**5. Conclusions**

This study applied a previously developed method to a new geographical location where in situ observations are lacking. Here, higher resolution could help various land use decisions such as construction of dams, agriculture, and aquaculture. In this study, soil moisture estimates of the Lower Mekong River Basin from April 2015–September 2018, from SMAP Enhanced L2 Radiometer Half-Orbit 9 km V.2., were enhanced to 1 km resolution. In this study, we applied an algorithm developed by Fang et al., 2018, based on soil moisture, LST, and NDVI to create 1 km soil moisture maps. Soil moisture daily values were negatively related to the daily temperature difference under varying vegetation conditions. The downscaling algorithm was based on LST, soil moisture, and NDVI and used the relationship between daily soil moisture and daily land surface temperature difference between satellite overpasses as well as the vegetation class to downscale soil moisture to a higher resolution. The months of April and May showed the best coverage of soil moisture at 1 km and July–September showed the least coverage at 1 km, due to LST/NDVI data with substantial cloud coverage and higher vegetation growth. It was discovered in this study that the 1 km SMAP did not perform as well during wet days due to the spatial coverage of the MODIS land surface temperature (LST) data being compromised by cloud contamination.

Soil moisture estimates are readily available at global scale from a multitude of satellite products but are represented at spatial scales that are often too coarse for effective hydrological modeling and decision-making purposes. Soil moisture at high resolution can be used in place of ground measurements for land and water management decisions in large river basins where in situ data are limited such as the LMB. The high resolution soil moisture estimates derived in this study can be more useful for assessing dry-down and wetting trends than coarser resolution data, such as the 9 km SMAP product in the LMB. Additionally, 1 km soil moisture retrievals can better aid drought and crop productivity monitoring, flood forecasting, and assist weather forecasting by providing greater spatial representation than coarser products. This high spatial resolution soil moisture at 1 km can be applied to a multitude of water resources applications in order to benefit large watershed management.

**Author Contributions:** Individual contributions from the authors include conceptualization, C.D.; B.F.; V.L.; methodology, B.F.; software, C.D.; validation, B.F.; V.L.; formal analysis, C.D.; B.F.; investigation, C.D.; resources, V.L.; data curation, C.D.; and B.F.; writing—original draft preparation, C.D.; writing—review and editing, C.D.; B.F.; V.L.; visualization C.D.; B.F.; V.L.; supervision, V.L.; project administration, V.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
