Trends and land surface phenological responses to climate variability in the Argentina Pampas

B. Lara, M. Gandini, P. Gantes, S.D. Matteucci


Understanding the interaction between land surface and atmosphere processes is fundamental for predicting the effects of future climate change on ecosystem functioning and carbon dynamics. The objectives of this work were to analyze the trends in land surface phenology (LSP) metrics from remote sensing data, and to reveal their relationship with precipitation and ENSO phenomenon in the Argentina Pampas. Using a time series of MODIS Normalized Difference Vegetation Index (NDVI) data from 2000 to 2014, the start of the growing season (SOS), the annual integral of NDVI (i-NDVI, linear estimator of annual productivity), the timing of the annual maximum NDVI (t-MAX) and the annual relative range of NDVI (RREL, estimator of seasonality) were obtained for the Argentina Pampas. Then, spatial and temporal relationships with the Multivariate ENSO Index (MEI) and precipitation were analyzed. Results showed a negative trend in annual productivity over a 53.6% of the study area associated to natural and semi-natural grassland under cattle grazing, whereas a 40.3% of Argentina Pampas showed a significant positive trend in seasonality of carbon gains. The study also reveals that climate variability has a significant impact on land surface phenology in Argentina Pampas, although the impact is heterogeneous. SOS and t-MAX showed a significant negative correlation with the precipitation indicating an earlier occurrence. 23.6% and 28.4% of the study area showed a positive correlation of the annual productivity with MEI and precipitation, respectively, associated to rangelands (in the first case) and to both rangeland and croplands, in the second case. Climate variability did not explain the seasonal variability of phenology. The relationships found between LSP metrics and climate variability could be important for implementation of strategies for natural resource management.


climate variability; land surface phenology; MODIS; ENSO; carbon gains

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Alcaraz-Segura, D., Cabello, J., Paruelo, J.M., Delibes, M. 2009. Use of descriptors of ecosystem functioning for monitoring a national park network: a remote sensing approach. Environmental Management 43, 38-48.

Alcaraz-Segura, D., Lomba, A., Sousa-Silva, R., Nieto-Lugilde, D., Alves, P., Georges, D., Vicente, J.R., Honrado, J.P. 2017. Potential of satellite-derived ecosystem functional attributes to anticipate species range shifts. International Journal of Applied Earth Observation and Geoinformation 57, 86-92.

Aliaga, V.S., Ferrelli, F., Alberdi-Algañaraz, E.D., Bohn, V.Y., Piccolo, M.C. 2016. Distribución y variabilidad de la precipitación en la región pampeana, Argentina. Cuadernos de Investigación Geográfica 42, 261-280.

Allan, R., Lindesay, J., Parker, D. 1996. El Niño Southern Oscillation and climate variability. CSIRO Publishing, Collingwood, Australia.

Andrade, M.I., Laporta, P., Iezzi, L. 2009. Sequías en el sudoeste bonaerense: Vulnerabilidad e incertidumbre. Geograficando 5, 213-233.

Arrieta, E.M., Cuchietti, A., Cabrol, D., González, A.D. 2018. Greenhouse gas emissions and energy efficiencies for soybeans and maize cultivated in different agronomic zones: a case study of Argentina. Science of the Total Environment 625, 199-208.

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., Eilers, P.H.C. 2011. A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America. Int. J. Digit. Earth 4, 365-386.

Baldi, G., Guerschman, J.P., Paruelo, J.M. 2006. Characterizing fragmentation in temperate South America grasslands. Agric. Ecosyst. Environ. 116, 197-208.

Baldi, G., Nosetto, M.D., Aragón, R., Aversa, F., Paruelo, J.M., Jobbágy, E.G. 2008. Long-term satellite data sets: evaluating their ability to detect ecosystem functional changes in South America. Sensors 8, 5397-5425.

Barnston, A.G., Tippett, M.K. 2013. Predictions of Nino3.4 SST in CFSv1 and CFSv2: a diagnostic comparison. Climate Dynamics 41, 1612-1633.

Bertin, R.I. 2008. Plant phenology and distribution in relation to recent climate change. Journal of the Torrey Botanical Society 135, 126-146.

Blanco, P.D., Colditz, R.R., López Saldaña, G., Hardtke, G.L.A., Llamas, R.M., Mari, N.A., Fischer, A., et al. 2013. A land cover map of Latin America and the Caribbean in the framework of the SERENA project. Remote Sensing of Environment 132, 13-31.

Broich, M., Huete, A., Tulbure, M.G., Ma, X., Xin, Q., Paget, M., Restrepo-Coupe, N., Davies, K., Devadas, R., Held, A. 2014. Land surface phenological response to decadal climate variability across Australia using satellite remote sensing. Biogeosciences 11, 5181-5198.

Cabello, J., Paruelo, J.M. 2008. La teledetección en estudios ecológicos. Ecosistemas 17, 1-3.

Cong, N., Wang, T., Nan, H., Ma, Y., Wang, X., Myneni, R.B., Piao, S. 2013. Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis. Global Change Biology 19, 881-891.

Dai, J., Wang, H., Ge, Q. 2013. The spatial pattern of leaf phenology and its response to climate change in China. International Journal of Biometeorology 58, 521-528.

Dessay, N., Laurent, H., Machado, L.A.T., Shimabukuro, Y.E., Batista, G.T., Diedhiou, A., Ronchail, J. 2004. Comparative study of the 1982-1983 and 1997-1998 El Niño events over different types of vegetation in South America. International Journal of Remote Sensing 25, 4063-4077.

Durbin, J., Watson, G.S. 1951. Testing for serial correlation in least squares regression. II. Bometrika 38, 159-178.

Forján, H., Manso, L. 2012. La secuencia de cultivos. In: H. Forján, L. Manso (Eds.), Rotaciones y secuencias de cultivos en la región mixta cerealera del centro-sur bonaerense: 30 años de experiencias. Ediciones INTA, Tres Arroyos, Argentina, pp. 25-34.

Friedl, M., Henebry, G.M., Reed, B.C., Huete, A., White, M.A., Morisette, J.T., Nemani, R., Zhang, X., Myneni, R. 2006. Land surface phenology, A community white paper requested by NASA. (accessed 13 November 2018).

Gandini, M., Lara, B., Moreno, L., Cañibano, A., Gandini, P. 2019. Trends in fragmentation and connectivity of Paspalum quadrifarium in the Buenos Aires province, Argentina. PeerJ 7, e6450.

Garreaud, R.D., Vuille, M., Compagnucci, R., Marengo, J. 2009. Present-day South American climate. Palaeogeography, Palaeoclimatology, Palaeoecology 281, 180-195.

Glade, F.E., Miranda, M.D., Meza, F.J., van Leeuwen, W.J.D. 2016. Productivity and phenological responses of natural vegetation to present and future inter-annual climate variability across semi-arid river basins in Chile. Environ. Monit. Assess. 188, 676.

Guerschman, J.P., Paruelo, J.M., Burke, I. 2003. Land use impacts on the Normalized Difference Vegetation Index in temperate Argentina. Ecol. Appl. 13, 616-628.[0616:LUIOTN]2.0.CO;2.

Guevara-Ochoa, C., Lara, B., Vives, L., Zimmermann, E., Gandini, M. 2018. A methodology for the characterization of land use using medium-resolution spatial images. Revista Chapingo Series Ciencias Forestales y del Ambiente 24, 207-218.

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

Helsel, D.R., Hirsch, R.M. 2002. Statistical methods in water resources. USGS, Reston Virginia, USA.

Horning, N., Robinson, J., Sterling, E., Turner, W., Spector, S. 2010. Remote Sensing for Ecology and Conservation: a handbook of techniques. Oxford Uni. ed., New York.

Hou, W., Gao, J., Wu, S., Dai, E. 2015. Interannual variations in growing-season NDVI and its correlation with climate variables in the southwestern Karst Region of China. Remote Sensing 7, 11105-11124.

Intergovernmental Panel on Climate Change (IPCC). 2013. Working Group I Contribution of the Fifth Assessment Report. Climate change 2013: the physical science basis.

Jönsson, P., Eklundh, L. 2004. Timesat - a program for analyzing time-series of satellite sensor data. Computer & Geosciences 30, 833-845

Kuenzer, C., Dech, S., Wagner, W. 2015. Remote Sensing Time Series: revealing land surface dynamics. Springer. ed.

Lara, B., Gandini, M. 2014. Quantifying the land cover changes and fragmentation patterns in the Argentina Pampas, in the last 37 years (1974-2011). Geofocus 14, 163-180.

Lara, B., Gandini, M. 2016. Assessing the performance of smoothing functions to estimate land surface phenology on temperate grassland. Int. J. Remote Sens. 37, 1801-1813.

Lara, B., Gandini, M., Gantes, P., Matteucci, S.D. 2018. Regional patterns of ecosystem functional diversity in the Argentina Pampas using MODIS time-series. Ecological Informatics 43, 65-72.

Lieth, H. 1974. Phenology and seasonality modeling. Springer, New York.

Luo, X., Chen, X., Xu, L., Myneni, R., Zhu, Z. 2013. Assessing performance of NDVI and NDVI3g in monitoring leaf unfolding dates of the deciduous broadleaf forest in northern China. Remote Sensing 5, 845-861

Manuel-Navarrete, D., Gallopin, G.C., Blanco, M., Díaz-Zorita, M., Ferraro, D.O., Herzer, H., Laterra, P., Murmis, R., Podesta, G.P., Rabinovich, J., Satorre, E.H., Torres, F., Viglizzo, E.F. 2009. Multi-causal and integrated assessment of sustainability: the case of agriculturization in the Argentine Pampas. Environ. Dev. Sustain. 11, 621-638.

Matteucci, S.D. 2012. Ecorregión Pampa. In: J.H. Morello, S.D. Matteucci, A. Rodríguez, M. Silva (Eds.), Ecorregiones y Complejos Ecosistémicos Argentinos. Buenos Aires, pp. 391-446.

Melendez-Pastor, I., Navarro-Pedreño, J., Koch, M., Gómez, I., Hernández, E.I. 2010. Land-cover phenologies and their relation to climatic variables in an anthropogenically impacted mediterranean coastal area. Remote Sens. 2, 697-716.

Mestelan, S.A., Ramaglio, J.C. 2011. Características, distribución y usos de los suelos del partido de Azul. In: E. Requesens (Ed.), Bases agroambientales para un desarrollo sustentable del partido de Azul. Universidad Nacional del Centro de la provincia de Buenos Aires. Facultad de Agronomía, pp. 61-75.

Monteith, J.L. 1981. Climatic variation and the growth of crops. Quaterly J. R. Meteorol. Soc. 107, 749-774.

Müller, O., Berbery, E., Alcaraz-Segura, D., Ek, M. 2014. Regional model simulations of the 2008 drought in southern South America using a consistent set of land surface properties. J. Clim. 27, 6754-6778.

Nemani, R., Keeling, C., Hashimoto, H., Jolly, W., 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, 1560-1563.

Paruelo, J.M. 2008. La caracterización funcional de los ecosistemas mediante sensores remotos. Ecosistemas 17, 4-22.

Paruelo, J.M., Garbulsky, M.F., Guerschman, J.P., Jobbágy, E.G. 2004. Two decades of Normalized Difference Vegetation Index changes in South America: identifying the imprint of global change. International Journal of Remote Sensing 25, 2793-2806.

Paruelo, J.M., Jobbágy, E.G., Sala, O.E. 2001. Current distribution of Ecosystem Functional Types in temperate South America. Ecosystems 4, 683-698.

Paruelo, J.M., Texeira, M., Staiano, L., Mastrángelo, M., Amdan, L., Gallego, F. 2016. An integrative index of Ecosystem Services provision based on remotely sensed data. Ecological Indicators 71, 145-154.

Podestá, G.P., Messina, C.D, Grondona, M.O., Magrin, G.O. 1999. Associations between grain crop yields in central-eastern Argentina and El Niño-Southern Oscillation. Journal of Applied Meteorology 38, 1488-1498.<1488:ABGCYI>2.0.CO;2.

Reed, B.C., Schwartz, M.D., Xiangming, X. 2009. Remote sensing phenology: status and the way forward. In: A. Noormets (Ed.), Phenology of Ecosystem Processes, Springer, New York, pp. 231-246.

Rojas, M.C., Vázquez, P.M., Verdier, M., Noseda, R. 2011. Componentes del paisaje que favorecen la aparición de carbunco en la Pampa Deprimida (provincia de Buenos Aires, Argentina). Revue Scientifique et Technique de l’ Office International Des Epizzoties 30, 897-909.

Rouse, J.W., Haas, R.H, Schell, J.A., Deering, D.W. 1973. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Prog. Rep. RSC 1978-1, Remote Sensing Center, Texas A & M Univ., College Station, 93p. (NTIS No. E73-106393).

Salio, P., Hobouchian, M.P., García Skabar, Y., Vila, D. 2015. Evaluation of high-resolution satellite precipitation estimates over southern South America using a dense rain gauge network. Atmospheric Research 163, 146-161.

Schmidt, M., Raupach, M., Briggs, P. 2010. Use of lagged time series correlations to relate climate drivers and vegetation response, in: Proceedings of the 15th Australasian Remote Sensing and Photogrammetry Conference. pp. 1-14.

Solano, R., Didan, K., Jacobson, A., Huete, A. 2010. MODIS Vegetation Index User’s Guide (MOD13 Series) C5. Vegetation Index and Phenology Lab, The University of Arizona.

Texeira, M., Oyarzabal, M., Pineiro, G., Baeza, S., Paruelo, J. 2015. Land cover and precipitation controls over long- term trends in carbon gains in the grassland biome of South America. Ecosphere 6, 196.

Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8, 127-150.

van Leeuwen, W.J.D., Hartfield, K., Miranda, M., Meza, F.J. 2013. Trends and ENSO / AAO driven variability in NDVI derived productivity and phenology alongside the Andes mountains. Remote Sensing 5, 1177-1203.

Vazquez, P., Zulaica, L. 2013. Intensificación agrícola y pérdida de servicios ambientales en el partido de Azul (provincia de Buenos Aires) entre 2002-2011. Rev. Soc. Nat. 25, 543-556.

Viglizzo, E.F., Frank, F.C., Carreño, L.V., Jobbágy, E.G., Pereyra, H., Clatt, J., Pincén, D., Ricard, M.F. 2011. Ecological and environment footprint of 50 years of agricultural expansion in Argentina. Global Change Biology 17, 959-973.

Viglizzo, E.F., Roberto, Z.E., Lértora, F., López Gay, E., Bernardos, J. 1997. Climate and land-use change in field-crop ecosystems of Argentina. Agriculture, Ecosystems and Environment 66, 61-70.

Vitousek, P.M., Mooney, H.A., Lubchenco, J., Melillo, J.M. 1997. Human domination of Earth's ecosystems. Science 277, 494-499.

Wang, C., Cao, R., Chen, J., Rao, Y., Tang, Y. 2015. Temperature sensitive of spring vegetation phenology correlates to within-spring warming speed over the Northern Hemisphere. Ecological Indicators 50, 62-68.

Wang, Q., Tenhunen, J., Dinh, N.Q., Reichstein, M., Vesala, T., Keronen, P. 2004. Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland. Remote Sensing Environment 93, 225-237.

Wang, Y., Huang, F. 2015. Identification and analysis of ecosystem functional types in the west of Songnen Plain, China, based on moderate resolution imaging spectroradiometer data. Journal of Applied Remote Sensing 9, 096096.

Weiss, J.L., Gutzler, D.S., Allred, J.E., Dahm, C.N. 2004. Seasonal and inter-annual relationships between vegetation and climate in central New Mexico, USA. Journal of Arid Environment 57, 507-534.

Wolter, K., Timlin, M.S. 2011. El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext). International Journal of Climatology 31, 1074-1087.

Zhang, Y., Song, C., Band, L.E., Sun, G., Li, J. 2017. Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening? Remote Sensing of Environment 191, 145-155.

Zhao, M., Running, S.W. 2010. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329: 940-943.

Zhou, J., Cai, W., Qin, Y., Lai, L., Guan, T., Zhang, X., Jiang, L., Du, H., Yang, D., Cong, Z., Zheng, Y. 2016. Alpine vegetation phenology dynamic over 16 years and its covariation with climate in a semi-arid region of China. Science of the Total Environment 572, 119-128.

Zhu, L., Meng, J. 2015. Determining the relative importance of climatic drivers on spring phenology in grassland ecosystems of semi-arid areas. Int. J. Biometeorol. 5, 237-248.


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