Crop type mapping provides essential information to control and make decisions related to agricultural practices and their regulations. To map crop types accurately, it is important to capture their phenological stages and fine spatial details, especially in a temporally and spatially heterogeneous landscape. The data availability of new generation multispectral sensors of Landsat-8 (L8) and Sentinel-2 (S2) satellites offers unprecedented options for such applications. Given this, our study aims to display how the synergistic use of these optical sensors can efficiently support crop type mapping research while integrating an object-based image analysis (OBIA). Through the applied methods, we used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) to blend L8 and S2 data and obtain reliable normalized difference vegetation index (NDVI) datasets with fine spatial and temporal resolution. Then the crop phenological information was extracted using a Savitzky–Golay filter and fused NDVI time series. Finally, a model combining phenological metrics and fused reconstructed NDVI as classification features was developed using a random forest (RF) classifier/OBIA approach. The results show that the FSDAF method creates more accurate fused NDVI and keeps more spatial details than ESTARFM. The FSDAF model was then used to create fused, high-resolution time-series products that were able to extract crop phenology in single-crop fields while providing a very detailed pattern relative to that from individual sensor time-series data. Moreover, combined L8 and S2 data by FSDAF produced highly significant overall classification accuracies (90.03% for pixel-based RF to 93.12% OBIA RF), outperforming individual sensor use (82.57% for L8-only; 88.45% for S2-only). Our proposed workflow highlights the advantage of spatiotemporal fusing and OBIA environment in spatiotemporally heterogeneous areas and fragmented landscapes, which represents a promising step toward generating fast, accurate, and ready-to-use agricultural data products.

中文翻译：

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摩洛哥中部农作物的分类和状况监测：OBIA方法与融合的Landsat-Sentinel-2数据的协同组合

作物类型图谱提供了控制和做出与农业实践及其法规相关的决策的必要信息。为了准确地绘制作物类型，重要的是捕捉它们的物候阶段和精细的空间细节，尤其是在时间和空间异质景观中。Landsat-8（L8）和Sentinel-2（S2）卫星的新一代多光谱传感器的数据可用性为此类应用提供了空前的选择。鉴于此，我们的研究旨在展示这些光学传感器的协同使用如何在集成基于对象的图像分析（OBIA）的同时有效地支持作物类型映射研究。通过应用的方法，我们使用增强的时空自适应反射融合模型（ESTARFM）和灵活的时空数据融合模型（FSDAF）来混合L8和S2数据，并获得具有良好时空分辨率的可靠归一化植被指数（NDVI）数据集。然后使用Savitzky-Golay滤波器和融合的NDVI时间序列提取作物物候信息。最后，使用随机森林（RF）分类器/ OBIA方法开发了将物候指标和融合重建的NDVI作为分类特征的模型。结果表明，与ESTARFM相比，FSDAF方法可创建更精确的融合NDVI，并保留更多空间细节。然后使用FSDAF模型创建融合的，高分辨率时间序列产品，能够提取单作物田间的作物物候，同时提供相对于单个传感器时间序列数据而言非常详细的模式。此外，FSDAF组合的L8和S2数据产生了非常重要的总体分类精度（基于像素的RF为90.03％，至OBIA RF为93.12％），优于单个传感器的使用（仅L8为82.57％；仅S2为88.45％）。我们提出的工作流程强调了时空融合区域和零散景观中的时空融合和OBIA环境的优势，这代表了朝着生成快速，准确和即用型农业数据产品迈出的有希望的一步。FSDAF组合的L8和S2数据产生了非常重要的整体分类精度（基于像素的RF为90.03％，至OBIA RF为93.12％），优于单个传感器的使用（仅L8为82.57％；仅S2为88.45％）。我们提出的工作流程强调了时空融合区域和零散景观中的时空融合和OBIA环境的优势，这代表了朝着生成快速，准确和即用型农业数据产品迈出的有希望的一步。FSDAF组合的L8和S2数据产生了非常重要的整体分类精度（基于像素的RF为90.03％，至OBIA RF为93.12％），优于单个传感器的使用（仅L8为82.57％；仅S2为88.45％）。我们提出的工作流程强调了时空融合区域和零散景观中的时空融合和OBIA环境的优势，这代表了朝着生成快速，准确和即用型农业数据产品迈出的有希望的一步。