Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass estimation from Unmanned Aerial System-based RGB imagery

Abstract

Crop biomass estimation with high accuracy at low-cost is valuable for precision agriculture and high-throughput phenotyping. Recent technological advances in Unmanned Aerial Systems (UAS) significantly facilitate data acquisition at low-cost along with high spatial, spectral, and temporal resolution. The objective of this study was to explore the potential of UAS RGB imagery-derived spectral, structural, and volumetric information, as well as a proposed vegetation index weighted canopy volume model (CVMVI) for soybean [Glycine max (L.) Merr.] aboveground biomass (AGB) estimation. RGB images were collected from low-cost UAS throughout the growing season at a field site near Columbia, Missouri, USA. High-density point clouds were produced using the structure from motion (SfM) technique through a photogrammetric workflow based on UAS stereo images. Two-dimensional (2D) canopy structure metrics such as canopy height (CH) and canopy projected basal area (BA), as well as three-dimensional (3D) volumetric metrics such as canopy volume model (CVM) were derived from photogrammetric point clouds. A variety of vegetation indices (VIs) were also extracted from RGB orthomosaics. Then, CVMVI, which combines canopy spectral and volumetric information, was proposed. Commonly used regression models were established based on the UAS-derived information and field- measured AGB with a leave-one-out cross-validation. The results show that: (1) In general, canopy 2D structural metrics CH and BA yielded higher correlation with AGB than VIs. (2) Three-dimensional metrics, such as CVM, that encompass both horizontal and vertical properties of canopy provided better estimates for AGB compared to 2D structural metrics (R2 = 0.849; RRMSE = 18.7%; MPSE = 20.8%). (3) Optimized CVMVI, which incorporates both canopy spectral and 3D volumetric information outperformed the other indices and metrics, and was a better predictor for AGB estimation (R2 = 0.893; RRMSE = 16.3%; MPSE = 19.5%). In addition, CVMVI showed equal prediction power for different genotypes, which indicates its potential for high-throughput soybean biomass estimation. Moreover, a CVMVI based univariate regression model yielded AGB predicting capability comparable to multivariate complex regression models such as stepwise multilinear regression (SMR) and partial least squares regression (PLSR) that incorporate multiple canopy spectral indices and structural metrics. Overall, this study reveals the potential of canopy spectral, structural and volumetric information, and their combination (i.e., CVMVI) for estimations of soybean AGB. CVMVI was shown to be simple but effective in estimating AGB, and could be applied for high-throughput phenotyping and precision agro-ecological applications and management.