Estimation of winter canola growth parameter from UAV multi-angular spectral-texture information using stacking-based ensemble learning model

The leaf chlorophyll content (LCC) and leaf area index (LAI) play a crucial role in assessing crop growth status and optimizing field water-fertilizer management. Compared to labor-intensive traditional measurement methods, low-cost unmanned aerial vehicle (UAV) remote sensing technology provides a unique opportunity for monitoring small-scale farmland crop growth information. Currently, the estimation method combining machine learning with spectral or texture information from UAV images has attracted much attention. However, most studies commonly used spectral or texture information from vertical observation, and the potential of multi-angle spectral -texture information has not been fully explored. Meanwhile, the simultaneous usage of multiple characteristic information increases the computational complexity of the model, presenting challenges for crop growth parameter estimation. To address this, this study proposes a multi-feature fusion framework for canola growth parameter estimation using stacking-based ensemble learning algorithm. Firstly, 17 spectral features (VI s and Band) and 136 texture features (TF s ) are extracted from UAV spectral images with seven view zenith angle (Nadir, +/- 20o, +/- 40o and +/- 60o), respectively. Secondly, important feature variables are selected through correlation analysis and then feature datasets are constructed. Finally, a base learners-meta learner stacking structure was employed to ensemble four machine learning models (SVM,PLSR,RF and GBDT) to predict canola growth parameters. The result shows that: (1) Compared with nadir observation, off -nadir observations (especially for -20o and -40o) can provide more spectral -texture information related to canola growth; (2) The stacking-based ensemble learning can achieve accurate estimation of canola LAI (R 2 = 0.72; RMSE = 0.74) and LCC (R 2 = 0.78; RMSE = 6.4 ug/cm 2 ); (3) Compared with single machine learning model (LAI: R 2 = 0.56 - 0.67, RMSE = 0.87 - 1.08; LCC: R 2 = 0.56 - 0.75, RMSE = 8.1 - 10.6 ug/cm 2 ), the stacking-based ensemble learning has advantage in growth parameters estimation. The corresponding spatiotemporal mapping reflects the impact of field treatment on the canola growth. Overall, These above results demonstrate the application value of multiangle spectral-textural information and stacking-based ensemble learning in accessing crop growth, providing new insights into remotely quantitative diagnosis of crop growth.