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

SMAP-HydroBlocks (SMAP-HB) is a hyper-resolution satellite-based surface soil moisture product that combines NASA's Soil Moisture Active-Passive (SMAP) L3 Enhanced 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 reports the top 5 cm of surface soil moisture in volumetric units (m3/m3).

This repository contains the following two versions of the SMAP-HydroBlocks dataset:

1. SMAP-HB_hru_6h.zip: SMAP-HydroBlocks data in the Hydrological Response Unit (HRU) space. Storing the data in the HRU space enables the entire 30-m dataset to be compressed to 33.8 GB. A Python script and instructions for post-processing and remapping the data from the HRU space to geographic coordinates (latitude, longitude) are available on GitHub. After post-processing, files are stored in netCDF4 format with a Plate Carrée projection. If you have trouble unzipping the file, please review the command instructions on GitHub.
2. SMAP-HB_1km_6h.zip: SMAP-HydroBlocks data at 1-km 6-h resolution. This aggregated version is already post-processed and is in geographic coordinates (latitude, longitude) in netCDF4 format, with a Plate Carrée projection, and comprises 31.5 GB of data. 

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 are available at https://waterai.earth/smaphb/ 

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

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). https://doi.org/10.1038/s41597-021-01050-2

Vergopolan, N., Chaney, N. W., Beck, H. E., Pan, M., Sheffield, J., Chan, S., & 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. https://doi.org/10.1016/j.rse.2020.111740

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

pip install zenodo-get
zenodo_get 5206725