Tree-Structured Parzan Estimator-Machine Learning-Ordinary Kriging: An Integration Method for Soil Ammonia Spatial Prediction in the Typical Cropland of Chinese Yellow River Delta with Sentinel-2 Remote Sensing Image and Air Quality Data

Spatial prediction of soil ammonia (NH3) plays an important role in monitoring climate warming and soil ecological health. However, traditional machine learning (ML) models do not consider optimal parameter selection and spatial autocorrelation. Here, we present an integration method (tree-structured Parzen estimator-machine learning-ordinary kriging (TPE-ML-OK)) to predict spatial variability of soil NH3 from Sentinel-2 remote sensing image and air quality data. In TPE-ML-OK, we designed the TPE search algorithm, which encourages gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGB) models to pay more attention to the optimal hyperparameters' high-possibility range, and then the residual ordinary kriging model is used to further improve the prediction accuracy of soil NH3 flux. We found a weak linear correlation between soil NH3 flux and environmental variables using scatter matrix correlation analysis. The optimal hyperparameters from the TPE search algorithm existed in the densest iteration region, and the TPE-XGB-OK method exhibited the highest predicted accuracy (R2 = 85.97%) for soil NH3 flux in comparison with other models. The spatial mapping results based on TPE-ML-OK methods showed that the high fluxes of soil NH3 were concentrated in the central and northeast areas, which may be influenced by rivers or soil water. The analysis result of the SHapley additive explanation (SHAP) algorithm found that the variables with the highest contribution to soil NH3 were O3, SO2, PM10, CO, and NDWI. The above results demonstrate the powerful linear-nonlinear interpretation ability between soil NH3 and environmental variables using the integration method, which can reduce the impact on agricultural nitrogen deposition and regional air quality.

Automated summary

This study presents an integration method (tree-structured Parzen estimator-machine learning-ordinary kriging) to predict spatial variability of soil NH3 by considering parameter selection and spatial autocorrelation. The TPE-XGB-OK method exhibited the highest accuracy in predicting soil NH3 flux compared to other models. Spatial mapping results showed that high fluxes of soil NH3 were concentrated in certain areas, possibly influenced by rivers or soil water.