Chang’E-5 reveals the Moon's secrets to a longer life

Mitchell, Ross N.

doi:10.1016/j.xinn.2021.100177

Cited by 13 publications

(7 citation statements)

References 5 publications

(9 reference statements)

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“…Moreover, a dry mantle source for the CE5 basalts excludes the possibility of hydrous mantle melting ( 13 ). Other hypotheses for prolonged lunar volcanism such as tidal heating ( 37 ), megaregolith insulation ( 38 ), and sluggish mantle convection ( 39 ) are possible but require further evidence. Our study shows that the young CE5 mantle source region had more clinopyroxene-ilmenite cumulates than those of older Apollo low-Ti basalts.…”

Section: Resultsmentioning

confidence: 99%

Fusible mantle cumulates trigger young mare volcanism on the cooling Moon

Yuan

Chen

et al. 2022

Sci. Adv.

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The Chang’E-5 (CE5) mission has demonstrated that lunar volcanism was still active until two billion years ago, much younger than the previous isotopically dated lunar basalts. How the small Moon retained enough heat to drive such late volcanism is unknown, particularly as the CE5 mantle source was anhydrous and depleted in heat-producing elements. We conduct fractional crystallization and mantle melting simulations that show that mantle melting point depression by the presence of fusible, easily melted components could trigger young volcanism. Enriched in calcium oxide and titanium dioxide compared to older Apollo magmas, the young CE5 magma was, thus, sourced from the overturn of the late-stage fusible cumulates of the lunar magma ocean. Mantle melting point depression is the first mechanism to account for young volcanism on the Moon that is consistent with the newly returned CE5 basalts.

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Section: Resultsmentioning

confidence: 99%

Fusible mantle cumulates trigger young mare volcanism on the cooling Moon

Yuan

Chen

et al. 2022

Sci. Adv.

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“…Here, δ crst and r l are constants arbitrarily chosen. We assumed δ crst = 79.5 × 10 3 K to mimic the temperature-increase with depth in the topmost 35 km (i.e., the crust) of the Moon, and r l = 550 km which is estimated from earlier numerical models of the post-overturn stratification (e.g., Boukaré et al, 2018;Mitchell, 2021). T p is the potential temperature; T c = 1875 K the initial temperature of the core.…”

Section: Appendix B: Details Of the Initial Conditionmentioning

confidence: 99%

The Volcanic and Radial Expansion/Contraction History of the Moon Simulated by Numerical Models of Magmatism in the Convective Mantle

Kameyama,

Ogawa

2023

JGR Planets

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To understand the evolution of the Moon, we numerically modeled mantle convection and magmatism in a two‐dimensional polar rectangular mantle. Magmatism occurs as an upward permeable flow of magma generated by decompression melting through the convecting matrix. The mantle is assumed to be initially enriched in heat‐producing elements (HPEs) and compositionally dense ilmenite‐bearing cumulates (IBC) at its base. Here, we newly show that magma generation and migration play a crucial role in the calculated volcanic and radial expansion/contraction history. Magma is generated in the deep mantle by internal heating for the first several hundred million years. A large volume of the generated magma ascends to the surface as partially molten plumes driven by melt buoyancy; the magma generation and ascent cause a volcanic activity and radial expansion of the Moon with the peak at 3.5–4 Gyr ago. Eventually, the Moon begins to radially contract when the mantle solidifies by cooling from the surface boundary. As the mantle is cooled, the activity of partially molten plumes declines but continues for billions of years after the peak because some basal materials enriched in the dense IBC components hold HPEs. The calculated volcanic and radial expansion/contraction history is consistent with the observed history of the Moon. Our simulations suggest that a substantial fraction of the mantle was solid, and there was a basal layer enriched in HPEs and the IBC components at the beginning of the history of the Moon.

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“…However, these methods not only consume a lot of manpower and material resources, there are also spatial and temporal gaps in the results. Since 2000, satellite remote sensing technology has played an important role in related fields such as resource surveys (Mitchell, 2021), urban planning (Guo et al, 2019), agricultural development (Qiao et al, 2021), and national security (Zhang et al, 2022). The use of satellite remote sensing has become an effective way of making yield predictions due to its advantages of simple data acquisition, low cost, efficiency, wide spatial coverage, and short operating cycles (Peng et al, 2014;Zhang et al, 2019;Wang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Wheat yield estimation using remote sensing data based on machine learning approaches

et al. 2022

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Accurate predictions of wheat yields are essential to farmers’production plans and to the international trade in wheat. However, only poor approximations of the productivity of wheat crops in China can be obtained using traditional linear regression models based on vegetation indices and observations of the yield. In this study, Sentinel-2 (multispectral data) and ZY-1 02D (hyperspectral data) were used together with 15709 gridded yield data (with a resolution of 5 m × 5 m) to predict the winter wheat yield. These estimates were based on four mainstream data-driven approaches: Long Short-Term Memory (LSTM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR). The method that gave the best estimate of the winter wheat yield was determined, and the accuracy of the estimates based on multispectral and hyperspectral data were compared. The results showed that the LSTM model, for which the RMSE of the estimates was 0.201 t/ha, performed better than the RF (RMSE = 0.260 t/ha), GBDT (RMSE = 0.306 t/ha), and SVR (RMSE = 0.489 t/ha) methods. The estimates based on the ZY-1 02D hyperspectral data were more accurate than those based on the 30-m Sentinel-2 data: RMSE = 0.237 t/ha for the ZY-1 02D data, which is about a 5% improvement on the RSME of 0.307 t/ha for the 30-m Sentinel-2 data. However, the 10-m Sentinel-2 data performed even better, giving an RMSE of 0.219 t/ha. In addition, it was found that the greenness vegetation index SR (simple ratio index) outperformed the traditional vegetation indices. The results highlight the potential of the shortwave infrared bands to replace the visible and near-infrared bands for predicting crop yields Our study demonstrates the advantages of the deep learning method LSTM over machine learning methods in terms of its ability to make accurate estimates of the winter wheat yield.

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Chang’E-5 reveals the Moon's secrets to a longer life

Cited by 13 publications

References 5 publications

Fusible mantle cumulates trigger young mare volcanism on the cooling Moon

Fusible mantle cumulates trigger young mare volcanism on the cooling Moon

The Volcanic and Radial Expansion/Contraction History of the Moon Simulated by Numerical Models of Magmatism in the Convective Mantle

Wheat yield estimation using remote sensing data based on machine learning approaches

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