## 4 Discussion
### Grouping by Cropland and Crop Rotation  
We evaluated the performances of grouping soil samples by cropland type and crop rotation systems for predicting soil pH (Table 5, Figure 5). On the one hand, grouping by cropland type did not increase soil pH prediction accuracy (Table 5, Figure 5a). This was inconsistent with the finding of [35], which conducted a separate prediction of SOM in drylands and paddy fields and received positive results. This inconsistency may arise from the two study regions, characterized by distinct soil and cropping environments. The study region of [35] is the Sanjiang Plain in Northeast China, where the agricultural landscapes are more homogenous and the croplands can be clearly divided into drylands and paddy fields. In addition, crops there can only be harvested once per year. The difference in SOM between paddy and dryland fields thus enabled the separate prediction in [35], reducing the influence of spectral differences on predicting the SOM [35]. Compared to the previous context, Zengcheng in Southern China is characterized by more complex agricultural landscapes and cropping systems. The soil pH in Zengcheng thus has a higher spatial heterogeneity that cannot be easily explained by cropland type (Figure 10a), limiting the strategy of grouping by cropland type.  
On the other hand, we found that grouping by crop rotation and carrying out separate predictions for the three main rotations gave rise to significant improvements in soil pH estimations (Table 5, Figure 5b). This was because the crop rotation systems managed to capture the spatial heterogeneity of the soil pH (Figure 10c). The proper clustering of soil samples made the models more capable of capturing the complex soil property-environment relationships. In other words, highly personalized RF models could be fitted, fully accounting for the characteristics of the subsamples. In this study, the important variables selected by the sub-models and their rankings noticeably differed from scenario 6 (Figure 6e vs. Figure 7) and each other (Figure 7a vs. Figure 7b vs. Figure 7c). As indicated in Figure 7c, the orchard sub-model with the highest accuracy used both Sentinel-1 and Sentinel-2 composites, which could have impacted performance as radar imagery is more sensitive to the structure of orchards. The sub-models for the paddy and vegetable systems determined band 7 (red edge) as the most influential variable, with the vegetable sub-model using more variables, including SAVI, the water index LSWI, and the soil salinity index SSII (Figure 7a,b). We directly compared grouping by crop rotation to the joint modeling of scenario 6 and found that the grouping strategy significantly improved the prediction performance for the vegetable and orchard rotations, probably benefiting from optimized RF models (Tables 5 and 6).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Crop Rotation** & **Number of Selected Variables** & **ME** & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline \hline \multirow{2}{*}{Paddy} & \multirow{2}{*}{9} & \(-0.07\) & \(0.47\) & \(0.15\) \\ \cline{3-5}  & & \(0.02\) & \(0.77\) & \(0.30\) \\ \hline \hline \multirow{2}{*}{Orchard} & \multirow{2}{*}{9} & \(0.15\) & \(0.70\) & \(-0.04\) \\ \cline{3-5}  & & \(0.02\) & \(0.66\) & \(0.36\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Individual statistical soil pH prediction accuracies for different crop rotations based on scenario 6.