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MRRT: Modeling of multiple reflections of radiation between terrains applied on the lunar surface region
  • Yunfei Liu
Yunfei Liu
Shanghai Institute of technical physics, Shanghai Institute of technical physics

Corresponding Author:[email protected]

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The lunar surface has a stable luminosity. To use the Moon as a calibration standard, the Robotic Lunar Observatory (ROLO) program models the integration of the radiance of the entire lunar surface. However, the albedos of the mare and the highlands are very different. The modeling based on the lunar global irradiance/reflected radiance is bound to result in higher uncertainty. In contrast, if the local calibration of the lunar surface is adopted, the lunar complex topography effect cannot be ignored. This paper presents a new model for quantifying multiple reflections of radiation between terrains (MRRT). The relationship between the bidirectional reflectance factor (BRF) of the observed pixel and the true microtopography reflectance is established, which shows that the BRF is mainly influenced by the true topography reflectance, the terrain undulation, the incident irradiance on the topography surface, and the masking in the observation direction. The new model applied on the lunar surface obtains clearer terrain details. The inversion reflectance of the Chang’e-3 landing area is closer to the reflectance measured in situ, and the reflectance curves of the Apollo 16 landing area are almost consistent under different illumination observation geometries. This shows that the MRRT model can effectively eliminate the topographic effect. Compared with the ROLO model, the MRRT model does not restrict the specific selection, so it can select a region with a uniform material distribution, small albedo difference, and low topography undulation to establish the lunar surface radiometric calibration field with the advantage of providing stable radiation characteristics.
2022Published in IEEE Transactions on Geoscience and Remote Sensing volume 60 on pages 1-17. 10.1109/TGRS.2022.3225645