# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 4 Research Methods
### Model Construction and Optimization  
Machine learning algorithms are data-driven modeling methods that optimize model structures and parameter configurations by learning from a large amount of historical data, thereby improving prediction accuracy. Unlike traditional fixed-model frameworks, machine learning is adaptive and iteratively adjusts to minimize the error between predicted and actual values, achieving more precise fitting. This approach is widely used in complex prediction and classification tasks, effectively handling data nonlinearity and feature complexity to enhance decision-making accuracy [33].  
\begin{table}
\begin{tabular}{c c} \hline
**Data Type** & **Feature Variables** \\ \hline Vegetation Index & ARVI, Clg, GNDVI, GOSAVI, MSAVI, \\  & MVI, NDVI, DVI, EVI, RGBVI, TVI, RVI \\ Meteorological Data & Monthly maximum temperature (\({}^{\circ}\)C), monthly minimum temperature (\({}^{\circ}\)C), \\  & monthly precipitation (mm), wind speed (m/s) \\ Soil Data & Soil organic carbon content (g/kg), soil nitrogen content (g/kg) \\  & soil total phosphorus content (g/kg), PH \\ Topographic Data & Elevation (m), slope (\({}^{\circ}\)), aspect \\ \hline \end{tabular}
\end{table}
Table 2: Characteristic variables.  
The Multilayer Perceptron (MLP) is a typical feedforward neural network model that includes at least one hidden layer and is trained using nonlinear activation functions and the backpropagation algorithm. Compared to traditional machine learning algorithms, MLP can automatically learn complex feature representations, excelling especially with nonlinear and high-dimensional data. Its primary advantage lies in the elimination of manual feature design, making it suitable for large-scale data, and it demonstrates strong generalization capabilities in tasks such as classification and regression [34].  
The MLP model employed in this study consists of a three-hidden-layer architecture, with neuron counts set to 128, 64, and 32 for each layer, respectively. All hidden layers use the ReLU activation function to introduce nonlinearity, while the output layer employs a linear activation function to predict continuous AGB values. The Adam optimizer was used with a fixed learning rate of 0.001 and a maximum of 500 training epochs. To prevent overfitting, an early stopping mechanism was implemented, terminating training if the validation error did not decrease for 10 consecutive epochs. These architectural parameters were determined through grid search combined with six-fold cross-validation, balancing model complexity and generalization capability.  
For modeling analysis, this study uses six models, including five traditional machine learning models--Random Forest Regression (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Multiple Linear Regression (MLR), Gradient Boosting Decision Trees (GBDT)--and a Multilayer Perceptron (MLP). The accuracy of these six models is compared, and the best-performing model is ultimately used to invert AGB. Hyperparameters are critical parameters for optimizing the performance of machine learning algorithms. Optimizing these parameters can achieve the best model performance. In this study, machine learning models were constructed using Python v.3.8, and grid search along with six-fold cross-validation was employed to find the optimal hyperparameters.  
The specific hyperparameter search spaces for each model were as follows: (1) MLP: Hidden layers = 2-4, neurons per layer = 64-256, activation function = {ReLU, Sigmoid}; (2) RF: Number of trees = 50-200, maximum depth = 10-30; (3) XGBoost: Learning rate = 0.01-0.1, number of trees = 100-300, max depth = 3-10; (4) SVR: Kernel = {'rbf', 'linear'}, C = 0.1-10; (5) GBDT: Number of estimators = 100-300, learning rate = 0.01-0.1. For linear models (MLR), no hyperparameter tuning was performed as they lack adjustable parameters beyond feature selection.