Abstract
The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active–passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are compared, and considerations for assimilation implementation are presented. A quantitative representation of results from the literature review, including ranges and variability of improvements in LSMs due to assimilation, are analyzed for both surface and root zone SM. A direction for future studies is then presented.
Highlights
Publisher’s Note: MDPI stays neutralSoil Moisture (SM) is a crucial variable in the partitioning of water and energy and has been considered in many atmospheric and hydrological studies [1,2]
Retrieved SM), the correction of Land Surface Models (LSMs) SM estimates can be divided into the following categories: (A) some papers followed the correction of LSM climatology by calibration of the LSM, (B) others conducted the correction of forcing data by assimilation
This study reviewed the studies that analyzed the improvement of LSM SM estimates by assimilation of satellite-derived SM and Brightness Temperature (BT)
Summary
Soil Moisture (SM) is a crucial variable in the partitioning of water (into infiltration and runoff) and energy (into sensible and latent heat flux) and has been considered in many atmospheric and hydrological studies [1,2]. Volumetric SM measurements at different depths are limited to sparse ground measurements, which cannot provide required spatial and temporal resolution data for numerical weather initialization To solve this problem, Land Surface Models (LSMs) are used to model the near-surface (~0–5 cm) and root zone (~5 to 100) SM (hereafter referred to as SSM and RSM) behavior through physical and hydrological laws. As stated in [5], the initial assimilation experiments based on the optimal interpolation method used screen-level temperature and relative humidity as assimilation inputs This approach had some built-in limitations, such as applied fix coefficients, the accumulation of errors induced by the model, forcing data in the root zone, and sparse data sampling [5,6,7].
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