Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning.

ABSTRACT: A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop growth model with a radiative transfer model to introduce biological constraints in a synthetic training dataset. In addition to the comparison of two datasets without and with biological constraints, we also investigated the effects of observation geometry, retrieval method, and wavelength range on estimation accuracy of four crop traits (leaf area index, leaf chlorophyll content, leaf dry matter, and leaf water content) of wheat. The theoretical analysis demonstrated potential advantages of adding biological constraints in synthetic training d

SUBMITTER: Chen Q 

PROVIDER: S-EPMC9629788 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature