A Conceptual Framework for the Simultaneous Extraction of Sub-pixel Spatial Extent and Spectral Characteristics of Crops

The sub-pixel spectral contribution of background soils and shadows hampers the accurate site-specific monitoring of agricultural crop characteristics from aerial or satellite images. To address this problem, the present study combines measured in situ and hyperspectral data in an alternative unmixing algorithm. The proposed algorithm, referred to as Soil Modeling Mixture Analysis (SMMA), incorporates a soil reflectance model in a traditional unmixing algorithm and as such opens up the opportunity to simultaneously extract the sub-pixel spatial extent of crops as well as its pure spectral information. The performance of the algorithm is evaluated using a soil moisture reflectance model, calibrated for an in situ measured Albic Luvisol dataset. Synthetic mixtures, i.e., compiled from in situ measured hyperspectral bare soil and citrus tree canopy spectra, were decomposed and the sub-pixel crop cover fractions (R 2 � 0.94, RMSE � 0.03) and pure vegetation signals (average extraction error 350 to 2,500 nm � 0.017, RMSE � 0.02) were adequately extracted from the mixtures.