Spectral Components Analysis: A Bridge between Spectral Observations and Agrometeorological Crop Models

Spectral observations have been acknowledged to indicate general plant conditions over large areas but have yet to be exploited in connection with agrometeorological crop models. One reason is that it is not yet appreciated how periodic spectral observations of row-cropped and natural plant canopies, as expressed by vegetation indices (VI), can provide information on important crop model parameters, such as leaf area index (LAI) and absorbed photosynthetically active radiation (APAR). Two experiments were conducted under AgRISTARS sponsorship, one with cotton and one with spring wheat, specifically to determine the relationships for each term in the " spectral components analysis" identity \begin{equation*} LAI/VI \times APAR/LAI = APAR/VI.\end{equation*} LAI and APAR could, indeed, be well estimated from vegetation indices such as normalized difference(ND) and perpendicular vegetation index (PVI)¿apparently because of the close relation between the VI and amount of photosynthetically active tissue in the canopy. APAR and VI measurements are similarly affected by solar zenith angle (SZA), and LAI can be divided by cos SZA at the time of the VI and APAR measurements to achieve correspondence. APAR, ND, and PVI plotted against LAI all asymptote to limiting values in the same way yield does as LAI exceeds 5, further linking canopy development to yield capability. In summary, the spectral components analysis results presented add credence to the information conveyed by spectral canopy observations about plant development and yield, and establish a bridge between remote observations and agrometeorological crop modeling through the variables of mutual concern, LAI, biomass, and yield.