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.

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.

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.

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.

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.

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.

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.

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.

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.

This U.S Geological Survey data release contains soil moisture, actual evapotranspiration, precipitation, and potential evapotranspiration by watershed for the United States from 1980 to 2020. Hourly values from NASA's North American Land Data Assimilation System Phase 2 (NLDAS-2) Noah model were aggregated to daily values and extracted by Geospatial Attributes of Gages for Evaluating Streamflow, version II (GAGES-II) watershed. The daily aggregated values include average soil moisture at four depths, 0 to 10 centimeters (SoilM_0_10cm), 10 to 40 centimeters (SoilM_10_40cm), 40 to 100 centimeters (SoilM_40_100cm), and 100 to 200 centimeters (SoilM_100_200cm); and total actual evapotranspiration (Evap), precipitation (Rainf), and potential evapotranspiration (PotEvap). Each daily value is presented in a table with a row from January 1, 1980 to December 31, 2020. Each column represents a GAGES-II watershed. References: Falcone, J.A., 2011, GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow: U.S. Geological Survey dataset, https://doi.org/10.3133/70046617. Xia, Y.L., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L.F., Alonge, C., Wei, H.L., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan, Q.Y., Mo, K., Fan, Y., and Mocko, D., 2012, Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products: Journal Of Geophysical Research-Atmospheres, v. 117, D03109, https://doi.org/10.1029/2011jd016048. Xia, Y.L., Mitchell, K., Ek, M., Cosgrove, B., Sheffield, J., Luo, L.F., Alonge, C., Wei, H.L., Meng, J., Livneh, B., Duan, Q.Y., and Lohmann, D., 2012, Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow: Journal Of Geophysical Research-Atmospheres, v. 117, D03110, https://doi.org/10.1029/2011jd016051.