Seasonal Variations of Regional Soil Moisture Measurement Accuracy Based on Cosmic-ray Neutron Sensing
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    Abstract:

    The cosmic-ray neutron sensing method is a mesoscale and non-contact method for measuring soil moisture, which has been widely studied and applied. However, whether the accuracy and the applicability of the measurement results was consistent under the influence of typical meteorological conditions have not been clearly confirmed. The seasons were divided into time scales and typical meteorological periods were selected to study the difference of continuous observation effect between cosmic-ray neutron sensing (CRNS) and frequency domain reflection(FDR) under mountainous terrain. The result indicated that the seasonal variation of CRNS horizontal footprint was relatively stable under mountainous terrain, while the vertical footprint was fluctuated within the tillage layer. Different precipitation levels in different seasons was the main factor causing CRNS measurement deviation, during the period of severe soil moisture change, such as the continuous loss of soil moisture caused by high temperature and little rain and the process of precipitation of larger magnitude in summer and autumn, the results of CRNS and FDR were consistent. The spring precipitation was also smaller magnitude, canopy interception results in slightly different consistency between CRNS and FDR. In winter, precipitation and evapotranspiration were basically balanced, and the single precipitation was smaller magnitude, CRNS and FDR had a minimal deviation. The variation of CRNS measurement accuracy under typical soil moisture conditions was analyzed to further explain the influence of precipitation on the accuracy of CRNS, while the consistency was good in the period of continuous high temperature and drought, the root mean square error(RMSE), Nash-Sutcliffe efficiency coefficient(NSE)and Kling-Gupta efficiency coefficient(KGE)was 0.014m3/m3, 0.925 and 0.919, respectively. The research results can provide a scientific basis for the selection of application scenarios and data quality control of CRNS.

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History
  • Received:December 04,2020
  • Revised:
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  • Online: January 10,2022
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