Accurate crop classification from satellite imagery can substantially enhance agricultural monitoring. Recent advances in deep learning have improved crop classification by employing models that take into account both spatial and temporal information contained in satellite image time series. However, the limited interpretability of these models remains a major challenge for their adoption in agricultural applications. This study presents a one-versus-rest classification framework for five crop types (wheat, maize, rapeseed, sunflower and watermelon and melon), in Ialomița County, Romania, using a CNN-LSTM (Convolutional Neural Network–Long Short-Term Memory) architecture and nine Sentinel-2 images acquired throughout the 2025 growing season. Each image sequence consisted of four spectral bands and NDVI (Normalized Difference Vegetation Index), enabling the exploitation of spectral, spatial and temporal information. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrices and the area under the receiver operating characteristic curve (ROC-AUC). The models achieved high validation accuracy, with values of 96.01% for wheat, 94.33% for maize, 97.48% for rapeseed, 97.06% for sunflower and 97.06% for watermelon and melon classifications. Explainable AI (XAI) techniques such as Integrated Gradients and SHAP (SHapley Additive exPlanations) were employed to quantify the contribution of individual spectral bands and acquisition dates to the classification decision. The Integrated Gradients analysis showed that for some crops, NDVI was the most influential input feature, accounting for 50.78% of the feature importance in wheat classification, 39.30% in rapeseed classification and 40.01% in watermelon and melon classification, while for other crops, the NIR (Near-infrared) spectral band was the most influential feature, accounting for 29.89% of the feature importance in maize classification and 41.00% in sunflower classification. The results demonstrate that the proposed framework achieves high classification performance while providing meaningful explanations of model predictions, highlighting the importance of both vegetation characteristics and seasonal dynamics in crop classification.