Above Ground Biomass Estimation By Multi-Source Data Based On Interpretable DNN Model

Above Ground Biomass (AGB) estimation is a basis for rational utilization of natural resources and ecological succession process. Recently, multiple sources of remote sensing data have been used to estimate AGB at high spatial resolution, overcoming the limitations of each type of data. In order to fully exploit the potential of deep learning models based on multi-source data in AGB estimation, an end-to-end Deep Neural Networks (DNN) model is developed using Sentinel-1/2 data, and a learnable weight matrix is designed to tune the contribution of different predictors in multi-source data, thus improving the performance of the model. The experimental results show that the designed DNN model can achieve accurate AGB estimation with a coefficient of determination of 0.7314. Compared with the widely employed XGBoost and Random Forest machine learning models, the proposed DNN model has improved by 6.29% and 5.07% for grassland biomass estimation, respectively.