Identifying native grasslands and key phenological stages using time series Sentinel-2 data and deep learning models

• Sentinel-2 NDVI time series distinguishes native and tame grasslands. • Conv1D-based model achieves superior accuracy (OA = 0.88) in classification. • DOY 170–200 is key phenological window for native grassland identification. • Optimal 10-day resolution enhances Conv1D in Southern grasslands. • Multi-model comparison: CNNs outperform LSTMs and RF. Canadian prairies are among the world’s most endangered ecosystems, and the identification of native grasslands is crucial for supporting grassland management and wildlife conservation. However, the current amount, distribution, and dynamic changes of native grassland remain uncertain, partly due to the difficulty of separating native grassland with other land cover types, especially tame grassland in landcover classification products. This study aims to identify native grasslands in the Mixed Grasslands ecoregion of Saskatchewan using Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series data through deep learning and multi-temporal approaches. Three classifiers, one-dimensional convolutional (Conv1D), Attention Long Short-Term Memory (At-LSTM), and Random Forest (RF), were applied to distinguish the native and tame grasslands, analyzing the key phenological stages. The results show that the Conv1D-based model outperformed the others in both the South (accuracy: 0.88; F1 score: 0.87) and North (accuracy: 0.78; F1 score: 0.77) sub-regions. In contrast, the At-LSTM and RF models performed worse, particularly in the North sub-region, with F1 scores of 0.64 and 0.68, respectively. The early July period (Day of Year 170 to 200) was critical for distinguishing native and tame grassland landcover, especially in the South sub-region. Additionally higher resolution data (5-day intervals) generally improved model accuracy compared to 10-day and 30-day intervals. Overall, the study demonstrates the effectiveness of time series NDVI data and deep learning approaches for identifying native grasslands, offering valuable information to assist in grassland ecosystem management and conservation strategies in the Canadian prairies.