Fourier analysis of historical NOAA time series data to estimate bimodal agriculture

In the present study, NDVI time-series 10-day composites derived from NOAA AVHRR data were used to estimate bimodal agriculture areas (where there are two seasons of cultivation per annum) using Fourier approach. The NDVI sequence was transformed into harmonic signals and the amplitude and phase of first and second harmonics were used for the analysis. A classification was applied, using a decision tree, to discriminate bimodal agriculture area from other land cover types, principally over the Asian sub-region. When the amplitude of second harmonics in a sample region, where bimodal agriculture is predominant, was compared with the irrigated area statistics developed by FAO-UF, a linear relationship was determined. The derived function was applied to transform the amplitude of second harmonics to bimodal agriculture area estimates. Thus large-scale irrigation projects appear on the map and provide an encouraging initial result. This result indicates that estimating bimodal agriculture area that is one of the main sources of information for irrigated area mapping at regional or global scale, with improved accuracy possible if greater spatial, temporal resolution is achieved, for instance from MODIS or SPOT vegetation time series NDVI data, combined with (1) an improved decision tree classification algorithm and (2) a greater precision and geographical distribution of ground-truth data. The principle merits of this approach are automation and repeatability.