This dataset contains in-situ soil moisture profile and soil temperature data collected at 30-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites since 2021 in the United States and New Zealand. The SoilSCAPE network has used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data over varying durations since 2011. Since 2021, the SoilSCAPE has upgraded the two previously active sites in Arizona and added several new sites in the United States and New Zealand. These new sites typically use the METER Teros-12 soil moisture sensor. At its maximum, the new network consisted of 57 wireless sensor installations (nodes), with a range of 6 to 8 nodes per site. Each SoilSCAPE site contains multiple wireless end-devices (EDs). Each ED supports up to five soil moisture probes typically installed at 5, 10, 20, and 30 cm below the surface. Sites in Arizona have soil moisture probes installed at up to 75 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional and national (e.g. NASA's Cyclone Global Navigation Satellite System - CYGNSS and Soil Moisture Active Passive - SMAP) scales. The data are provided in NetCDF format.

Water content measurements by gravimetric methods involve weighing a wet sample, removing the water via drying in an oven, and reweighing the sample to determine the amount of water removed. Water content then is obtained by dividing the difference between wet and dry masses by the mass of the dry sample to obtain the ratio of the mass of water to the mass of dry soil. When multiplied by 100, this becomes the percentage of water in the sample on a dry-mass (or, as often expressed, on a dry-weight) basis. Soil moisture determined using the gravimetric method was measured at 800 sites along 24 transects. These transects were over flown by the airborne Gamma Radiation System used to measure soil moisture. These data are useful for comparison of airborne and ground soil moisture data. This analysis for the airborne Gamma Radiation System, using completely independent soil moisture data showed that the root mean square error of 97 flights was 3.02 percent soil moisture, with a bias of less than 0.5 percent soil moisture (Carroll et al., 1988).

This data set comprises gravimetric soil moisture and soil bulk density data collected during the Soil Moisture Experiment 2003 (SMEX03), which was conducted during June and July of 2003 in northern Alabama and southern Tennessee, USA.

The gravimetrical soil moisture data were collected from many stations spread over the FIFE study area. These data were collected to characterize the spatial and temporal patterns of moisture content of the soils over this area during and between the FIFE Intensive Field Campaigns. The aim of the FIFE soil moisture work was to characterize spatial and temporal patterns of soil moisture at the FIFE site, to validate and calibrate remote sensing measurements of soil water, and to evaluate alternative methods of measuring soil moisture both from the air and on the ground. A further goal was to develop techniques for comparing point and spatially continuous measurements of soil moisture. The FIFE soil moisture research was designed to advance the technology for characterizing soil moisture and contribute useful data to other FIFE investigations.

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 combines data for several parameters measured for the Soil Moisture Experiment 2003 (SMEX03). This study was conducted between 2 July 2003 and 17 July 2003 in the Little Washita watershed in Oklahoma, USA.