Estimating spatial patterns of rainfall interception from remotely sensed vegetation indices and spectral mixture analysis

Rainfall interception by vegetation is an important factor in the water balance. Consequently, rainfall interception should also be an important factor in models simulating processes such as evaporation, transpiration, surface runoff, soil erosion, and crop growth. In practice, however, it is difficult to make quantitative assessments of the spatial and temporal distribution of rainfall interception loss at the catchment level, for instance, and to make these values available as model input. In this paper, we present a novel method using earth observation images to estimate local quantitative values of rainfall interception loss. Leaf Area Index (LAI) and fractional vegetation cover per grid cell are important process variables for rainfall interception. These two variables are estimated from images using spectral vegetation indices and using spectral mixture analysis, respectively. Relations between canopy storage capacity and LAI exist for several plant species and vegetation types, but limited data are found on crops, and more research is needed in this field. The new method is explained and illustrated for a study area in southern France with a variety of land-cover types. It is found to be a valuable and practical approach to quantitatively assess spatial patterns of interception loss for given rainfall events.