Estimating vertically growing crop above-ground biomass based on UAV remote sensing

The accurate estimation of crop above-ground biomass (AGB) can assist in crop growth monitoring and grain yield prediction. Remote sensing has been widely used for AGB estimation at regional and local scales in recent years. However, optical remote sensing spectral indices (SIs) become saturated at medium-to-high crop covers. The combined use of remote sensing techniques and statistical regression models is not based on an understanding of how crop leaves and vertical organs contribute to the crop AGB. This causes difficulties in measuring the biomass stored in vertical organs (e.g., plant stem, wheat-spike, maize-tassel; abbreviated as AGBv) using optical remote sensing. This study aims to develop an unmanned aerial vehicle (UAV)-based vertically growing crop AGB (VGC-AGB) model. We defined Csm (g/m) to describe the crop stem and reproductive organs’ average dry mass content. This was done to improve the estimation of AGBv. The crop leaf area index (LAI, m2/m2), leaf dry matter content (Cm, g/m2), height (Ch, m), and density (Cd, m−2) were used in the VGC-AGB. The VGC-AGB calculated crop leaf AGB (AGBl) using LAI × Cm (g/m2) and AGBv using Cd × Ch × Csm (g/m2). The proposed VGC-AGB (AGB = LAI × Cm + Cd × Ch × Csm) was verified using field and UAV-based hyperspectral datasets of winter-wheat and summer-maize at three growth stages. Our results indicate that UAV-based VGC-AGB (R2 = 0.92–0.93, RMSE = 68.82–75.15 g/m2) is superior to the statistical regression model that is based on remote sensing SIs and CSMs (R2 = 0.77, RMSE = 134.94 g/m2). The results indicate that the UAV-based VGC-AGB supports the analysis of crop photosynthetic product transfers and high-performance UAV-based high-performance non-destructive AGB monitoring.