Land cover mapping is of great importance to provide reliable quantification of agricultural landscapes. However, one of the limitations in tropical regions is cloud and cloud shadow coverage, resulting in imagery gaps. In this study, we tested four methods of gap filling: Interpolation k = 1 and k = 2, Mean and Median using VENµS satellite time series. Further, we assessed these filled time series in an object-based classification using Random Forest algorithm in the center of São Paulo state, Brazil. We used a 10-day composite NDVI as input data for the gap-filling methods. The linear interpolation (k=1) showed good adaptation to high dynamics temporal profile crops over time, such as sugarcane and annual crops. This same database with Interpolation (k=1) achieved high overall accuracy in the classification (0.81) allowing better discrimination on land use classes.