Consequently, the production of high-resolution active-passive soil moisture product got hampered, and only ~2.5 months (April 15th, 2015 to July 7th, 2015) of data remain available. 2017b, Reichle et al. L Band Microwave Remote Sensing and Land Data Assimilation Improve the Representation of Prestorm Soil Moisture Conditions for Hydrologic Forecasting. Standard statistical metrics include correlation (In order to determine the success of each downscaling technique, a statistical analysis on the Distribution of (a) spatial, (b) temporal standard deviations, and (c) soil moisture values for the Satellite-derived soil moisture distributions are much narrower for both original (not downscaled) low-resolution SMOS and SMAP products (top left and bottom left, respectively, Distribution of soil moisture values for original, DS1, DS2, and DS3 from SMOS (top, left to right) and SMAP (bottom, left to right) for WGEW over the time period. Statistical evaluation was done through a comparison between original SMOS and SMAP soil moisture products and each subsequently derived downscaled soil moisture product (DS1, DS2, and DS3). Any platform. (PDF, 3.2 MB; see Reichle, R. H., et al. The precipitation observations used to correct the GMAO precipitation estimates are obtained from the NOAA Climate Prediction Center (Reichle et al. Soil moisture analyses at higher spatial and temporal resolutions based on more modern LSMs are available. 2018.

The foremost objective of the SMAP Level-4 Surface and Root Zone Soil Moisture (SPL4SM) product is to fill this gap and provide estimates of root zone soil moisture that are informed by and consistent with SMAP observations. Koster, R. D., Q. Liu, S. P. P. Mahanama, and R. H. Reichle. These spatial contrasts are a necessary condition for accurately downscaling low-resolution soil moisture products. Sens. For example, T013000 corresponds to the time average from 00:00:00 UTC to 03:00:00 UTC on a given day.The date/time indicates the time of the analysis update. You currently do not have any folders to save your paper to!

The version 4.0 SMAP-SSS level 3, 8-Day running mean gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS). For more information on this SMAP outage, users should refer to the Three basic time steps are involved in the generation of the Level-4 soil moisture products, including:SMAP observations are assimilated in an EnKF analysis update step at the nearest 3-hourly analysis time such as 0z, 3z, ..., and 21z (where z indicates Zulu). These data can be imported and visualized but not geolocated. < 2017b, Reichle et al. 2010. The surface and root zone soil moisture estimates in the SMAP Level-4 soil moisture products are the outputs of a land surface model into which SMAP observations of brightness temperature have been assimilated. Exploiting soil moisture, precipitation and streamflow observations to evaluate soil moisture/runoff coupling in land surface models. Any sensor. “LR” refers to the low-resolution (original) soil moisture product prior to downscaling. All values are expressed in m3/m3, except for R and R2, which are unitless.Statistical metrics reported here are similar to those in Morelo et al.,Additionally, it is important to take into consideration the differences between the validation extent and the SMAP and SMOS resolution. GLDAS (Rodell et al. Any parameter.

There are a few reasons that the soil moisture data values in SMAP Level-4 data products may vary from what you expect in a particular region. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations. Downscaled soil moisture estimates (1 km) were evaluated at point and spatial scales over a densely instrumented watershed.The study area is the Walnut Gulch Experimental Watershed (WGEW) operated by the U.S. Department of Agriculture’s Agricultural Research Service and located in semiarid southeastern Arizona (DEM of the WGEW and 19 of the surface soil moisture sensors.

Visual assessment of downscaled soil moisture maps for both SMOS and SMAP show that each disaggregation is capable of revealing spatial discrepancies and heterogeneity of the surface. The assimilation system will typically provide some weight to the model background and thus buffers the impact of anomalous observations that are not caught in the flagging process.For more quality control information, refer to the Appendix of this doocument.The data assimilation system weighs the relative errors of the assimilated lower-level product (such as radiance or retrieval) and the land model forecast. Any spatial coverage. Variations of the downscaling method were chosen as they are computationally efficient, require no ground-based ancillary data sources, and require no site/region-specific calibration, allowing each to be implemented in near-real time. 2019).Utilizing the baseline data assimilation algorithm discussed below, input data sources are used with the SMAP Level-4 soil moisture model to provide enhanced estimates of surface soil moisture, root zone soil moisture, and related geophysical variables.The SPL4SM science algorithm consists of two key processing elements:The GEOS-5 Catchment Land Surface and Microwave Radiative Transfer Model is a numerical description of the water and energy transport processes at the land-atmosphere interface, augmented with a model that describes the land surface microwave radiative transfer (refer to section 4.1.1 of the ATBD: Reichle et al. Two kinds of quality control (QC) measures are applied.

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