Hyperspectral Imaging and Stress Mapping in Agriculture: A Case Study on Wheat in Beauce (France)

Abstract

In an attempt to extract information relevant for agriculture in remotely sensed wheat crops, MIVIS hyperspectral images are analyzed in the visible and near-infrared domains. Through the selection, by means of a principal component analysis (PCA), of two endmembers of wheat, related respectively to well-developed and stressed plants, a water deficiency is detected among the spectral population of wheat. The image is then modeled by a spectral mixture analysis (unmixing) of these two wheat endmembers, soil, and shade. Resulting fraction images are interpreted in terms of crop vitality (level of green biomass) in relation to stress presence and compared to field knowledge. In addition, these images allow mapping the leaf area index (LAI) over the whole scene, with an empirical relationship based on 12 ground measurements of this variable. This work shows the interest of the approach combining PCA and unmixing for stress detection and mapping of agronomic variables, with a good accuracy compared to spectral ratio analysis. It provides relevant support for crop monitoring and precision agriculture, by means of numerical cartographic products obtained by hyper- (super-) spectral remote sensing. It demonstrates the need for improved methodologies derived from hyperspectral data analysis, and reveals that, through such methods, one can, however, retrieve a significant amount of information with limited number of spectral channels (10–20), highlighting the potential of superspectral observations.