A VI-based phenology adaptation approach for rice crop monitoring using UAV multispectral images

Accurate monitoring of crop biophysical parameters such as the plant area index (PAI) is essential for regional crop growth simulations and crop management. Many efforts have been made to estimate PAI by vegetation index (VI)-based methods, such as the widely used single empirical regression method and the piecewise relationship method. However, model structure error is inherent to the single-relationship method since it neglects the influence of phenology, and the discontinuity of the piecewise method can result in abrupt changes in estimates in the stage-transition period. Here we introduce a generalized and more accurate approach, termed the VI-based phenology adaptation (VPA) method, to estimate PAI of rice (Oryza sativa L.) using unmanned aerial vehicle (UAV)-based multispectral data. The VPA method automatically adapted the VI-PAI relationship at each observation time by bridging the VI, phenology and PAI into a continuous model. Moreover, the capability to monitor aboveground biomass of rice via the VPA method was evaluated. Intensive aerial and ground experiments were conducted in the controlled experimental plots and the randomly selected farmer-managed plots during two consecutive years (one year for calibration and another year for testing). The trajectory of the anchorpoint that linked the discrete VI-PAI/biomass relationship for each phenological stage was developed and parameterized. The estimates of the single-relationship method were scattered when PAI was high, and this method failed to estimate PAI for the entire growing season. Significant underestimation was observed at the flowering stage (BBCH 64) for the piecewise methods. In contrast, the VPA approach outperformed the other methods for both controlled plots (R2 of 0.799 and RMSE of 0.536) and farmer-managed plots (R2 of 0.543 and RMSE of 0.671). Moreover, the VPA method exhibited superior generality for estimating other crop biophysical parameters such as biomass. Compared with the piecewise methods, the proposed method best estimated aboveground biomass, with 93% of biomass variation captured. The results demonstrated the steady performance of the VPA method for estimating various biophysical parameters throughout the entire growing season, indicating the promising potential of this method for cross-year and cross-site crop status monitoring.