Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #366832

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: The Texas Soil Observation Network: A comprehensive soil moisture dataset for remote sensing and land surface model validation

item CALDWELL, T. - University Of Texas At Austin
item BONGIOVANNI, T. - University Of Texas At Austin
item Cosh, Michael
item Jackson, Thomas
item COLLIANDER, A. - Jet Propulsion Laboratory
item ABOLT, C. - University Of Texas At Austin
item CASTEEL, R. - University Of Texas At Austin
item LARSON, T. - University Of Texas At Austin
item SCANLON, B.R. - University Of Texas At Austin
item YOUNG, M.H. - University Of Texas At Austin

Submitted to: Vadose Zone Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/1/2019
Publication Date: 9/26/2019
Citation: Caldwell, T., Bongiovanni, T., Cosh, M.H., Jackson, T.J., Colliander, A., Abolt, C., Casteel, R., Larson, T., Scanlon, B., Young, M. 2019. The Texas Soil Observation Network: A comprehensive soil moisture dataset for remote sensing and land surface model validation. Vadose Zone Journal. 18:1.

Interpretive Summary: In situ network design is usually focused on a particular feature or parameter, such as precipitation or vegetation. With the recent innovations in soil moisture measurement, new networks are beginning to be deployed with the specific intention of monitoring near surface soil moisture. The Texas Soil Observation Network (TxSON) was established in 2015 with the specific goal to provide a resource for remote sensing validation from the Soil Moisture Active Passive mission launched by NASA. There were a variety of scales built into the design to meet the different scales of soil moisture products to be produced. A validation campaign was conducted to prove the accuracy of the network. This network will be useful for understanding the unique characteristics of the Texas hill country as well as provide another point of reference for satellite soil moisture validation.

Technical Abstract: The spatiotemporal variability of soil water content (SWC) at the remote sensing or land surface modelling scale requires dense monitoring networks for calibration and validation; however, there is a limited amount of such data. Here, we present an overview of the The Texas Soil Observation Network (TxSON), is an intensively monitored area (1300 km2) located near Fredericksburg, Texas within the middle reaches of the Colorado River. The area typifies the dissected, carbonate-rock terrain of the Texas Hill Country with semiarid rangelands of mixed oak/grasslands and juniper woodlands. TxSON currently serves as a Core Calibration and Validation Site for NASA’s Soil Moisture Active Passive (SMAP) mission, as well as the ESA Soil Moisture Ocean Salinity (SMOS) and Sentinel-1 satellite products. Our The goal of this work is to provide a comprehensive data set of SWC to calibrate and validate soil moisture retrievals from satellite-based sensors, while also serving as a hydrological testbed for land surface models. TxSON is a dense monitoring network consisting of 40 in situ locations nested at 36, 9 and 3 km within the Equal-Area Scalable Earth Grid 2.0. Beginning in 2015, the four-year data set consists of mean hourly volumetric SWC and temperature measured at 5, 10, 20 and 50 cm depths. The SWC data are upscaled using arithmetic, Voronoi, and inverse distance spatial weighting at 36, 9 and 3 km grid cells. Ancillary soil characterization data includes bulk density, particle size and gravel content, organic and inorganic carbon for each site and sensor depth. We also provide an automated quality assurance routine for each in situ location and scripts to use our binary flagging in various upscaling methods. To summarize these data, the 36 km grid cell has a mean bulk density of 1.34 ± 0.18 g cm-3 and surficial soil texture is 36.5 ± 13.0% sand and 13.7 ± 8.1% clay indicating a loam textural class. The in situ mean soil water content 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 and instrumentation, with additional dense networks planned for the coming years in other ecotones of the Edwards Plateau in Texas. This paper presents an overview of the TxSON sensor network, quality assurance flagging, upscaling algorithms, and its data structure. We also present background data on site selection, climate, geology, soils, and data structure. The time series data along with scripts to import, plot and upscale SWC are available in a full data repository at