3.2.3. Decision-Based Fusion Approach

To reduce the error of modeled RCF based on remote sensing data due to the weaknesses of different algorithms, in the proposed strategy, the results of four RFR, SVM, ANN and PLSR algorithms were combined based on Equation (1).

$$\text{RCF}\_{\text{f}} = \sum\_{\text{i}=1}^{n} \text{W}\_{\text{i}} \text{RCF}\_{\text{model}(\text{i})} \tag{1}$$

In Equation (1), RCFf is the modeled RCF based on the remote sensing data by combining the results of different algorithms, RCFmodel(i) is the fraction of the modeled RCF based on the remote sensing data obtained from the ith algorithm, Wi is the degree of importance of the ith algorithm and n is the number of used algorithms. Equation (2) is used to calculate the significance of the ith algorithm.

$$\mathcal{W}\_{\text{i}} = \frac{\text{RMSE}\_{\text{model}(\text{i})}}{\sum\_{\text{i}=1}^{n} \text{RMSE}\_{\text{model}(\text{i})}} \tag{2}$$

In Equation (2), RMSEmodel(i) is the mean squares root of the of the estimated fraction and is based on the ith algorithm. The lower the RMSE of an algorithm in estimating the RCF, the greater its impact and importance in the result of the RCF estimation. MATLAB 2019a software was used to implement various indices and algorithms for RCF modeling.
