This study assessed whether the relationship of climate with foliar phenology is sufficiently robust to use a measure of foliar phenology to interpolate climate statistics in areas where observations are sparse. The normalized difference vegetation index (NDVI) was used to represent vegetation activity. As a measure of foliar phenology, we used parameters obtained by modelling NDVI time series with a Fast Fourier Transform (FFT) applied to a 9-year time series of monthly National Oceanographic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) NDVI global area coverage (GAC) images. The FFT decomposes the series into an average signal and to sinusoidal components. The selected FFT parameters were mean NDVI, and amplitude and phase for a 1-year period. Our specific objective was to relate the ratio of precipitation, P, over potential evapotranspiration, ETP, to the FFT parameters in two complementary ways. The first was to use them as attributes in a numerical classification to obtain a map of foliar isophenology, and then associate these classes with bioclimatic types, thus generating a bioclimatic map. The second was to fit a multiple linear regression model with P/ETP as predicted variable and the FFT parameters as predictive variables. The regression model was then applied to obtain a map of the ratio P/ETP. The latter gave a second bioclimatic map. Foliar isophenology classes show a north-south decrease in phase value and increase in amplitude and mean NDVI values, thus reflecting the transition in climate conditions from hotter and drier to wetter and cooler. The model explains 92% (p-value <10 −12 ) of the spatial variation in the P/ETP ratio. When using a single FFT parameter, no significant relationship was obtained. The three parameters provide complementary information to understand phenological variability in response to climate variability. Modelling bioclimate by means of monthly NDVI series summarized by Fourier analysis is an adequate tool to extend climate data where they are sparse.

This paper describes a method to detect the impact of rainfall anomalies on vegetation phenology, in terms of timing (phase) and greenness, by using Fourier series to fit a time series of Normalized Difference Vegetation Index (NDVI) observations. The study was conducted in the northern semiarid region of Argentina, where rainfall is the driving factor of vegetation phenology. A 9-year time series of monthly NDVI Global Area Coverage (GAC) images, obtained with the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR), was split into nine series of 12monthly images, each corresponding to a yearly growth cycle. A Fast Fourier Transform (FFT) algorithm was applied to each cycle, and derived parameters were analysed according to rainfall anomalies for irrigated and rainfed crops, grasslands and native forest. Derived Fourier parameters were: mean NDVI, amplitude and phase. Both negative and positive rainfall anomalies had a significant impact on the Fourier parameters. Amplitude and phase were the most sensitive parameters. Droughts modified the monomodal structure of the yearly cycle by decreasing the contribution of the 12-month periodic component and increasing the contribution of the 6-month component. The impact of drought on the Fourier parameters was highest for rainfed crops. Yearly values of Fourier parameters for grasslands and native forest were affected by prevailing hydrological conditions over the previous year.

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The aim of this study was to analyse bimodal histogram patterns of monthly National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) global area coverage (GAC) data and their relation to vegetation dynamics and climatic conditions for the period 1982-1991 in Argentina. The proposed method was to split up bimodal histograms by the median criterion and to study each mode as a separate unimodal frequency distribution. Modes were analysed based on their histogram shape and statistical parameters, geographical distribution and dynamics, and climatic significance. For the latter, a multinomial statistical analysis was used. The split-up criterion yielded coherent results. Histogram shapes and statistical parameters changed according to season. For geographical dynamics, 84% of pixels remained in the same mode through the seasons, and 16% shifted temporarily to the other mode. Changes from low-NDVI mode to high-NDVI mode were caused by an improvement in water supply, rainfall or irrigation, and higher temperatures. Changes in the opposite direction were due to a reduction in vegetation cover produced by drought, harvest, or autumn effects. The low-NDVI mode was strongly related to the arid zone with 74.6% probability (a = 0.05), and the high-NDVI mode was related to humid (58.8%) and semiarid zones (38.4%). This contribution helps explain the dynamics of vegetation cover along the latitudinal range from 22° to 55°S, for nine growing cycles, with a simple methodology. Improving the knowledge of multimodal histograms may allow a better understanding of difficult classification results.Resume. Le but a ete d'analyser les patrons des histogrammes bimodaux correspondantes aux donnees mensuelles NOAA AVHRR NDVI GAC et leur rapport avec la dynamique de la vegetation et les conditions climatiques, pour la periode 1982-1991 en Argentine. La methode proposee a etc' couper les histogrammes bimodaux par la mediane pour etudier chaque mode separement comme une distribution unimodale des frequences. Les modes ont ete etudiees par la forme des histogrammes et par leur statistiques, par leur distribution geographique et leur dynamique, et par leur rapport avec les conditions climatiques. Ce dernier a Me etudie par une analyse statistique multinomiale. Le critere de coupe par la mediane a donne des resultats coherents. Les formes des histogrammes ainsi que les statistiques ont ete modifiees selon les saisons. En ce qui concerne Ia dynamique geographique, 84% des pixels sont restes dans la meme mode a travers les saisons, to reste a change temporairement 3 l'autre mode. Les changements de la mode des has NDVI a celle des haut NDVI ont ete explique par ('amelioration dans l'approvisonement de ('eau, soi par les pluies ou par ['irrigation, et par des temperatures plus elevees.Les changements contraires ont ete dus a la reduction du convert vegetale a cause de secheresses, recoltes ou ('arrive de l'automne. La mode aux has NDVI a ete fortement associ...