Estimation of Sugarcane Yield by Assimilating UAV and Ground Measurements Via Ensemble Kalman Filter

摘要

Sugarcane is a water-intensive crop that plants in a large region of Southern China. It is important to estimate sugarcane yield under various soil water conditions during sugar production. A field experiment of 39 plots under five irrigation levels was implemented. Extensive unmanned aerial vehicle (UAV) measurements (including RGB and multispectral images) and ground observations (including soil water content (SWC), leaf area index (LAI), and meteorological data) have been collected. The objective of this study was to improve sugarcane yield estimation by assimilating UAV-derived LAI and ground measured SWC data into SWAP-WOFOST-Sugarcane crop model via different data assimilation strategies. Results showed that ensemble Kalman filter (EnKF) method produces the most satisfactory estimation than forcing and calibration method. Ground SWC measurement alone is not sufficient to guarantee the estimation accuracy, while joint utilization of UAV-derived LAI measurement is necessary. This study demonstrated the benefit of assimilating UAV-derived data and provided methodological reference of fusing various data into soil-water-atmosphere-plant system.