## 1 Introduction  
As an \"Essential Climate Variable\", as identified by the Global Climate Observing System, soil moisture plays a critical role in the water and energy balance at the soil-atmosphere boundary, which has a large effect on climate [1; 2]. At a watershed scale, soil moisture also controls the partitioning of rainfall into runoff and infiltration, and therefore has an important effect on the runoff response of catchments [3]. Hence, soil moisture has been widely used in various applications, such as numerical weather forecasting [4], water and energy budgets simulation [5], soil evaporation evaluation [6],stream flow prediction [7], and water resource management [8]. Located in the south bank of the middle reaches of the Yangtze River, Poyang Lake is the largest freshwater lake of china. The ungauged zone of Poyang Lake, which lacks hydrological observations, generally refers to the large alluvial plain stretching from seven stream flow gauging stations to the lake boundary. This region covers a majority of the Poyang Lake eco-economic zone, which is occupied by intensive rice agriculture, supporting the high-density local population. Moreover, the significant seasonal fluctuations of Poyang Lake water levels and the associated inundation areas create extensive wetland ecosystems in this region [9]. However, both droughs and floods have occurred frequently around the lake in recent decades, which are primarily caused by climate change and human activities [10]. For example, the severe continuous drought during 2006-2007 brought the lake water storage down to less than 1% of the lake capacity [11], and the extrema flood event in 1998 resulted in direct economic losses of about \(\Psi\)38 billion over the Poyang Lake watershed [12], with most of the losses occurring within the ungauged zone. Accordingly, soil moisture estimation over Poyang Lake ungauged zone is of vital importance for flood forecasting and drought monitoring, irrigation scheduling, agriculture management, and wetland protection. Besides, knowledge of soil moisture is beneficial to the Predictions in Ungauged Basins (PUB) researches [13]. Soil moisture, especially surface soil moisture, is highly variable in space and time [14]. In situ measurements can provide accurate estimation of soil moisture, but they are both time consuming and expensive, and only represent a small area (few square decimeters). Nevertheless, a number of strategies can be adopted to upscale the spatially sparse ground-based observations [15; 16], which are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals [17]. Microwave remote sensing techniques have been used to obtain surface soil moisture, commonly referred to as the water content of the uppermost soil layer, at various temporal and spatial scales since the 1970s [18]. The strengths lie in that both microwave scattering and emission are directly related to the water content of soil surface and microwave sensors can acquire imagery in all weather, during day and night. In recent years, various soil moisture products have been derived globally from microwave radiometer and scatterometer systems with high temporal resolution [19; 20; 21; 22], but they are generally too coarse for watershed or field scale applications. Using downscaling techniques, the spatial resolution of those soil moisture products can be largely improved, while uncertainties can be introduced during this process [23; 24]. Conversely, Synthetic Aperture Radar (SAR) systems can achieve much higher spatial resolutions (<1 km) and allow for soil moisture mapping at fine scales directly. However, apart from soil moisture, SAR measurements are also sensitive to several other surface parameters such as surface roughness, vegetation cover, and topography, making it difficult to separate the soil moisture contribution to the backscatter signal from other factors.