遥感反演技术为悬浮泥沙浓度（SSC）监测提供了高效手段，但对于高浓度宽范围河流SSC的适用性亟需验证。以黄河干流石嘴山水文站和吴堡水文站为测站，构建了基于交叉验证递归特征消除-随机森林（RFECV-RF）的机器学习模型，通过哨兵2号卫星多光谱反射率信息遥感反演测站高浓度悬沙。研究结果表明，RFECV-RF模型预测精度指标R2大于0.8，总体上能够估算测站（0-44.5）kg/m3区间的SSC，但对高值SSC的估算偏低；高浓度宽范围SSC与光谱信息呈现明显的非线性关系，且可见光主导的特征对反演低浓度SSC较重要，红光-短波红外光谱区间主导的特征对反演高浓度SSC较重要；在关键光谱特征中，B8A、B7/B5、B8-B11波段光谱特征在SSC（0-44.5）kg/m3 区间保持灵敏，而B3/B8、B4/B8、B5/B6随着SSC升高而渐趋饱和；数据输入以及机器学习模型结构的不确定性是反演模型不确定性的主要来源，且当反演的SSC范围变宽时，模型预测的95%置信宽度增大且低值SSC的相对偏差也增大。综上，RFECV-RF模型可用于定量反演高浓度宽范围SSC并估算河段SSC空间分布，为高含沙河流SSC自动监测方法提供技术参考

Remote sensing inversion offers an efficient approach for monitoring suspended sediment concentration (SSC). However, its applicability for rivers with high and wide-range SSC requires further validation. This study aims to develop a machine learning model based on Recursive Feature Elimination with Cross-Validation and Random Forest (RFECV-RF) to retrieve SSC from multispectral imagery, using the Shizuishan and Wubu Station on the main stream of the Yellow River as study sites. Sentinel-2 multispectral reflectance data were matched with in-situ daily SSC measurements from 2020 to 2024. After outlier removal using the Isolation Forest algorithm, datasets of 267 and 256 samples were obtained for the Shizuishan and Wubu Station, respectively. Spectral features, including single bands and band combinations (ratios and differences) from visible to shortwave infrared, were constructed. The RFECV method was applied to select optimal features, and a Random Forest model was subsequently built to establish the relationship between spectral features and SSC. Model performance was evaluated using R2, RMSE, and MAE. Spectral sensitivity was analyzed using regression fitting and slope decay ratios. Parameter sensitivity was assessed via the perturbation method by varying key parameters and evaluating corresponding changes in performance metrics using sensitivity coefficients. Uncertainty arising from data input, model parameters, and model structure was quantified using a Monte Carlo simulation framework with 500 iterations, and variance decomposition was applied to quantify the contribution of each uncertainty source. The RFECV-RF model achieved R2 values greater than 0.8 for both stations, with RMSE below 1.6 kg/m3, demonstrating reliable SSC estimation within the range of 0–44.5 kg/m3, though with underestimation of extreme high SSC values. A strong nonlinear relationship was observed between spectral features and SSC. The B8A band, along with B7/B5 and B8–B11, remained sensitive across the entire SSC range, while features such as B3/B8, B4/B8, and B5/B6 tended to saturate as SSC increased. Parameter sensitivity analysis indicated that model performance was robust, with the most sensitive parameters being the subsampling rate and the number of features removed per iteration. Uncertainty analysis revealed that data input and model structure were the dominant sources of uncertainty, contributing over 98% of total variance, with larger uncertainties observed for low SSC values when modeling wide-range SSC. When applied to river reaches, the inversion model effectively captured temporal variations and spatial transport dynamics of SSC, with higher SSC values in the middle reaches compared to the upper reaches, consistent with actual observations. The RFECV-RF model provides a reliable and effective approach for retrieving high and wide-range SSC in sediment-laden rivers using multispectral remote sensing. The identified key spectral features and quantified uncertainties offer valuable insights for improving inversion accuracy and support the development of automated SSC monitoring methods in complex river systems.