**4. Discussion**

### *4.1. Comparison of S1-Retrieved SM with SM Products*

We chose the Tuotuo River basin as an example, where the detailed investigation was conducted, to compare with widely used SM products, such as GLDAS-Noah, ESA CCI, and ERA5-Land (Figure 12). At the local scale, the retrieval results can present the distribution characteristics of SM in different surface environments. The widely used SM data products are unable to characterize the heterogeneity of SM spatial distribution in detail. For example, the GLDAS and CCI can only identify high SM in the southernmost glacial regions, while the distribution of SM in other areas is varied. They can only give a very rough description of the moisture distribution over tens of kilometers limited by its coarse resolution, and the details on SM distribution are lost. In addition, the distribution of the three SM products in this region is also different, which also confirms the demand for high-accuracy SM data in the QTP.

**Figure 12.** The comparison of SM distribution between retrieved SM and widely used SM products. (**a**) The place circled by the black line indicates the location of the Tuotuo River basin. (**b**–**i**): The SM distribution in this region with different data. The white areas in the retrieval results are caused by post-processing, where water bodies, mountain shadows, and regions with negative Δ*σ* are masked. The white areas of SM products are caused by the lack of effective data in QTP.

Figure 13 shows the statistics of SM distribution in the study area on 2 July 2018, from three SM products and S1 retrieval results. The average SM content of ESA CCI, ERA5- Land, GLDAS-Noah, and retrieval result is 0.34, 0.5, 0.29, and 0.19, respectively. The upper and lower quartiles of the in situ SM for the thawing season are 0.29 and 0.09, respectively. Compared with the in situ data, the SM values of the three products are significantly overestimated, while the retrieval results are in a reasonable range. In addition, previous research found that the SM data of the ESA CCI product has the best accuracy on the QTP compared with in situ observations, with an r of 0.63 [16]. In terms of accuracy, our SM retrieval results also showed substantially higher accuracy, and r reached 0.9.

**Figure 13.** The box plot of SM for the three SM products and S1 retrieval results for the Tuotuo river basins on 2 July 2018. The box line diagram has six parts: lower edge, lower quartile, median, upper quartile, upper edge, and outliers beyond the upper and lower edges.

### *4.2. SM Distribution Characteristics at the Local Scale*

Figure 14 shows the spatial distribution of different vegetation types [86] and SM in the Tuotuo River basin. We summarized the characteristics of SM over different vegetation types, as shown in Figure 15. In areas with high vegetation cover, such as alpine swamp meadows and alpine meadow areas, the SM content is significantly higher than in alpine steppe and alpine desert areas. The average SM content over different vegetation types from high to low is alpine swamp meadow (0.26), alpine meadow (0.23), alpine steppe (0.20), and alpine desert (0.16).

**Figure 14.** The vegetation types and spatial distribution of SM in the Tuotuo River basin. (**a**): The vegetation type. (**b**): S1retrieved SM.

**Figure 15.** Violin plots of SM content of the four vegetation types. Each violin plot contains a box plot and a kernel density plot. A kernel density plot overlays each box plot. The black marks indicate the median, and the boxes indicate the quarter range of values. The number of samples defines the width of the violin in each subplot.

### *4.3. Regions with Very Low σ*◦ *in the Thawing Season*

Normally, the value of *σ*◦ in the thawing season is higher than that in winter because the liquid water content in the thawing state is usually higher than in the frozen state [87]. However, during the SM retrieving process, we noticed that *σ*◦ during the thawing season in some regions is close to or even lower than *σ*◦ in winter. Therefore, we tried to find the reason by comparing the variation of the backscattering coefficients for long time series. As shown in Figure 16a, we consider the regions with significant seasonal variations in the *σ*◦ as normal areas, i.e., *σ*◦ is higher in summer than in winter, and the disordered areas as abnormal areas. To explain the potential reasons for this phenomenon, we further examined the precipitation, vegetation, and soil texture in these particular regions.

• Precipitation

The precipitation process during the thawing season is one of the main reasons for SM variations [88]. During precipitation events, the wet or flooded ground surface will cause the *σ*◦ to deviate from its normal range. We examined the precipitation conditions in three regions as in Figure 16a to test the possibility of this conjecture. The precipitation data is from ERA5-Land precipitation reanalysis data [13], and the temporal curves are shown in Figure 16b. The average annual precipitation of the three regions is 606 mm, 574 mm, and 624 mm, respectively, with little difference in precipitation. Therefore, precipitation is not the cause of low *σ*◦ in the thawing season, and the speculation that the wet ground surface causes low *σ*◦ is not true. The inference of the abnormal *σ*◦ caused by the accumulation of surface water is also not valid.

• Vegetation and soil texture

The impact of vegetation coverage and soil texture on SM content should not be neglected [89–91]. We found some differences between the two regions by examining the temporal changes in the NDVI values in the normal and abnormal regions. As shown in Figure 16c, the NDVI values in the abnormal areas are all relatively low (smaller than 0.1) and do not exhibit seasonal variations. Meanwhile, we referred to the soil texture dataset published by Liu et al. [92,93] and combined it with the field records in the anomaly areas. We found that the soil in this abnormal area is composed of sand. There is a big chance that these abnormal areas are bare ground and are extremely dry during the particular period in the thawing season, therefore leading to low values of *<sup>σ</sup>*◦.

**Figure 16.** Comparison of normal and abnormal regions. (**a**): *σ*◦; (**b**): Precipitation; (**c**): NDVI.
