Crop biophysical and spectral features seasonality

Remote sensing has entered into its application stage when the goal is to bring investigation results to an operational use. Interest is rapidly spreading in the application of hyperspectral data to precision farming. Agricultural monitoring supplies information on crop growth and condition and is used for yield prediction. In this paper, we investigate the performance of an approach for cereals state assessment during different stages of crop seasonal development. The approach comprises the establishment of statistical relationships between crop biophysical and spectral reflectance features, derived from field measurements. Predictions from single-date and temporal spectral data are verified through comparison with estimations from biophysical models. High-resolution visible and near-infrared reflectance data have been acquired throughout the growing season, along with detailed datasets of crop growth variables. Spectral-biophysical models have been developed relating crop agronomic parameters and yield to various spectral predictors. The algorithm has been tested and validated using airborne remote sensing data. A good correspondence has been found between estimations from spectral and biophysical models. This is a preposition for obtaining reliable information on crop condition from remotely sensed multispectral data.