The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examine the effects of vegetation and its relationship with soil moisture on the simulated land–atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Finally, this study evaluates the model improvements for each simulation method.
Keywords: land-atmosphere interactions, satellite remote sensing, soil moisture, vegetation, WRF model