Evaluation of Reflectance and Canopy Scattering Coefficient Based Vegetation Indices to Reduce the Impacts of Canopy Structure and Soil in Estimating Leaf and Canopy Chlorophyll Contents

Chlorophyll is of great physiological and ecological significance. Leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) can be retrieved from remotely sensed data based on vegetation indices (VIs). However, the impacts of canopy structure and soil remain open problems. VIs are typically calculated from spectral reflectance. In this study, we also constructed and examined VIs based on canopy scattering coefficients (
$W_{\lambda }$
) from spectral invariants theory. Based on extensive leaf and canopy radiative transfer simulations, linear regression and artificial neural network models were built with reflectance-based and
$W_{\lambda }$
-based VIs to retrieve LCC and CCC. The results showed that the canopy structure and soil significantly affected the retrievals.
$W_{\lambda }$
can effectively suppress the impacts of the leaf angle distribution (LAD) but not the leaf area index (LAI). The
$W_{\lambda }$
, estimated as the ratio reflectance/directional area scattering factor (DASF), contained a large error when the soil effect was strong. The
$W_{\lambda }$
-based VIs did not yield very accurate results in LCC estimation but exhibited higher accuracy for CCC estimation compared to reflectance-based VIs. Of all the VIs investigated, the best VI was D99 [(
$R_{850}$
–
$R_{710}$
)/(
$R_{850}$
–
$R_{680}$
)] for LCC and Wmul (
$W_{749} \times W_{956}$
) for CCC. Compared to D99 for LCC, Wmul for CCC was less accurate, and the accuracy varied more among canopies with different LADs. The main reason was that CCC equals LCC multiplied by LAI, but LCC and LAI impact VIs in a similar manner.