## 4 Experiment and Analysis
### 500 m Daily Soil Moisture Map Generation
In terms of time, (e1)-(e3) is compared with (a1)-(a3), (b1)-(b3), (c1)-(c3), (d1)-(d3), and it can be seen that in most areas the soil moisture have risen drastically. This is due to three consecutive days of continuous rainfall in the eastern coastal and central regions during 27 February 2014-1 March 2014, which significantly increased the soil moisture value. At the same time, during this period, the soil moisture map (e4) of NASA-USDA products also increased significantly compared to (a4), (b4), (c4), and (d4). The soil moisture increase area in the soil moisture map (e4) of NASA-USDA products is the same as the soil moisture map (e1)-(e3) retrieved by GA-BP. This preliminarily shows that the soil moisture map retrieved by GA-BP in this paper is accurate. In order to further verify the reliability of the soil moisture map retrieved by GA-BP in this study, we compared rainfall, NDVI map, and soil moisture. Existing studies have shown that NDVI can reflect the vegetation coverage information and reflect the soil moisture information under the vegetation coverage state [59]. In addition, precipitation can be said to be the first influencing factor in bare-soil environmental areas. It also plays a vital role in vegetation growth and significantly impacts soil moisture [60]. Figure 8 shows the rainfall, NDVI, and GA-BP inverted soil moisture map from 27 February 2014 to 1 March 2014. As seen in Figure 8(a1)-(a3), the NDVI in this region, which fluctuates up and down in a small range, does not vary significantly. However, because NDVI is calculated by waveband, the optical image information is highly susceptible to interference. Meanwhile, in the desert and urban building areas without vegetation cover, the NDVI calculation is seriously affected because of the low vegetation cover. This results in NDVI values greater than 1 or less than 1 in a small number of places, making negative soil moisture values in these areas. In later experiments, this study will be improved for different NDVI calculations in these different areas. The distribution of NDVI in the region is also generally consistent with the land cover type map in Figure 5b. The southwestern part is a desert, barren area with basically no vegetation cover. For this reason, the NDVI values are low, which also makes the soil moisture values in this region poorer compared to other regions. The northeastern and central areas are primarily arable land, surrounded by arable land as grassland; more peripheral are tall shrub-like vegetation and coniferous forests, resulting in higher NDVI values in the eastern coastal and central areas of the test area. (b1)-(b3) shows the rainfall distribution during the period, as we can see. The central-eastern coastal area had substantial rainfall on 27 February 2014 (red box range), as rainfall was directly and strongly influenced. The soil moisture model of this experiment captures this vital information better, and the soil moisture values in the red-boxed area of the GA-BP Inversion soil moisture map (c1) on that day have increased significantly. The continuous rainfall at the exact location from 28 February 2014-1 March 2014 has further increased the soil moisture values in this area. Our GA-BP Inversion soil moisture map all reflects the soil moisture changes in these areas better. From the red boxed areas of the GA-BP Inversion soil moisture maps of (c1)-(c3), we can see that the soil moisture at these locations is directly affected by rainfall. The GA-BP inversion soil moisture map of this experiment can better describe the soil moisture change.