## 1 Introduction
Since then, whether it is mono-spectral (Visible Infrared Imaging Radiometer Suite/Day-Night-Band (VIIRS/DNB), Scientific Application Satellite-C High Sensitivity Technological Camera (SAC-C HSTC), Scientific Application Satellite-D High Sensitivity Camera (SAC-D HSC), CubeSat Multispectral Observing System (CUMULOS), Luojia1-01 (LJ 1-01), Earth Remote Observation System-B (EROS-B) or multi-spectral (Aerocube 4, International Space Station (ISS), Aerocube 5, Landsat-8, Jilin-1), new sensors have been developed with the main focus of detecting and identifying self-luminous objects at night [14]. Applications using these night-time remote sensing data include mapping urban areas [15; 16; 17; 18; 19], estimating population, GDP, and poverty [20; 21; 22; 23], monitoring disasters and conflicts [24; 25; 26], as well as understanding the influence of light pollution [27; 10; 28; 29]. These studies often focus on urban lights or self-luminous bodies, such as fisheries, oil, gas extraction, etc., with very few exploring the potential of nightlight remote sensing data to study natural processes. Ironically, it seems to have long been ignored that the original purpose of DMSP/OLS was to detect clouds under moonlight illumination [30]. Although moonlight has been an important factor that affect many nocturnal animals and plants, mainstream nightlight remote sensing image processes either try their best to totally avoid moonlight, or to remove the moonlight component from observations with tuned algorithms. For example,to generate annual DMSP/OLS composites, only sunlit and moonlight-free observations have been used, and moonlit observations are simply discarded [31]. To produce the VIIRS/DNB daily black marble product, an algorithm was developed to remove moonlight components from daily nightlight observations [32]. With the recent development of low-light detection technology, the nightlight remote sensing community started to realize that moonlight can be a very useful illumination source for detecting weather and climate parameters at night, instead of being treated as a noise source for city light detection [33; 34]. [PERSON] et al. [4] made a detailed insight of many potential applications for nocturnal low-light visible satellite observations and presented a long list of key variables that could be obtained under moonlight using VIIRS/DNB from space. They found that sometimes night-time moonlight remote sensing even showed advantages over the daytime sunlight remote sensing. These include the detection of snow cover, rainfall distributions across arid/semi-arid surfaces, the ability to peer through optically thin clouds to reveal sea ice, and the detection of oceanic currents, etc. Although these studies demonstrated a comprehensive potential for night-time low-light measurements, quantitative assessment is still needed. How to quantitatively assess the potential of moonlight remote sensing needs a thorough investigation and more questions must be addressed. Currently, there exist many different satellite sensors, as mentioned above, with different characteristics in terms of spectra and spatial resolutions. Furthermore, drones, as a new near-ground remote-sensing platform, also have great potential to study changes in lighting at night [14]. These sensors have not been discussed for Earth observation under moonlight, thus, it is of great significance to analyze and compare them in the context of radiometric correction of nightlight remote sensing data and for the design of next generation night-time sensors. Another important question is the quantitative characterization of non-self-lighting objects under night-time low-light environments, considering that there is no rigorous quantitative analysis to date, such as land cover classification under moonlight lighting conditions. We first compare the differences in night-time observations under moonlight using different sensors, the mono-spectral VIIRS/DNB night-time image, the multi-spectral nighttime photos taken by astronauts from the International Space Station, and UAVs.