Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China

Abstract

Crop biomass is an important ecological indicator of growth, light use efficiency, and carbon stocks in agro-ecosystems. Light detection and ranging (LiDAR) or laser scanning has been widely used to estimate forest structural parameters and biomass. However, LiDAR is rarely used to estimate crop parameters because the short, dense canopies of crops limit the accuracy of the results. The objective of this study is to explore the potential of airborne LiDAR data in estimating biomass components of maize, namely aboveground biomass (AGB) and belowground biomass (BGB). Five biomass-related factors were measured during the entire growing season of maize. The field-measured canopy height and leaf area index (LAI) were identified as the factors that most directly affect biomass components through Pearson's correlation analysis and structural equation modeling (SEM). Field-based estimation models were proposed to estimate maize biomass components during the tasseling stage. Subsequently, the maize height and LAI over the entire study area were derived from LiDAR data and were used as input for the estimation models to map the spatial pattern of the biomass components. The results showed that the LiDAR-estimated biomass was comparable to the field-measured biomass, with root mean squared errors (RMSE) of 288.51g/m2 (AGB), and 75.81g/m2 (BGB). In conclusion, airborne LiDAR has great potential for estimating canopy height, LAI, and biomass components of maize during the peak growing season.