# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 6 Discussion  
Crop growth models have performed well in biomass prediction, as they integrate biological theory with computational algorithms, offering high scientific accuracy [38]. However, the reliance of these models on various data sources (such as water content, soil nutrients, etc.) and ecological theories limits their application in rapid biomass estimation [39]. In contrast, machine learning models based on the relationship between spectral  
Figure 4: Spatial distribution map of above-ground biomass (AGB) in the study area. reflectance or vegetation indices and biomass, with their higher simulation accuracy, robustness, and reliability, are less affected by complex environmental factors and are widely applied in biomass prediction. Machine learning models continuously optimize model parameters through feedback errors, outperforming traditional parametric models in terms of generalization ability and predictive performance, making them suitable for large-scale rapid biomass estimation [40]. The Multilayer Perceptron (MLP) model selected in this study captures the nonlinear relationships between features through its multilayer structure, better adapting to the complex variations of grassland above-ground biomass. The MLP model has demonstrated excellent performance in handling high-dimensional, nonlinear data, further improving the accuracy of biomass inversion and the model's adaptability. After feature selection, 14 key variables were chosen as the driving factors for the model. Among these, vegetation indices like MVI, Clg, and GNDVI show a high correlation with grassland above-ground biomass, indicating that these indices effectively reflect the biomass status of the grassland. Notably, MVI's sensitivity to vegetation growth in arid/semi-arid environments aligns with the water limitation theory in grassland ecology, where red and shortwave infrared bands are critical for detecting water stress. This highlights how MVI captures both canopy structure and physiological status, two key indicators of grassland resilience to drought. This result is consistent with the findings of [PERSON] et al. [41]. In addition, meteorological data such as precipitation and maximum temperature are significantly positively correlated with biomass, reflecting the dynamic changes of grassland biomass under different climatic conditions. The positive correlation between soil organic carbon (SOC) and AGB (R = 0.428) reflects the soil-nutrient feedback mechanism, where SOC acts as a reservoir for plant-available nitrogen and phosphorus in semi-arid systems. This underscores the importance of soil carbon pools in sustaining grassland productivity, particularly in the eastern study area with higher SOC content (Figure 3). In the comparison of various machine learning models, the MLP model showed the best predictive accuracy in this study, with an R\({}^{2}\) value of 0.765, RMSE of 38.066 g/m\({}^{2}\), and rRMSE of 33.058%. In contrast, other traditional machine learning models, such as Random Forest (RF) and Extreme Gradient Boosting (XGBoost), also achieved relatively high accuracy (with R\({}^{2}\) values of 0.708 and 0.723, respectively), but they were slightly less effective than MLP in fitting nonlinear complex relationships. This superiority stems from MLP's ability to model nonlinear climate-vegetation interactions, such as the synergistic effect of precipitation and vegetation indices (e.g., NDVI). For instance, the model reveals that the positive impact of precipitation on AGB is amplified in areas with denser vegetation (higher NDVI), a phenomenon rooted in improved water-use efficiency via canopy interception. Traditional linear models (e.g., MLR) and tree-based algorithms (e.g., RF) struggle to capture such context-dependent dynamics, limiting their ecological interpretability [42]. The performance of Support Vector Regression (SVR) and Multiple Linear Regression (MLR) models was poorer, indicating limitations when handling high-dimensional and nonlinear feature data.