Modelling evapotranspiration and plant growth at the landscape scale using remote sensing data

The determination of vegetation parameters is one of the most important remote sensing applications. Vegetation parameters such as LAI or biomass are usually estimated by utilising simple empirical relationships of plant parameters and spectral reflectance. Especially NDVI measurements are used frequently. The applicability of these relationships is limited to periods with suitable observation conditions with regards to environmental (cloud) or object conditions (signal level). Despite these limitations, remote sensing data are used successfully to quantitatively determine plant parameters and their spatial patterns. However, whereas remote sensing observes environmental processes, it does not explain them. As opposed to remote sensing, plant-physiological and physical models simulate these processes. Due to their complexity, these models are often limited to single fields. The availability of appropriate computing power, the advances in modelling, remote sensing, and GIS technology allow the development and use of physically based coupled plant growth and hydrology models for modelling on the landscape scale. Thus, based upon an existing evapotranspiration model, W. Mauser et al. (1998), a process-oriented modular environment and vegetation model (PROMET-V) was developed, which uses remote sensing data as input data, for updating model parameters and for model validation. This paper demonstrates the use and benefits of including remote sensing observations in a process-oriented spatial model