Soil moisture sensors infer volumetric soil water content (SWC) from other properties of the bulk porous media. The CS655 water content reflectometer is a relatively new, low-frequency electromagnetic sensor that determines relative permittivity (K a ) using the two-way travel period and voltage attenuation of the applied signal along two 12-cm rods. This measured attenuation is quadratically related to bulk electrical conductivity (EC). Along with an onboard thermistor, the CS655 allows a more robust correction of propagation time and K a , which its predecessors, the CS615 and CS616, lacked. However, with new sensors it is necessary to quantify their practical accuracy in the field. Here, we present an overview of the CS655 sensor and an evaluation under both laboratory and field conditions, using five surface soils (0-10-cm depth) in the laboratory and gravimetric samples collected in the field. Overall, a site-specific calibration using a two-term linearization of the SWC-K a function reduced the root mean square error (RMSE) of the factory-derived SWC of 0.073 and 0.043 m 3 m −3 during batch and infiltration experiments, respectively, to 0.025 and 0.028 m 3 m −3 . Results further indicate that a soil-specific calibration additionally reduced the RMSE to <0.02 m 3 m −3 . Field evaluation across the Texas Soil Observation Network found that calibration reduced the variance across the network but did not affect the arithmetic mean or the RMSE against gravimetric sampling, which remained ?0.05 m 3 m −3 regardless of the SWC-K a -EC function applied. At the regional scale, a global calibration is sufficient.Abbreviations: BD, bulk density; CAL, calibration; EC, electrical conductivity; K a , relative dielectrical permittivity; PA, period average; SMAP, Soil Moisture Active Passive; STD, standardized; SWC, soil water content; TDR, time domain reflectometry; TLO, transmission line oscillator; TxSON, Texas Soil Observation Network; VR, voltage ratio.Soil moisture is a key state variable in hydrology and climate systems, coupling the water and energy cycles (Vereecken et al., 2008;Seneviratne et al., 2010). Much like soil formation itself (Jenny, 1941), climate, topography, vegetation, and soil physical properties each manifest themselves at decreasing scale to produce a heterogeneous soil moisture field. Interpreting soil moisture measurements are inherently challenging because of this variability in both time and space and the discrepancy of scales between measurement volumes and data requirements (Robinson et al., 2008;Ochsner et al., 2013).Soil moisture is quantitatively measured as volumetric soil water content (SWC). All soil moisture sensors infer SWC from some change in either thermal properties (Bristow et al., 1993;Mori et al., 2003) or electrical properties of the soil; the latter tends to be more popular due to the wider availability of commercial sensors and perceived simplicity of measurement. Most electrical sensors used to measure SWC are based on the propagation of an electromagnetic wave in a porou...

This study investigates the effects of agricultural drought on crop yields, through integration of crop growth models and remote sensing observations. The soil moisture (SM) product from SM and Ocean Salinity (SMOS) mission obtained at 25 km was downscaled to a spatial resolution of 1 km, compatible with the crop models. The downscaling algorithm is based upon information theoretic learning and uses data-driven probabilistic relationships between high-resolution remotely sensed products that are sensitive to SM and in situ SM. The downscaled SM values are assimilated in the crop model using an Ensemble Kalman filter-based augmented state-vector technique that estimates states and parameters simultaneously. The downscaling and assimilation framework are implemented for predominantly agricultural region of the lower La-Plata Basin (LPB) in Brazil during two growing seasons. This rain-fed region was affected by agricultural drought in the second season, indicated by markedly lower precipitation compared to the first growing season. The downscaled SM was compared with the in situ SM at a validation site and the root mean square difference (RMSD) was . The crop yields estimated by the downscaling-assimilation framework were compared with those provided by the Companhia Nacional de Asastecimento (CONAB) and Instituto Brasileiro de Geografia e Estatistica (IBGE). The assimilated yields are improved during both seasons with increased improvement during the second season that was affected by agricultural drought. The differences between the assimilated and observed crop yields were 16.8% during the first growing season and 4.37% during the second season.

Core Ideas TxSON is a Core Cal/Val Site for the NASA SMAP, SMOS, and Sentinel programs. TxSON consists of 40 in situ locations nested within a 36‐km EASE2 grid cell. Locations monitor soil moisture, temperature, and precipitation at 5, 10, 20, and 50 cm. Hourly, quality‐controlled data from 2015 to 2018 are available. The spatiotemporal variability of soil water content (SWC) at the remote sensing scale requires dense monitoring for calibration and validation. Here, we present an overview of the Texas Soil Observation Network (TxSON), an intensively monitored area in the semiarid rangelands of the central Texas Hill Country. TxSON is a dense network consisting of 40 in situ locations nested at 36, 9, and 3 km within the Equal‐Area Scalable Earth Grid and serves as a Core Calibration and Validation Site for NASA's Soil Moisture Active Passive mission. The 4‐yr dataset consists of hourly SWC measured at 5, 10, 20, and 50 cm. The SWC data are upscaled using arithmetic, Voronoi, and inverse distance weighting for 36‐, (2) 9‐, and (5) 3‐km grid cells. We present the site selection, environmental characteristics, network design, quality assurance (QA), upscaling algorithms, and data structure. Ancillary data include bulk density, particle size, and carbon content for each site and depth. We also provide automated QA for each location and scripts to use our binary flagging in upscaling methods. To summarize, the 36‐km grid cell has a mean bulk density of 1.34 ± 0.18 g cm−3 and a loam textural class. The in situ SWC has a root mean square error of 0.029 m3 m−3 against gravimetric data from 14 field campaigns. TxSON continues to add new locations with additional dense networks planned in other ecotones of the Edwards Plateau. The time series data along with scripts to import, plot, and upscaled SWC are available at https://doi.org/10.18738/T8/JJ16CF.