482 Making Soil Moisture Sensors Better for Hydroclimatic Applications

Tuesday, 24 January 2017
4E (Washington State Convention Center )
Chen Xu, University of Oklahoma, Norman, OK; and K. Zhang and Y. Hong

Soil Moisture (SM) information is key to understanding the flows of water and heat energy between the surface and atmosphere that impact weather and climate. The recent advances in remote sensing sensors, remarkably passive microwave, have provided significant information on soil water content and, if augmented with existing soil and other geographic information, such as terrain elevation and slope, may provide accurate data on soil water content. NASA’s Soil Moisture Active Passive (SMAP) mission is an orbiting observatory that measures the top 5 cm of SM everywhere on Earth’s surface over a three-year period, every 2-3 days. ESA’s Soil Moisture and Ocean Salinity (SMOS) mission is partly dedicated to making global observations of SM. JAXA’s Advanced Microwave Scanning Radiometer (AMSR)-2 measures weak microwave emissions from the surface and the atmosphere of the Earth and offers a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. Factors such as vegetation cover, soil properties (density and texture), and surface roughness may affect the accuracy of remotely-sensed SM. Therefore, it is critical to compare the remotely-sensed SM data with in situ observations for calibration. The Oklahoma Mesonet monitors a wealth of atmospheric and hydrologic variables including solar radiation, humidity, temperature, wind speed and direction, and SM to aid in operational weather forecasting and environmental research across the state. The objective of this study is to evaluate the potential utility of the SM data retrieved from remote sensing techniques (SMAP, SMOS, and AMSR-2) by comparing them to Oklahoma Mesonet data. The correlation between the remotely-sensed SM data and daily Mesonet SM observations from the top 5, 25, and 60 cm of soil are determined for each site. This work is aimed at assessing the effectiveness of remotely-sensed data at observing hydro-climatological phenomena, calibrating the error in remote sensing observations, and developing more functional satellite sensors. After calibration, the results show that the surface SM data derived from satellite are highly correlated with the Mesonet data at 5 cm depth. This work validates the remotely-sensed SM data for the top-most soil layer, but more research is still needed to validate the retrieved SM data at further depths.
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