We undertake an alternative and novel approach to assimilation of near-surface soil moisture into land surface models by means of an extension of multiscale Kalman filtering (MKF). While most data assimilation studies rely on the assumption of spatially independent near-surface soil moisture observations to attain computational tractability in large-scale problems, MKF allows us to explicitly and very efficiently model the spatial dependence and scaling properties of near-surface soil moisture fields. Furthermore, MKF has the appealing ability to cope with model predictions and observations made at different spatial scales. Yet another essential feature of our approach is that we resort to the use of the expectation maximization (EM) algorithm in conjunction with MKF so that the statistical parameters inherent to MKF may be optimally determined directly from the data at hand and allowed to vary over time. This constitutes a significant advantage since these parameters (e.g., observation and model error noise variances) essentially determine the performance of the assimilation approach and have so far been most commonly prescribed heuristically and not allowed to evolve in time. We test our approach by assimilating the near-surface soil moisture fields derived from electronically scanned thinned array radiometer (ESTAR) during the Southern Great Plains Hydrology experiment of 1997 (SGP97) into the three-layer variable infiltration capacity (VIC-3L) land surface model. The results show that assimilation significantly improves the short-term predictions of soil moisture and energy fluxes from VIC-3L, especially with regard to capturing the spatial structure of these state variables. Additionally, we find that allowing the statistical parameters of the assimilation algorithm to evolve in time allows for an adequate representation of the time-varying uncertainties in land surface model predictions.