Technical Abstract: This paper investigates the spatial relationships between land-surface fluxes and near-surface atmospheric properties (AP), and the potential errors in flux estimation due to homogeneous atmospheric inputs over heterogeneous landscapes. A Large Eddy Simulation (LES) model is coupled to a surface energy balance scheme with remotely sensed surface temperature (Ts) as a key boundary condition. Simulations were performed for different agricultural regions having major contrasts in Ts, canopy cover, and surface roughness (z0) between vegetated/irrigated and bare soil areas. If AP from a single weather station in a not representative location are applied uniformly over the domain, significant differences in surface flux estimation with respect to the LES are observed. The spatial correlations of AP with the fluxes, the land cover properties, and surface states were examined and the spatial scaling of these fields is analyzed using a two-dimensional wavelet technique. The results indicate a significant local correlation of the spatial distributions of the air temperature (Ta) with the sensible heat flux (H), of the specific humidity (q) with the latent heat flux (LE), and of the wind speed (U) with z0. These relationships can be described by a general linear form, suggesting that a simple regression relation may be applicable for most agricultural landscapes to estimate spatially variable AP fields. A simple yet practical method is proposed using remotely sensed observations and the land surface scheme, based on a general linear expressions derived between Ta and H, q and LE, and U and z0. The method is shown to reproduce the main spatial patterns of AP, to reduce potential local errors in heat flux estimation by more than 50%, and to reduce biases in regionally-averaged fluxes. This approach is recommended when only local weather station observations are available.