To study the Earth’s energy balance and to extend exoplanet research, the Earth’s outward radiative flux and its radiance in the Moon-based view were simulated according to the Earth–Sun–Moon geometry model, with the help of ERA5. A framework was developed to identify the angular distribution model (ADM) of Earth’s surface and its scene types, according to the surface and atmospheric data from ERA5. Our simulation shows that the specific viewing geometry controls the periodical variations in the Moon-based view radiative flux and its radiance, which reflect the orbital period of the Moon. The seasonal variations in shortwave and longwave radiative flux follow the energy balance in general, which is probably influenced by the Earth albedo. The derived global ADM would help to identify the anisotropic factor of observations at DSCOVR. Our simulations prove that Moon-based observation is a valuable source for Earth observation and that the orbital information of exoplanets could be derived from the radiance observation.

The variation in the radiation budget at Earth’s top of the atmosphere (TOA) represents the most fundamental metric defining the status of global climate change. The accurate estimation of Earth’s shortwave radiant exitance is of critical importance to study Earth’s radiation budget (ERB) at TOA. Measuring Earth’s outgoing shortwave radiance (OSR) is a key point to estimate Earth’s shortwave radiant exitance. Compared with space-borne satellite systems, Moon-based sensors (MS) could provide large-scale, continuous, and long-term data for Earth radiation observations, bringing a new perspective on ERB. However, the factors affecting the estimation of Earth’s OSR in the lunar direction have not yet been fully explored, for example, anisotropic surface reflection and the effects of clouds and aerosols on radiation budget. In this work, we only focused on the influence of anisotropic surface reflection. To evaluate the extent of this influence, we constructed a model to estimate Earth’s OSR in the lunar direction (EOSRiLD), integrating the variables of anisotropic surface reflection (scene types, solar zenith angles, viewing zenith angles, and relative azimuth angles) and radiant flux in Moon-viewed sunlit regions. Then, we discussed it over three time periods (Earth’s rotation, revolution period, and synodic month cycle) and analyzed the impact of three variables (area of the Moon-viewed sunlit region, scene types, and incident-viewing angular bins) on anisotropic EOSRiLD. Our results indicate that EOSRiLD based on the assumptions of anisotropic and isotropic reflection is different but they all show the same monthly cycle change, which is related to the area of the Moon-viewed sunlit region. At the beginning and end of the lunar month, the differences between anisotropy and isotropy are greatest in each cycle; when it is close to the first half of each cycle, there is a small difference peak. Both anisotropy and isotropy are caused by the relative azimuth angles between the Sun and Moon. In conclusion, even if the Moon-based platform has a wider scope than space-borne satellites, the difference is still large between anisotropy and isotropy. Therefore, we still need to consider the anisotropic surface reflection based on the Moon-based observation.

Generally, it is difficult to acquire remote sensing data whose resolution is both highly spatial and highly temporal from a single satellite. In this paper, a novel compressed sensing (CS)-based spatiotemporal data fusion (CSBS) method is proposed to synthesize such high-spatiotemporal resolution images. With CSBS, a low-spatial resolution remote senisng image is treated as a sampling of the high-spatial resolution image. The down-sampling in the spatial domain of images is modeled as a CS measurement matrix in CSBS. Moreover, continuity constraints in the temporal domain are also introduced into the CSBS object function for CS reconstruction. To better represent the intrinsic features of the data, images are segmented into many small patches and clustered into several groups via K-means. Dictionary training, measurement matrix identification, and high-resolution prediction are carried out group-by-group. Based on features learned from patch groups, the transformational relationship between spatial-temporal images having different resolutions are easily identified. Compared with previous compressed sensing and dictionary learning methods, CSBS is characterized by: (1) the patch-group stratagem in dictionary learning and measurement matrix learning; (2) the combination of continuity in temporal domain and sparsity in spatial domain. The proposed method is then comprehensively compared with different methods using land-surface reflectance data. Experiment results validate the effectiveness and advancement of CSBS for spatiotemporal data fusion.

An eye-safe 1567 nm continuous-wave laser with a maximum output power of 50 mW and a slope efficiency of 21.1% was demonstrated in an Er:Yb:Ba₃Gd(PO₄)₃ crystal. By using a Co²⁺:MgAl₂O₄ crystal with an initial transmission of 95% as a saturable absorber, a stable passively Q-switched pulsed laser was also realized in the crystal. The effects of the output coupler transmission and cavity length on pulsed performance were investigated. At an absorbed pump power of 350 mW, a 1541 nm Er:Yb:Ba₃Gd(PO₄)₃ pulsed laser with a repetition frequency of 0.86 kHz, duration of 38 ns, energy of 21.2 µJ, and peak output power of 0.56 kW was obtained.