Reducing Uncertainty in Modeling the NDVI-Precipitation Relationship: A Comparative Study Using Global and Local Regression Techniques

Abstract

The spatial relationship between vegetation and rainfall in Central Kazakhstan was modeled using the Normalized Difference Vegetation Index (NDVI) and rainfall data from weather stations. The modeling is based on the application of two statistical approaches: conventional ordinary least squares (OLS) regression, and geographically weighted regression (GWR). The results support the assumption that the average impression provided by the OLS model may not accurately represent conditions locally. The GWR approach, dealing with spatial non-stationarity, significantly increases the model's accuracy and prediction power. The GWR provides a better solution to the problem of spatially autocorrelated errors in spatial modeling compared to the OLS modeling.