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Land surface temperature (LST) is a key indicator of the land surface state and can provide information on surface-atmosphere heat and mass fluxes, vegetation water stress, and soil moisture. Split-window algorithms have been used with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data to estimate instantaneous LST for nearly 20 years. However, the low accuracy of LST retrievals associated with intractable variability has often hindered its wide use. In this study, we developed a six-year daily (day and night) NOAA-14 AVHRR LST dataset over continental Africa. By combining vegetation structural data available in the literature and a geometric optics model, we estimated the fractions of sunlit and shaded endmembers observed by AVHRR for each pixel of each overpass. Although our simplistic approach requires many assumptions (e.g., only four endmember types per scene), we demonstrate through correlation that some of the AVHRR LST variability can be attributed to angular effects imposed by AVHRR orbit and sensor characteristics, in combination with vegetation structure. These angular effects lead to systematic LST biases, including "hot spot" effects when no shadows are observed. For example, a woodland case showed that LST measurements within the "hot-spot" geometry were about 9 K higher than those at other geometries. We describe the general patterns of these biases as a function of tree cover fraction, season, and satellite drift (time past launch). In general, effects are most pronounced over relatively sparse canopies (tree cover <60%), at wet season sun-view angle geometries (principal plane viewing) and early in the satellite lifetime. These results suggest that noise in LST time series may be strongly reduced for some locations and years, and that long-term LST climate data records should be normalized to a single sun-view geometry, if possible. However, much work remains before these can be accomplished.