## 1 Introduction  
Soil moisture (SM) accounts for just 0.05% of global fresh water [1], but it plays an outsized role in our world through its influence on such critical biosphere functions as river flows and flood timing [2], agricultural water management [3, 4], terrestrial photosynthesis [5], and recharging of groundwater aquifers [6, 7]. Accurate information on the spatial and temporal occurrence of SM is essential for understanding its influence on a wide range of critical zone processes. SM refers to the quantity of water present within the unsaturated zone of the soil [8]. SM levels are impacted by a range of environmental factors, including precipitation [9, 10], land-use patterns [11, 12, 13], soil type and depth [15, 16, 17, 18], topography [19, 20, 21], and local atmospheric factors including radiation, temperature, humidity, and pressure [22]. The impact of these factors on SM variation varies based on the spatial scale of the study area, the temporal resolution, and the prevailing environmental conditions [23; 24; 25; 26]. Various methods have been proposed for monitoring SM, which can be divided into two categories: pointwise and regional measurements (Figure 1). Pointwise measurements can be further differentiated into direct and indirect measurements. Direct measurement, such as the gravimetric method, which is independent of soil type and salinity, is standard and the most reliable method for estimating SM. However, its application is limited due to the impracticality of obtaining continuous spatial and temporal coverage and its destructive nature [27; 28; 29; 30]. To overcome these challenges, numerous indirect SM measurement techniques have evolved, including neutron scattering, time domain reflectometer (TDR), frequency domain reflectometer (FDR), gamma attenuation, thermal dissipation, heat flux, capacitance, electrical resistance (gypsum block), tensiometer, and pressure plate. Despite their benefits, these methods are often expensive, require calibration for different soil types, pose health risks (e.g., radiation exposure, electrical hazards), and may not be suitable for fast-draining soils [31; 32; 33; 34; 35; 36]. In response to these limitations, remote sensing (RS)-based SM estimation has emerged as a viable alternative, offering regular updates on a global scale at a lower cost [37; 38]. A comprehensive review of traditional methods of SM estimation can be found in [PERSON] et al. [39] and [PERSON] and [PERSON] [40]. Since the 1970s, various RS techniques have been introduced to analyze SM, utilizing different segments of the electromagnetic spectrum ranging from optical to microwave [34; 38; 41; 42; 43; 44; 45; 46]. Each type of sensor comes with its own set of advantages and disadvantages, offering a range of capabilities in the measurement of SM. The wealth of information from optical, thermal, and passive and active microwave sensors has led to the development of numerous techniques aimed at estimating SM. Figure 1 provides a detailed representation of SM retrieval methods using these sensors' outputs. A decade ago, early RS research provided promising insights in estimating SM using optical and thermal infrared RS [40]. Although optical and thermal RS technologies offer  
Figure 1: Comprehensive mapping of methodologies for estimating SM based on point observations and remote sensing techniques. high spatial resolution, widespread satellite availability, and mature technology, they often face challenges in retrieving SM due to vegetation interference, cloud contamination, night effects, poor temporal resolution, and atmospheric effects [47; 48; 49; 50]. To overcome these issues, microwave RS techniques have been adopted, offering advantages such as all-weather observation capabilities and robust physical foundations [34; 51; 52]. Since 1978, passive and active microwave satellite sensors have been delivering valuable global-scale surface SM (SSM) data [53; 54; 55].