# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
## 4 Results and Analysis
### Comparisons of the Downscaled Results  
Ground evaluation is a key step before utilizing satellite inversion data. Boxplots (Figure 4a) and probability density function (PDF) curves (Figure 4b) were used to compare the statistical distribution of the five SM datasets of in situ SM, the original SMAP, GABP, RF, and CNN in Sichuan Province during the summer and autumn of 2017. The sample sizes of the first two were 45,364 and 8670, and those of the latter three were 15, 937, and 221. The in situ SM can be measured in all weather and every day with a larger range. The measurement depth is different between the satellite (5 cm) and in situ SM (10 cm). As shown in Figure 4a, the data range is reduced from left to right. GABP was more consistent with SMAP than with the RF and CNN. GABP maintained the dynamic range and mean level of SMAP, whereas the RF and CNN reduced the dynamic range. As shown in Figure 4b, GABP and SMAP had the best consistence among these curves. SMAP data showed an obvious normal distribution. The RF and CNN data distributions were more concentrated and showed two or more peaks, which were quite different from that of SMAP. The in situ SM had a wider distribution and higher average. The main reasons were as follows: Because of the gravity infiltration of soil water and the evaporation of surface water, in situ measurements (10 cm) contain more water than SMAP (5 cm); in situ measurements contain data during cloud and rain weather; the stations are denser in the southeast and sparser in northwest Sichuan. Moreover, the southeast area has a stronger water-holding capacity because the terrain is low-lying and the vegetation is dominated by woody savannas, savannas, cropland/natural vegetation mosaics, evergreen broadleaf forests, and deciduous broadleaf forests. In the following section, the results from the three ML methods are compared in the time and space domains. After time and space matching, the sample numbers of SMAP were 3092, 1247, and 1845 in total, summer, and autumn, respectively, and those of GABP (RF or CNN) were 4904, 1911, and 2933. In situ SM and the four datasets both excluded cloud and rain areas, invalid value areas, unscanned areas, and low-precision SMAP areas such as ice, snow, water bodies, and bare ground. MAEs were used to analyze the differences in the time domain. Figure 5 shows the bars of monthly, seasonal, and total MAEs of the SMAP, GABP, RF, and CNN SM relative to the in situ SM. It can be seen that the MAE of the downscaled results was generally better than that of SMAP, in which GABP had the lowest and the CNN had the highest MAE. The MAE was significantly higher for the CNN in autumn and slightly higher for the RF in summer. The four datasets were more accurate in autumn than in summer, with August having the highest MAE and GABP and RF having the lowest MAE in September. This may be attributed to the abrupt changes of SM with heavy precipitation during the summer. In summer, the MAEs of GABP were much lower than those of the RF and CNN, indicating that GABP was better able to capture abrupt variations in the time domain. Figure 3: Bar (**a**) and stacked bar (**b**) plots for Pearson correlation coefficients (CCs) between SM and predictors, and bar plot for the importance values (**c**) of predictors in the RF model. Figure 4: Boxplots (**a**) and PDF curves (**b**) of in situ SM, SMAP, GABP, RF, and CNN SM data in Sichuan during summer and autumn 2017. Figure 5: Monthly, seasonal, and total MAEs of SMAP, GABP, RF, and CNN SM relative to in situ SM in Sichuan during summer and autumn of 2017. Figure 6 shows the spatial distributions of the mean SM of the in situ SM (a), GABP (b), RF (c), CNN (d), and SMAP (e) data in Sichuan during the summer and autumn of 2017. The downscaled results of the GABP network, RF, and CNN in the red-box region of Figure 6e are enlarged in Figure 6f-h, respectively. As can be seen from Figure 6, the five distributions were all wet in the southeast and dry in the northwest. However, the northwest boundary areas became wet.