Biophysical drought metrics extraction by time series analysis of SPOT Vegetation data

The repeated occurrence of severe wildfires has highlighted the need for development of effective vegetation moisture monitoring tools. The normalized difference infrared index (NDII) derived from SPOT Vegetation satellite data and the Keetch-Byram drought index derived from temperature and rainfall data are both related to vegetation moisture dynamics. Autocorrelation of time series is a major issue when time series derived from remote sensing and meteorological variables are analyzed. Autocorrelation affects cross-correlation between variables measured in time, and violates the basic regression assumption of independence. Therefore this study focuses on the extraction of independent drought metrics from seasonal time series to define quantitive relationships between remote sensing and meteorological time series. First, the correlation between time series of satellite- and climate-data based indices is investigated by cross-correlation analysis. Secondly, a novel method for extraction of drought metrics is optimized for satellite- and in-situ derived time series. The method is based on a nonlinear least squares fit of asymmetric Gaussian model functions. The smooth model functions are then used for defining key seasonality parameters. The hypothesis is that the 'seasonal shapes' of satellite- and in-situ derived time series are correlated. Based on this hypothesis, the performance for parameter extraction from time series is explored.