As Earth observation satellite data grows, the need for higher temporal and spatial resolutions becomes crucial for accurate monitoring and decision-making. Achieving both of high temporal and spatial resolutions is challenging due to trade-offs in sensor design; for instance, Sentinel-2 and Landsat offer higher temporal but lower spatial resolution, while high-resolution sensors like NEONSAT with small coverages. This study introduces a deep learning-based spatiotemporal image fusion method that integrates multi-sensor data, combining low and high spatial resolution images from different sensors over time. The method estimates adjustment features from temporal and spatial differences, using fusion and convolutional blocks to enhance resolution. Trained on Sentinel-2 and Planet images, the method effectively maintains spectral integrity and enhances spatial details under varying conditions. By leveraging multi-sensor data, this approach addresses sensor quality and stability issues, expanding NEONSAT’s potential applications. Future research will refine the method by incorporating more datasets, including NEONSAT imagery, to advance spatiotemporal fusion techniques.
Global climate changes as well as abnormal climate phenomena have affected the agricultural environment on a great
scale. Thus, there is a strong need for countermeasures by making full use of agriculture related information. As
agricultural lands in South Korea are mostly operated by private farmers on a small parcel level, it is difficult to gather
information for an overview on changing crop condition and to construct database necessary for disease management,
production estimation and compensation measures on a regional or governmental level. The objective of this study is to
evaluate the multispectral reflectance characteristics of RapidEye image data to classify agricultural land cover as well as
crop condition in South Korea. As the RapidEye sensor offers the spectral information in red edge range as a first
multispectral satellite system, we focus on the usefulness of red edge reflectance for identifying crop species and for
interpreting crop growth or stress condition.
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