One of the most important characteristics of porphyry copper deposits (PCDs) is the type and distribution pattern of alteration zones which can be used for screening and recognizing these deposits. Hydrothermal alteration minerals with diagnostic spectral absorption properties in the visible and near-infrared (VNIR) through the shortwave infrared (SWIR) regions can be identified by multispectral and hyperspectral remote sensing data. Six Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands in SWIR have been shown to be effective in the mapping of Al-OH, Fe-OH, Mg-OH group minerals. The five VNIR bands of Landsat-8 (L8) Operational Land Imager (OLI) are useful for discriminating ferric iron alteration minerals. In the absence of complete hyperspectral coverage area, an opportunity, however, exists to integrate ASTER and L8-OLI (AO) to compensate each other’s shortcomings in covering area for mineral mapping. This study examines the potential of AO data in mineral mapping in an arid area of the Duolong porphyry Cu-Au deposit(Tibetan Plateau in China) by using spectral analysis techniques. Results show the following conclusions: (1) Combination of ASTER and L8-OLI data (AO) has more mineral information content than either alone; (2) The Duolong PCD alteration zones of phyllic, argillic and propylitic zones are mapped using ASTER SWIR bands and the iron-bearing mineral information is best mapped using AO VNIR bands; (3) The multispectral integration data of AO can provide a compensatory data of ASTER VNIR bands for iron-bearing mineral mapping in the arid and semi-arid areas.

The fragile alpine vegetation in the Tibetan Plateau (TP) is very sensitive to environmental changes, making TP one of the hotspots for studying the response of vegetation to climate change. Existing studies lack detailed description of the response of vegetation to different climatic factors using the method of multiple nested time series analysis and the method of grey correlation analysis. In this paper, based on the Normalized Difference Vegetation Index (NDVI) of TP in the growing season calculated from the MOD09A1 data product of Moderate-resolution Imaging Spectroradiometer (MODIS), the method of multiple nested time series analysis is adopted to study the variation trends of NDVI in recent 17 years, and the lag time of NDVI to climate change is analyzed using the method of Grey Relational Analysis (GRA). Finally, the characteristics of temporal and spatial differences of NDVI to different climate factors are summarized. The results indicate that: (1) the spatial distribution of NDVI values in the growing season shows a trend of decreasing from east to west, and from north to south, with a change rate of −0.13/10° E and −0.30/10° N, respectively. (2) From 2001 to 2017, the NDVI in the TP shows a slight trend of increase, with a growth rate of 0.01/10a. (3) The lag time of NDVI to air temperature is not obvious, while the NDVI response lags behind cumulative precipitation by zero to one month, relative humidity by two months, and sunshine duration by three months. (4) The effects of different climatic factors on NDVI are significantly different with the increase of the study period.

The Inner Mongolia Autonomous Region (IMAR) is a major source of rivers, catchment areas, and ecological barriers in the northeast of China, related to the nation’s ecological security and improvement of the ecological environment. Therefore, studying the response of vegetation to climate change has become an important part of current global change research. Since existing studies lack detailed descriptions of the response of vegetation to different climatic factors using the method of grey correlation analysis based on pixel, the temporal and spatial patterns and trends of enhanced vegetation index (EVI) are analyzed in the growing season in IMAR from 2000 to 2015 based on moderate resolution imaging spectroradiometer (MODIS) EVI data. Combined with the data of air temperature, relative humidity, and precipitation in the study area, the grey relational analysis (GRA) method is used to study the time lag of EVI to climate change, and the study area is finally zoned into different parts according to the driving climatic factors for EVI on the basis of lag analysis. The driving zones quantitatively show the characteristics of temporal and spatial differences in response to different climatic factors for EVI. The results show that: (1) The value of EVI generally features in spatial distribution, increasing from the west to the east and the south to the north. The rate of change is 0.22/10°E from the west to the east, 0.28/10°N from the south to the north; (2) During 2000–2015, the EVI in IMAR showed a slightly upward trend with a growth rate of 0.021/10a. Among them, the areas with slight and significant improvement accounted for 21.1% and 7.5% of the total area respectively, ones with slight and significant degradation being 24.6% and 4.3%; (3) The time lag analysis of climatic factors for EVI indicates that vegetation growth in the study area lags behind air temperature by 1–2 months, relative humidity by 1–2 months, and precipitation by one month respectively; (4) During the growing season, the EVI of precipitation driving zone (21.8%) in IMAR is much larger than that in the air temperature driving zone (8%) and the relative humidity driving zone (11.6%). The growth of vegetation in IMAR generally has the closest relationship with precipitation. The growth of vegetation does not depend on the change of a single climatic factor. Instead, it is the result of the combined action of multiple climatic factors and human activities.

As an important ecological security barrier in northern China, the Inner Mongolia Autonomous Region (hereinafter referred to as Inner Mongolia) is seriously affected by drought. It is of great significance to characterize the spatial distribution of drought and identify the influencing factors of drought. Due to complex interactions among drought driving factors, it is difficult to quantify the contribution of each driving factor to drought using linear correlation analysis alone. In this study, we used the Standardized Precipitation Evapotranspiration Index (SPEI) as a quantitative indicator of drought to discuss the spatiotemporal variation of drought during growing seasons in the Inner Mongolia from 2000 to 2018. We quantitatively characterized mode, scope, and intensity of changes in SPEI caused by drought-influencing factors such as weather, water, topography, soil, and human activities using the Geodetector and Geographically Weighted Regression (GWR) models. We concluded that about 20.3% of the region showed a downward trend in SPEI, with the fastest rate of decline in the central and western Inner Mongolia. Air temperature, precipitation, elevation, and distance to rivers are the main controlling factors in drought change, and the factor interactions showed nonlinear enhancement. The drought driving effect was obvious in Alxa League, Wuhai City, Ulanqab City, and Baotou City. The results will help us to understand the effects of the driving factors on drought and eventually help policymakers with water-resource management.

Snow depth distribution in the Qinghai-Tibetan plateau is important for atmospheric circulation and surface water resources. In-situ observations at meteorological stations and remote observation by passive microwave remote sensing technique are two main approaches for monitoring snow depth at regional or global levels. However, the meteorological stations are often scarce and unevenly distributed in mountainous regions because of inaccessibility, so are the in-situ snow depth measurements. Passive microwave remote sensing data can alleviate the unevenness issue, but accuracy and spatial (e.g., 25 km) and temporal resolutions are low; spatial heterogeneity in snow depth is thus hard to capture. On the other hand, optical sensors such as moderate resolution imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites can monitor snow at moderate spatial resolution (1 km) and high temporal resolution (daily) but only snow area extent, not snow depth. Fusing passive microwave snow depth data with optical snow area extent data provides an unprecedented opportunity for generating snow depth data at moderate spatial resolution and high temporal resolution. In this article, a linear multivariate snow depth reconstruction (LMSDR) model was developed by fusing multisource snow depth data, optical snow area extent data, and environmental factors (e.g., spatial distribution, terrain features, and snow cover characteristics), to reconstruct daily snow depth data at moderate resolution (1 km) for 16 consecutive hydrological years, taking Qinghai-Tibetan Plateau (QTP) as a case study. We found that snow cover day (SCD) and environmental factors such as longitude, latitude, slope, surface roughness, and surface fluctuation have a significant impact on the variations of snow depth over the QTP. Relatively high accuracy (root mean square error (RMSE) = 2.26 cm) was observed in the reconstructed snow depth when compared with in-situ data. Compared with the passive microwave remote sensing snow depth product, constructing a nonlinear snow depletion curve product with an empirical formula and fusion snow depth product, the LMSDR model (RMSE = 2.28 cm, R² = 0.63) demonstrated a significant improvement in accuracy of snow depth reconstruction. The overall spatial accuracy of the reconstructed snow depth was 92%. Compared with in-situ observations, the LMSDR product performed well regarding different snow depth intervals, land use, elevation intervals, slope intervals, and SCD and performed best, especially when the snow depth was less than 3 cm. At the same time, a long-time snow depth series reconstructed based on the LMSDR model reflected interannual variations of snow depth well over the QTP.