Gradient boosting how can accuracy increase when we lower the depth of tree? What I don't understand about gradient boost is, doesn't lowering height of the tree means we use fewer features in our model?

From my model I get the highest accuracy when the depth is one. Meaning there is just root node at my trees, and uses one feature. How can a model that uses one feature gives such accuracy?
In boosting, a low biased &amp; low variance model is ensembled by additively combining high biased &amp; low variant models. Here, biases are gradually diminished while keeping variances low. Therefore, a tree with a single depth, that is highly biased, isn't an issue because additively errors will be reduced.