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

Publication date: Available online 18 July 2019Source: The Crop JournalAuthor(s): Xiuliang Jin, Zhenhai Li, Haikuan Feng, Zhibin Ren, Shaokun LiAbstractAccurate 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 ...

Source: The Crop Journal - July 18, 2019 Category: Food Science Source Type: research