Estimation of Rice Aboveground Biomass by Combining Canopy Spectral Reflectance and Unmanned Aerial Vehicle-Based Red Green Blue Imagery Data.

ABSTRACT: Estimating the aboveground biomass (AGB) of rice using remotely sensed data is critical for reflecting growth status, predicting grain yield, and indicating carbon stocks in agroecosystems. A combination of multisource remotely sensed data has great potential for providing complementary datasets, improving estimation accuracy, and strengthening precision agricultural insights. Here, we explored the potential to estimate rice AGB by using a combination of spectral vegetation indices and wavelet features (spectral parameters) derived from canopy spectral reflectance and texture features and texture indices (texture parameters) derived from unmanned aerial vehicle (UAV) RGB imagery. This study aimed to evaluate the performance of the combined spectral and texture parameters and improve rice A

SUBMITTER: Wang Z 

PROVIDER: S-EPMC9197132 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature