Satellite-based time series of land surface temperature (LST) have the potential to be an important tool to diagnose climate changes of the past several decades. Production of such a time series requires addressing several issues with using asynchronous satellite observations, including the diurnal cycle, clouds, and angular anisotropy. Here we evaluate the angular anisotropy of LST using one full year of simultaneous observations by two Geostationary Operational Environment Satellites, GOES-EAST and GOES-WEST, at the locations of five surface radiation (SURFRAD) stations. We develop a technique to convert directionally observed LST into direction-independent equivalent physical temperature of the land surface. The anisotropy model consists of an isotropic kernel, an emissivity kernel (LST dependence on viewing angle), and a solar kernel (effect of directional inhomogeneity of observed temperature). Application of this model reduces differences of LST observed from two satellites and between the satellites and surface ground truth - SURFRAD station observed LST. The techniques of angular adjustment and temporal interpolation of satellite observed LST open a path for blending together historical, current, and future observations of many geostationary and polar orbiters into a homogeneous multi-decadal data set for climate change research.