The ultimate goals of hyperspectral and thermal sensing in precision agriculture are to estimate biophysical and biochemical properties of agricultural crops (BB-PACs) and to delineate and characterize homogeneous management zones for optimal agricultural management like such fertilization, irrigation, or other agro-technical operations. This chapter concentrates on three characteristics of hyperspectral images: (1) their unique spectral properties, (2) the spatial attribute of hyperspectral images, and (3) the state-of-the art algorithms for hyperspectral image processing that show the added value of spatial information when combined with spectral information for mapping plant biophysical and biochemical properties of agricultural crops (BB-PACs). In addition, thermal panchromatic imaging is presented as an image type complementary to the hyperspectral images. Numerous hyperspectral vegetation indices (VIs) and multivariate spectral models have been developed to estimate crop status. Thus, to address the observed interchangeability of wavelengths and indices along crops, cultivars, growth stages, years, and sites, use of relative spectral/thermal indices is suggested for delineation of management zones and creation of prescription maps for variable-rate application.

A multiscale approach may address the limited resolutions (temporal and spatial) of the hyperspectral and thermal satellite images. Unmanned airborne vehicle (UAV) hyperspectral and thermal imaging systems could be used as sampling systems to establish relationships between the UAV and multi-spectral satellite images which can then be used to extrapolate to wider areas.