Archean-Paleoproterozoic Lithospheric Mantle at the Northern Margin of the North China Craton Represented by Tectonically Exhumed Peridotites

Tectonically emplaced peridotites from North Hebei Province, North China Craton, have retained an original harzburgite mineral assemblage of olivine （54%-58%） ＋ orthopyroxene （40%-46%） ＋minor clinopyroxene （〈1%）＋spinel. Samples with honinite-like chemical compositions also coexist with these peridotites. The spinels within the peridotites have high-A1 end-members with A1203 content of 30 wt%-50 wt%, typical of mantle spinels. When compared with experimentally determined melt extraction trajectories, the harzburgites display a high degree of melting and enrichment of SiO2, which is typical of cratonic mantle peridotites. The peridotites display variably enriched light rare earth elements （REEs）, relatively depleted middle REEs and weakly fractionated heavy REEs, which suggest a melt extraction of over 25% in the spinel stability field. The occurrence of are- and SSZ-type chromian spinels in the peridotites suggests that melt extraction and metasomatism occurred mostly in a subduction-related setting. This is also supported by the geochemical data of the coexisting boninite-like samples. The peridotites have lS7Os/lSSOs ratios ranging from 0.113-0.122, which is typical of cratonic iithospheric mantle. These lSTOs/ISSOs ratios yield model melt extraction ages （TRD） ranging from 981 Ma to 2054 Ma, which may represent the minimum estimation of the melt extraction age. The Ai203- lSTOs/lSSOs-proxy isochron ages of 2.4 Ga-2.7 Ga suggest a mantle melt depletion age between the Late Achaean and Early Paleoproterozoic. Both the peridotites and boninite-like rocks are therefore interpreted as tectonically exhumed continental lithospheric mantle of the North China Craton, which has experienced mantle melt depletion and subduction-related mantle metasomatism during the Neoarchean- Paleoproterozoic.

Estimation of 30 m land surface temperatures over the entire Tibetan Plateau based on Landsat-7 ETM+data and machine learning methods

Land surface temperature(LST)is an important parameter in land surface processes.Improving the accuracy of LST retrieval over the entire Tibetan Plateau(TP)using satellite images with high spatial resolution is an important and essential issue for studies of climate change on the TP.In this study,a random forest regression(RFR)model based on different land cover types and an improved generalized single-channel(SC)algorithm based on linear regression(LR)were proposed.Plateau-scale LST products with a 30 m spatial resolution from 2006 to 2017 were derived by 109,978 Landsat 7 Enhanced Thematic Mapper Plus images and the application of the Google Earth Engine.Validation between LST results obtained from different algorithms and in situ measurements from Tibetan observation and research platform showed that the root mean square errors of the LST results retrieved by the RFR and LR models were 1.890 and 2.767 K,respectively,which were smaller than that of the MODIS product(3.625 K)and the original SC method(5.836 K).