## 5 Discussion  
### Discussion of the Consistency between the OWCM-/GM-Derived SM and RFSM  
#### 5.1.1 Spatial Consistency  
Typically, renormalization is included in the downscaling procedures so that spatial resampling of fine-RES SM back to the original coarse-resolution grid will exactly match the original coarse-scale products being downscaled (e.g., RFSM in this case). However, it is worth noting that the eight downscaled fine-RES SM products obtained by GMs and OWCMs were not renormalized in this way, so that we could better understand the relative performance of the GMs and OWCMs. Within the QTP, Figure 5 shows images of monthly averaged SM estimates from corresponding GM- and OWCM-derived fine-RES SM products, along with the original RFSM. All eight fine-RES SM products generally match the temporal dynamic changes of the original RFSM. However, SM estimates derived from the ATA-GM and ATA-OWCM approaches are consistently better matched to the spatial patterns of RFSM; RF-GM and RF-OWCM SM also have good performance, especially in the middle, south, and west areas in summer. Considering the residual function in the ATA approach, it is reasonable to infer that ATA has the best ability to reflect the spatial pattern of the original RFSM product accurately. For both RF-GM and RF-OWCM, despite the machine learning model often being characterized by a sliding and smoothing effect, a spurious mosaic phenomenon (i.e., the reflection of the underlying 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) grid in fine-RES SM results) is still seen in certain areas. However, on the whole, the GM and OWCM of ER and NN have a stronger smoothing effect than those of RF, and underestimate the spatial variability present in the original RFSM product. It can be observed that the most evident spatial characteristic of the original RFSM is the increasing trend of SM from west to east over the QTP. Higher SM typically occurs in the east, south, and southeast, while lower SM appears in the central and western regions. The dynamic range of RFSM is better reflected in the OWCM-derived SM products than in the GM-derived SM--especially for ER- and NN-based SM. Overall, the OWCM-derived SM datasets represent a significant improvement with respect to the corresponding GM-derived SM datasets, and the GM algorithms are deficient in preserving the dynamic range of coarse-resolution SM information [24]. Moreover, the R and ubRMSE between the aggregated OWCM-/GM-derived SM datasets and the original RFSM products were calculated spatially, as shown in Figure 6. This echoes the comparison results above. ATA-OWCM SM behaves nearly the same as ATA-GM SM, with a similar spatial distribution of R and ubRMSE. Moreover, the ATA-OWCM and ATA-GM SM products preserve the spatial characteristics of the original RFSM over most areas, with clearly higher R and lower ubRMSE compared with the other three GM- and OWCM-derived SM products. RF-OWCM SM shows overall higher R and lower ubRMSE than RF-GM SM. ER-OWCM SM also obtains overall better R values--especially over the west and middle of the QTP, where the ER-GM SM product does not perform well. Likewise, the ubRMSE of ER-OWCM SM shows a better spatial distribution than that of ER-GM SM compared with the original RFSM products. It can be observed that the NN-GM and NN-OWCM SM datasets have the worst performance among the four GM- and OWCM-derived SM datasets, with NN-OWCM still outperforming NN-GM. Figure 5: The spatial distribution of the monthly averaged SM products over the QTP during the 2016 unfrozen season, calculated from the corresponding GM-/OWCM-derived fine-RES SM, along with the original RFSM. As a summary of the information presented in Figure 6, Figure 7 shows the spatial frequency distributions of the statistical metrics R, ubRMSE, and bias between the aggregated OWCM-/GM-derived SM and the original RFSM over the QTP.