LAI estimation based on multi-temporal CHRIS/PROBA data and radiative transfer modelling

Abstract

Leaf area index (LAI) is a key variable for the understanding and modeling of several ecophysiological processes within a vegetation canopy. The LAI could thus provide vital information for the management of the environment and agricultural practices when estimated continuously over time and space thanks to remote sensing sensors such as CHRIS/PROBA. The spaceborne ESA-mission CHRIS/PROBA provides multi-temporal observations of the land surface in the spectral and directional information dimensions. This system represents a rich source of information for Earth observation purposes specifically adapted for monitoring the high dynamic of agricultural crops. For this purpose a radiative transfer model (RTM) is coupled to a canopy structure dynamic model (CSDM). The coupled models are used to exploit the complementary content of the spectral and temporal information dimensions for LAI estimation over a maize canopy. The resulting estimation of the temporal and spatial variation of LAI is improved by integrating multi-temporal CHRIS/PROBA data and ground meteorological observations. Further, the presented method provides the continuous LAI time course over the season, which is required by crop growth and land surface process models.