## 4 Discussion  
### Modeling for Each Scenario  
We evaluated the performance of Sentinel-1/2 images, commonly used natural variables, cropland, and crop rotation maps in predicting soil pH by different data configurations (Table 4, Figure 4). Firstly, the results indicated that Sentinel-1 radar images played an important role in predicting soil pH (Figure 6). Prediction efforts benefit from the uniqueness of radar sensors, which operate in the microwave spectrum and respond to different physical processes [42; 55]. Sentinel-1 thus supplemented the optical perceptions of Sentinel-2 through the retrieval of geophysical variables related to soil pH. Long-term radar data have been shown to also be important in mapping the soil pH of Europe, and SAR-optical fusion has been previously shown to achieve better results [56]. Another advantage of active radar sensors is that they can obtain good-quality observations under all weather conditions, allowing the usage of dense time series. Secondly, as indicated by Figure 6, all the natural variables except for soil thickness were excluded from the optimal variable sets selected by MGFS, contrary to most previous findings [31; 37; 57]. This could be attributed to the site being a small agricultural region with relatively homogenous natural conditions and the fact that we only focused on cropland soils that are predominantly influenced by human activities. The limited spatial resolution of the data used may also help explain this. Thirdly, in agreement with the soil organic carbon modeling work by [31], we found that the cropland map had no positive effects, while the crop rotation systems remarkably  
Figure 9: Predicted soil pH distribution in 2021 derived from the two soil sample grouping strategies: grouping by cropland type (**a**) and crop rotation systems (**b**). Figures (**a1**,**b1**) correspond to the zoomed-in maps for the black squares in (**a**,**b**). The legend is identical to that of Figure 8.  
enhanced the prediction accuracy of soil pH (Figure 4e). This was likely because the soil pH in this area was largely influenced by the intensive and diversified cropping systems, and the crop rotation maps managed to capture the spatio-temporal heterogeneity of cropping systems. Several studies have also reported the effectiveness of cropping-related factors in DSM [58; 59; 60]. On the other hand, the cropland map could hardly provide valuable information related to the heterogeneity of soil pH, and there could have been large intra-class variations for rainfed and irrigated cropland. This hypothesis was supported by the statistical analyses. The one-way analysis of variance (ANOVA) suggested that the differences in soil pH between the rainfed and irrigated cropland were not significant, whereas significant differences were observed among the crop rotation systems (\(p<0.05\)). The least significant difference test (LSD) revealed that the differences were primarily from the main systems, especially for the crop rotation map produced in 2021 (Figure 10c). The crop rotation map in 2021 distinguished soil pH differences more effectively than that of 2020. This may indicate that the soil pH values kept changing under high-intensity cropping and the soil sampling date influenced the relationship between the soil pH and the crop rotation system containing dynamic crop information. As shown in Figure 10b,c, the paddy system had the highest soil pH, followed by the vegetable and orchard systems. Fertilization, water, and tillage management changing the soil moisture, redox reactions, microbial characteristics, and C/N-cycling processes should explain the differences [23]. The paddy system had a higher soil pH mainly because of less chemical fertilizer application and a larger acid buffering capacity due to flooded fields [21]. On the contrary, vegetables and orchards were severely overfertilized, resulting in a lower soil pH. In Zengcheng, vegetables consumed huge amounts of fertilizers, over three times those of grain crops.