From the early days of remote sensing until today, there has been a wide range of applications of remote sensing data for agricultural management. Improvements in spatial, spectral and temporal resolution of available data products together with precision agriculture have resulted in an increase in the availability of services and products that help to manage agricultural operation more efficiently and profitably. Image-based remote sensing offers the potential to provide spatially and temporally distributed information for agricultural management. Remote sensing information can improve the capacity and accuracy of decision support systems (DSS) and agronomic models by providing accurate input information or as a means of within-season calibration or validation. Crop phenology is an important variable required by precision crop management systems (PCMS) in support of time-critical crop management (TCCM). Estimates of crop development, which are used for nutrient deficiencies detection, crop yield prediction or timing of forthcoming harvest are important in agricultural planning and policy making.
In this paper, a methodology to track the main development stages of two cereals relevant for agricultural purposes and precision farming needs, based on hyperspectral data, is presented. An investigation of the suitability of four key parameters to track a crop stand’s vitality and an error assessment are performed. Leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), water content and chlorophyll content are defined as the main parameters reflecting vitality and therefore alter with the plants’ phenological stage.