Comparing methods for estimating leaf area index by multi-angular remote sensing in winter wheat.

ABSTRACT: The reflectance of wheat's canopy exhibits angular sensitivity, which can influence the accuracy of different methods for its leaf area index (LAI) estimation through multi-angular remote sensing. The primary objective of this study was to assess and compare the ability of various methods for LAI estimation from 13 view zenith angles (VZAs). The four methods included: (1) common hyper-spectral vegetation indices (VIs), (2) optimal two-band combination VIs (i.e., VIs: normalized difference index, simple ratio index, and difference vegetation index), (3) back-propagation neural network (BPNN), and (4) partial least squares regression (PLSR). Our results demonstrated that the red-edge plays a key role in estimating LAI, in that the traditional VIs, optimal two-band VIs, and PLSR including the

SUBMITTER: He L 

PROVIDER: S-EPMC7435181 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature