Combining Multi-temporal NDVI and Abundance from UAV Remote Sensing Data for Oilseed Rape Growth Monitoring

Using unmanned aerial vehicle (UAV) remote sensing method, this paper proposes a new approach to monitor the situation for oilseed rape based on multi-temporal spectral analysis. According to the rape canopy structure, this experiment determines the end-member combination of different growth stages and obtains the abundance data with the linear decomposition model. Aimed at the accumulation process of rape yield, we analyze the relation between the abundance data in three growth stages and the final yield, and propose three independent variable schemes to establish the multi-temporal rape yield estimation model. Experimental analysis shows that the combination of abundance and normalized difference vegetation index (NDVI) is more effective than the widely used NDVI. And the optimal abundance combinations are the same under different planting methods. This model is also suitable for different rape planting patterns. Experiment results verify the strong stability of the proposed model, and the multi-temporal combination of abundance data and NDVI can improve the effect of yield estimation model.