Sentence examples for moisture retrieval from inspiring English sources

The first part discusses the developments made in active and passive microwave soil moisture retrieval algorithms.

If feasible, the empirical dielectric model based method in [66] may be applied to GNSS-based soil moisture retrieval.

Furthermore, two different soil moisture retrieval approaches have been evaluated: a physically-based modeling approach and change detection technique.

The most widely applied algorithm i.e. Single channel algorithm (SCA), based on τ ω is used in this study for the soil moisture retrieval.

Thus, the surface soil moisture retrieval in the steep slope region of the Loess Plateau need to be further investigated in the future.

Therefore, the SMAP based SWDI (SMAP_SWDI) was computed to improve agriculture drought monitoring by using the latest soil moisture retrieval satellite technology.

This article presents a study carried out using EnKF based assimilation of coarser-scale SMOS soil moisture retrievals to improve the streamflow simulations and forecasting performance of SWAT model in a large catchment.

The regional bias parameter is added to satellite soil moisture retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm.

However, typical measurement depths of microwave-based soil moisture retrievals are generally considered too shallow (top 2 5 cm of the soil column) for many important water cycle and agricultural applications.

Most significantly, improvements in root-zone soil moisture accuracy are seen even for cases in which root-zone soil moisture retrievals are assumed to be relatively inaccurate (i.e. retrievals errors of up to 0.070 m3 m−3) or limited to only very sparse sampling (i.e. one instantaneous measurement every eight days).

In this analysis, we examine the potential benefits of simultaneously assimilating both microwave-based surface soil moisture retrievals and thermal infrared-based root-zone soil moisture estimates into a soil water balance model using a series of synthetic twin data assimilation experiments conducted at the USDA Optimizing Production Inputs for Economic and Environmental Enhancements (OPE3) site.