Modeling and understanding the relationship between vegetation and rainfall of a tropical watershed using remote sensing data
A model is developed to understand the relationship between satellite-derived NDVI and rainfall data in a large tropical catchment. Two Fourier-based modeling techniques with a seasonal component, viz. a seasonal model (SM) and a linear perturbation model (LPM) are tested, and their performance in reproducing the observed NDVI was evaluated. The methodology makes use of 15 years of 10-day composite time series data of rainfall and NDVI, which is estimated from NOAA-AVHRR data, both of which constitute concurrent data from 1982-96. The models are applied to a large catchment system of the Rufiji basin in Tanzania, with a network of 26 stations rainfall record and Thiessen polygon-interpolated spatially averaged NDVI data.