A first approach towards estimating nutrient uptake of grasslands by assimilating multi- and hyperspectral remote sensing data in growth models

Abstract

This paper presents a design for improving the predictive power of a growth model for grasslands by assimilating hyperspectral remote sensing data. The ultimate goal of the method is to use the growth model to get a clearer understanding of the nutrient uptake by vegetation within differently managed grasslands. In a first experiment, at three stages during the growing season of 2003, airborne multi- and hyperspectral data will be recorded over the catchment of Lake Waging-Taching in the foreland of the Bavarian Alps, Germany. Biophysical parameters of different types of grassland use, such as canopy water content, chlorophyll content and leaf area index (LAI) will be retrieved from the atmospherically corrected imagery by the inversion of the leaf/canopy reflectance model PROSAIL. This model inversion will be optimized for LAI retrieval by fixing the water content, specific leaf weight, soil reflection and the chlorophyll content for three fields. The LAI values retrieved from the inversion of the optimized PROSAIL model will be used to update the LAI values as simulated by the growth prediction model. In this way the model for predicting grass growth will be optimized for the actual growing conditions. No concrete results of the approach can be presented yet.