Development of low latency, high resolution SMAP soil moisture retrievals in support of near-real-time applications

Soil moisture plays a crucial role in weather forecasting and climate prediction, and provides useful information for flood and drought monitoring, and agricultural irrigation scheduling. In recent decades, soil moisture measurements have been enabled by space-borne passive microwave sensors. Nonetheless, the application of satellite remote sensing soil moisture data in operational meteorological and hydrological contexts is constrained by their prolonged latency and relatively coarse spatial resolution. This study describes the development of an operational enhanced Soil Moisture Active Passive (SMAP) soil moisture retrieval processor, referred to as SMAP_E_OPL, which addresses these limitations. SMAP_E_OPL produces near-real-time (NRT) soil moisture data at a 9-km grid resolution utilizing the single channel retrieval procedure employed in the long-latency SMAP Level 2 Enhanced soil moisture products (i.e., SMAP_L2SMP_E). In contrast to SMAP_L2SMP_E, the SMAP_E_OPL processor uses the Inverse Distance Squared Weighted interpolation approach to resample the NRT SMAP L1B brightness temperature ( T B ) data from a 33-km resolution to the target grid for enhanced computational efficiency. Moreover, SMAP_E_OPL also allows the interoperable use of soil temperature and snow depth estimates from various operational land surface model (LSM) configurations. A bias-correction process is implemented to ensure consistency in the effective soil temperature ( T eff ) between SMAP_E_OPL and SMAP_L2SMP_E. Evaluation based on in situ soil moisture measurements across the continental United States demonstrates that SMAP_E_OPL delivers the NRT soil moisture at 9-km grid resolution with accuracy comparable to the official (but high latency) SMAP enhanced soil moisture products. The use of different LSMs in SMAP_E_OPL to estimate T eff yields similar quality in soil moisture retrievals, highlighting the flexibility of SMAP_E_OPL with respect to LSMs. These results confirm the viability of the SMAP_E_OPL processor in providing high-spatial-resolution soil moisture data with low latency for operational forecasting, data assimilation, and monitoring systems.