The core concepts and fundamental issues of remote sensing science
Since the 1960s, remote sensing science and technology has emerged as a competitive high-tech field, with major countries striving to advance their capabilities. It has become a fundamental tool for human research in the earth system science and the comprehensive application of aerospace information across multiple domains. Recently, two significant developments warrant attention: First, in 2022, the Ministry of Education of China officially recognized Remote Sensing Science and Technology as a first-level interdisciplinary discipline within the graduate education framework, thereby strengthening foundational research in remote sensing and broadening its application areas. Second, the rise of artificial intelligence technologies, particularly deep learning, has ushered in a new paradigm for data-driven analysis and application of remote sensing data. While remote sensing fundamentally belongs to the domain of electromagnetic radiation physics, the associated physical models have been indispensable for the development of quantitative remote sensing. Nevertheless, the data-driven deep learning paradigm has introduced transformative ideas and methodologies to the field. Moving forward, the synergy between physical models and artificial intelligence will undoubtedly shape the future trajectory of remote sensing research and applications. In this context, a deeper exploration of the core concepts and fundamental issues in remote sensing science is crucial for achieving significant technological breakthroughs and scientific discoveries within this discipline.This article begins by examining the physical origins of remote sensing science, focusing on the interaction between ground objects and electromagnetic waves, which produces spectral radiation images under specific conditions. It explores the characteristics of various remote sensing methods across the electromagnetic spectrum, including solar reflected radiation in the visible to shortwave infrared remote sensing, daylight-induced chlorophyll fluorescence (SIF) remote sensing, laser remote sensing, both medium and longwave infrared remote sensing, and microwave remote sensing. The fundamental theoretical issues in remote sensing science are categorized into three primary characteristics: radiative, spectral, and temporal characteristics, along with five major effects: scale, atmospheric, angular, adjacent, and transfer effects. The former pertains to the intrinsic physical and chemical properties of ground objects within the electromagnetic spectrum, while the latter relates to factors such as imaging scale, atmospheric conditions, observation angle, and background environment. This discussion includes the expression and variation patterns of remote sensing features of land objects formed under diverse observation modes and conditions.The radiative characteristics reflect overall difference in term of radiation across different electromagnetic bands for various land covers, closely tied to geophysical and chemical properties. The spectral characteristics of land cover manifest as variations in the intensity of reflected and emitted signals with wavelength, highlighting significant differences in absorption, reflection, and emission behaviors among different materials, known as spectral characteristics. Temporal characteristics pertain to the systematic changes in spectral reflection or emission over time, aiding in remote sensing identification or feature inversion of land cover. The scale effect refers to the changes in remote sensing observation characteristics due to variations in pixel area size, influenced by spatial resolution or point scanning density (e.g., laser scanning spot density). The atmospheric effect describes how electromagnetic waves are impacted by the absorption, scattering, and emission from atmospheric particles during remote sensing imaging, leading to radiation distortion in image data. The angular effect highlights the directional nature of the interaction between land cover and electromagnetic waves, resulting in significant anisotropic characteristics and variations in radiation values based on the angles of incident radiation, remote sensing observation, and electromagnetic wave wavelength. The adjacent effect refers to the influence of spatial structure heterogeneity among land features, which can create cross-radiation contributions from non-target pixels to target pixels, dependent on spatial distribution and remote sensing observation mode. Finally, the transfer effect encompasses the changes in imaging quality after the electromagnetic signal of the ground objects entering the remote sensing system, including the processes such as photoelectric conversion, signal transmission, and digital recording.The review and discussion presented in this article on the fundamental issues of remote sensing science aim to deepen theoretical research in the field, particularly in the context of artificial intelligence. This exploration is intended to foster innovative methods in remote sensing technology and applications, promote the collaborative evolution of AI for Science and Science for AI in remote sensing, and encourage profound cross-disciplinary integration between remote sensing and other fields.
> Review
