesca y Alimentación -Instituto para la Conservación de la Naturaleza (ICONA).

Rodrigues, A., Marcal, A.R.S., Cunha, M., 2013. Monitoring vegetation dynamics inferred by satellite data using the pheno sat tool. IEEE Transactions on Geoscience and Remote Sensing, 51(4), 2096-2104.

Rodriguez-Galiano, V.F., Dash, J., Atkinson, P.M., 2015a. Characterising the land surface phenology of Europe using decadal MERIS data. Remote Sensing, 7(7), 9390-9409.

Rodriguez-Galiano, V.F., Dash, J., Atkinson, P.M., 2015b. Intercomparison of satellite sensor land surface phenology and ground phenology in Europe. Geophysical Research Letters, 42(7), 2253-2260.

Rodriguez-Galiano, V.F., Sanchez-Castillo, M., Dash, J., Atkinson, P.M., Ojeda-Zujar, J., 2016. Modelling interannual variation in the spring and autumn land surface phenology of the European forest. Biogeosciences, 13(11), 3305-3317.

Savitzky, A., Golay, M.J.E., 1964. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36(8), 1627-1639.

Schaber, J., Badeck, F.W., 2005. Plant phenology in Germany over the 20th century. Regional Environmental Change, 5(1), 37-46.

Sobrino, J.A., Julien, Y., Soria, G., 2013. Phenology estimation from meteosat second generation data. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), 1653-1659.

Tong, X., Tian, F., Brandt, M., Liu, Y., Zhang, W., Fensholt, R., 2019. Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992–2012. Remote Sensing of Environment, 232.

Valderrama-Landeros, L.H., España-Boquera, M.L., Baret, F., 2016. Deforestation in Michoacan, Mexico, from CYCLOPES-LAI Time Series (2000-2006). Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(12), 5398-5405.

Verger, A., Filella, I., Baret, F., Peñuelas, J., 2016. Caracterización de la fenología de la vegetación a escala global mediante series temporales SPOT VEGETATION. Revista de Teledeteccion, 2016(47), 1-11.

Vermote, E., 2015. MOD09A1 MODIS/terra surface reflectance 8-day L3 global 500m SIN grid V006 [Dataset]. NASA EOSDIS Land Processes DAAC. Accessed 2018-02-25 from

Vrieling, A., De Leeuw, J., Said, M.Y., 2013. Length of growing period over africa: Variability and trends from 30 years of NDVI time series. Remote Sensing, 5(2), 982-1000.

Vrieling, A., Meroni, M., Darvishzadeh, R., Skidmore, A.K., Wang, T., Zurita-Milla, R., Oosterbeek, K., O'Connor, B., Paganini, M., 2018. Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island. Remote Sensing of Environment, 215, 517-529.

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., 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.

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.





Research articles