Comparative study of interpolation methods for missing latent heat flux data
[Objective]The aim of this study is to analyze and compare the interpolation accuracy of different interpolation methods for missing latent heat flux value(FLE)in ecosystem.[Method]Eddy-covariance(EC)method was used to continuously monitor FLE and environmental factors of a typical natural deciduous broad-leaved forest ecosystem in Songshan National Nature Reserve,Beijing in 2019.Three interpolation methods,namely marginal distribution sampling method(MDS),linear regression method(REG),and artificial neural network method(ANN)were applied to interpolate the missing FLE data(randomly removed from 0.5 h data),and the relationship between measured FLE,interpolated FLE and environmental factors was analyzed.[Result]All the three interpolation results underestimated the measured FLE,among which ANN interpolation value was the closest to the measured one(R2=0.40).The measured FLE showed an exponential relationship with air temperature(Ta)and saturated vapor pressure deficit(DVP).MDS interpolated the relationship between FLE and Ta and DVP,and was the closest to the measured FLE.All the three interpolation methods changed the sensitivity of FLE to Ta and DVP to varying degrees.[Conclusion]The interpolation results of ANN are the closest to the measured values.The relationship between the results of MDS and environmental factors is the closest to the relationship between measured FLE and environmental factors.Therefore,appropriate interpolation methods should be selected in future research based on the research purpose.[Ch,5 fig.1 tab.41 ref.]
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