In the present work, a deep learning-based network called LeNet is applied for accurate grassland map production from Sentinel-2 data for the Greater Sydney region, Australia. First, we apply the technique to the base date Sentinel-2 data (non-seasonal) to make the vegetation maps. Then, we combine short time-series (seasonal) data and enhanced vegetation index (EVI) information to the base date imagery to improve the classification results and generate high-resolution grassland maps. The proposed model obtained an overall accuracy (OA) of 88.36% for the mono-temporal data, and 92.74% for the multi-temporal data. The experimental products proved that, by combining the short time-series images and EVI information to the base date, the classification maps' accuracy is increased by 4.38%. Moreover, the Sentinel-2 produced grassland maps are compared with the pre-existing maps such as Australian Land Use and Management (ALUM) 50 m resolution and Dynamic Land Cover Dataset (DLCD) with 250 m resolution as well as some traditional machine learning methods such as Support Vector Machine (SVM) and Random Forest (RF). The results show the effect of the LeNet network's performance and efficiency for grassland map production from short time-series data. As a result, decision-makers and urban planners can benefit from this work in terms of grassland change identification, monitoring, and planning assessment.

在目前的工作中，一个名为 LeNet 的基于深度学习的网络被应用于根据澳大利亚大悉尼地区的 Sentinel-2 数据生成准确的草原地图。首先，我们将该技术应用于基准日期 Sentinel-2 数据（非季节性）以制作植被图。然后，我们将短时间序列（季节性）数据和增强植被指数（EVI）信息结合到基准日期图像中，以改进分类结果并生成高分辨率草原地图。所提出的模型在单时态数据上获得了 88.36% 的整体准确率 (OA)，在多时态数据上获得了 92.74% 的整体准确率 (OA)。实验产品证明，通过将短时间序列图像和EVI信息结合到基准日期，分类图的准确率提高了4.38%。而且，Sentinel-2 制作的草原地图与现有地图如澳大利亚土地利用和管理 (ALUM) 50 m 分辨率和动态土地覆盖数据集 (DLCD) 250 m 分辨率以及一些传统的机器学习方法如支持向量机 (SVM) 和随机森林 (RF)。结果显示了 LeNet 网络的性能和效率对短时间序列数据的草地地图制作的影响。因此，决策者和城市规划者可以在草原变化识别、监测和规划评估方面从这项工作中受益。