Monitoring soil salinity in coastal wetlands with Sentinel-2 MSI data: Combining fractional-order derivatives and stacked machine learning models - Congcong Lao; Xiayang Yu; Lucheng Zhan; Pei Xin (Agricultural Water Management)
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.