Estimating surface-soil moisture for retrieving maize leaf-area index from SAR data

The leaf-area index (LAI) is a key parameter for coupling earth-observation data with crop-growth models from the perspective of crop-yield forecasting. Remote sensing is of particular interest in estimating LAI over large areas. SAR data, thanks to their systematic acquisition, offer an ideal temporal resolution throughout the crop-growing season. Nevertheless, surface soil dielectric permittivity, which is strongly correlated with soil moisture, also affects the SAR signal. Thus, surface-soil permittivity or moisture has to be taken into account. This study tackles the issues related to soil influence on the SAR signal in monitoring maize crop growth. Different methods of assessing surface-soil moisture or permittivity are explored in order to retrieve LAI values from SAR data. The first method is based on a hydrological model-the soil, water, atmosphere, and plant (SWAP) model-with which the surface-soil moisture level can be estimated as a function of time. This method is tested with two kinds of meteorological data as inputs for the hydrological model: ground meteorological data and estimated meteorological data. The second method resorts to ground-penetrating radar, an alternative means of estimating surface-soil permittivity. This study demonstrates that both soil-moisture levels estimated by the SWAP model and soil permittivity measured by ground-penetrating radar can be successfully used for retrieving maize LAI values from SAR data using the water cloud model.