Calibrating a Coupled SVAT–Vegetation Growth Model with Remotely Sensed Reflectance and Surface Temperature—A Case Study for the HAPEX-Sahel Grassland Sites

Abstract

Models simulating the seasonal growth of vegetation have been recently coupled to soil–vegetation–atmosphere transfer schemes (SVATS). Such coupled vegetation–SVATS models (V–S) account for changes of the vegetation leaf area index (LAI) over time. One problem faced by V–S models is the high number of parameters that are required to simulate different sites or large areas. Therefore, efficient calibration procedures are needed. This study describes an attempt to calibrate a V–S model with satellite [Advanced Very High Resolution Radiometer (AVHRR)] data in the shortwave and longwave domains. A V–S model is described using ground data collected over three semiarid grassland sites during the Hydrological Atmospheric Pilot Experiment (HAPEX)-Sahel experiment. The effect of calibrating model parameters with time series of normalized difference vegetation index (NDVI) and thermal infrared (TIR) data is assessed by examining the simulated latent heat flux (LE) and LAI for a suite of calibration experiments. A sensitivity analysis showed that the parameters related to plant growth vigor and to soil evaporative resistance were the best candidates for calibration. The NDVI and TIR time series were used to calibrate these parameters, both independently and simultaneously, to assess their synergy. Ground-based, airborne, and satellite sensor (AVHRR) data were successively investigated. Both airborne and AVHRR NDVI data could be used to constrain the vegetation growth vigor. These calibrations significantly improved the simulation of the LAI and LE (rmse decreased by 21% for LE), and the site-to-site variability was greatly enhanced. The soil resistance could also be calibrated with ground-based TIR data, but the effect on the simulated variables was small. Although both NDVI and ground-based TIR data were suitable to constrain the V–S model, the synergy between the two wavelengths was not clearly established. Last, satellite TIR data from the AVHRR proved unsuitable for model calibration. Indeed, the AVHRR surface temperature values were systematically lower than both ground-based data and model outputs. The authors conclude that the calibration of a vegetation–SVAT model with shortwave AVHRR time series can be used to scale the energy and water fluxes up to the regional scale.