The surface latent heat flux is a large term in the surface energy balance and difficult to estimate remotely. The main difficulty for its estimation remotely is a poor ability to measure near-surface humidity. Current methods to retrieve near-surface specific humidity approach the problem statistically and have errors of approximately 1 g kg^-1 even in global-annual averages. Using extensive measurements from the EUREC⁴A field campaign (ElUcidating the RolE of Clouds, Circulation Coupling in Climate), we demonstrate that remote-sensing measurements of cloud base height can provide useful estimates of near-surface humidity. Applying the method to 171 coincident radiosonde and ceilometer pairings collected from a research vessel yields skillful predictions of near-surface specific humidity regarding the mean (mean bias 0.33 g kg^-1 compared to observed) and its variability (r = 0.76). We next apply this method using an airborne lidar to estimate cloud base height from above. In two case studies, we find similar skill in the predicted humidity, with low mean biases (-0.06 and -0.03 g kg^-1 compared to observed) with substantial variability captured (r=0.61 and r=0.57, respectively). Two main error sources, (i) the relative humidity lapse rate below cloud base and (ii) the temperature difference between the sea surface and near-surface air, are identified and quantified. Our proposed approach allows for estimates of the near-surface specific humidity using downward-staring space-borne lidar. This proof of concept raises the potential for its global application and for improved observational constraints on the surface energy budget.