Maize tasseling date forecast from canopy height time series estimated by UAV LiDAR data

Timely identification and forecast of maize tasseling date (TD) are very important for agronomic management, yield prediction, and crop phenotype estimation. Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season. A recent development in maize phenology detection research is to use canopy height (CH) data instead of spectral indices, but its robustness in multiple treatments and stages has not been confirmed. Meanwhile, because data of a complete growth season are needed, the need for timely in-season TD identification remains unmet. This study proposed an approach to timely identify and forecast the maize TD. We obtained RGB and light detection and ranging (LiDAR) data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments. After CH estimation, the feature points (inflection point) from the Logistic curve of the CH time series were extracted as TD. We examined the impact of various independent variables (day of year vs. accumulated growing degree days (AGDD)), sensors (RGB and LiDAR), time series denoise methods, different feature points, and temporal resolution on TD identification. Lastly, we used early CH time series data to predict height growth and further forecast TD. The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from LiDAR to estimate maize CH was the most stable across treatments and stages (R2: 0.928 to 0.943). For TD identification, the best performance was achieved by using LiDAR data with AGDD as the independent variable, combined with the knee point method, resulting in RMSE of 2.95 d. The high accuracy was maintained at temporal resolutions as coarse as 14 d. TD forecast got more accurate as the CH time series extended. The optimal timing for forecasting TD was when the CH exceeded half of its maximum. Using only LiDAR CH data below 1.6 m and empirical growth rate estimates, the forecasted TD showed an RMSE of 3.90 d. In conclusion, this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.