Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Abstract

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield. Biomass is also a key trait in increasing grain yield by crop breeding. The aims of this study were (i) to identify the best vegetation indices for estimating maize biomass, (ii) to investigate the relationship between biomass and leaf area index (LAI) at several growth stages, and (iii) to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network (DNN) algorithm. The results showed that biomass was associated with all vegetation indices. The three-band water index (TBWI) was the best vegetation index for estimating biomass and the corresponding R2, RMSE, and RRMSE were 0.76, 2.84 t ha−1, and 38.22% respectively. LAI was highly correlated with biomass (R2 = 0.89, RMSE = 2.27 t ha−1, and RRMSE = 30.55%). Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm (R2 = 0.83, RMSE = 1.96 t ha−1, and RRMSE = 26.43%). Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices (R2 = 0.91, RMSE = 1.49 t ha−1, and RRMSE = 20.05%). Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass. Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices (R2 = 0.87, RMSE = 1.84 t ha−1, and RRMSE = 24.76%). The DNN algorithm was effective in improving the estimation accuracy of biomass. It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region. Keywords: Biomass estimation, Maize, Vegetation indices, Deep neural network algorithm, LAI