Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system

• Stereo images were used to estimate maize canopy height and aboveground biomass.
• The stereo images were collected by a low-cost unmanned aerial vehicle (UAV) system.
• Estimation models were established based on the UAV metrics and field measurements.
• Structure from motion point metrics can be promising indicators in estimating maize parameters.

Canopy height (Hcanopy) and aboveground biomass (AGB) of crops are two basic agro-ecological indicators that can provide important indications on the growth, light use efficiency, and carbon stocks in agro-ecosystems. In this study, hundreds of stereo images with very high resolution were collected to estimate Hcanopy and AGB of maize using a low-cost unmanned aerial vehicle (UAV) system. Millions of point clouds that are related to the structure from motion (SfM) were produced from the UAV stereo images through a photogrammetric workflow. Metrics that are commonly used in airborne laser scanning (ALS) were calculated from the SfM point clouds and were tested in the estimation of maize parameters for the first time. In addition, the commonly used spectral vegetation indices calculated from the UAV orthorectified image were also tested. Estimation models were established based on the UAV variables and field measurements with cross validation, during which the performance of the UAV variables was quantified. Finally, the following results were achieved: (1) the spatial patterns of maize Hcanopy and AGB were predicted by a multiple stepwise linear (SWL) regression model (R2 = 0.88, rRMSE = 6.40%) and a random forest regression (RF) model (R2 = 0.78, rRMSE = 16.66%), respectively. (2) The UAV-estimated maize parameters were proved to be comparable to the field measurements with a mean error (ME) of 0.11 m for Hcanopy, and 0.05 kg/m2 for AGB. (3) The SfM point metrics, especially the mean point height (Hmean) greatly contributed to the estimation model of maize Hcanopy and AGB, which can be promising indicators in the detection of maize biophysical parameters. To conclude, the variations in spectral and structural attributes for maize canopy should be simultaneously considered when only simple RGB images are available for estimating maize AGB. This study provides some suggestions on how to make full use of the low-cost and high-resolution UAV stereo images in precision agro-ecological applications and management.