# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 6 Discussion
Additionally, this study relies on single-date Landsat imagery acquired in August, which may not fully capture seasonal variations in grassland phenology (e.g., spring growth peaks or autumn senescence). Seasonal differences in vegetation structure and spectral responses could introduce biases when applying the model to other time points. For instance, early-season biomass is more sensitive to soil moisture, while late-season biomass may be dominated by litter accumulation, both of which are not captured by the current single-date dataset. This temporal limitation highlights the need for multi-temporal imagery or time-series analysis in future studies to improve the model's robustness across different growth stages and climatic conditions. Future studies could consider integratinghigher spatial resolution remote sensing data or advanced deep learning models with multi-source data to further enhance model accuracy. Our approach using Landsat 9 30 m imagery and MLP models offers a balance between spatial coverage and computational efficiency, differing from recent studies with alternative sensors or models. [PERSON] et al. achieved an R\({}^{2}\) of 0.85 using UAV-based multispectral imagery at 0.5 m resolution for small grassland plots, but acknowledged challenges in scaling to regional extents due to data acquisition costs and flight time limitations. In contrast, our 30 m resolution dataset enables cost-effective mapping of thousands of square kilometers, and it is suitable for semi-arid grasslands where high-resolution surveys are logistically infeasible [43]. [PERSON] et al. applied an LSTM model to Sentinel-2 time-series data, achieving an R\({}^{2}\) of 0.79 by capturing crop phenological dynamics. While their framework excels in modeling temporal patterns, it requires \(\geq\)8 cloud-free images per season and high GPU resources. Our single-date MLP model, though limited in temporal analysis, demonstrates robustness with minimal data input, making it accessible for regions with sparse historical imagery or computational constraints [44]. [PERSON] et al. compared optical (Landsat-8) and SAR (Sentinel-1) data in semi-arid grasslands, finding that optical vegetation indices outperformed SAR for low-to-moderate biomass. While SAR maintained consistent performance in cloudy conditions (R\({}^{2}\) = 0.71), its sensitivity to canopy structure was weaker than optical signals in grassland ecosystems. Our optical-based approach leverages this spectral advantage to maximize accuracy in clear-sky environments, a common scenario in semi-arid regions [45]. The results of this study reveal the spatial distribution pattern of grassland above-ground biomass in Xiwuzhumuqin Banner, with biomass increasing from the northern region to the central and southeastern areas. This trend provides important references for the rational utilization and protection of grassland resources: 1 This trend provides important references for grassland management. Low-biomass zones (<100 g/m\({}^{2}\), northern desert-steppe): implement strict grazing exclusion and plant drought-resistant species (e.g., Caragana microphylla) to combat desertification, as these areas have <50% vegetation cover and high soil erosion risk. High-biomass zones (>300 g/m\({}^{2}\), southeastern meadow steppe): adopt rotational grazing with a maximum stocking rate of 0.8 LU/ha (livestock units per hectare) and four-paddock rotation systems to maintain biodiversity and soil health. Medium-biomass zones (100-300 g/m\({}^{2}\), central typical steppe): use dynamic grazing management, adjusting intensity based on seasonal rainfall, with a post-grazing biomass threshold of \(\geq\)50 g/m\({}^{2}\) to sustain carbon storage. 2 The strong SOC-AGB correlation suggests that soil carbon conservation--through reduced tillage and rotational grazing--is critical for maintaining grassland productivity under climate change. Degraded areas with low SOC could benefit from organic amendments to enhance nutrient cycling.