This data archive contains gridded and regionally averaged records of North American climate and soil moisture as well as the 1,498 tree-ring width index (RWI) chronologies used for gridded soil-moisture reconstructions across western North America.

Modelled top-layer soil moisture (0-10 cm) using the Australian Water Resources Assessment model via assimilation of satellite soil moisture products: 50th percentile of the ensemble of model estimates. Note that the data represents the mean of an ensemble of 100 modelled estimates for each data derived via perturbed meteorological forcing. Resolution of the output data is 0.05-degree for the whole country. The unit of measure is relative wetness (0-1, indicating 0 and 100% degree of saturation).

Modelled top-layer soil moisture (0-10 cm) using the Australian Water Resources Assessment model via assimilation of satellite soil moisture products: 2.5th percentile of the ensemble of model estimates. Note that the data represents the mean of an ensemble of 100 modelled estimates for each data derived via perturbed meteorological forcing. Resolution of the output data is 0.05-degree for the whole country. The unit of measure is relative wetness (0-1, indicating 0 and 100% degree of saturation).

Modelled top-layer soil moisture (0-10 cm) using the Australian Water Resources Assessment model via assimilation of satellite soil moisture products: 97.5th percentile of the ensemble of model estimates. Note that the data represents the mean of an ensemble of 100 modelled estimates for each data derived via perturbed meteorological forcing. Resolution of the output data is 0.05-degree for the whole country. The unit of measure is relative wetness (0-1, indicating 0 and 100% degree of saturation).

This data set consists of in-situ point measurements of soil moisture within three 25-km by 25-km Meso-cell Study Areas (MSAs) in northern Colorado (Fraser, North Park , and Rabbit Ears).

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 data set consists of in-situ point measurements of soil moisture within three 25-km by 25-km Meso-cell Study Areas (MSAs) in northern Colorado (Fraser, North Park , and Rabbit Ears).

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.