In order to assess the pattern of soil moisture in densely vegetated areas, obtained from the new version of the algorithm LPRM calibrated for Brazil (LPRM_BR), we used data of brightness temperature sensor MIRAS aboard the SMOS satellite during the period 04-09 March to different regions. These locations were selected according to availability of soil moisture data measured "in situ". Thus, observational data of volumetric water content in the soil (at 10 cm) were collected from automatic meteorological stations of CPTEC/INPE distributed in different regions with dense vegetation. To calculate the degree of association between satellite and observed data, we used statistical tools such as correlation (R) and BIAS. The results of both R and BIAS were satisfactory in various locations. The correlation coefficients were relatively high for regions with dense vegetation, such as Esec Maraca (RR), Parna Jaú (AC), Tailândia (PA) e Votuporanga (AM), whose coefficients were 0.84, 0.99 and 1, respectively. Therefore, we conclude that the LPRM_BR infer accurate values of soil moisture to areas with dense vegetation, using the MIRAS sensor L band. So, products derived from the LPRM_BR can be used for different purposes such as: monitoring of soil moisture in extreme rain events contributing to issuing warnings of landslides; support in planning for planting and / or irrigation for certain crops; data assimilation in models of numerical weather prediction, which may contribute to the improvement of weather forecasting. However, research with more detailed data from SMOS become necessary to better evaluate the performance of LPRM_BR.Pages: 9248-925
