Monitoring seasonal grass cover dynamics in a multi-use savanna rangeland is important for sustaining the coexistence of livestock and wildlife. Moreover, population growth is driving increased livestock production, which further limits resources for both livestock and wildlife. To better understand the effect of grazing on grass cover dynamics, we developed a multi-scale remote sensing approach to study the monthly variation in grass cover in two types of conservation areas: a wildlife sanctuary and a communal livestock grazing and wildlife conservancy. The study was carried out in a semi-arid region in Kenya during an exceptionally dry year of 2022 when grazing resources were limited. The Excess Greenness color index was first used to develop a model predicting green fractional vegetation cover (fCover) of field photographs. This model was then applied to upscale fCover to the landscape level using very-high-resolution Pleiades satellite data. The resulting fCover maps were subsequently used to predict grass cover from medium-resolution Sentinel-2 multispectral satellite imagery using Random Forest machine learning. The final model showed high predictive power of grass cover in May (R2 = 0.96, root mean square error (RMSE) = 4.95%), while predictions were less accurate yet promising for January (R2 = 0.67, RMSE = 7.1%). The monthly grass cover maps demonstrate differences between the two conservation areas; the grazing area experienced low grass cover throughout the year, whereas grass cover in the wildlife sanctuary was more driven by rainfall. The results demonstrate the usability of digital cameras as the basis for vegetation cover models. Furthermore, this method can be used for adaptive land management to monitor within-season resources for both livestock and wildlife.