Application of UAV-retrieved canopy spectra for remote evaluation of rice full heading date

Abstract

The heading date is an important fundamental trait in rice, which determines the length of growing duration and influences final yield. The traditional method to measure rice heading date involves frequent field work based on manual observations, which is slow, often subjective and feasible only in small areas. In this study, a Random Forest model was used to remotely estimate rice full heading (FH) date by unmanned aerial vehicle (UAV) imaging over the study sites throughout rice growing periods. The model using time-series Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge index (NDRE), retrieved from UAV multi-spectral images, was able to accurately estimate FH date for more than 1000 rice cultivars with root mean square errors below 4 days. The developed model was applied to map rice FH date variations under different environments. The results showed that most rice cultivars tend to heading later in response to colder temperatures while heading earlier at higher planting density, which has the sounded biological background. This study shows the great potential of using remote sensing method to assist in breeding studies, which is easy to implement across many fields and seasons, evaluating and comparing the crop trait for the large number of cultivars with high efficiency at low cost.