Soil water content is an important indicator used to maintain the ecological balance of farmland. The efficient spatial prediction of soil water content is crucial for ensuring crop growth and food production. To this end, 104 farmland soil samples were collected in the Yellow River Delta (YRD) in China, and the soil water content was determined using the drying method. A gradient boosting decision tree (GBDT) model based on a tree-structured Parzen estimator (TPE) hyperparametric optimization was developed, and then the soil water content was predicted and mapped based on the soil texture and vegetation index from Sentinel-2 remote sensing images. The results of statistical analysis showed that the soil water content had a high coefficient of variation (55.30%), a non-normal distribution, and complex spatial variability. Compared with other models, the TPE-GBDT model had the highest prediction accuracy (RMSE = 6.02% and R² = 0.71), and its mapping results showed that the areas with high soil water content were distributed on both sides of the river and near the estuary. Furthermore, the results of Shapley additive explanation (SHAP) analysis showed that the soil texture (PC2 and PC5), modified normalized difference vegetation index (MNDVI), and Sentinel-2 red edge position (S2REP) index provided important contributions to the spatial prediction of soil water content. We found that the hydraulic physical properties of soil texture and the vegetation characteristics (such as vegetation coverage, root action, and transpiration) are the key factors affecting the spatial migration and heterogeneity of the soil water content in the study area. The above results show that the TPE algorithm can quickly capture the hyperparameters that are most suitable for the GBDT model, so that the GBDT model can ensure prediction accuracy, reduce the loss function with less training data, and accurately learn of the nonlinear relationship between soil water content and environmental factors. This paper proposes a machine learning method for hyperparameter optimization that shows considerable potential to predict the spatial heterogeneity of soil water content, which can effectively support regional farmland soil and water conservation and high-quality agricultural development.

Monitoring and evaluation of soil ecological environments are very important to ensure saline–alkali soil health and the safety of agricultural products. It is of foremost importance to, within a regional ecological risk-reduction strategy, develop a useful online system for soil ecological assessment and prediction to prevent people from suffering the threat of sudden disasters. However, the traditional manual or empirical parameter adjustment causes the mismatch of the hyperparameters of the model, which cannot meet the urgent need for high-performance prediction of soil properties using multi-dimensional data in the WebGIS system. To this end, this study aims to develop a saline–alkali soil ecological monitoring system for real-time monitoring of soil ecology in the Yellow River Delta, China. The system applied advanced web-based GIS, including front-end and back-end technology stack, cross-platform deployment of machine learning models, and a database embedded in multi-source environmental variables. The system adopts a five-layer architecture and integrates functions such as data statistical analysis, soil health assessment, soil salt prediction, and data management. The system visually displays the statistical results of air quality, vegetation index, and soil properties in the study area. It provides users with ecological risk assessment functions to analyze heavy metal pollution in the soil. Specially, the system introduces a tree-structured Parzan estimator (TPE)-optimized machine learning model to achieve accurate prediction of soil salinity. The TPE–RF model had the highest prediction accuracy (R² = 94.48%) in the testing set in comparison with the TPE–GBDT model, which exhibited a strong nonlinear relationship between environmental variables and soil salinity. The system developed in this study can provide accurate saline–alkali soil information and health assessment results for government agencies and farmers, which is of great significance for agricultural production and saline–alkali soil ecological protection.

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.

Saline–alkali soils represent a significant reserve of arable land, playing a vital role in ensuring national food security. Given that saline–alkali soil has low soil organic matter (SOM) and soil nutrient contents, and that soil quality degradation poses a threat to regional high-quality agricultural development and ecological balance, this study took coastal saline–alkali land as a case study. It adopted the extreme gradient boosting (XGB) model optimized by the tree-structured Parzen estimator (TPE) algorithm, combined with in situ hyperspectral (ISH) and spaceborne hyperspectral (SBH) data, to predict and map soil organic matter and four soil nutrients: alkali nitrogen (AN), available phosphorus (AP), and available potassium (AK). From the research outputs, one can deduce that superior predictive efficacy is exhibited by the TPE-XGB construct, employing in situ hyperspectral datasets. Among these, available phosphorus (R² = 0.67) exhibits the highest prediction accuracy, followed by organic matter (R² = 0.65), alkali-hydrolyzable nitrogen (R² = 0.56), and available potassium (R² = 0.51). In addition, the spatial continuity mapping results based on spaceborne hyperspectral data show that SOM, AN, AP, and AK in soil nutrients in the study area are concentrated in the northern, eastern, southern, and riverbank and estuarine delta areas, respectively. The variability of soil nutrients from large to small is phosphorus, potassium, nitrogen, and organic matter. The SHAP (SHapley Additive exPlanations) analysis results reveal that the bands with the greatest contribution to the fitting of SOM, AN, AP, and AK are 612 nm, 571 nm, 1493 nm, and 1308 nm, respectively. Extending into realms of hierarchical partitioning (HP) and variation partitioning (VP), it is discerned that climatic factors (CLI) alongside vegetative aspects (VEG) wield dominant influence upon the spatial differentiation manifest in nutrients. Meanwhile, comparatively diminished are the contributions possessed by terrain (TER) and soil property (SOIL). In summary, this study effectively assessed the significant variation patterns of soil nutrient distribution in coastal saline–alkali soils using the TPE-XGB model, providing scientific basis for the sustainable advancement of agricultural development in saline–alkali coastal regions.