## 3 Results and Discussion
### Satellite/UAV/Ground-Based Observation for SM Estimation  
Out of 144 studies, the most widely used methods were Sentinel-1 (36 studies) followed by Moderate Resolution Imaging Spectroradiometer (MODIS; 35 studies), Sentinel-2 (27 studies), Soil Moisture Active Passive (SMAP; 26 studies), Landsat (20 studies), and Unmanned Aerial Vehicle (UAV; 19 studies) (Figure 6). The details of the spatial and temporal resolution of all RS products used in SM estimation can be found in Supplementary Table S2. These sensors differ considerably in spatial resolution, revisit frequency, and soil depth sensitivity, which directly affect their estimation accuracy and scale of application. Sentinel-1 uses C-band Synthetic Aperture Radar (SAR), allowing it to penetrate cloud cover and operate in all weather conditions [77]. SAR backscatter is highly sensitive to soil dielectric properties, which are directly influenced by SM content [78]. Backscattering coefficient with different polarization modes, incident angles, and Polarimetric Decompositions are commonly used features from SAR [79; 80; 81; 82]. Although SMAP was launched in 2015, it has been widely used in SM estimation due to its ability to directly provide SSM using L-band microwave radiometry, which is less affected by atmospheric conditions and vegetation cover compared to optical and thermal sensors [54]. However, its coarse spatial resolution (-36 km) limits its application in fine-scale mapping, although its high revisit frequency (2-3 days) and surface sensing (-5 cm) make it valuable for large-area monitoring. Many studies have used SMAP-derived SSM for RZSM estimation using ML models and reported enhancement of SM prediction using SSM from SMAP [83; 84; 85]. Optical sensors, MODIS, Sentinel-2, and Landsat provide information on vegetation conditions, land-surface temperature, and evapotranspiration, making them suitable for SM estimation. MODIS offers daily observations but at coarser resolution (250-1000 m), while Sentinel-2 and Landsat provide finer resolution (10-30 m) but with longer revisit cycles (5 to 16 days). However, their inability to penetrate clouds or vegetation canopies make them less reliable for direct SM estimation, necessitating integration with microwave sensors or models for improved accuracy [40; 47; 48; 49].  
Figure 6: The top 10 widely used remote sensing techniques used in SM estimation. MODIS: Moderate Resolution Imaging Spectroradiometer, SMAP: Soil Moisture Active Passive, UAV: Unmanned Aerial Vehicle, CYGNSS: Cyclone Global Navigation Satellite System, ERA-5: ECMWF Reanalysis 5 th Generation.  
UAVs are widely used to estimate SM at field scale due to its high spatial and temporal resolution, flexibility, and real-time or near-real-time data collection for site-specific monitoring. Multispectral, thermal, and RGB cameras have been widely used to derive different spectral indices from UAVs to estimate SM [86; 87; 88; 89]. However, UAVs are limited by short flight times, dependence on weather conditions, and challenges in processing large volumes of data for accurate SM estimation [90]. These differences in spatial resolution, revisit frequency, and soil depth sensitivity underline the importance of selecting appropriate RS products based on the spatial scale, crop cover, and temporal dynamics of the study area.