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In the present study, we tested the applicability of multi-sensor satellite data to account for key natural factors of annual livestock number changes in county-level <i>soum</i> districts of Mongolia. A schematic model of nomadic landscapes was developed and used to select potential drivers retrievable from multi-sensor satellite data. Three alternative methods (principal component analysis, PCA; stepwise multiple regression, SMR; and random forest machine learning model, RF) were used to determine the key drivers for livestock changes and <i>Dzud</i> outbreaks. The countrywide <i>Dzud</i> in 2010 was well-characterized by the PCA as cold with a snowy winter and low summer foraging biomass. The RF estimated the annual livestock change with high accuracy (R² > 0.9 in most <i>soums</i>). The SMR was less accurate but provided better intuitive insights on the regionality of the key factors and its relationships with local climate and <i>Dzud</i> characteristics. Summer and winter variables appeared to be almost equally important in both models. The primary factors of livestock change and <i>Dzud</i> showed regional patterns: dryness in the south, temperature in the north, and foraging resource in the central and western regions. This study demonstrates a synergistic potential of models and satellite data to understand climate–vegetation–livestock interactions in Mongolian nomadic pastures.