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The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy. [ABSTRACT FROM AUTHOR]

The tectonic inversion of the Songliao Basin during the Cenozoic may have played an important role in controlling the development of sandstone-type uranium deposits. The widely distributed mafic intrusions in the host sandstones of the Qianjiadian U ore deposits provided new insights to constrain the regional tectonic evolution and the genesis of the U mineralization. In this study, zircon U-Pb dating, whole-rock geochemistry, Sr-Nd-Pb isotope analysis, and mineral chemical compositions were presented for the mafic rocks from the Qianjiadian area. The mafic rocks display low SiO2 (44.91–52.05 wt.%), high TFe2O3 contents (9.97–16.46 wt.%), variable MgO (4.59–15.87 wt.%), and moderate K2O + Na2O (3.19–6.52 wt.%), and can be subdivided into AB group (including basanites and alkali olivine basaltic rocks) and TB group (mainly tholeiitic basaltic rocks). They are characterized by homogenous isotopic compositions (εNd (t) = 3.47–5.89 and 87Sr/86Sr = 0.7032–0.7042) and relatively high radiogenic 206Pb/204Pb (18.13–18.34) and Nb/U ratios (23.0–45.6), similar to the nearby Shuangliao basalts, suggesting a common asthenospheric origin enriched with slab-derived components prior to melting. Zircon U-Pb and previous Ar-Ar dating show that the AB group formed earlier (51–47 Ma) than the TB group (42–40 Ma). Compared to the TB group, the AB group has higher TiO2, Na2O, K2O, P2O5, Ce, and HREE contents and Ta/Yb and Sr/Yb ratios, which may have resulted from variable depth of partial melting in association with lithospheric thinning. Combined with previous research, the Songliao Basin experienced: (1) Eocene (~50–40 Ma) lithospheric thinning and crustal extension during which mafic rocks intruded into the host sandstones of the Qianjiadian deposit, (2) a tectonic inversion from extension to tectonic uplift attributed to the subduction of the Pacific Plate occurring at ~40 Ma, and (3) Oligo–Miocene (~40–10 Ma) tectonic uplift, which is temporally associated with U mineralization. Finally, the close spatial relation between mafic intrusions and the U mineralization, dike-related secondary reduction, and secondary oxidation of the mafic rocks in the Qianjiadian area suggest that Eocene mafic rocks and their alteration halo in the Songliao Basin may have played a role as a reducing barrier for the U mineralization. [ABSTRACT FROM AUTHOR]

Temperature and precipitation are considered to be the most important indicators affecting the green-up date. Sensitivity of the green-up date to temperature and precipitation is considered to be one of the key indicators to characterize the response of terrestrial ecosystems to climate change. We selected the main grassland types for analysis, including temperate steppe, temperate meadow steppe, upland meadow, and lowland meadow. This study investigates the variation in key meteorological indicators (daily maximum temperature (Tmax), daily minimum temperature (Tmin), and precipitation) between 2001 and 2018. We then examined the partial correlation and sensitivity of green-up date (GUD) to Tmax, Tmin, and precipitation. Our analysis indicated that the average GUD across the whole area was DOY 113. The mean GUD trend was −3.1 days/decade and the 25% region advanced significantly. Tmax and Tmin mainly showed a decreasing trend in winter (p > 0.05). In spring, Tmax mainly showed an increasing trend (p > 0.05) and Tmin a decreasing trend (p > 0.05). Precipitation showed no significant (p > 0.05) change trend and the trend range was ±10 mm/decade. For temperate steppe, the increase in Tmin in March promotes green-up (27.3%, the proportion of significant pixels), with a sensitivity of −0.17 days/°C. In addition, precipitation in April also promotes green-up (21.7%), with a sensitivity of −0.32 days/mm. The GUDs of temperate meadow steppe (73.9%), lowland meadow (65.9%), and upland meadow (22.1%) were mainly affected by Tmin in March, with sensitivities of −0.15 days/°C, −0.13 days/°C, and −0.14 days/°C, respectively. The results of this study reveal the response of vegetation to climate warming and contribute to improving the prediction of ecological changes as temperatures increase in the future. [ABSTRACT FROM AUTHOR]

The medium resolution spectral imager-2 (MERSI-2) is one of the most important sensors onboard China's latest polar-orbiting meteorological satellite, Fengyun-3D (FY-3D). The National Satellite Meteorological Center of China Meteorological Administration has developed four precipitable water vapor (PWV) datasets using five near-infrared bands of MERSI-2, including the P905 dataset, P936 dataset, P940 dataset and the fusion dataset of the above three datasets. For the convenience of users, we comprehensively evaluate the quality of these PWV datasets with the ground-based PWV data derived from Aerosol Robotic Network. The validation results show that the P905, P936 and fused PWV datasets have relatively large systematic errors (−0.10, −0.11 and −0.07 g/cm2), whereas the systematic error of the P940 dataset (−0.02 g/cm2) is very small. According to the overall accuracy of these four PWV datasets by our assessments, they can be ranked in descending order as P940 dataset, fused dataset, P936 dataset and P905 dataset. The root mean square error (RMSE), relative error (RE) and percentage of retrieval results with error within ± (0.05 + 0.10 ∗ P W V A E R O N E T ) (PER10) of the P940 PWV dataset are 0.24 g/cm2, 0.10 and 76.36%, respectively. The RMSE, RE and PER10 of the P905 PWV dataset are 0.38 g/cm2, 0.15 and 57.72%, respectively. In order to obtain a clearer understanding of the accuracy of these four MERSI-2 PWV datasets, we compare the accuracy of these four MERSI-2 PWV datasets with that of the widely used MODIS PWV dataset and AIRS PWV dataset. The results of the comparison show that the accuracy of the MODIS PWV dataset is not as good as that of all four MERSI-2 PWV datasets, due to the serious overestimation of the MODIS PWV dataset (0.40 g/cm2), and the accuracy of the AIRS PWV dataset is worse than that of the P940 and fused MERSI-2 PWV datasets. In addition, we analyze the error distribution of the four PWV datasets in different locations, seasons and water vapor content. Finally, the reason why the fused PWV dataset is not the one with the highest accuracy among the four PWV datasets is discussed. [ABSTRACT FROM AUTHOR]

Northern China has been long threatened by aeolian desertification. In recent years, all levels of the Chinese government have performed a series of ecological protection and sand control projects. To grasp the implementation effects of these projects and adjust policies in time, it is necessary to understand the process of aeolian desertification quickly and accurately. Remote sensing technologies play an irreplaceable role in aeolian desertification monitoring. In this study, the Zhenglan Banner, which is in the hinterland of the Hunshandake Sandy Land, was considered as the research area. Based on unmanned aerial vehicle (UAV) images, ground survey data, and Landsat images called in Google Earth Engine (GEE), the aeolian desertified land (ADL) in 2000, 2004, 2010, 2015, and 2019 was extracted using spectral mixture analysis. A desertification index (DI) was constructed to evaluate the spatial and temporal dynamics of the ADL in the Zhenglan Banner. Finally, a residual analysis explored the driving forces of aeolian desertification. The results showed that (1) the ADL area in the Zhenglan Banner has been trending downwards over the past 20 years but rebounded from 2004 to 2010; (2) over the past 20 years, the area of slightly, moderately, and severely desertified land has decreased at annual rates of 0.4%, 2.7%, and 3.4%, respectively; (3) human activities had significantly positive and negative impacts on the aeolian desertification trend for 20.0% and 21.0% of the study area, respectively, but not for the rest. This paper explored new techniques for rapid aeolian desertification monitoring and is of great significance for controlling and managing aeolian desertification in this region. [ABSTRACT FROM AUTHOR]

The accumulation of heat and moderate precipitation are the primary factors that are used by grasslands to trigger a green-up date. The accumulated growing degree-days (AGDD) requirement over the preseason is an important indicator of the response of grassland spring phenology to climate change. This study adopted the Normalized Difference Phenology Index (NDPI), which derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), to extract annual green-up dates in the Hulun Buir grassland in China between 2001–2015. Our analysis indicated that the range (standard deviation) and trend for the green-up date were DOY (day of year) 104 to DOY 144 (10.6 days) and −2.0 days per decade. Nine point two percent of the study area had significant (p < 0.05) changes in AGDD requirements. The partial correlations between the AGDD requirements and chilling days (67.04%, pixels proportion) were negative and significant (p < 0.05). The partial correlations between the AGDD requirement and precipitation (28.87%) were positive and significant (p < 0.05). Finally, the partial correlation between the AGDD requirement and insolation (97.65%) were positive and significant (p < 0.05). The results of this study could reveal the response of vegetation to climate warming and contribute to improving the phenological mechanism model of different grassland types in future research. [ABSTRACT FROM AUTHOR]