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The Belt and Road initiative has a significant focus on infrastructure, trade, and economic development across a vast region, and it also provides significant opportunities for sustainable development. The combined pressure of climate variability, intensified use of resources, and the fragility of ecosystems make it very challenging, however, to achieve future sustainability. To develop the path in a sustainable way, it is important to have a comprehensive understanding of these issues across nations and evaluate them in a scientific and well-informed approach. In this context, the Digital Belt and Road (DBAR) program was initiated as an international venture to share expertise, knowledge, technologies, and data to demonstrate the role of Earth observation science and technology and big Earth data applications to support large-scale development. In this paper, we identify pressing challenges, present the research priorities and foci of the DBAR program, and propose solutions where big Earth data can make significant contributions. This paper calls for further joint actions and collaboration to build a digital silk road in support of sustainable development at national, regional and global levels.  相似文献   
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An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.  相似文献   
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Scholars have long discussed the introduction and spread of iron metallurgy in different civilizations. The sporadic use of iron has been reported in the Eastern Mediterranean area from the late Neolithic period to the Bronze Age. Despite the rare existence of smelted iron, it is generally assumed that early iron objects were produced from meteoritic iron. Nevertheless, the methods of working the metal, its use, and diffusion are contentious issues compromised by lack of detailed analysis. Since its discovery in 1925, the meteoritic origin of the iron dagger blade from the sarcophagus of the ancient Egyptian King Tutankhamun (14th C. BCE) has been the subject of debate and previous analyses yielded controversial results. We show that the composition of the blade (Fe plus 10.8 wt% Ni and 0.58 wt% Co), accurately determined through portable x‐ray fluorescence spectrometry, strongly supports its meteoritic origin. In agreement with recent results of metallographic analysis of ancient iron artifacts from Gerzeh, our study confirms that ancient Egyptians attributed great value to meteoritic iron for the production of precious objects. Moreover, the high manufacturing quality of Tutankhamun's dagger blade, in comparison with other simple‐shaped meteoritic iron artifacts, suggests a significant mastery of ironworking in Tutankhamun's time.  相似文献   
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Computational Geosciences - Anthropogenic land subsidence can be evaluated and predicted by numerical models, which are often built over deterministic analyses. However, uncertainties and...  相似文献   
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