The Soil Moisture Active Passive Marena, Oklahoma, In Situ Sensor Testbed (SMAP‐MOISST): Testbed Design and Evaluation of In Situ Sensors

Abstract

Core Ideas Soil moisture sensors have varying accuracies that can be improved with calibration. In situ sensors require scaling to improve their representativeness of large areas. Soil moisture sensors in profile have decreasing ability to accurately represent the surface soil moisture. In situ soil moisture monitoring networks are critical to the development of soil moisture remote sensing missions as well as agricultural and environmental management, weather forecasting, and many other endeavors. These in situ networks utilize a variety of sensors and installation practices, which confounds the development of a unified reference database for satellite calibration and validation programs. As part of the Soil Moisture Active Passive Mission, the Marena, Oklahoma, In Situ Sensor Testbed (SMAP‐MOISST) was initiated to perform inter‐comparisons and study sensor limitations. Soil moisture sensors that are deployed in major monitoring networks were included in the study, along with new and emerging technologies, such as the Cosmic Ray Soil Moisture Observing System (COSMOS), passive/active distributed temperature sensing (DTS), and global positioning system reflectometers (GPSR). Four profile stations were installed in May of 2010, and soil moisture was monitored to a depth of 1 m on an hourly basis. The four stations were distributed within a circular domain of approximately 600 m diameter, adequate to encompass the sensing range of COSMOS. The sensors included in the base station configuration included the Stevens Water Hydra Probe, Campbell Scientific 616 and 229, Decagon EC‐TM, Delta‐T Theta Probe, Acclima, and Sentek EnviroSMART capacitance system. In addition, the Pico TRIME system and additional time‐domain reflectometry (TDR) systems were deployed when available. It was necessary to apply site‐specific calibration to most sensors to reach an RMSE below 0.04 m3 m−3. For most sensor types, a single near surface sensor could be scaled to represent the areal‐average of a field domain by simple linear regression, resulting in RMSE values around 0.03 m3 m−3.

Soil Moisture Remote Sensing: State‐of‐the‐Science

“…The variety of networks and technologies has necessitated the development of an in situ sensor testbed to provide some estimate of the interoperability and accuracy of these different sensors. The MOISST (Cosh et al, 2016) network was established in 2010 to provide a long‐term data series of diverse soil moisture technologies to address what impact these technology selections have on calibration and validation efforts. Most networks are available through the International Soil Moisture Network (Dorigo et al, 2011) and have been used for satellite soil moisture validation (e.g., de Jeu et al, 2014; Paulik et al, 2014; van der Schalie et al, 2016; Wu et al, 2016).…”

Considerations for Atmospheric Measurements with Small Unmanned Aircraft Systems

“…the Marena Oklahoma In Situ Sensor Testbed (MOISST). MOISST was established in 2010 to evaluate and compare existing and emerging in situ and proximal sensing technologies for soil moisture monitoring [13]. The DOE ARM SGP site consists of in situ and remote-sensing instrument clusters arrayed across approximately 143,000 km 2 in north-central Oklahoma and is the largest and most extensive climate research field site in the world, making it an invaluable resource for CLOUD-MAP researchers [14].…”

The integrated water balance and soil data set of the Rollesbroich hydrological observatory

“…However, the highly heterogeneous pattern of soil water content leading to complex and scale-dependent patterns of water, energy, and matter fluxes makes it challenging to predict terrestrial system responses for both scientists and policymakers (Jaeger and Seneviratne, 2011;Seneviratne et al, 2010). Therefore, integrated observations of soil water content and the exchange of water and heat between the soil, vegetation, and atmosphere are critical to improving our understanding of the terrestrial system response to changes in climatic conditions and land management (Dirnbock et al, 2003;Foley et al, 1998;Hinzman et al, 2005;Refsgaard, 1997;Seneviratne et al, 2010;Guo and Lin, 2016) and serve as key data in validating remote sensing data products (e.g., Rötzer et al, 2014;Cosh et al, 2016).…”