Enhancing Model Skill by Assimilating SMOPS Blended Soil Moisture Product into Noah Land Surface Model

Abstract

Many studies that have assimilated remotely sensed soil moisture into land surface models have generally focused on retrievals from a single satellite sensor. However, few studies have evaluated the merits of assimilating ensemble products that are merged soil moisture retrievals from several different sensors. In this study, the assimilation of the Soil Moisture Operational Products System (SMOPS) blended soil moisture (SBSM) product, which is a combination of soil moisture products from WindSat, Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite sensors is examined. Using the ensemble Kalman filter (EnKF), a synthetic experiment is performed on the global domain at 25-km resolution to assess the impact of assimilating the SBSM product. The benefit of assimilating SBSM is assessed by comparing it with in situ observations from U.S. Department of Agriculture Soil Climate Analysis Network (SCAN) and the Surface Radiation Budget Network (SURFRAD). Time-averaged surface-layer soil moisture fields from SBSM have a higher spatial coverage and generally agree with model simulations in the global patterns of wet and dry regions. The impacts of assimilating SMOPS blended data on model soil moisture and soil temperature are evident in both sparsely and densely vegetated areas. Temporal correlations between in situ observations and net shortwave radiation and net longwave radiation are higher with assimilating SMOPS blended product than without the data assimilation.

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

“…Ground sensor measurements (points) can continuously measure soil moisture but are limited in spatial coverage. Available soil moisture measurements based on optical, thermal, and radar satellites provide spatial coverage, but the coarse spatial resolution (pixel size) or untimely information during the irrigation season limit their use for crop field operations [11,12]. Finally, existing land surface models developed for estimation of soil moisture are not necessarily spatial in nature, or they require information not generally available for their calibration [13].…”

A Method for Downscaling FengYun-3B Soil Moisture Based on Apparent Thermal Inertia

“…However, the radar sensor has stopped working since 7 July 2015. The improved soil moisture products are combined by other soil moisture products [9,10], and the spatial resolution is still at about 25 km-resolution. Since soil moisture at higher spatial resolutions, e.g., 1-10 km, is often needed for many applications [11,12], it is therefore urgent to develop algorithms to obtain such fine resolution data.…”

Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture

“…It has been widely used for hydrological assimilation [8], especially for soil moisture assimilation [39][40][41]. The EnKF algorithm is a Bayesian filtering process, which alternates between an ensemble forecast step, where an ensemble of model states is propagated forward in time using the model equations, and a state variable update step, where the simulated state is updated with the observation states when the observations are available.…”

“…Normally distributed zero mean additive perturbation was applied for downward longwave radiation, and log-normally distributed (mean 1) multiplicative perturbations for precipitation and downward shortwave radiation. Additionally, the Noah LSM estimated soil moisture for all four layers are perturbed with additive normal distribution with zero mean based on study by [41]. …”