**1. Introduction**

Estimating the water balance in large watersheds is of great interest for water resource management and soil moisture is a key variable in this estimation as it effects evaporation, infiltration, and runoff [1]. Soil moisture acts as a link between energy and water fluxes at Earth's surface-atmosphere interface, and knowledge of soil moisture variation is the key to understanding the hydrological cycle [2]. Soil moisture is the primary source of water for agriculture and directly influences crop growth and food production [3]. Even though it only accounts for a small portion of global freshwater, it is still an important factor in global hydrologic cycles [3]. This seemingly small layer (top few centimeters) controls the regulation and distribution of precipitation between runoff and water storage [4]. Soil moisture observations over large areas are increasingly necessary for a range of applications such as meteorology, hydrology, water resource management, and climatology [5]. Remote sensing has provided valuable data sets for understanding land surface hydrological and meteorological processes [6–9].

Obtaining soil moisture measurements can be achieved using a variety of remote sensing instruments or ground-based systems. Satellite-based radars can measure soil moisture at high resolution but are limited in spatial coverage and temporal frequency. Satellite data products can produce global soil moisture estimates but are usually too coarse for practical use in modelling and decision-making [10]. High resolution soil moisture estimates can be applied directly to improve flood prediction and assessment as well as drought monitoring, agricultural productivity prediction, and irrigation management [11–14]. With improved prediction of extreme events, we can also better prepare for their effects on the natural environment and future climate change [2]. NASA's soil moisture active passive (SMAP) will help determine whether there will be more or less water, regionally, in the future compared to today [15,16]. Monitoring these changes in future water resources is a very important aspect of climate change as this will affect the future water supply and food production in areas like the Lower Mekong Basin [17–19]. High resolution soil moisture can aid in crop yield forecasting as well as by providing earlier monitoring of droughts and better understanding of hydrologic processes [4].

This research uses global soil moisture data derived from the L-band radiometer aboard NASA's SMAP observatory [20]. However, satellite microwave radiometers are much coarser than active microwave and optical systems [6]. This coarseness reduces satellite applicability in large watershed models and for regional flood prediction [21]. This study aims to downscale SMAP soil moisture estimates, from gridded 9 km resolution to 1 km resolution, in the Lower Mekong Basin (LMB). This will be done using the regression relationship between daily temperature changes and daily soil moisture under different vegetation conditions with the algorithm developed by Fang et al., 2013. Soil moisture estimates with high spatial resolution can be very useful for watershed scale hydrological modeling due to the fact that soil moisture estimates can be used to constrain errors during extensive wetting and dry downs [21]. The downscaling algorithm and methodology implemented in this research were developed in a previous study by Fang et al., 2018. This algorithm has been applied to the Black Bear-Red Rock watershed in Oklahoma and validated with in situ soil moisture from the ISMN (International Soil Moisture Network). Regions with low elevation are vulnerable to flooding and other water-resource related problems. With these problems, it is important to increase the capacity of flood and drought monitoring. Here we apply this validated algorithm to the Lower Mekong Basin, an area with no functioning in situ soil moisture network. With higher resolution soil moisture, this region would have greater modelling capabilities and the ability to make better decisions concerning water resource management. This algorithm can be applied to other watersheds worldwide, with little absent from the in situ soil moisture systems.

The Mekong River in Southeast Asia provides food, water, and energy resources to the countries of China, Laos, Myanmar, Thailand, Cambodia, and Vietnam [2]. It is the 12th longest river in the world, extending over 4300 km [22]. The basin can be divided into two major catchments also known as the upper and lower river basins. The upper basin is mostly mountainous, rising in the Tibetan Plateau (Figure 1). The Lower Mekong Basin (LMB) is subject to high levels of flooding due to the combination of low-lying terrain and seasonal precipitation cycles [22]. The LMB is home to the rice paddy fields of Vietnam, which would benefit greatly from consistent soil moisture data. Unfortunately, the LMB does not have a consistent in situ soil moisture measuring system, which makes satellite-derived soil moisture estimates appealing for application in watershed-scale hydrological modelling in this region. The lack of ground measurements for soil moisture also complicated the validity of remotely-sensed estimates of the LMB [2].

**Figure 1.** Topography and river networks in Lower Mekong River Basin (LMB).

#### **2. Data**

#### *2.1. SMAP Data*

Developed by NASA, the soil moisture active passive (SMAP) observatory was designed to distinguish between frozen and thawed land surfaces [14]. This mission was launched in January 2015 with the goal of combining radar and radiometer at L-band frequencies to record high resolution soil moisture measurements and freeze/thaw detection at global scale. Unfortunately, shortly after the launch a hardware failure caused the radar to stop working, leaving the radiometer as the only operational mechanism to record data [23]. Since the launch, the radiometer aboard the observatory has been collecting data at a spatial resolution of 36 km and providing global coverage every 2 to 3 days [23]. Observations from SMAP will provide improved estimates of water, energy, and transfers between land and atmosphere [24,25]. SMAP uses lower frequency microwave radiometry (L Band) to map soil moisture at Earth's land surface because at lower frequencies the atmosphere is less opaque, vegetation is more transparent, and the results were more representative of the soil below the skin surface than when higher frequencies were used [26,27]. This research utilizes the SMAP Level 2 enhanced passive soil moisture product (L2\_SM\_P\_E), which is available on a 9-km grid for downscaling to 1-km resolution.

#### *2.2. GLDAS Data*

NASA's global land data assimilation system (GLDAS) was designed to combine satelliteand observation-based data to produce high resolution, global information on Earth's land surface states and fluxes [28]. GLDAS is able to provide 36 land surface fields from 2000 to the present, including soil moisture, surface temperature, surface runoff, and rainfall. The product of 3-hourly data (GLDAS\_NOAH025\_3H) with 0.25◦ × 0.25◦ spatial resolution was used in this study [29]. Our downscaling approach utilized soil moisture with 0 to 10 cm depth and surface skin temperature from GLDAS that corresponded to the closest overpass times of the Aqua satellite for the LMB, which was approximately 12:00 and 24:00 local time.
