Leaf and canopy cholorophyll content retrieval from hyperspectral remote sensing imagery

Chlorophyll content is the essential parameter in photosynthesis determining leaf spectral variation in visible bands. Therefore, the accurate estimation of the forest canopy chlorophyll content is a significant foundation in assessing forest growth and diseases. Hyperspectral remote sensing with high spatial resolution can be used for estimating chlorophyll content. And it is also provided to diagnose and examine chlorophyll spectral characteristics through narrow bands spectral reflection. In this study, the chlorophyll content was retrieved by using Hyperion. Firstly, Hyperion data was processed with smear correction, echo correction, background removal, radiometric correction, bad pixels repair, and image quality checking. Secondly, the canopy reflectance was converted into leaf reflectance by geometrical-optical model 4-scale and look-up table. Following by that, the spectral curve of the leaf was studied and 25 spectral characteristic parameters were identified through the correlation coefficient matrix. Moreover, leaf chlorophyll content inversion model was established by using these parameters through stepwise regression. Finally, leaf chlorophyll content was retrieved, and canopy chlorophyll content per unit ground surface area was estimated based on leaf chlorophyll content and leaf area index. The result indicated that the effect of the leaf chlorophyll content inversion model was very robust, and the precision achieved 88.74%. Leaf chlorophyll content was estimated with R
²
= 0.5735, RMSE = 7.3574 ¿g/cm
²
. An empirical relationship between simple ratio derived from the Hyperion imagery and the ground-measured leaf area index was developed, with R
²
= 0.7947.