## 3 Innovation
### Relationship Between Metadata and Prediction Accuracy  
The study focused on papers analyzing depths of 0-60 cm and investigated the relationship between metadata and modeling accuracy. Box plots indicated that the mean modeling accuracy (R\({}^{2}\) = 0.76) surpassed the mean validation accuracy (R\({}^{2}\) = 0.69). We speculated that this discrepancy may be due to the differing numerical distribution characteristics of the modeling and validation datasets, which do not completely overlapped. Figure 2: The study analyzes the types of remote sensing data and machine learning techniques employed in these 104 articles. The graphs on the left and right utilize pie charts to illustrate the percentage of each sensor type (used individually or in combination) and the various machine learning algorithms applied, respectively. Additionally, the allocation ratio of modeling to validation samples might influence model accuracy, with the 7:3 allocation pattern being the most commonly employed, followed by 8.2. The analysis revealed that the distribution of salinization study areas primarily occurred in coastal zones, farmland, lakes, and oases (farmland and desert areas), with their accuracies ranked as follows (Figure 3a): terminal lake (R\({}^{2}\) = 0.81) > oasis (R\({}^{2}\) = 0.76) > coastal region (R\({}^{2}\) = 0.74) > crop (R\({}^{2}\) = 0.71). This ranking illustrated that accuracy diminishes as vegetation coverage increases, transitioning from high salinity areas around lakes with minimal vegetation to regions with extensive agricultural coverage. However, it is important to note that the number of papers included in the assessment varied by region. Soil salinity statistics indicated that the mean and maximum values in these studies ranged from 0.54 to 69.50 dS/m and 2.4 to 355.20 dS/m (Figure 3c), respectively. The statistics also showed that the mean values of the samples used for modeling and validating the model were 203 and 110 (Figure 3d), respectively, which can give a reference to how many samples were actually collected for different regional studies. The selection of environmental variables was frequently addressed in the literature, with the number of variable types and the total number of variables ranging from 1 to 11 and 5 to 63, respectively, yielding mean values of 4 and 22 (Figure 3e). The study investigated the relationship between metadata and accuracy, yielding the following insights (Figure 4): (1) Validation accuracy, sample size, number of variables, and mean sample salinity exhibited some correlation with modeling accuracy, while sampling depth, variable type, sampling time, and maximum sample salinity did not influence modeling accuracy. (2) Statistical analysis indicated a correlation of 0.6 between modeling accuracy (R\({}^{2}\)) and validation accuracy (R\({}^{2}\)) (Figure 4a). (3) A large sample size at extensive scales may lead to error accumulation, subsequently reducing modeling accuracy, which is associated with the geographic diversity of the study area (Figure 4b). (4) The use of additional environmental variables did not necessarily result in higher modeling accuracy (Figure 4e). This phenomenon may be attributed to the limited information content of individual environmental variables and the uncertain contributions of these variables. Figure 3: Statistical characteristics of the metadata include modeling accuracy, validation accuracy, mean and maximum values of ECE, prediction accuracy across different regions, sample size, number of variables, and types of variables: (**a**) Range of accuracy of calibration model and validation model; (**b**) Range of R\({}^{2}\) values for modeling soil salinity in different regions; (**c**) Mean and maximum values of observed values; (**d**) Number of samples used to construct calibration model and validation model; (**e**) Number of variables and types involved in model construction. Rhombus are statistics for particular cases.