The recent availability of satellite hyperspectral imaging combined with the developments in the classification techniques have paved the way towards improving our ability to obtain information on the spatiotemporal distribution of land use/land cover (LULC) at improved accuracy. In this context, the present study aims at evaluating the combined use of the recently launched Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite mission with two powerful machine learning (ML) classifiers. In particular, the Support Vector Machines (SVM) and Random Forest (RF) are used synergistically with EnMAP imagery in performing LULC mapping at a typical Mediterranean setting located in northern Greece. Evaluation of the derived LULC maps is based on the computation of a series classification accuracy metrics. The McNemar's chi-square statistical significance testing was also computed to confirm the statistical significance of the differences between the classifiers. In overall, results showed that SVM slightly outperformed RF, exhibiting a higher overall accuracy, of 92.6% and 88.1%, respectively, whereas the statistical significance of the findings was also attested by the McNemar's statistical test results. To our knowledge, this study is one of the first published so far focusing on exploring the capabilities of ENMAP imagery when combined with different ML pixel-based classifiers in the context of LULC mapping. Our results, indeed provided useful insights on the potential of EnMAP datasets in deriving information on the spatiotemporal distribution of LULC at a highly fragmented Mediterranean landscape, evidencing the EnMAP promising potential in this field.

最近卫星高光谱成像的出现与分类技术的发展相结合，为提高我们以更高的精度获取土地利用/土地覆盖（LULC）时空分布信息的能力铺平了道路。在此背景下，本研究旨在评估最近启动的环境测绘和分析计划（EnMAP）高光谱卫星任务与两个强大的机器学习（ML）分类器的组合使用。特别是，支持向量机 (SVM) 和随机森林(RF) 与 EnMAP 图像协同使用，在希腊北部的典型地中海环境中执行 LULC 制图。导出的 LULC 地图的评估基于一系列分类精度指标的计算。还计算了麦克尼马尔卡方统计显着性检验，以确认分类器之间差异的统计显着性。总体而言，结果表明 SVM 略优于 RF，表现出更高的总体准确率，分别为 92.6% 和 88.1%，而 McNemar 统计测试结果也证明了研究结果的统计显着性。据我们所知，这项研究是迄今为止首次发表的研究之一，重点是探索 ENMAP 图像在 LULC 映射背景下与不同的基于 ML 像素的分类器相结合时的功能。我们的结果确实为 EnMAP 数据集在获取高度分散的地中海景观中 LULC 时空分布信息方面的潜力提供了有用的见解，证明了 EnMAP 在该领域的巨大潜力。