## 4 Experiment and Analysis
### Point-Surface Fusion Results of NMRI  
Owing to the good prediction ability of the RF model, a spatially continuous 500 m spatial resolution NMRI product was obtained. Figure 9 shows the fused NMRI map compared with the NDVI and GPP in summer and winter. The blank area in the map is unable to be retrieved because the auxiliary data has been removed for the effects of ice- and snow-covered areas, water bodies, buildings, and other features. In general, the spatial distributions of the NMRI are consistent with that of NDVI and GPP. That is, in summer, the three indices are reported to have higher values in the middle of the northern region and the north-east corner, whereas the southern region and the central inland region are lower. When it comes to the winter condition, the spatial distribution changes significantly, but the consistency between the three indices retains. Most areas in the central inland and north-east regions suffer a reduction of vegetation growth owing to the coming of winter. However, the vegetation in the areas of the California experience a growth, shown as all the three indices increased obviously in this region. This is mainly due to special climate of California, Mediterranean climate, which is characterized as dry, hot in summer and mild and wet in winter. Therefore, in summer the dry and hot climate will inhibit the vegetation growth, while in winter the suitable mild climate can bring a growing season to the vegetation [40]. The consistency of NMRI spatial distribution with NDVI and GPP further proves the accuracy of the point-surface fusion results. However, there is still some inconsistency in the NMRI map, such as the higher NDVI and GPP values in the west of Washington and Oregon along the southern coastal alongside smaller NMRI values. One of the reasons could be that the vegetation type in this area is mainly tree cover, as shown in Figure 1b, while the PBO H2O network is always located in sites with low vegetation, like grassland, cropland, and shrubland. When the NMRI measured by the PBO network directly extends to forests with tree covers, the index may not be as applicable as before. Furthermore, although a small number of PBO H2O sites are also distributed in the forest area, they are usually located in open spaces 10 m away from the nearest trees in the forest. Because these GPS sites are originally designed for a position needed to reduce multipath effects [40]. Therefore, the current PBO sites are mainly designed to monitor the water content of nearby shrubs, herbs, mosses, and lichens, but NDVI and GPP products  
Figure 9: Point–surface fusion results of 500 m NMRI compared with NDVI and GPP: (**a**) NMRI-Summer; (**b**) NDVI-Summer; (**c**) GPP-Summer; (**d**) NMRI-Winter; (**e**) NDVI-Winter; (**f**) GPP-Winter. have a lower spatial resolution and usually measure the vegetation growth condition of all the green plants in the range, including trees, shrubs, and herbs. As a result, NMRI has some limitations in higher vegetation areas, which is shown as an underestimation of VWC information. Meanwhile, there are still some shortcomings in the RF-based NMRI map, e.g., the blocky effect in Figure 9a,d, which affects the continuity of the whole picture. Such blocky effects have also been found in other regression studies using RF models [53; 54]. This phenomenon is mainly due to the characteristics of the RF model. RF is a model based on a decision tree, which selects different features to judge the bifurcation and direction of the decision tree to obtain the final regression result. Therefore, when the range of the judgment conditions is broad and similar variables are input to the trained model, multiple distinct input variables can easily correspond to the same output variable, thus producing the blocky effect.