Dataset Open Access

SMAP-HydroBlocks: Hyper-resolution satellite-based soil moisture over the continental United States

Noemi Vergopolan; Nathaniel W. Chaney; Ming Pan; Justin Sheffield; Hylke E. Beck; Craig R. Ferguson; Laura Torres-Rojas; Eric F. Wood


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    <dct:title>SMAP-HydroBlocks: Hyper-resolution satellite-based soil moisture over the continental United States</dct:title>
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    <dcat:keyword>SMAP, HydroBlocks, hyper-resolution, soil moisture, hydrology, remote sensing, satellite, machine learning</dcat:keyword>
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    <dct:description>&lt;p&gt;&lt;a href="https://waterai.earth/smaphb/"&gt;SMAP-HydroBlocks (SMAP-HB)&lt;/a&gt;&amp;nbsp;is a hyper-resolution satellite-based surface soil moisture product that combines NASA&amp;#39;s Soil Moisture Active-Passive (SMAP) L3 Enhance product, hyper-resolution land surface modeling, radiative transfer modeling, machine learning, and in-situ observations. The dataset was developed over the continental United States at 30-m 6-hourly resolution (2015&amp;ndash;2019), and it reports the top 5-cm surface soil moisture in volumetric units (m3/m3).&lt;/p&gt; &lt;p&gt;This repository contains the following two versions of the SMAP-HydroBlocks dataset:&lt;/p&gt; &lt;ol&gt; &lt;li&gt;&lt;strong&gt;SMAP-HB_hru_6h.zip&lt;/strong&gt;: SMAP-HydroBlocks data in the Hydrological Response Unit (HRU) space. Storing the data in the HRU space enables the entire 30-m 6-h dataset to be compressed to 33.8 GB. A python script and instructions to post-process and remap the data from the HRU-space into geographic coordinates (latitude, longitude) is provided at &lt;a href="https://github.com/NoemiVergopolan/SMAP-HydroBlocks_postprocessing"&gt;GitHub&lt;/a&gt;. After post-processed, files are stored in netCDF4 format with a Plate Carr&amp;eacute;e projection.&lt;/li&gt; &lt;li&gt;&lt;strong&gt;SMAP-HB_1km_6h.zip&lt;/strong&gt;: SMAP-HydroBlocks data at 1-km 6-h resolution. This aggregated version is already post-processed, and thus it is already in geographic coordinates (latitude, longitude), stored in netCDF4 format, with a Plate Carr&amp;eacute;e projection, and comprising 31.5 GB of data.&amp;nbsp;&lt;/li&gt; &lt;/ol&gt; &lt;p&gt;Different subsets of the original dataset can be made available on request from Noemi Vergopolan (noemi.v.rocha@gmail.com). Data visualization, updates, and more information is available at &lt;a href="http://waterai.earth/smaphb/"&gt;https://waterai.earth/smaphb/&lt;/a&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;Please cite the following paper when using the dataset in any publication:&lt;/p&gt; &lt;p&gt;Vergopolan, N., Chaney, N. W., Beck, H. E., Pan, M., Sheffield, J., Chan, S., &amp;amp; Wood, E. F. (2020). Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates. Remote Sensing of Environment, 242, 111740. &lt;a href="https://doi.org/10.1016/j.rse.2020.111740"&gt;https://doi.org/10.1016/j.rse.2020.111740&lt;/a&gt;&lt;/p&gt; &lt;p&gt;Vergopolan, N., Chaney, N.W., Pan, M.&amp;nbsp;&lt;em&gt;et al.&lt;/em&gt;&amp;nbsp;SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US.&amp;nbsp;&lt;em&gt;Sci Data&lt;/em&gt;&amp;nbsp;&lt;strong&gt;8,&amp;nbsp;&lt;/strong&gt;264 (2021). &lt;a href="https://doi.org/10.1038/s41597-021-01050-2"&gt;https://doi.org/10.1038/s41597-021-01050-2&lt;/a&gt;&lt;/p&gt;</dct:description>
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