# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 7 Conclusions  
This study underscores the transformative potential of machine learning in remote sensing for grassland ecology by demonstrating that the MLP model can effectively integrate multi-source data to uncover complex vegetation-climate interactions. For remote sensing applications, the framework establishes medium-resolution satellite imagery (e.g., Landsat 9) as a viable tool for cost-efficient regional biomass monitoring, particularly in data-scarce semi-arid regions where high-resolution surveys are logistically challenging. The identification of vegetation indices (e.g., MVI) and precipitation as key drivers provides a scientific basis for designing targeted remote sensing protocols that prioritize spectral bands sensitive to water stress and canopy structure. In the context of grassland management, the study's spatial biomass patterns offer actionable insights for climate-adaptive strategies. The northern low-biomass zone, linked to arid conditions and soil degradation, can be prioritized for restoration initiatives such as rainwater harvesting and drought-resistant vegetation planting. Conversely, the high-biomass southeastern region supports sustainable grazing practices with defined stocking rates and rotational systems, balancing productivity with carbon storage objectives. Moreover, the strong soil organic carbon-AGB correlation highlights the need for soil health management policies, reinforcing the role of grasslands in carbon neutrality agendas. The specific conclusions are as follows: 1 The MLP model outperformed traditional machine learning models in predicting grassland biomass, highlighting its superiority in handling nonlinear and high-dimensional data. 2 Vegetation indices, meteorological data, and soil data are critical factors influencing grassland above-ground biomass. Among them, MVI, Clg, and precipitation contributed the most to inversion accuracy. 3 The results revealed spatial variations in biomass within the study area, showing lower biomass in the northern region and higher biomass in the central and southern regions. This trend offers significant references for the management of grassland ecosystems. 4 To enhance model robustness, future research should explore multi-sensor fusion strategies, such as combining Landsat 9 optical data with Sentinel-1 SAR for cloud resilience and UAV-based hyperspectral imagery (0.5 m resolution) to capture microscale heterogeneity in degraded grasslands. Regarding model architecture, Transformer-based networks could be applied to analyze multi-temporal Sentinel-2 time-series (5-10-day resolution) and model seasonal phenological dynamics, while LSTM (Long Short-Term Memory) networks may improve predictions under inter-annual climate variability (e.g., droughs). Additionally, incorporating soil moisture dynamics and grazing intensity datasets into the model could refine the representation of human-environment interactions. These advancements would enable more precise spatiotemporal AGB mapping, supporting adaptive management strategies in semi-arid ecosystems. Conceptualization, Z.W. ; methodology, Z.W. and S.M. ; software, S.M. ; validation, S.M. and Z.W. ; formal analysis, S.M. ; investigation, S.M., J.C., Q.E. and Z.W. ; resources, Z.W. ; data curation, Z.W., S.M., X.Y. and J.C.; writing--original draft preparation, S.M. ; writing--review and editing, S.M. and Z.W. ; visualization, S.M. ; supervision, Z.W., P.H., Y.Z., Q.E. and X.Y. ; project administration, Z.W., P.H. and Y.Z. ; funding acquisition, Z.W., Y.Z. and P.H. All authors have read and agreed to the published version of the manuscript.