## 3 Innovation
### Innovative Mapping and Forecasting Methods
#### 3.3.2 Application of Machine Learning to Soil Salinity Inversion  
Various ML algorithms have been extensively employed for the rapid remote sensing monitoring of regional salinized soils [78]. The process of DSM in soil salinity mapping is shown in Figure 1. The uniformity and representativeness of the samples directly influence the model parameters and prediction accuracy. In the modeling process, data-driven ML models adjust and iterate parameters solely based on the characteristics of the current input data [79]. When certain sample data used in modeling exhibit pronounced regional local features, the resulting model may suffer from inadequate generality and generalization ability [80]. For instance, localized anomalies in environmental factors such as soil particle size distribution, moisture content, and vegetation cover can lead to atypical spectra in soil samples. These anomalies diminish the regional salinity information withinthe sample data and disrupt the mapping relationship between the actual soil salinity on the ground and the spectral features captured in the images [15]. A notable characteristic of geographic sample data is its spatial dependence. To enhance the accuracy and generalization performance of the model, [PERSON] et al. [81] introduced a spatial correlation module in RF to identify and exclude these anomalous samples, thereby facilitating more accurate SSC mapping. While ML algorithms excel in modeling high-dimensional big data, achieving higher prediction accuracy typically necessitates a substantial number of training samples. In soil salinity mapping, acquiring extensive soil samples through field surveys is not only labor-intensive but also fraught with challenges. Specifically, field sampling often yields sparse and non-normally distributed soil sample data [82]. The high feature complexity of remote sensing image-derived variables not only complicates model construction but also frequently leads to diminished model prediction performance. To tackle this challenge, [PERSON] et al. [34] proposed a model framework for soil salinity prediction that integrates sample data enhancement, feature selection, and model optimization. This strategy employs the Mix-up method to generate additional artificial samples, addressing the overfitting problem associated with sparse samples. Joint parameter tuning through Bayesian parameter optimization and Light Gradient Boosting Machine (LightGBM) enhances the model's generalization capability. SHapley additive explanation (SHAP) values are utilized to compare the impact of features on SSC across different experimental scenarios, improving the model's interpretability and reducing uncertainty. Recently, the CNN algorithm has garnered attention in various computer vision applications, including semantic segmentation, target detection, and image classification [83]. It extracts spatial and local distribution features using a sliding window approach and is applicable to data with three-dimensional characteristics. Compared to traditional ML algorithms and neural networks, CNN can effectively address the issue of neglecting spatial context in its architecture [84]. However, there are relatively few studies exploring the potential of CNN algorithms for soil salinity mapping. To comprehensively evaluate the application potential of CNN algorithms in soil science, [PERSON] et al. [85] conducted CNN spatial distribution mapping of surface soil salinity in Lake Urmia and employed the Frequency Ratio (FR) model and Weights of Evidence (WOE) model to assess the classification results. Other specifically adopted ML methods include PLSR [52; 57], Back Propagation Neural Network (BPNN) [35; 51], SVR [35; 39; 86], RF [35; 39; 62; 71; 81], GPR [87], Extreme Learning Machine (ELM) [44], Cubist [9], MIXup_LGBM [34; 69], Gradient Boosting Regression Trees (GBRT) [22; 88; 89], XGBoost [58; 90], Grasshopper Optimization Algorithm (GOA) [87], Temporal Convolutional Network (TCN) [26], CNN [27; 31; 85], and Deep Neural Network (DNN) [87].