Satellite remote sensing technology has shown promising results in characterizing the environment in which plants and animals thrive. Remote sensing scientists, biologists, and epidemiologists are adopting remotely sensed imagery to compensate for the paucity of weather information measured by weather stations. With measured humidity from three stations as baselines, our study reveals that Normalised Difference Vegetation Index (NDVI) and atmosphere saturation deficits at the 780 hPa pressure level (DMODIS), both of which were derived from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensor, were significantly correlated with station saturation deficits (Dstn)(ȣrȣ = 0.42-0.63, p < 0.001). These metrics have the potential to estimate saturation deficits over east Africa. Four to nine days of lags were found in the NDVI responding to Dstn. For the daily estimations of Dstn, DMODIS had a better performance than the NDVI. However, both of them poorly explained the variances in daily Dstn using simple regression models (adj. R2 = 0.17-0.39). When the estimation temporal scale was changed to 16-day, their performances were similar, and both were better than daily estimations. For Dstn estimations at coarser geographic scales, given that many factors such as soil, vegetation, slope, aspect, and wind speed might complicate the NDVI response lags and model construction, DMODIS is more favourable as a proxy of the saturation deficit over ground due to its simple relationship with Dstn.