Fine-tuning foundation models is a key step in adapting them to a particular task. In the case of Geospatial Foundation Models (GFMs), fine-tuning can be particularly challenging given data scarcity both in terms of the amount of labeled data and, in the case of Satellite Image Time Series (SITS), temporal context. Under these circumstances, the optimal GFM fine-tuning strategy across different labeled data regimes remains poorly understood. In this paper, we thoroughly assess and study the performances of two different GFMs given several combinations of two data scarcity factors: number of labeled samples and sequence length. Specifically, we analyze the performances on a crop classification task, i.e., semantic segmentation, of the Sentinel-2 images contained in the PASTIS-HD dataset. We compare GFMs to U-TAE, a fully supervised baseline, across varying amounts of labeled data (1\%, 10\%, 50\%, 100\%) and temporal input lengths (1, 6, 15, 25 and 35) under different training configurations. Among these explorations, we find that using a smaller learning rate for the pre-trained encoders improves performance in moderate and high data regimes (50\%-100\%). In contrast, full fine-tuning outperforms partial fine-tuning in very low-label settings (1\%-10\%). This behavior suggests a nuanced trade-off between feature reuse and adaptation that defies the intuition of standard transfer learning.