Analysis of plant phenology dynamics in Spain from 1983 to 2020 using satellite imagery

Authors

  • Maria Adell Michavila Universidad de Zaragoza
  • Sergio M. Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE–CSIC) https://orcid.org/0000-0003-2892-518X
  • Raquel Universidad de Zaragoza https://orcid.org/0000-0001-7403-1764
  • ZangZang Department of Physical Geography and Ecosystem Science, Lund University
  • Lars Department of Physical Geography and Ecosystem Science, Lund University

DOI:

https://doi.org/10.18172/cig.5739

Keywords:

fenología vegetal, teledetección, cambio global, NOAA-AVHRR

Abstract

This study spatially analyzes plant phenology and its variations over time in mainland Spain and the Balearic Islands. To conduct the analysis, a nearly 40-year span time series (1983-2020) was generated by merging NDVI vegetation index values from satellite images sourced from NOAA-AVHRR and MODIS sensors. The phenological variables were calculated using TIMESAT 3.3, which extracted 13 phenometrics whose trends were evaluated using the Theil-Sen model, and their significance was assessed with the Mann-Kendall test. The results reveal regional differences between Eurosiberian Spain and the Mediterranean region regarding the start and end phases of the season. On average, the Eurosiberian zones have experienced delays in their season start and end dates, by approximately 0.35 and 0.22 days per year over the study period, respectively, while the Mediterranean region has seen an advancement in leaf-out and senescence dates by about 0.07 and 0.05 days per year. A greening trend across the entire study area and significant contrasts among land covers have also been observed, opening avenues for future studies to delve deeper into these behavioral differences and their interactions with changes in climate and land management.

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References

Ahas, R., Aasa, A., Menzel, A., Fedotova, V. G., & Scheifinger, H. 2002. Changes in European spring phenology. International Journal of Climatology, 22(14), 1727-1738. https://doi.org/10.1002/joc.818

Alcaraz Segura, D. 2006. Caracterización del funcionamiento de los ecosistemas ibéricos mediante teledetección. Ecosistemas, 15, 113–117.

Alcaraz-Segura, D., Cabello, J., & Paruelo, J. 2009. Baseline characterization of major Iberian vegetation types based on the NDVI dynamics. Plant Ecology, 202, 13–29. https://doi.org/10.1007/s11258-008-9555-2

Alcaraz-Segura, D., Liras, E., Tabik, S., Paruelo, J., & Cabello, J. 2010. Evaluating the Consistency of the 1982–1999 NDVI Trends in the Iberian Peninsula across Four Time-series Derived from the AVHRR Sensor: LTDR, GIMMS, FASIR, and PAL-II. Sensors, 10(2), 1291-1314. https://doi.org/10.3390/s100201291

Amorós-López, J., Gómez-Chova, L., Alonso, L., Guanter, L., Zurita-Milla, R., Moreno, J., & Camps-Valls, G. 2013. Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring. International Journal of Applied Earth Observation and Geoinformation, 23, 132-141. https://doi.org/10.1016/j.jag.2012.12.004

Badeck, F., Bondeau, A., Böttcher, K., Doktor, D., Lucht, W., Schaber, J., & Sitch, S. 2004. Responses of spring phenology to climate change. New Phytologist, 162(2), 295-309. https://doi.org/10.1111/j.1469-8137.2004.01059.x

Batllori, E., & Gutiérrez, E. 2008. Regional tree line dynamics in response to global change in the Pyrenees. Journal of Ecology, 96(6), 1275-1288. https://doi.org/10.1111/j.1365-2745.2008.01429.x

Bertin, R. I. 2008. Plant Phenology And Distribution In Relation To Recent Climate Change. The Journal of the Torrey Botanical Society, 135(1), 126-146. https://doi.org/10.3159/07-RP-035R.1

Caparros-Santiago, J. A., & Rodríguez-Galiano, V. F. 2020. Estimación de la fenología de la vegetación a partir de imágenes de satélite: El caso de la península ibérica e islas Baleares (2001-2017). Revista de Teledetección, 57, 25. https://doi.org/10.4995/raet.2020.13632

Chmielewski, F.-M., Müller, A., & Bruns, E. 2004. Climate changes and trends in phenology of fruit trees and field crops in Germany, 1961–2000. Agricultural and Forest Meteorology, 121(1-2), 69-78. https://doi.org/10.1016/S0168-1923(03)00161-8

Cleland, E., Chuine, I., Menzel, A., Mooney, H., & Schwartz, M. 2007. Shifting plant phenology in response to global change. Trends in Ecology & Evolution, 22(7), 357-365. https://doi.org/10.1016/j.tree.2007.04.003

Cleland, E. E., Allen, J. M., Crimmins, T. M., Dunne, J. A., Pau, S., Travers, S. E., Zavaleta, E. S., & Wolkovich, E. M. 2012. Phenological tracking enables positive species responses to climate change. Ecology, 93(8), 1765-1771. https://doi.org/10.1890/11-1912.1

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. https://doi.org/10.1111/j.1365-2486.2005.00949.x

del Río, S., Herrero, L., Pinto-Gomes, C., & Penas, A. 2011. Spatial analysis of mean temperature trends in Spain over the period 1961–2006. Global and Planetary Change, 78(1-2), 65-75. https://doi.org/10.1016/j.gloplacha.2011.05.012

del Río, S., Cano-Ortiz, A., Herrero, L., & Penas, A. 2012. Recent trends in mean maximum and minimum air temperatures over Spain (1961–2006). Theoretical and Applied Climatology, 109(3-4), 605-626. https://doi.org/10.1007/s00704-012-0593-2

Díaz-Delgado, R., Lloret, F., Pons, X., & Terradas, J. 2002. Satellite evidence of decreasing resilience in mediterranean plant communities after recurrent wildfires. Ecology, 83(8), 2293-2303. https://doi.org/10.1890/0012-9658(2002)083[2293:SEODRI]2.0.CO;2

Eastman, J. R., Sangermano, F., Machado, E. A., Rogan, J., & Anyamba, A. 2013. Global trends in seasonality of Normalized Difference Vegetation Index (NDVI), 1982-2011. Remote Sensing, 5(10), 4799–4818. https://doi.org/10.3390/rs5104799

Eklundh, L., & Jönsson, P. 2015. TIMESAT: A Software Package for Time-Series Processing and Assessment of Vegetation Dynamics. En C. Kuenzer, S. Dech, & W. Wagner (Eds.), Remote Sensing Time Series (Vol. 22, pp. 141-158). Springer International Publishing. https://doi.org/10.1007/978-3-319-15967-6_7

Eklundh, L., & Jönsson, P. 2017. TIMESAT 3.3 with seasonal trend decomposition and parallel processing Software Manual. Sweden: Lund and Malmo University.

Fernandes, R., & G. Leblanc, S. 2005. Parametric (modified least squares) and non-parametric (Theil–Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors. Remote Sensing of Environment, 95(3), 303-316. https://doi.org/10.1016/j.rse.2005.01.005

Fisher, J., Mustard, J., & Vadeboncoeur, M. 2006. Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Remote Sensing of Environment, 100(2), 265-279. https://doi.org/10.1016/j.rse.2005.10.022

Fu, Y. H., Piao, S., Op de Beeck, M., Cong, N., Zhao, H., Zhang, Y., Menzel, A., & Janssens, I. A. 2014. Recent spring phenology shifts in western Central Europe based on multiscale observations: Multiscale observation of spring phenology. Global Ecology and Biogeography, 23(11), 1255-1263. https://doi.org/10.1111/geb.12210

García-Mozo, H., Mestre, A., & Galán, C. 2010. Phenological trends in southern Spain: A response to climate change. Agricultural and Forest Meteorology, 150(4), 575-580. https://doi.org/10.1016/j.agrformet.2010.01.023

Ge, Q., Wang, H., Rutishauser, T., & Dai, J. 2015. Phenological response to climate change in China: A meta‐analysis. Global Change Biology, 21(1), 265-274. https://doi.org/10.1111/gcb.12648

Gill, A. L., Gallinat, A. S., Sanders-DeMott, R., Rigden, A. J., Short Gianotti, D. J., Mantooth, J. A., & Templer, P. H. 2015. Changes in autumn senescence in northern hemisphere deciduous trees: A meta-analysis of autumn phenology studies. Annals of Botany, 116(6), 875-888. https://doi.org/10.1093/aob/mcv055

Gordo, O., & Sanz, J. J. 2009. Long-term temporal changes of plant phenology in the Western Mediterranean. Global Change Biology, 15(8), 1930-1948. https://doi.org/10.1111/j.1365-2486.2009.01851.x

Gutiérrez-Hernández, O. 2020. Fenología de los ecosistemas de alta montaña en Andalucía: Análisis de la tendencia estacional del SAVI (2000-2019). Pirineos, 175, e055. https://doi.org/https://doi.org/10.3989/piri- neos.2020.175005

Gutiérrez Hernández, O. 2022. Tendencias recientes del NDVI en Andalucía: los límites del reverdecimiento. Boletín de La Asociación de Geógrafos Españoles, 94. https://doi.org/10.21138/bage.3246

Helman, D. 2018. Land surface phenology: What do we really ‘see’ from space? Science of the Total Environment, 618, 665–673. https://doi.org/10.1016/j.scitotenv.2017.07.237

Jato, V., Rodríguez-Rajo, F., Méndez, J. et al. 2002. Phenological behaviour of Quercus in Ourense (NW Spain) and its relationship with the atmospheric pollen season. International Journal of Biometeorology, 46(4), 176-184. https://doi.org/10.1007/s00484-002-0132-4

Jeong, S.-J., Ho, C.-H., Gim, H.-J., & Brown, M. E. 2011. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982-2008: PHENOLOGY SHIFTS AT START VS. END OF GROWING SEASON. Global Change Biology, 17(7), 2385-2399. https://doi.org/10.1111/j.1365-2486.2011.02397.x

Jeong, S.-J., Schimel, D., Frankenberg, C., Drewry, D. T., Fisher, J. B., Verma, M., Berry, J. A., Lee, J.-E., & Joiner, J. 2017. Application of satellite solar-induced chlorophyll fluorescence to understanding large-scale variations in vegetation phenology and function over northern high latitude forests. Remote Sensing of Environment, 190, 178-187. https://doi.org/10.1016/j.rse.2016.11.021

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. https://doi.org/10.1109/TGRS.2002.802519

Jönsson, P., & Eklundh, L. 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Computers & Geosciences, 30(8), 833-845. https://doi.org/10.1016/j.cageo.2004.05.006

Karkauskaite, P., Tagesson, T., & Fensholt, R. 2017. Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone. Remote Sensing, 9(5), 485. https://doi.org/10.3390/rs9050485

Kendall, M. G. 1948. Rank correlation methods.

Kharouba, H. M., Ehrlén, J., Gelman, A., Bolmgren, K., Allen, J. M., Travers, S. E., & Wolkovich, E. M. 2018. Global shifts in the phenological synchrony of species interactions over recent decades. Proceedings of the National Academy of Sciences, 115(20), 5211-5216. https://doi.org/10.1073/pnas.1714511115

Khorchani, M., Vicente-Serrano, S. M., Azorin-Molina, C., Garcia, M., Martin-Hernandez, N., Peña-Gallardo, M., El Kenawy, A., & Domínguez-Castro, F. 2018. Trends in LST over the peninsular Spain as derived from the AVHRR imagery data. Global and Planetary Change, 166, 75-93. https://doi.org/10.1016/j.gloplacha.2018.04.006

Kim, S.-R., Prasad, A. K., El-Askary, H., Lee, W.-K., Kwak, D.-A., Lee, S.-H., & Kafatos, M. 2014. Application of the Savitzky-Golay Filter to Land Cover Classification Using Temporal MODIS Vegetation Indices. Photogrammetric Engineering & Remote Sensing, 80(7), 675-685. https://doi.org/10.14358/PERS.80.7.675

Kudo, G., & Ida, T. Y. 2013. Early onset of spring increases the phenological mismatch between plants and pollinators. Ecology, 94(10), 2311-2320. https://doi.org/10.1890/12-2003.1

Lieth, H. 1974. Purposes of a Phenology Book. En H. Lieth (Ed.), Phenology and Seasonality Modeling (Vol. 8, pp. 3-19). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-51863-8_1

Lim, P. O., Kim, H. J., & Gil Nam, H. 2007. Leaf Senescence. Annual Review of Plant Biology, 58(1), 115-136. https://doi.org/10.1146/annurev.arplant.57.032905.105316

Liu, Q., Fu, Y. H., Zeng, Z., Huang, M., Li, X., & Piao, S. 2016. Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. Global Change Biology, 22(2), 644-655. https://doi.org/10.1111/gcb.13081

Lunetta, R. S., Knight, J. F., Ediriwickrema, J., Lyon, J. G., & Worthy, L. D. 2006. Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sensing of Environment, 105(2), 142-154. https://doi.org/10.1016/j.rse.2006.06.018

Mann, H. B. 1945. Nonparametric Tests Against Trend. Econometrica, 13(3), 245. https://doi.org/10.2307/1907187

MAPA. 1992. Mapa de Cultivos y Aprovechamientos 1980-1990. Ministerio de Agricultura Pesca y Alimentación. Gobierno de España. https://www.mapa.gob.es/es/cartografia-y-sig/publicaciones/agricultura/mac_1980 _1990.aspx

Marraccini, E., Debolini, M., Moulery, M., Abrantes, P., Bouchier, A., Chéry, J.-P., Sanz Sanz, E., Sabbatini, T., & Napoleone, C. 2015. Common features and different trajectories of land cover changes in six Western Mediterranean urban regions. Applied Geography, 62, 347-356. https://doi.org/10.1016/j.apgeog.2015.05.004

Martínez, B., & Gilabert, M. A. 2009. Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sensing of Environment, 113(9), 1823-1842. https://doi.org/10.1016/j.rse.2009.04.016

Matesanz, A. Escudero, F. Valladares. 2009. Impact of three global change drivers on a Mediterranean shrub. Ecology, 90 (2009), pp. 2609-2621

Menzel, A. 2002. Phenology: its importance to the global change community. Climatic Change, 54(4), 379-385. https://doi.org/10.1023/A:1016125215496

Menzel, A., Sparks, T. H., Estrella, N., Koch, E., Aasa, A., Ahas, R., Alm-Kübler, K., Bissolli, P., Braslavská, O., Briede, A., Chmielewski, F. M., Crepinsek, Z., Curnel, Y., Dahl, Å., Defila, C., Donnelly, A., Filella, Y., Jatczak, K., Måge, F., … Zust, A. 2006. European phenological response to climate change matches the warming pattern: EUROPEAN PHENOLOGICAL RESPONSE TO CLIMATE CHANGE. Global Change Biology, 12(10), 1969-1976. https://doi.org/10.1111/j.1365-2486.2006.01193.x

Miao, L., Müller, D., Cui, X., & Ma, M. 2017. Changes in vegetation phenology on the Mongolian Plateau and their climatic determinants. PLOS ONE, 12(12), e0190313. https://doi.org/10.1371/journal.pone.0190313

MITECO. s.f. Fenología y cambio climático en la Red Española de Reservas de Biosfera. Recuperado de https://www.miteco.gob.es/es/ceneam/grupos-de-trabajo-y-seminarios/red-espanola-reservas-biosfera/fenologia-cambio-climatico-reservas-biosfera.aspx

Motohka, T., Nasahara, K. N., Oguma, H., & Tsuchida, S. 2010. Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology. Remote Sensing, 2(10), 2369-2387. https://doi.org/10.3390/rs2102369

Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C., Tucker, C. J., Myneni, R. B., & Running, S. W. 2003. Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999. Science, 300(5625), 1560-1563. https://doi.org/10.1126/science.1082750

Novillo, C., Arrogante-Funes, P., & Romero-Calcerrada, R. 2019. Recent NDVI Trends in Mainland Spain: Land-Cover and Phytoclimatic-Type Implications. ISPRS International Journal of Geo-Information, 8(1), 43. https://doi.org/10.3390/ijgi8010043

Oteros, J., García-Mozo, H., Botey, R., Mestre, A., & Galán, C. 2015. Variations in cereal crop phenology in Spain over the last twenty-six years (1986–2012). Climatic Change, 130(4), 545-558. https://doi.org/10.1007/s10584-015-1363-9

Palazón, A., Aragonés, L., & López, I. 2016. Evaluation of coastal management: Study case in the province of Alicante, Spain. Science of The Total Environment, 572, 1184-1194. https://doi.org/10.1016/j.scitotenv.2016.08.032

Pastor, F., Valiente, J. A., & Khodayar, S. 2020. A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature. Remote Sensing, 12(17), 2687. https://doi.org/10.3390/rs12172687

Peng, H., Wang, S., & Wang, X. 2008. Consistency and asymptotic distribution of the Theil–Sen estimator. Journal of Statistical Planning and Inference, 138(6), 1836-1850. https://doi.org/10.1016/j.jspi.2007.06.036

Peñuelas, J., Filella, I., & Comas, P. (2002). Changed plant and animal life cycles from 1952 to 2000 in the Mediterranean region. Global Change Biology, 8(6), 531-544.S.

Peñuelas, J., Filella, I., & Comas, P. 2002. Changed plant and animal life cycles from 1952 to 2000 in the Mediterranean region: PHENOLOGICAL EFFECTS OF CLIMATE WARMING. Global Change Biology, 8(6), 531-544. https://doi.org/10.1046/j.1365-2486.2002.00489.x

Piao, S., Fang, J., Zhou, L., Ciais, P., & Zhu, B. 2006. Variations in satellite-derived phenology in China’s temperate vegetation: SATELLITE-DERIVED PHENOLOGY IN CHINA. Global Change Biology, 12(4), 672-685. https://doi.org/10.1111/j.1365-2486.2006.01123.x

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. https://doi.org/10.1111/gcb.14619

Prieto, F., RUIZ, P., & Martínez, J. (2008). Prospectiva 2030 en los cambios de ocupación del suelo en España y sus impactos en el ciclo hidrológico. In VI Congreso Ibérico sobre Gestión y Planificación del Agua. Fundación Nueva Cultura del Agua (pp. 4-7).

Prislan, P., Gričar, J., Čufar, K., de Luis, M., Merela, M., & Rossi, S. 2019. Growing season and radial growth predicted for Fagus sylvatica under climate change. Climatic Change, 153(1-2), 181-197. https://doi.org/10.1007/s10584-019-02374-0

R Core Team 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/

Rathcke, B., & Lacey, E. P. 1985. Phenological Patterns of Terrestrial Plants. Annual Review of Ecology and Systematics, 16(1), 179-214. https://doi.org/10.1146/annurev.es.16.110185.001143

Reed, B. C. 2006. Trend Analysis of Time-Series Phenology of North America Derived from Satellite Data. GIScience & Remote Sensing, 43(1), 24-38. https://doi.org/10.2747/1548-1603.43.1.24

Reed, B. C., Schwartz, M. D., & Xiao, X. 2009. Remote Sensing Phenology. En A. Noormets (Ed.), Phenology of Ecosystem Processes (pp. 231-246). Springer New York. https://doi.org/10.1007/978-1-4419-0026-5_10

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. https://doi.org/10.1016/j.agrformet.2012.09.012

Rodriguez-Galiano, V. F., Dash, J., & Atkinson, P. M. 2015. Intercomparison of satellite sensor land surface phenology and ground phenology in Europe: Inter-annual comparison and modelling. Geophysical Research Letters, 42(7), 2253-2260. https://doi.org/10.1002/2015GL063586

Rossi, S., Morin, H., Deslauriers, A., & Plourde, P.-Y. 2011. Predicting xylem phenology in black spruce under climate warming: XYLEM PHENOLOGY UNDER CLIMATE WARMING. Global Change Biology, 17(1), 614-625. https://doi.org/10.1111/j.1365-2486.2010.02191.x

Rubio-Cuadrado, Á., Camarero, J. J., Rodríguez-Calcerrada, J., Perea, R., Gómez, C., Montes, F., & Gil, L. 2021. Impact of successive spring frosts on leaf phenology and radial growth in three deciduous tree species with contrasting climate requirements in central Spain. Tree Physiology, 41(12), 2279-2292. https://doi.org/10.1093/treephys/tpab076

Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., & Ohno, H. 2005. A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, 96(3-4), 366-374. https://doi.org/10.1016/j.rse.2005.03.008

Schwartz, M. D. 2013. Phenology: An Integrative Environmental Science. In M. D. Schwartz (Ed.), Phenology: An Integrative Environmental Science. Springer Netherlands. https://doi.org/10.1007/978-94-007-6925-0

Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F., & Stahl, K. 2015. Candidate Distributions for Climatological Drought Indices (SPI and SPEI). International Journal of Climatology, 35(13), 4027-4040. https://doi.org/10.1002/joc.4267

Stellmes, M., Röder, A., Udelhoven, T., & Hill, J. 2013. Mapping syndromes of land change in Spain with remote sensing time series, demographic and climatic data. Land Use Policy, 30(1), 685-702. https://doi.org/10.1016/j.landusepol.2012.05.007

Stöckli, R., & Vidale, P. L. 2004. European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset. International Journal of Remote Sensing, 25(17), 3303-3330. https://doi.org/10.1080/01431160310001618149

Van Oort, P. A. J., Timmermans, B. G. H., & van Swaaij, A. C. P. M. 2012. Why farmers’ sowing dates hardly change when temperature rises. European Journal of Agronomy, 40, 102-111. https://doi.org/10.1016/j.eja.2012.02.005

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. https://doi.org/10.1016/j.rse.2016.02.057

Vicente-Serrano, S. M., López-Moreno, J. I., Beguería, S., Lorenzo-Lacruz, J., Azorin-Molina, C., & Morán-Tejeda, E. 2012. Accurate Computation of a Streamflow Drought Index. Journal of Hydrologic Engineering, 17(2), 318-332. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000433

Vicente-Serrano, S. M., Rodríguez-Camino, E., Domínguez-Castro, F., El Kenawy, A., & Azorín-Molina, C. 2017. An updated review on recent trends in observational surface atmospheric variables and their extremes over Spain. Cuadernos de Investigación Geográfica, 43(1), 209-232. https://doi.org/10.18172/cig.3134

Vicente-Serrano, S. M., Martín-Hernández, N., Reig, F., Azorin-Molina, C., Zabalza, J., Beguería, S., Domínguez-Castro, F., El Kenawy, A., Peña-Gallardo, M., Noguera, I., & García, M. 2020. Vegetation greening in Spain detected from long term data (1981–2015). International Journal of Remote Sensing, 41(5), 1709-1740. https://doi.org/10.1080/01431161.2019.1674460

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. https://doi.org/10.1016/j.rse.2018.03.014

White, K., Pontius, J., & Schaberg, P. 2014. Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty. Remote Sensing of Environment, 148, 97-107. https://doi.org/10.1016/j.rse.2014.03.017

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., … 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. https://doi.org/10.1111/j.1365-2486.2009.01910.x

Willmott, C. J. 1981. ON THE VALIDATION OF MODELS. Physical Geography, 2(2), 184-194. https://doi.org/10.1080/02723646.1981.10642213

Wolfe, D. W., Schwartz, M. D., Lakso, A. N., Otsuki, Y., Pool, R. M., & Shaulis, N. J. 2005. Climate change and shifts in spring phenology of three horticultural woody perennials in northeastern USA. International Journal of Biometeorology, 49(5), 303-309. https://doi.org/10.1007/s00484-004-0248-9

Yang, L. H., & Rudolf, V. H. W. 2010. Phenology, ontogeny and the effects of climate change on the timing of species interactions. Ecology Letters, 13(1), 1-10. https://doi.org/10.1111/j.1461-0248.2009.01402.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. https://doi.org/10.1016/S0034-4257(02)00135-9

Zhang, X., Wang, J., Gao, F., Liu, Y., Schaaf, C., Friedl, M., Yu, Y., Jayavelu, S., Gray, J., Liu, L., Yan, D., & Henebry, G. M. 2017. Exploration of scaling effects on coarse resolution land surface phenology. Remote Sensing of Environment, 190, 318-330. https://doi.org/10.1016/j.rse.2017.01.001

Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., & Myneni, R. B. 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research: Atmospheres, 106(D17), 20069-20083. https://doi.org/10.1029/2000JD000115

Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., Samanta, A., Piao, S., Nemani, R., & Myneni, R. 2013. Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. Remote Sensing, 5(2), 927-948. https://doi.org/10.3390/rs5020927

Published

11-03-2024

How to Cite

1.
Adell Michavila M, Sergio M., Raquel, ZangZang, Lars. Analysis of plant phenology dynamics in Spain from 1983 to 2020 using satellite imagery. CIG [Internet]. 2024 Mar. 11 [cited 2024 May 20];. Available from: https://publicaciones.unirioja.es/ojs/index.php/cig/article/view/5739

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