## 3 Analysis and Discussion
### Measurement and Monitoring of Soil Moisture Dynamics
#### 3.2.4 Remote Sensing: Unmanned Aerial Vehicle (UAV) for Monitoring Soil Moisture Dynamics  
The principal advantages of using drone-based SM monitoring include precision, efficiency, timeliness, scalability, and cost-effectiveness. UAVs can be equipped with diverse sensors, including optical, infrared, and laser imaging detection and ranging (LIDAR), serving as a crucial platform for remote sensing observations. Through UAV-based remote sensing, high-resolution imagery can be acquired, achieving pixel sizes down to the centimeter or millimeter scale, enabling detailed and precise SM monitoring and analysis [15].  
In our review, we identified the most prevalent sensors equipped on UAVs for SM monitoring:  
* _RGB cameras_: which capture images in the visible spectrum primarily for vegetation mapping, offering high-quality RGB band images at a relatively low cost [77];
* _Multispectral and hyperspectral cameras_: capturing images across multiple spectral bands to compute various spectral indices (Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), and others.) for assessing vegetation conditions, soil physical parameters, and SM content; this image generally require radiometric and atmospheric corrections [78; 79];
* _Thermal cameras_: commonly used for mapping temperature, evapotranspiration, and SM by capturing infrared radiation emitted by the targets, allowing for temperature differences and thermal anomalies identification. Thermal cameras have been introduced for SM mapping by utilizing the relationship between water content and land surface temperature (LST) [80], with their most common application in agriculture for monitoring crop water stress and irrigation management [81];  
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* _Shortwave near infrared (SWIR) camera_: helps generate various SWIR reflectance indices that correlate more closely with the total water content of the vegetation pores and infer SM content [82];
* _LIDAR_: significantly applied for 3D reconstruction, flood monitoring, snow depth, and erosion assessments, though they are costlier and require ground filtering corrections [83].  
Given the nature of SM measurement via UAV remote sensors, it is essential to develop models linking SM content to derivable remote sensing indicators like surface temperature and vegetation indices [84]. For instance, [PERSON] et al., (2020) [85] mounted multi-type cameras at plot scale (visible RGB camera, near-infrared camera, and thermal infrared camera) and calculates the GNDVI (green normalized difference vegetation index) in the Tibetan semi-arid lands to analyze the role of the size and type of patchiness in grassland to monitoring the SM. Similarly, [PERSON] et al., (2020) [86] in China's semi-arid lands found a significant correlation with the brightness of UAV visible images and SM, suggesting the SM can be estimated using image brightness combined with vegetation coverage. [PERSON] et al., (2022) [77] also demonstrated significant correlations between soil texture properties and surface temperature for SM estimation in Iran's arid lands, using RGB and thermal imaging sensors from UAV platforms. Figure 5 shows the principal sensors carried by UAVs used for monitoring SM at project scales.