There is an increasing need of effective monitoring systems for habitat quality assessment. Methodsbased on remote sensing (RS) features, such as vegetation indices, have been proposed as promisingapproaches, complementing methods based on categorical data to support decision making.Here, we evaluate the ability of Earth observation (EO) data, based on a new automated, knowledge-driven system, to predict several indicators for oak woodland habitat quality in a Portuguese Natura 2000site.We collected in-field data on five habitat quality indicators in vegetation plots from woodland habitatsof a landscape undergoing agricultural abandonment. Forty-three predictors were calculated, and a multi-model inference framework was applied to evaluate the predictive strength of each data set for the severalquality indicators.Three indicators were mainly explained by predictors related to landscape and neighbourhood struc-ture. Overall, competing models based on the products of the automated knowledge-driven system hadthe best performance to explain quality indicators, compared to models based on manually classifiedland cover data.The system outputs in terms of both land cover classes and spectral/landscape indices were consideredin the study, which highlights the advantages of combining EO data with RS techniques and improvedmodelling based on sound ecological hypotheses. Our findings strongly suggest that some features ofhabitat quality, such as structure and habitat composition, can be effectively monitored from EO datacombined with in-field campaigns as part of an integrative monitoring framework for habitat statusassessment.