# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 1 Introduction  
Grasslands are one of the most widely distributed vegetation types globally, covering approximately 25% of the Earth's surface and storing approximately 34% of global soil carbon [1], with Inner Mongolia's grasslands alone accounting for 1.79 PgC [2]. [PERSON] et al. 's national-scale machine learning model identified precipitation as a critical predictor for forest aboveground biomass (AGB) across China, reinforcing its role in shaping carbon storage potential in diverse ecosystems [3]. They play an extremely important role in the global ecosystem. grasslands not only act as crucial carbon and nitrogen reservoirs, effectively mitigating the adverse effects of global climate change, but also play a key role in water conservation, soil protection, and biodiversity maintenance [4]. Above-ground biomass (AGB), a key indicator of grassland health, directly influences carbon sequestration capacity. For instance, [PERSON] et al. found that AGB in Xilinhot ranges from 120 g/m\({}^{2}\) (desert steppe) to 350 g/m\({}^{2}\) (meadow steppe), with soil organic carbon (SOC) contributing 32% tothis spatial variation--a relationship critical for regional carbon accounting [5]. Therefore, accurately estimating AGB over large areas is of great practical significance for scientifically assessing the ecological health of grasslands, developing reasonable grassland resource management strategies, and maintaining regional ecological security. Traditional ground-based harvesting methods, while precise, are labor-intensive and limited to small scales. For example, [PERSON] et al. 's comprehensive review revealed that traditional field measurement methods for grassland AGB require destructive sampling of numerous quadrats (typically 30-50 plots per hectare), making large-scale monitoring labor-intensive and ecologically disruptive [6]. Although this method provides high precision, it also has clear limitations [6]. On one hand, the ground harvesting method is complex, time-consuming, and costly, making it unsuitable for large-scale, long-term dynamic monitoring. On the other hand, because it is limited to a small sample area, it cannot meet the demand for biomass estimation at large spatial scales. As global environmental changes intensify, there is an increasing need for efficient and accurate large-scale dynamic monitoring of grassland biomass. In contrast, remote sensing technology, with its wide spatial coverage, high timeliness, and relatively low cost, has become an ideal tool for large-scale grassland biomass monitoring [7]. By obtaining wide-area spectral information, remote sensing imagery can provide data related to grassland vegetation cover, canopy structure, and growth conditions, which can be further used to extract vegetation indices and other variables closely related to biomass [8]. In recent years, with the enrichment of remote sensing data sources and the improvement of resolution, researchers have gradually explored statistical models based on the relationship between spectral reflectance, vegetation indices, and biomass. These methods can not only achieve large-scale grassland biomass estimation but also partially overcome the limitations of traditional ground-based measurement methods [9]. Moreover, some researchers have combined remote sensing data with crop growth models to simulate grassland biomass [10]. However, crop growth models rely heavily on numerous environmental factors (such as water and soil nutrients) and ecological theories for biomass estimation, making them dependent on large amounts of data. This reliance makes it difficult to meet the practical demand for rapid and large-scale estimation of grassland biomass [11]. Against this backdrop, remote sensing-based biomass inversion models using machine learning algorithms have gradually emerged.