Monitoring soil salinity is essential for understanding the behavior of coastal wetland ecosystems and implementing effective management strategies. Despite the advantages of the Multi-Spectral Instrument (MSI) data for large-scale, high-frequency soil salinity monitoring, challenges remain in data preprocessing and model construction. We combined fractional-order derivative (FOD) technology with stacked machine learning models to monitor and map soil salinity using Sentinel-2 MSI data. The base models included Elastic Net Regression, Support Vector Regression, Artificial Neural Network, Extreme Gradient Boosting, and Random Forest, with Non-Negative Least Squares as the meta-learner. The results showed that low-order FOD enhanced image gradients and maintained a high peak signal-to-noise ratio, thereby improving the correlation with soil salinity. Notably, the 0.25-order FOD showed the best performance, increasing the correlation coefficient with soil salinity by up to 13 %. The stacked machine learning models effectively combined the strengths of different base models, enhancing prediction accuracy by more than 8 % compared to single models. Furthermore, combining stacked models with FOD further improved prediction accuracy, with an increase in R² of up to 9 %. The combination of 0.25-order FOD and the stacked machine learning model achieved the best performance (R² = 0.82, RMSE = 10.19 ppt, RPD = 2.38, RPIQ = 4.69). This approach provides a reference for rapid and effective large-scale digital mapping of soil salinity in coastal wetlands.

中文翻译：

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使用 Sentinel-2 MSI 数据监测沿海湿地的土壤盐度：结合分数阶导数和堆叠机器学习模型


监测土壤盐度对于了解沿海湿地生态系统的行为和实施有效的管理策略至关重要。尽管多光谱仪器 （MSI） 数据在大规模、高频土壤盐度监测方面具有优势，但在数据预处理和模型构建方面仍然存在挑战。我们将分数阶导数 （FOD） 技术与堆叠机器学习模型相结合，以使用 Sentinel-2 MSI 数据监测和绘制土壤盐度。基本模型包括 Elastic Net Regression、Support Vector Regression、Artificial Neural Network、Extreme Gradient Boosting 和 Random Forest，其中非负最小二乘法作为元学习器。结果表明，低阶 FOD 增强了图像梯度并保持了较高的峰值信噪比，从而提高了与土壤盐分的相关性。值得注意的是，0.25 阶 FOD 表现出最佳性能，与土壤盐度的相关系数提高了 13 %。堆叠机器学习模型有效地结合了不同基础模型的优势，与单个模型相比，预测准确性提高了 8% 以上。此外，将堆叠模型与 FOD 相结合进一步提高了预测准确性，R² 提高了 9%。0.25 阶 FOD 和堆叠机器学习模型的组合实现了最佳性能 （R² = 0.82，RMSE = 10.19 ppt，RPD = 2.38，RPIQ = 4.69）。该方法为快速有效的沿海湿地土壤盐分大规模数字绘图提供了参考。