Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation
Résumé
Because the canopy chlorophyll content (CCC) is an essential ecophysiological variable for photosynthetic functioning, remote sensing of CCC is vital for a wide range of ecological and agricultural applications. Simple and robust algorithms were explored for spectral assessment of CCC using the diverse hyperspectral datasets for six vegetation types (rice, wheat, corn, soybean, sugar beet, natural grass) acquired in four locations (Japan, France, Italy, USA). A simple model using the ratio spectral index RSI(R815,R704) with the reflectance at 815 and 704 nm, proved to have the highest accuracy and applicability based on comprehensive analysis on the accuracy, linearity, sensitivity and applicability of various spectral models. The model would work as a robust algorithm for canopy-chlorophyll-metre and/or remote sensing of CCC in ecosystem and regional scales.