| The estimation of Leaf Area Index (LAI) is a key parameter controlling biophysical processes of the vegetation canopy, and ultimately yield. Defined as one half the total green leaf area per unit ground surface area, LAI is an essential component of precision crop management. Direct field techniques are tedious, time-consuming and labour-intensive. Indirect techniques, such as determining gap fraction with optical instruments have proven to be a good alternative, but their use is limited to rigid field sampling techniques. Vegetation indices have been useful to estimate LAI but are limited mostly due to its background reflectance noise. LAI can be estimated using different types of data, but only hyperspectral remote sensing has the potential to distinguish effectively the crop from other field components using spectral mixture analysis. Once the crop fraction has been derived, LAI is estimated using a crop fraction inversion technique. The application of this technique under agricultural field conditions has been very limited and not rigorously validated. The main objective of this study is to validate the crop fraction inversion technique for LAI estimation, and to examine the potential for LAI estimation using hyperspectral remote sensing data in the context of precision agriculture. This research will provide a unique scientific contribution to the field of hyperspectral remote sensing and greatly contribute to the advancement of remote sensing agriculture applications in Canada. (Abstract shortened by UMI.)... |
