## 6 Conclusions  
Most nightlight remote-sensing studies focus on artificial lights that are emitted at night and can be observed from space, especially those of cities. Little attention has been paid to examine the potential use of reflected moonlight. The present study systematically evaluated the potential of moonlight remote sensing. (1) The reliability of the moonlight remote-sensing imagery. Using VIIRS/DNB, ISS and UAV moonlight images, the possibilities of moonlight remote sensing were discussed. VIIRS/DNB data successfully acquired spatial distribution and spectral information of land surface, such as snowy mountains, forests, farmlands, and rivers. The ISS data successfully identified snow and forests in the wilderness. In addition, the spatial distribution and texture characteristics of the land surface could be obtained as clearly as optical data in the daytime, especially for the observation of non-self-luminous objects, such as fish ponds, bare land, farmland, and even greenhouses. Therefore, it is believable that moonlight remote sensing is feasible for obtaining non-luminous land surfaces under faint lunar illumination at night, providing a practical way to increase observation frequencies of optical remote sensing. (2) Land surface classification of moonlight remote-sensing imagery. VIIRS/DNB, ISS, UAV images were classified to explore the potential of moonlight remote sensing. The overall accuracy (OA) and kappa coefficient of the VIIRS/DNB moonlight image are 79.80% and 0.45, respectively. In the low-light suburban areas of Calgary, the overall accuracy and kappa coefficient of the classification result are 87.16% and 0.77, respectively. While the overall accuracy and kappa coefficient of Komsomsolsk-nature are 91.49% and 0.85, respectively. The land surface classification of UAV moonlight images well reflected the spatial distribution characteristics of each land type. The overall accuracy and kappa coefficient are 82.33% and 0.77, respectively. The above results show that these moonlight remote sensing data can be applied well to the classification of a non-self-luminous land surface at night. (3) The characteristics of current moonlight remote sensing. Finally, the characteristics of current moonlight remote sensing were compared from three aspects of bands, spatial resolutions, and sensors. First of all, multi-spectral moonlight remote sensing is more suitable for Earth observation under complex environments at night. Then, the spatial resolution of the moonlight data directly affects the application scenario of moonlight sensors; both CCD and CMOS cameras have great potential to achieve night-time Earth observations under fine lunar illumination. The present study has systematically proved the huge potential of moonlight remote sensing in detecting non-self-emitting objects at night, which has been overlooked in traditional applications of night-light remote sensing. Although moonlight remote sensing has great potential for Earth observations, there is still more work to be done to use moonlight as an illuminating source for nightlight remote sensing. It is more difficult for the nocturnal atmospheric radiative transfer model to establish that the moonlight irradiance is much smaller than the sunlight irradiance and atmospheric changes at night are more complicated. In addition, the irradiances of moonlight under different moon phases from a new moon to a full moon also need to be carefully measured and calculated in the future. Meanwhile, studies on the nocturnal atmospheric radiative transfer model and the influence of different moon phase irradiances on the quality of nightlight data are also the basis for promoting quantitative research of moonlight remote sensing. Conceptualization, D.L. and Q.Z. ; methodology, D.L. and Y.W. ; writing--original draft preparation, D.L., Q.Z. and J.W. ; writing--review and editing, D.L., J.W., Y.S. ([PERSON]) and Q.Z. ; supervision, Q.Z. ; project administration, Q.Z. and Y.S. ([PERSON]); funding acquisition, Q.Z. and Y.S. ([PERSON]).