  Nudging is a data assimilation technique that has proved to be capable of
reconstructing several highly turbulent flows from a set of partial
spatiotemporal measurements. In this study we apply the nudging protocol on the
temperature field in a Rayleigh-B\'enard Convection system at varying levels of
turbulence. We assess the global, as well as scale by scale, success in
reconstructing the flow and the transition to full synchronization while
varying both the quantity and quality of the information provided by the sparse
measurements either on the Eulerian or Lagrangian domain. We asses the
statistical reproduction of the dynamic behaviour of the system by studying the
spectra of the nudged fields as well as the correct prediction of the heat
transfer properties as measured by the Nusselt number. Further, we analyze the
results in terms of the complexity of the solutions at various Rayleigh numbers
and discuss the more general problem of predicting all state variables of a
system given partial or full measurements of only one subset of the fields, in
particular temperature. This study sheds new light on the correlation between
velocity and temperature in thermally driven flows and on the possibility to
control them by acting on the temperature only.
