Calibrating a soil–vegetation–atmosphere transfer model with remote sensing estimates of surface temperature and soil surface moisture in a semi arid environment

A series of numerical experiments has been designed to investigate how effective satellite estimates of radiometric surface temperatures and soil surface moisture are for calibrating a Soil–Vegetation–Atmosphere Transfer (SVAT) model. Multi–objective calibration based on error minimization of temperature and soil moisture model outputs is performed in a semi–arid environment.

Model accuracy when calibrated using in situ versus satellite objectives is explored in detail. Observational meteorological datasets from the African Monsoon Multidisciplinary Analysis (AMMA) were used to force a column model during a growing season in Mali. Fourier Amplitude Sensitivity Test (FAST) revealed the most sensitive parameters to model outputs.

Parameters found sensitive were subsequently optimized in a series of model calibrations to reveal trade-offs between model objectives. Our main findings are (1) the SVAT model performs well in the semi–arid environment, but underestimates peak growing season evapotranspiration and overestimates soil moisture, (2) most of the parameters important for flux estimates can be constrained using surface temperature and soil surface moisture with the three exceptions: root depth, the extinction coefficient and unstressed stomatal resistance, (3) flux simulations are improved when the model is calibrated using in situ surface temperature and soil surface moisture versus satellite estimates.