Estimation of Soil Organic Matter in Moso Bamboo (Phyllostachys edulis) Forests Based on a Synergistic Matching Mechanism Between Feature Selection and Models

Rapid and effective estimation of soil organic matter (SOM) is crucial for the scientific management of Moso bamboo forests. This study investigated Moso bamboo forest soils in Yongan City, Fujian Province, and systematically evaluated the synergistic adaptation strategies coupling spectral preprocessing methods, feature extraction strategies, and machine learning models based on visible and shortwave near-infrared (Vis-NIR) spectroscopy. The results indicated that: (1) Conventional preprocessing algorithms attenuated the SOM spectral feature signals dominated by soil color within the limited wavelength range of field in situ spectral data, resulting in a general decline in the accuracy of the estimation models. (2) Feature extraction and modeling algorithms exhibited distinct adaptability across different content intervals. Within the low-content interval (<15 g/kg), simple physical indices combined with random forest (RF) achieved effective estimation at a lower computational cost (RPD = 2.18). Within the high-content interval (>25 g/kg), the synergistic strategy of the CARS algorithm combined with support vector regression (SVR) yielded the optimal estimation performance (R2 = 0.83, RPD = 2.45) and effectively mitigated the underestimation of high values caused by data imbalance. In conclusion, this study proposed a feature–model synergistic estimation approach, validating its feasibility for estimating SOM in Moso bamboo forests under the specific constraints of the current study area, thereby serving as a valuable reference for forest soil SOM monitoring in specific regions.