•A remote sensing based approach predicts habitat quality of protected dry grassland.•Models benefit from multiple Landsat-based phenology metrics, particularly in case of management.•Phenology metrics performed best for management intensity and nutrient content.•Model relationships of phenology metrics differ between management types.•Models provide in-depth information on conservation risks for managers and science. Dry grasslands are species rich and ecologically valuable habitats that have experienced a massive decline in Switzerland during the last century due to agricultural intensification and land abandonment. Appropriate management is a key factor in maintaining habitat quality of the remaining most valuable sites and should thus be an essential part of monitoring studies. However, information on management is often missing and fine-scale patterns are difficult to assess, especially over large areas and for past decades. The aim of this study was to predict habitat quality of protected dry grasslands in Switzerland. Using a nation-wide in-situ vegetation data set with plot-based species lists, we derived six habitat quality indicators (management tolerance, light availability, nutrient content, moisture content and species richness). We then tested how well satellite-based phenology metrics, in combination with environmental and climate data, can predict these dry grassland habitat quality indicators. We expected that the seasonal pattern of vegetation activity, based on the Normalized Difference Vegetation Index (NDVI), would represent local productivity and management patterns, two crucial indicators of dry grassland habitat quality. Linear regression analysis was conducted to assess the relative importance and ecological relationship of different NDVI metrics and other environmental and climate predictors for habitat quality. Variance partitioning was applied to assess model contributions of the three variable groups which represent different data sources for productivity and management. Accuracies for the habitat quality prediction models ranged between 34% and 57% and significant correlations with multiple NDVI metrics were found. Including NDVI phenology improved all models by 7–12%. Single contributions of NDVI phenology were highest for management tolerance and nutrient content. However, we found high variation of contributions between management types. NDVI metrics were highly informative for the habitat qualities of abandoned sites, but grazing and mowing reduced or even cancelled their predictive power. Moreover, our results demonstrate the limitation of single-date NDVI values in predicting habitat quality of dry grasslands, in particular pastures and meadows. For monitoring applications of dry grasslands, we propose using a combination of NDVI metrics, as our results showed that they greatly improve prediction results of essential habitat qualities. The Landsat legacy dataset facilitates the assessment of habitat changes during past decades and can be complemented in the future with higher resolution data, such as Sentinel-2, to increase the temporal and spatial resolution so analyses are more appropriate for the typically limited size of dry grassland habitat sites in Switzerland.