# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 5 Results and Analysis
### Model Accuracy Evaluation and Analysis  
In this study, the dry weight of above-ground biomass from 78 sampling plots within the study area was used as the dependent variable, while the 14 selected feature variables served as driving factors. The data were randomly split into training and testing sets in an 8:2 ratio. Six models were employed for modeling analysis: five traditional machine learning models, including Random Forest Regression (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine Regression (SVR), Multiple Linear Regression \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Type** & **Feature Variable** & **Correlation** \\ \hline \multirow{8}{*}{Vegetation Index} & MVI & 0.637 ** \\  & Clg & 0.577 ** \\  & GNDVI & 0.558 ** \\  & GOSAVI & 0.558 ** \\  & NDVI & 0.550 ** \\  & ARVI & 0.535 ** \\  & MSAVI & 0.533 ** \\  & Maximum Temperature & 0.519 ** \\ Meteorological Data & Minimum Temperature & \(-\)0.145 * \\  & Precipitation & 0.597 ** \\ Soil Data & Organic Carbon Content & 0.428 ** \\  & Total Nitrogen Content & 0.364 ** \\  & Elevation & 0.356 ** \\ Topographical Data & Slope & 0.372 ** \\ \hline \hline \end{tabular} Note: “**” indicates significance at the 0.05 level, “**” indicates significance at the 0.01 level.  
\end{table} 
Table 3: Correlation of Driving Factors. (MLR), and Gradient Boosting Decision Tree (GBDT), along with the Multilayer Perceptron (MLP) model. A detailed evaluation of the performance of different feature combinations was conducted. From Table 4, it is evident that feature combinations significantly enhance model performance. In particular, the inclusion of meteorological factors (e.g., precipitation) and soil factors (e.g., organic carbon content) increased the R\({}^{2}\) of all models by more than 0.05, while RMSE and rRMSE values decreased by 5% to 15%. In the MLP model, when all feature factors were included, the R\({}^{2}\) reached 0.765, and RMSE and rRMSE were reduced to 38.066 g/m\({}^{2}\) and 33.058%, respectively. This result indicates that the combination of multiple feature factors is key to improving model prediction accuracy. To further analyze the overall performance and strengths of each model, Table 5 summarizes the accuracy evaluation results for different models under the condition of all features. As shown in Table 5, the MLP model performed best across the R\({}^{2}\), RMSE, and rRMSE metrics, while the SVR and MLR models exhibited relatively poorer performance. The training and testing results of traditional machine learning models and the Multilayer Perceptron (MLP) model are shown in Figure 3. The Support Vector Machine Regression (SVR) model exhibited the poorest performance (R\({}^{2}\) = 0.530, RMSE = 53.835 g/m\({}^{2}\), rRMSE = 46.752%), followed by the Multiple Linear Regression (MLR) model (R\({}^{2}\) = 0.590, RMSE = 50.307 g/m\({}^{2}\), rRMSE = 43.688%). The Gradient Boosting Decision Tree (GBDT), Random Forest Regression (RF), and Extreme Gradient Boosting (XGBoost) models demonstrated similar overall performance, with R\({}^{2}\) values ranging from 0.658 to 0.723, RMSE values from 41.354 g/m\({}^{2}\) to 45.913 g/m\({}^{2}\), and rRMSE values from 35.913% to 39.872%. The Multilayer Perceptron (MLP) model achieved the best results (R\({}^{2}\) = 0.765, RMSE = 38.066 g/m\({}^{2}\),