The Texas Soil Observation Network:A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation

Abstract

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.

Field evaluation of portable soil water content sensors in a sandy loam

“…The CS659 water content sensor for HydroSense II (Campbell Scientific) is a 12‐cm electrode version of the CS65x (e.g., CS650/655), and all CS65x are new versions of the original CS615 WCR (Figure 2e; Caldwell, Bongiovanni, Cosh, Halley, & Young, 2018). It is worth noting that all the new CSI TLO are referred to as “CS65x.” The CS65x has been used in various research fields that require ground‐based SWC measurements; for instance, it was used in optimizing satellite‐based SWC data (Bai, He, & Li, 2016), investigating tree establishment conditions (Morrison, Holdo, Rugemalila, Nzunda, & Anderson, 2019), and validating satellite‐ and model‐based SWC data (Caldwell et al., 2018, 2019; Moller, Jovanovic, Garcia, Bugan, & Mazvimavi, 2018). In this study, we used the CS659 with the HydroSense II portable device.…”

Validation of Soil Moisture Data Products from the NASA SMAP Mission

“…Perhaps most importantly, the spacing of the station measurements (number and distribution) must allow a reliable estimation of the average SM over the SMAP footprint. The required minimum number of point-scale sensors and their spacing is dictated by the spatial variability of SM within the area of interest and the desired accuracy for the estimate at that spatial scale (e.g., [64], [65], [66], [67], [68], [9], [10], [69], [70], [71]). For the SMAP CVS, Voronoi diagrams ( [72]; or see Thiessen Polygons in [73]) were chosen as the baseline upscaling approach to avoid geographical overweighting of clustered parts of the pixels [54].…”

“…Conversely, the time-varying component of SM typically has a large autocorrelation over long distances; that is, point measurements can better represent the SM temporal changes over domains of several km [76], [77]. Many studies of temporal stability of SM very effectively illustrate these differences of spatial and temporal evolution of SM (e.g., [78], [79], [80], [11], [81], [70]). Generally, the sparse networks lack adequate representation for resolving bias and RMSE at the scale of satellite SM retrieval footprints.…”

Assessing Disaggregated SMAP Soil Moisture Products in the United States

“…Detailed descriptions of the areas and experiments have been reported in [69], [71]- [80], and are summarized in TABLE II. These previous studies have demonstrated the spatial representativeness of the in situ networks for the watersheds through intensive field campaigns, thus providing reliable in situ soil moisture observations to validate data products from existing satellite SM missions [11], [14], [16], [46], [74], [81], [82].…”