Spatiotemporal fusion for spectral remote sensing: A statistical analysis and review

Abstract

Remote sensing images obtained by a variety of sensors have been widely used in different Earth observation tasks. However, owing to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both high spatial and temporal resolution. This brings difficulties to remote sensing research which requires high spatial and temporal resolution data. To solve the above constraints, the spatiotemporal fusion (STF) method was proposed and has received widespread attention. The main challenge for remote sensing STF is to reconstruct both phenological and land cover type changes. To overcome this challenge, many STF methods have been proposed based on different principles and strategies. Since there are STF methods have been proposed recently, there is a need for new review to reflect the current research status. Therefore, in this review, we summarize the existing studies, discusses their basic principle and limitations, collect some recent applications, and provide a comprehensive overview of current advances. Furthermore, to facilitate and promote the research in this community, we also collect publicly available resources and introduce the most used quantitative metrics. Finally, we take a conversation about some open problems and challenges that require attention in the future.

Keywords

• Remote sensing image
• Spatiotemporal fusion
• Weight
• Unmixing
• Machine learning
• Deep learning