# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 1 Introduction
Machine learning models, due to their powerful data processing and nonlinear fitting capabilities, can establish mapping relationships between grassland biomass and variables such as vegetation indices, terrain, and soil, by learning from a large amount of historical data. These models demonstrate strong robustness, and even with limited data, they can still provide reliable biomass estimation results, making them effective tools for remote sensing-based grassland biomass inversion [12]. However, most existing machine learning models for AGB estimation still rely on single vegetation indices (e.g., NDVI) or neglect the coupled effects of terrain, climate, and soil factors [13]. For example, models using only spectral data have shown poor performance in mountainous regions with high spatial heterogeneity, where topographic shading and soil nutrient gradients strongly influence biomass distribution. Although auxiliary variables like meteorological data have been introduced, the nonlinear interactions between multi-source features are often oversimplified in traditional algorithms, limiting estimation accuracy in complex ecosystems [14,15]. This study addresses this gap by leveraging MLP's deep learning architecture to capture hierarchical relationships among vegetation, environment, and biomass. In machine learning algorithms, Multilayer Perceptron (MLP), as a typical deep learning model, has been widely applied to complex environmental variable estimation and classification tasks in recent years due to its multilayer network structure and powerful nonlinear fitting capability. MLP can effectively capture the combined effects of vegetation indices, meteorological data, terrain, and soil on grassland biomass, making it highly suitable for biomass inversion tasks [16]. Against this background, this study aims to achieve the following: 1 develop an MLP model integrating vegetation indices, meteorological data, topographic features, and soil properties to achieve high-precision AGB estimation in heterogeneous grasslands; 2 quantitatively compare the performance of MLP with five traditional machine learning models (RF, XGBoost, SVR, MLR, GBDT) in capturing nonlinear feature interactions; 3 analyze the spatial distribution patterns of AGB in Xiwuzhumuqin Banner and identify key environmental drivers (e.g., precipitation, soil organic carbon) using correlation and regression analysis; 4 provide a scalable remote sensing framework for large-scale grassland monitoring, supporting sustainable grazing management and carbon sequestration assessments. This study focuses on the Xilinhot region in Inner Mongolia, utilizing Landsat 9 remote sensing data combined with ground-truth measurements to construct a Multilayer Perceptron (MLP) model based on vegetation indices, meteorological data, terrain data, and soil data for precise inversion of grassland above-ground biomass. Additionally, the performance of the MLP model will be compared with traditional machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) to explore the applicability and accuracy of different models in grassland biomass estimation. The results of this study will provide scientific support for dynamic monitoring and resource management of grassland ecosystems, as well as data support for the sustainable use and ecological protection strategies of grassland resources.