A 1 km soil moisture data over eastern CONUS generated through assimilating SMAP data into the Noah-MP land surface model

Abstract. An improved fine-scale soil moisture (SM) dataset at 1-km grid spacing, covering much of the eastern continental U.S., was generated by assimilating 9-km SMAP SM data into the v4.0.1 Noah-MP land surface model. The assimilation, conducted using the Ensemble Kalman Filter algorithm within NASA’s Land Information System, involved 12 ensemble members. The SM analysis for 2016 was fully validated against in-situ observations from four different networks and compared with four other existing datasets. Results indicate that this SM analysis surpasses other datasets in top-layer SM distribution, including a machine learning-based product, despite all SM estimates being less heterogeneous than observed. The analysis of anomalous errors suggests that large similarity in intrinsic errors is likely due to overlapping data sources among the selected SM datasets. By assessing the product using the ARM SGP data, we show that soil temperature and surface heat fluxes are concurrently simulated in good accuracy. A specific investigation into the 2016 southeastern U.S. drought response further indicates drier conditions and higher evapotranspiration estimates compared to GLEAMv4.1. Notably, large errors are associated with grids having clay soil textures, highlighting the need for refined model treatments for specific soil types to further improve SM estimates.