Improved Crop Biomass Algorithm with Piecewise Function (iCBA-PF) for Maize Using Multi-Source UAV Data

Abstract

Maize is among the most important grain crops. Aboveground biomass (AGB) is a key agroecological indicator for crop yield prediction and growth status monitoring, etc. In this study, we propose two new methods, improved crop biomass algorithm (iCBA) and iCBA with piecewise function (iCBA-PF), to estimate maize AGB. Multispectral (MS) images, visible-band (RGB) images, and light detection and ranging (LiDAR) data were collected using unmanned aerial vehicles (UAVs). Vegetation indices (VIs) and the VI-weighted canopy volume model (CVMVI) were calculated and used as input variables for AGB estimation. The two proposed methods and three benchmark methods were compared. Results demonstrated that: (1) The performance of MS and RGB data in AGB estimation was similar. (2) AGB was estimated with higher accuracy using CVMVI than using VI, probably because the temporal trends of CVMVI and AGB were similar in the maize growing season. (3) The best estimation method was the iCBA-PF (R2 = 0.90 ± 0.02, RMSE = 190.01 ± 21.55 g/m2), indicating that AGB before and after maize heading should be estimated with different methods. Our method and findings are possibly applicable to other crops with a heading stage.

Keywords

• aboveground biomass (AGB)
• unmanned aerial vehicle (UAV)
• vegetation-index-weighted canopy volume model (CVM_VI)
• heading
• growth stage