A novel methodology is proposed for soil moisture content (SMC) estimation using the feature level fusion of Senti-nel-1 and Landsat-8 satellite datasets. This fusion consists of concatenating Temperature Vegetation Dryness Index (TVDI) to the feature vector (radar and physical features) of the inversion of the Integral Equation Model (IEM) through Artificial Neural Networks (ANN) to reduce vegetation effects on Sentinel-1 estimation. This methodology is applied on Blackwell farms, Guildford, United Kingdom, where ground truth and satellite data were collected dur-ing 2017. The preliminary SMC estimation results show lower RMSE errors (by 0.474%) and less bias than the IEM inversion method.
A novel decision level data fusion algorithm for soil moisture content estimation is proposed in this paper. Firstly, individual estimations are determined, respectively, from the inversion of the Integral Equation Model (IEM) for Sentinel-1 and from the Temperature Vegetation Dryness Index (TVDI) for LANDSAT-8. Then, a feature level fusion of these methods is performed using an Artificial Neural Network (ANN). Finally, all estimations including the feature level fusion estimation are fused at the decision level using a novel weights based estimation. The area of interest for this study is Blackwell Farms, Guildford, United Kingdom and datasets were taken on 17/11/2017 for both Landsat-8 and Sentinel-1. Estimation from the proposed decision level fusion method produces a Root Mean Square Error RMSE (1.090%) which is lower than RMSE of the individual estimations of each sensor as well as that of the feature level fusion estimation.