Nathalie Novello

A Combined Optical–Microwave Method to Retrieve Soil Moisture Over Vegetated Areas

A simple approach for correcting for the effect of vegetation in the estimation of the surface soil moisture (wS) from L-band passive microwave observations is presented in this study. The approach is based on semi-empirical relationships between soil moisture and the polarized reflectivity including the effect of the vegetation optical depth which is parameterized as a function of the normalized vegetation difference index (NDVI). The method was tested against in situ measurements collected over a grass site from 2004 to 2007 (SMOSREX experiment). Two polarizations (horizontal/vertical) and five incidence angles (20°, 30°, 40°, 50°, and 60°) were considered in the analysis. The best wS estimations were obtained when using both polarizations at an angle of 40°. The average accuracy in the soil moisture retrievals was found to be approximately 0.06 m3/m3, improving the estimations by 0.02 m3/m3 with respect to the case in which the vegetation effect is not considered. The results indicate that information on vegetation (through a vegetation index such as NDVI) is useful for the estimation of soil moisture through the semi-empirical regressions.

Investigating temporal variations in vegetation water content derived from SMOS optical depth

This study investigates the temporal behavior of the gravimetric vegetation water content (Mg) derived from SMOS (L-band) optical depth (τ) values. The analysis is done for the year 2010 over a coniferous forest site in the U.S. Resulting values of Mg are compared to values of leaf water potential obtained with the Soil-Plant-Atmosphere model and in situ data. A significant nonlinear correlation is found between the two (R=0.72, p<;0.001). It is found that the temporal behavior of Mg, based on plant water status, is different from that of existing optical-based vegetation indices, which are mainly based on chlorophyll/biomass changes. Therefore, τ-derived Mg could provide complementary information for future global-scale dynamic monitoring of vegetation water status.