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

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Noemi Vergopolan</dc:creator>
  <dc:creator>Nathaniel W. Chaney</dc:creator>
  <dc:creator>Ming Pan</dc:creator>
  <dc:creator>Justin Sheffield</dc:creator>
  <dc:creator>Hylke E. Beck</dc:creator>
  <dc:creator>Craig R. Ferguson</dc:creator>
  <dc:creator>Laura Torres-Rojas</dc:creator>
  <dc:creator>Eric F. Wood</dc:creator>
  <dc:description>SMAP-HydroBlocks (SMAP-HB) is a hyper-resolution satellite-based surface soil moisture product that combines NASA'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 (2015–2019), and it reports the top 5-cm surface soil moisture in volumetric units (m3/m3).

This repository contains the following two versions of the SMAP-HydroBlocks dataset: 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 GitHub. After post-processed, files are stored in netCDF4 format with a Plate Carrée projection. 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 Carrée projection, and comprising 31.5 GB of data. 

Different subsets of the original dataset can be made available on request from Noemi Vergopolan ( Data visualization, updates, and more information is available at 


Please cite the following paper when using the dataset in any publication:

Vergopolan, N., Chaney, N. W., Beck, H. E., Pan, M., Sheffield, J., Chan, S., &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.

Vergopolan, N., Chaney, N.W., Pan, M. et al. SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US. Sci Data 8, 264 (2021).


To download all the files via command line, please try zenodo_get:

pip install zenodo-get
zenodo_get 5206725</dc:description>
  <dc:subject>SMAP, HydroBlocks, hyper-resolution, soil moisture, hydrology, remote sensing, satellite, machine learning</dc:subject>
  <dc:title>SMAP-HydroBlocks: Hyper-resolution satellite-based soil moisture over the continental United States</dc:title>
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