## 3 Results and Discussions
### Radar Feature Analysis  
The synthetic aperture radar (SAR) is a useful spaceborne sensor in various remote-sensing application aspects, such as natural resource monitoring, crop production assessment and ground object identification [32; 33; 34; 35]. In this investigation, we analyze a set of features by using GF-3 SAR images so as to enhance the robustness of the trained classification model and diversify the type and number of remote-sensing data in the study area. Figure 7 shows a pair of slices in the study area comparing GF-3 SAR imagery and GF-2 MS imagery. Four types of samples are assigned letters A-D. It was shown that SAR recognizes higher biomass (spot A) under the plastic film than the camera on board the GF-2 satellite, because of the penetrating ability of the microwave from using the SAR sensor. In other words, the electromagnetic waves of the SAR could detect the earth in cases where a thin plastic film is placed. Spot B shows a higher NDVI feature index yet is interpreted as a smaller NRCS (i.e., radar backscattering coefficient) value than that of spot A, which shows a lower NDVI index. This example implies that SAR imagery is a useful data set to identify land cover. Spots C and D, on the other hand, produced images with lower backscattering samples, resulting from the small surface roughness of bare soil, including those growing shrubber. We summarize the radar features of five typical types of samples extracted from GF-3 SAR images (UFS) collected in this investigation (Table 5). As shown in Table 5, the difference between typical land covers can be drawn by MVN features. The highest value of NRCS in dB is observed for building, \(-1.33\) dB on average. However, this statistic should be recognized as the radar backscattering of the entire building rather than of the rooftop only, which dominates the spectral reflectance of pixels in GF-2 multispectral images. The reason is that the structural parameters, such as the shape and material of the rooftop, the location of scattering center, etc., determine the NRCS of a building. The lowest value of NRCS in dB, on the other hand, is observed for water, at \(-26.43\) dB on average, with a small  
Figure 7: GF-3 SAR image of Dumkuotan Village (**left**), which was acquired on 28 June 2021 (in HH polarization, 3 m), and the GF-2 multispectral image pair (**right**), which was acquired on 24 May 2021 (R/C/B: NIR/red/green or band 4/3/2). A: land covered with higher biomass (vegetation); B: land covered with lower biomass (vegetation); C: bare soil covered by medium biomass (shrubs); D: bare soil covered by lower biomass (Gobi). SDN of 1.23 dB. The desert is detected as a dark area in the C-band SAR images because the dryer the object is, the lower the signal backscattered is. We can see that the averaged NRCS for the desert is \(-21.69\) dB, half of that of bare soil, which is covered by rock, clay or salt crust in the study area. The vegetation, shown as bright spot in SAR images, is identified as \(-7.95\) dB of NRCS, resulting from the large biomass, as expected in summer. To demonstrate the seasonally varied characteristics of NRCS in SAR images, we produced a pseudo-colored SAR image (Figure 8) from multitemporal Sentinel-1 SAR data by the GEE platform ([[https://eartheingine.google.com/](https://eartheingine.google.com/)]([https://eartheingine.google.com/](https://eartheingine.google.com/)), accessed on 30 December 2022). From Figure 8, we can see the image showed stronger brightness in the green band, which is related to the SAR image acquired in June, when higher biomass is expected than that in spring or winter. In addition, we can see the color changed among the cultivated lands in the study area. This is because the seasonal variation of NRCS is observed. Moreover, many extreme bright spots are displayed in Figure 8. These spots are related to the buildings that intensively reflect the incidence electromagnetic waves.