Vis–NIR Spectroscopy Characteristics of Wetland Soils with Different Water Contents and Machine Learning Models for Carbon and Nitrogen Content

Soil nutrient detection in wetlands is critical for rapidly and effectively managing these ecosystems. Our objective was to provide a methodological framework for identifying optimal data processing methods and machine learning model for predicting soil organic carbon (SOC) and total nitrogen (TN) content using Vis–NIR spectroscopy, under the confounding influence of varying soil moisture. Soil samples (474) were collected from the Shaanxi Yellow River Wetland Provincial Nature Reserve with five moisture levels (0, 5, 10, 20, and 30%). Using a Vis–NIR spectroscopy system (ASD FS4 spectrometer), soil organic carbon (SOC) and total nitrogen (TN) were detected within the 350–2500 nm spectral range. Machine learning models were established using the Random Forest model (RF), eXtreme Gradient Boosting (XGBoost), and Partial Least Squares Regression (PLSR). The results indicated: (1) spectral reflectance values increased as soil moisture content decreased, with the 0% moisture model being consistently more accurate; (2) models for SOC and TN on first-derivative spectra had higher accuracy; and (3) the RF exhibited higher inversion accuracy and stability (R2 = 0.30–0.69). (4) The SHAP analysis confirmed 1865 nm and 1419 nm as the most contributory bands for SOC and TN prediction respectively, validating the RF model’s spectral interpretation capability.