NDVI (Normalized Difference Vegetation Index) time-series have been used for permitting a land surface phenology retrieval but these time series are affected by clouds and aerosols, which add noise to the signal sensor. In this sense, several smoothing functions are used to remove noise introduced by undetected clouds and poor atmospheric conditions, but a comparison between methods is still necessary due to disagreements about its performance in the literature. The application of a smoothing function is a necessarily previous step to describe land surface phenology in different ecosystems. The aims of this research were to evaluate the consistency of different smoothing functions from TIMESAT software and their impacts on phenological attributes of temperate grasslanda complex mosaic of land uses with natural vegetated and agricultural regions using NDVI-MODIS time series. An adaptive Savitzky-Golay (SG) filter, Asymmetric Gaussian (AG) and Double Logistic (DL) functions to fitting NDVI data were used and their performances were assessed using the measures root mean square error (RMSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and bias. Besides, differences on the estimation of the start of the growing season (SOS) and the length of the growing season (LOS) were obtained. High and low RMSE over croplands and grassland were observed for the three smoothing functions; in the rest of the region, the SG filter showed more reliable results. Patterns of difference on the estimation of SOS and LOS between SG filter and the other two models were randomly distributed, where differences of 20-50 days were found. This study demonstrated that methods from TIMESAT software are robust and spatially consistent but must be carefully used.

Introducción: La caracterización de los usos del suelo representa uno de los insumos indispensables para el manejo de los recursos naturales a diferentes escalas.Objetivo: Desarrollar una metodología para caracterizar el uso del suelo en la cuenca superior del arroyo del Azul (Buenos Aires, Argentina), a través de la fusión de imágenes satelitales de media resolución espacial.Materiales y métodos: Se utilizó una serie temporal de 23 imágenes del índice de vegetación de diferencia normalizada (NDVI, por sus siglas en inglés) del satélite MODIS-Terra (producto MOD13Q1) para el periodo mayo 2015 - mayo 2016. Además, se emplearon imágenes Landsat 8 para discriminar algunas categorías difíciles de clasificar con NDVI-MODIS. El mapa final de coberturas se validó considerando puntos de verificación independientes al proceso de clasificación; su precisión se evaluó a través del estadístico Kappa.Resultados y discusión: La serie temporal de NDVI permitió reconocer los patrones fenológicos de las coberturas y usos del suelo de mayor representatividad en la región. Se discriminaron siete coberturas; los usos agrícolas representaron 81.5 % de la superficie, siendo el sistema de doble cultivo trigo-soya (soja en Argentina) el predominante (39.4 %). La precisión global del mapa final fue alta (88.9 %, coeficiente Kappa = 0.86).Conclusión: La metodología empleada tiene la ventaja de ser rápida y replicable, para caracterizar los usos del suelo de una región determinada y evaluar sus cambios potenciales a lo largo del tiempo.

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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.