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

SMAP-HydroBlocks: Hyper-resolution satellite-based soil moisture over the continental United States
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 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-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 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-HB_1km_6h.zip: 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 (noemi.v.rocha@gmail.com). Data visualization, updates, and more information is available at https://waterai.earth/smaphb/ 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., & 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 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 To download all the files via command line, please try zenodo_get: pip install zenodo-get zenodo_get 5206725
Zenodo
Ferguson, Craig R.
b9d14f97-34c8-44c2-a0d7-f97147d0063c
Torres-Rojas, Laura
56c0d075-de31-47af-93ac-387e9bef7b8f
Vergopolan, Noemi
3c455209-3f04-4ef3-9687-d637239ec4b4
Beck, Hylke E.
edbdb027-f978-47dd-a9d3-43a1cce92e9a
Chaney, Nathaniel W.
bc3ca362-9e26-46af-bd26-f99983445106
Wood, Eric F.
49f16ef9-1dbf-4527-be9a-b69c9c880d68
Pan, Ming
9f6dfdc0-e281-4985-8e4d-0ce9537bd39f
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Ferguson, Craig R.
b9d14f97-34c8-44c2-a0d7-f97147d0063c
Torres-Rojas, Laura
56c0d075-de31-47af-93ac-387e9bef7b8f
Vergopolan, Noemi
3c455209-3f04-4ef3-9687-d637239ec4b4
Beck, Hylke E.
edbdb027-f978-47dd-a9d3-43a1cce92e9a
Chaney, Nathaniel W.
bc3ca362-9e26-46af-bd26-f99983445106
Wood, Eric F.
49f16ef9-1dbf-4527-be9a-b69c9c880d68
Pan, Ming
9f6dfdc0-e281-4985-8e4d-0ce9537bd39f
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b

(2021) SMAP-HydroBlocks: Hyper-resolution satellite-based soil moisture over the continental United States. Zenodo doi:10.5281/zenodo.4441211 [Dataset]

Record type: Dataset

Abstract

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-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 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-HB_1km_6h.zip: 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 (noemi.v.rocha@gmail.com). Data visualization, updates, and more information is available at https://waterai.earth/smaphb/ 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., & 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 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 To download all the files via command line, please try zenodo_get: pip install zenodo-get zenodo_get 5206725

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More information

Published date: 18 August 2021

Identifiers

Local EPrints ID: 474109
URI: http://eprints.soton.ac.uk/id/eprint/474109
PURE UUID: f565c08e-35fd-4b43-9923-081d593c6dbf
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

Catalogue record

Date deposited: 13 Feb 2023 18:11
Last modified: 28 Nov 2023 02:47

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Contributors

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

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