Remote Sensing Derived Leaf Area Index and Potential Applications for Crop Modeling

Integration of meteorological and remote sensing data in crop growth models offers a potentially powerful tool for yield monitoring. Leaf area index (LAI) is a key variable in crop growth models. The derivation of reliable LAI maps from satellite imagery would provide a means of spatially extrapolating these models. As part of a two-year project to develop an intelligent sensorweb system for yield prediction in agricultural crops and rangeland, the ability to obtain reliable LAI estimates from Compact High Resolution Imaging Spectrometer (CHRIS) data was investigated. Throughout the 2004 and 2005 growing season, data from the CHRIS sensor were acquired over two contrasting sites in Alberta, a wheat crop and rangeland. The modified triangular vegetation index (MTVI2) was used to derive LAI values which were compared to ground- based LAI data collected weekly or tri-weekly in wheat and monthly on the rangeland. A strong relationship was observed between ground-based and remote sensing derived LAI in the case of wheat (r=0.91-0.93). In, the rangeland, where senescent vegetation is a confounding factor, LAI was consistently overestimated using the CHRIS imagery.