Sentinel-2 and Sentinel-3 are two remote sensing satellites implemented by the European Space Agency for global observation. The temporal resolution of Sentinel-2 images and the spatial resolution of Sentinel-3 images may not be sufficient for local and precise monitoring. With spatiotemporal image fusion of Sentinel-2 and Sentinel-3 sensors, images with 1.4 days temporal resolution and 10-m spatial resolution can be produced. However, strong temporal change is a challenging factor for spatiotemporal fusion. The aim of this study was to compare the success of the deep learning-based DMNet model with flexible spatiotemporal data fusion (FSDAF) 2.0 and reliable and adaptive spatiotemporal data fusion (RASDF) algorithms for the spatiotemporal fusion of Sentinel-2 and Sentinel-3 images over strong temporal changes. Thus, the Kansas dataset was developed for the spatiotemporal fusion of Sentinel-2 and Sentinel-3 images in this study. It contained a large number of surface changes due to large wheat harvests. The results of this investigation show that in case of strong temporal change, the deep learning-based DMNet model performed better than the FSDAF 2.0 and RASDF methods. On the other hand, in the case of less temporal change, the FSDAF 2.0 and RASDF methods had a very high success compared with the DMNet model.

中文翻译：

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Sentinel-2和Sentinel-3图像在强时空变化下的时空融合对比研究

Sentinel-2 和 Sentinel-3 是欧洲航天局实施的两颗用于全球观测的遥感卫星。Sentinel-2 图像的时间分辨率和 Sentinel-3 图像的空间分辨率可能不足以进行局部和精确监测。通过 Sentinel-2 和 Sentinel-3 传感器的时空图像融合，可以生成 1.4 天时间分辨率和 10 米空间分辨率的图像。然而，强烈的时间变化是时空融合的一个挑战因素。本研究的目的是比较基于深度学习的 DMNet 模型与灵活时空数据融合 (FSDAF) 2.0 以及可靠和自适应时空数据融合 (RASDF) 算法在 Sentinel-2 和 Sentinel-3 时空融合方面的成功在强烈的时间变化上的图像。因此，Kansas 数据集是为本研究中 Sentinel-2 和 Sentinel-3 图像的时空融合而开发的。由于小麦丰收，它包含了大量的表面变化。这项调查的结果表明，在强烈的时间变化的情况下，基于深度学习的 DMNet 模型的性能优于 FSDAF 2.0 和 RASDF 方法。另一方面，在时间变化较小的情况下，与 DMNet 模型相比，FSDAF 2.0 和 RASDF 方法取得了非常高的成功率。