Leaf area index (LAI) is a key driver of forest productivity and evapotranspiration; however, it is a difficult and labor-intensive variable to measure, making its measurement impractical for large-scale and long-term studies of tropical forest structure and function. In contrast, satellite estimates of LAI have shown promise for large-scale and long-term studies, but their performance has been equivocal and the biases are not well known. We measured total, overstory, and understory LAI of an Amazon-savanna transitional forest (ASTF) over 3 years and a seasonal flooded forest (SFF) during 4 years using a light extinction method and two remote sensing methods (LAI MODIS product and the Landsat-METRIC method), with the objectives of (1) evaluating the performance of the remote sensing methods, and (2) understanding how total, overstory and understory LAI interact with micrometeorological variables. Total, overstory and understory LAI differed between both sites, with ASTF having higher LAI values than SFF, but neither site exhibited year-to-year variation in LAI despite large differences in meteorological variables. LAI values at the two sites have different patterns of correlation with micrometeorological variables. ASTF exhibited smaller seasonal variations in LAI than SFF. In contrast, SFF exhibited small changes in total LAI; however, dry season declines in overstory LAI were counteracted by understory increases in LAI. MODIS LAI correlated weakly to total LAI for SFF but not for ASTF, while METRIC LAI had no correlation to total LAI. However, MODIS LAI correlated strongly with overstory LAI for both sites, but had no correlation with understory LAI. Furthermore, LAI estimates based on canopy light extinction were correlated positively with seasonal variations in rainfall and soil water content and negatively with vapor pressure deficit and solar radiation; however, in some cases satellite-derived estimates of LAI exhibited no correlation with climate variables (METRIC LAI or MODIS LAI for ASTF). These data indicate that the satellite-derived estimates of LAI are insensitive to the understory variations in LAI that occur in many seasonal tropical forests and the micrometeorological variables that control seasonal variations in leaf phenology. While more ground-based measurements are needed to adequately quantify the performance of these satellite-based LAI products, our data indicate that their output must be interpreted with caution in seasonal tropical forests.