Improvement of sugarcane yield estimation by assimilating UAV-derived plant height observations

Abstract

Crop yield estimation plays a significant role in agricultural development and management decision making. Simulation of crop growth at field scales requires high-resolution remote sensing (RS) data and an applicable model. However, RS data based on satellite sensors are affected by atmospheric interference, limiting its use in precision agriculture. The objective of this study is to assimilate plant height data into the crop model to improve yield estimation and optimize agricultural water management. The plant height data are monitored regularly in 60 plots at Chongzuo experimental station (Guangxi, China) from an Unmanned Aerial Vehicle (UAV) system and ground-based measurements through a two-year sugarcane experiment (2016 and 2017). A SWAP-PH model (i.e., SWAP model considering plant height simulation) coupled with an iterative ensemble smoother algorithm is constructed to simulate plant height and crop growth procedure. We analyze its performance under the effects of different observation errors, ensemble sizes and development stages on crop growth and yield estimation, and compare the plant height assimilation results from two types of observation platforms. Our results reveal that the crop growth simulation at field scales is improved by incorporating the plant height data into the model. Moreover, incorporating the plant height measurements in the late growth stage with an absolute error of 1–2 cm and ensemble size over 50 could achieve an acceptable crop yield estimation result. In addition, assimilating the UAV-derived measurements could contribute to better yield estimation results than ground-measured plant height data. Finally, we propose a supplemental irrigation and drainage strategy in July and August to maximize the sugarcane yield in Guangxi region.