A framework for monitoring crop growth by combining directional and spectral remote sensing information

Abstract

For monitoring agricultural crop production, growth of crops is modeled, for example, by using simulation models. Estimates of crop growth often are inaccurate for practical field conditions. Therefore, model simulations must be improved by incorporating information on the actual growth and development of field crops, for example, by using remote sensing data. Such data can be used to initialize, calibrate, or update crop growth models, and it can yield parameter estimates to be used as direct input into growth models: 1) leaf area index (LAI), 2) leaf angle distribution (LAD), and 3) leaf colour (optical properties in the PAR region). LAI and LAD determine the amount of light interception. Leaf (or crop) color influences the fraction of absorbed photosynthetically active radiation (APAR) and the maximum (potential) rate of photosynthesis of the leaves. A framework is described for integrating optical remote sensing data from various sources in order to estimate the mentioned parameters. In this article, the above concepts for crop growth estimation are elucidated and illustrated using groundbased and airborne data obtained during the MAC Europe 1991 campaign. Quantitative information concerning both LAI and LAD was obtained by measurements at two viewing angles (using data from the CAESAR scanner in dual-look mode). The red edge index was used for estimating the leaf optical properties (using AVIRIS data). Finally, a crop growth model (SUCROS) was calibrated on time series of optical reflectance measurements to improve the estimation of crop yield.