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.
The characterization of ecosystem functioning is significant for different purposes such as biodiversity conservation and ecosystem services. A key aspect of ecosystem functioning is carbon gains, since it represents the energy available for upper trophic levels. In this sense, remote-sensing methods have allowed the study of ecosystem dynamics and spatial distribution at different spatial and temporal scales. The objectives were to describe the regional patterns of ecosystem functional diversity and to establish the importance of interannual variability in the definition of Ecosystem Functional Types (EFTs) in the Argentina Pampas. EFTs were obtained from carbon gains using a set of seven functional attributes and their interannual variations, which were retrieved from 14-year NDVI time-series. An ISODATA technique was applied to all the analyzed variables, and the clusters that best separate in the n-dimensional space were selected using discriminant analysis. The Argentina Pampas shows a high heterogeneity in the spatial patterns of ecosystem functional attributes. The annual integral of NDVI (i-NDVI, a linear estimator of net primary productivity), a complex of ecosystem functional attributes that describe the interannual variability, and the annual relative range of NDVI (RREL, ecosystem seasonality) had the highest relevance to distinguish nine EFTs in the study area. This study shows a novel approach for mapping ecosystem functioning, which reveals the importance of interannual variations. This methodology includes the effects of climate variability on ecosystem dynamics, thus enhancing our understanding of ecosystem functional diversity. The results
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