A Generic Model to Estimate Wheat LAI over Growing Season Regardless of the Soil-Type Background.

ABSTRACT: It is valuable to develop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration. To achieve this objective, 2 strategies were investigated to improve our existing random forest regression (RFR) model, which was trained with simulations from a radiative transfer model (PROSAIL). The 2 strategies consisted of (a) broadening the reflectance domain of soil background to generate training data and (b) finding an appropriate set of indicators (band reflectance and/or vegetation indices) as inputs of the RFR model. The RFR models were tested in diverse soils representing varying soil types in Australia. Simulation analysis indicated that adopting both strateg

SUBMITTER: Chen Q 

PROVIDER: S-EPMC10205590 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature