3-hour, 1-km surface soil moisture dataset for the contiguous United States for 2019
We simulated a 3-hour, 1-km spatially seamless surface soil moisture (SSM) dataset (called STF_SSM) in the Contiguous United States (CONUS) using a virtual image pair-based spatio-temporal fusion method. This proposed approach effectively fuses the distinct advantages of two long-term SSM datasets, namely, the Soil Moisture Active Passive (SMAP) L4 SSM product and the Crop Condition and Soil Moisture Analytics (Crop-CASMA) dataset. The SMAP L4 product provides spatially seamless SSM observations with a 3-hour temporal resolution but at a 9-km spatial resolution, while the Crop-CASMA SSM dataset offers a finer spatial resolution of 1 km but has a daily temporal resolution and contains spatial gaps. By referring to the ground-based in-situ data, the mean correlation coefficients (CC) are 0.716 at the daily scale and 0.689 at the 3-hour scale. This dataset provides a critical data source for the calibration and validation of land surface models.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "010:12" ] |
| contactPoint |
{ "fn": "Haoxuan Yang", "@type": "vcard:Contact", "hasEmail": "mailto:yhx965665334@163.com" } |
| description | We simulated a 3-hour, 1-km spatially seamless surface soil moisture (SSM) dataset (called STF_SSM) in the Contiguous United States (CONUS) using a virtual image pair-based spatio-temporal fusion method. This proposed approach effectively fuses the distinct advantages of two long-term SSM datasets, namely, the Soil Moisture Active Passive (SMAP) L4 SSM product and the Crop Condition and Soil Moisture Analytics (Crop-CASMA) dataset. The SMAP L4 product provides spatially seamless SSM observations with a 3-hour temporal resolution but at a 9-km spatial resolution, while the Crop-CASMA SSM dataset offers a finer spatial resolution of 1 km but has a daily temporal resolution and contains spatial gaps. By referring to the ground-based in-situ data, the mean correlation coefficients (CC) are 0.716 at the daily scale and 0.689 at the 3-hour scale. This dataset provides a critical data source for the calibration and validation of land surface models. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Digital Data", "format": "XML", "accessURL": "https://doi.org/10.5066/P13CCN69", "mediaType": "application/http", "description": "Landing page for access to the data" }, { "@type": "dcat:Distribution", "title": "Original Metadata", "format": "XML", "mediaType": "text/xml", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.67e30ff3d34ee7f142216e7e.xml" } ] |
| identifier | http://datainventory.doi.gov/id/dataset/USGS_67e30ff3d34ee7f142216e7e |
| keyword |
[ "USGS:67e30ff3d34ee7f142216e7e", "climatologyMeteorologyAtmosphere", "droughts", "geoscientificInformation", "geospatial datasets", "hydrology", "imageryBaseMapsEarthCover", "remote sensing", "soil moisture" ] |
| modified | 2025-05-05T00:00:00Z |
| publisher |
{ "name": "U.S. Geological Survey", "@type": "org:Organization" } |
| spatial | -127.8420, 23.2527, -65.4026, 51.5388 |
| theme |
[ "geospatial" ] |
| title | 3-hour, 1-km surface soil moisture dataset for the contiguous United States for 2019 |
