Publication

Abstract : Soil moisture plays an important role in water, carbon, and energy exchanges between the land surface and the atmosphere. Therefore, a better representation of the soil moisture in the land surface models could significantly reduce the uncertainties associated with carbon flux estimates, especially for methane (CH4) flux, where the status of soil water content can largely affect the anaerobic condition that stimulates methanogenesis activity. In this study, the SMAP soil moisture has been confronted to the simulated top-soil layer of LPJ land surface model for the years 2015-2019, to evaluate its potential to improve the model using data assimilation techniques. Several metrics were applied to validated the simulated CO2 and CH4 fluxes using the eddy covariance measurements in the ABoVE domain. The performance of modeled CH4 across time scales has been evaluated by a wavelet analysis to characterize the influence of assimilation of SMAP soil moisture in the time-frequency domain. This study provides an opportunity to utilize the large-volume of satellite measurements by the ABoVE experiments and to improve model structure more precisely by using data assimilation techniques.