Construction and estimation of soil moisture site with FDR and COSMIC-ray (SM-FC) sensors for calibration/validation of satellite-based and COSMIC-ray soil moisture products in Sungkyunkwan university, South Korea

Abstract

In this study, Frequency Domain Reflectometry (FDR) and COSMIC-ray soil moisture (SM) stations were installed at Sungkyunkwan University in Suwon, South Korea. To provide reliable information about SM, soil property test, time series analysis of measured soil moisture, and comparison of measured SM with satellite-based SM product are conducted. In 2014, six FDR stations were set up for obtaining SM. Each of the stations had four FDR sensors with soil depth from 5 cm to 40 cm at 5~10 cm different intervals. The result showed that study region had heterogeneous soil layer properties such as sand and loamy sand. The measured SM data showed strong coupling with precipitation. Furthermore, they had a high correlation coefficient and a low root mean square deviation (RMSD) as compared to the satellite-based SM products. After verifying the accuracy of the data in 2014, four FDR stations and one COSMIC-ray station were additionally installed to establish the Soil Moisture site with FDR and COSMIC-ray, called SM-FC. COSMIC-ray-based SM had a high correlation coefficient of 0.95 compared with mean SM of FDR stations. From these results, the SM-FC will give a valuable insight for researchers into investigate satellite-and model-based SM validation study in South Korea.

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“…This resulted in a collection of more than 100,000 10-day scale surface soil moisture records obtained from 728 stations belonging to 29 networks. There are 19 dense networks (usually with multiple stations within one 0.1° pixel (Dorigo et al, 2015)): AMMA-CATCH (Cappelaere et al, 2009;De Rosnay et al, 2009;Lebel et al, 2009;Pellarin et al, 2009), BIEBRZA_S-1 (http://www.igik.edu.pl/en), BNZ-LTER (Van Cleve et al, 2015) (http://www.lter.uaf.edu/), CTP_SMTMN (Yang et al, 2013), FLUXNET-AMERIFLUX (http://ameriflux.lbl.gov/), FR_Aqui (Al-Yaari et al, 2018), HiWATER_EHWSN (Jin et al, 2014;Kang et al, 2014), HOBE (Bircher et al, 2012), HYDROL-NET_PERUGIA (Morbidelli et al, 2014), iRON (Osenga et 6 al., 2019), MAQU (Su et al, 2011), OZNET (Smith et al, 2012;Young et al, 2008), REMEDHUS (http://campus.usal.es/~hidrus/), SASMAS (Rüdiger et al, 2007), SKKU (Hyunglok et al, 2016), SOILSCAPE (Moghaddam et al, 2010;Moghaddam et al, 2016), SWEX_POLAND (Marczewski et al, 2010), VAS (http://nimbus.uv.es/) and WSMN (http://www.aber.ac.uk/wsmn). The remaining 10 are sparse networks, including ARM (http://www.arm.gov/), CARBOAFRICA (Ardö, 2013), COSMOS (Zreda et al, 2008;Zreda et al, 2012), DAHRA (Tagesson et al, 2015), RSMN (http://assimo.meteoromania.ro), SMOSMANIA (Albergel et al, 2008;Calvet et al, 2007), TERENO (Zacharias et al, 2011), UDC_SMOS (Loew et al, 2009;Schlenz et al, 2012), USCRN (Bell et al, 2013), USDA-ARS (Jackson et al, 2010).…”