Spatial improvement of arable land use characterisation to support precision farming using airborne remote sensing technique

Spatial variability of crops within fields depend on soil variability. Understanding the magnitude and pattern in spatial variability of plant growth conditions detected by airborne remote sensing is important in order to provide modelers, regional planners and at least farmers with valuable information. Ground truth data of crop parameters were collected and related to spectral information in red, middle -and thermal infrared wavebands derived from airborne scanner data. Simple regression analysis have been used to obtain classification functions. The classified spectral images are finally compared with soil properties and N-balances for an extracted example. Results of two years under study indicate a high applicability of airborne remote sensing technique in order to detect spatial variability of plant growth conditions on field level.