Fine particulate matter (PM_2.5) is a major pollutant in Guanzhong Urban Agglomeration (GUA) during the winter, and GUA is one of China’s regions with the highest concentrations of PM_2.5. Daily surface PM_2.5 maps with a spatial resolution of 1 km × 1 km can aid in the control of PM_2.5 pollution. Thus, the Random Forest and eXtreme Gradient Boosting (RF-XGBoost) model was proposed to fill the missing aerosol optical depth (AOD) at the station scale before accurately estimating ground-level PM_2.5 using the recently released MODIS AOD product derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC), high density meteorological and topographic conditions, land-use, population density, and air pollutions. The RF-XGBoost model was evaluated using an out-of-sample test, revealing excellent performance with a coefficient of determination (R²) of 0.93, root-mean-square error (RMSE) of 12.49 μg/m³, and mean absolution error (MAE) of 8.42 μg/m³. The result derived from the RF-XGBoost model indicates that the GUA had the most severe pollution in the winter of 2018 and 2019, owing to the burning of coal for heating and unfavorable meteorological circumstances. Over 90% of the GUA had an annual average PM_2.5 concentrations decrease of 3 to 7 μg/m³ in 2019 compared to the previous year. Nevertheless, the air pollution situation remained grim in the winter of 2019, with more than 65% of the study area meeting the mean PM_2.5 values higher than 35 μg/m³ and the maximum reaching 95.57 μg/m³. This research would be valuable for policymakers, environmentalists, and epidemiologists, especially in urban areas.

The Xiemisitai area, West Junggar, Xinjiang, China, is situated at a potential copper mineralization zone in association with small granitic intrusions. In order to identify the alteration zones and mineralization characteristics of the intrusions, Landsat Enhanced Thematic Mapper (ETM+) and Quickbird data of the study area were evaluated in mapping lithological units, small intrusions, and alteration zones. False color composites of the first principal component analyses (PCA1), PCA2, and PCA4 in red (R), green (G), and blue (B) of the ETM+ image, and relevant hue-saturation-intensity (HSI) color model transformations, were performed. This led to the identification of lithologic units and discrimination of granitic intrusions from wall-rocks. A new geological map was generated by integrating the remote sensing results with two internally published local geologic maps and field inspection data. For the selected region, false color composites from PCA and relevant HSI-transformed images of the Quickbird data delineated the details of small intrusions and identified other unknown similar intrusions nearby. Fifteen separate potash-feldspar granites and three separate hornblende biotite granites were identified using ETM+ and Quickbird data. The principal component analysis-based Crosta technique was employed to discriminate alteration minerals. Some of the mapped alteration zones using the Crosta technique agreed very well with the known copper deposits. Field verification led to the discovery of three copper mineralizations and two gold mineralizations for the first time. The results show that the PCA and HSI transformation techniques proved to be robust in processing remote sensing data with moderate to high spatial resolutions. It is concluded that the utilized methods are useful for mapping lithology and the targeting of small intrusion-type mineral resources within the sparsely vegetated regions of Northwest China.

Eutrophication is a significant factor that damages the water ecosystem’s species balance. The total phosphorus (TP) concentration is a vital water quality indicator in assessing surface water eutrophication. This paper predicts the spatial distribution of TP concentration using remote sensing, measured data, and the partial least squares regression (PLSR) method. Based on the correlation analysis, the models were built and tested using the TP concentration and Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) image spectra. The results demonstrated that the best technique based on band combinations of the Sentinel-2 and Landsat-8 images achieved good precision. The coefficient of determination (R²), root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) were 0.771, 0.023 mg/L, and 2.086 for Sentinel-2 images and 0.630, 0.032 mg/L, and 1.644 for Landsat-8 images, respectively. The TP concentration maps were interpolated using the inverse distance weighting method, and the inversion results obtained from the images were in good agreement. The western and northwestern regions of Taihu Lake, where significant cyanobacterial blooms occurred, had TP concentrations greater than 0.20 mg/L; nevertheless, the central and eastern regions had amounts ranging from 0.05 to 0.20 mg/L. In order to prove the extensibility of the model, the optimal algorithm was applied to the Sentinel-2 and Landsat-8 images in 2017. The optimal algorithm based on Landsat-8 images has a better verification effect (RMSEP = 0.027 mg/L, and R = 0.879 for one Landsat-8 image), and the optimal algorithm based on Sentinel-2 images has moderate verification effect (RMSEP = 0.054 mg/L and 0.045 mg/L, and R = 0.771 and 0.787 for two Sentinel-2 images). The interpolation and inversion maps are in good agreement, indicating that the model is suitable for the Landsat-8 and Sentinel-2 images, which can be complementary for higher temporal resolutions. Monitoring water quality using multiple remote sensing images can provide the scientific basis for water quality dynamic monitoring and prevention in China.

The Chagai porphyry Cu belt in Pakistan is an important metallogenic terrain extending approximately 400 km in an east-west direction. Most of the known porphyry Cu deposits, such as the world-class Reko Diq deposit and Saindak deposit, are located in the western Chagai belt. In this study, the ZY1-02D hyperspectral data acquired by a recently launched spaceborne imaging spectrometer with 166 bands within a 0.4- to 2.5-μm spectral region were used to map mineral information over 8,000 km² for exploring potential targets of porphyry Cu mineralization in the western Chagai belt. False color composite, spectral angle mapper, and wavelength position mapping methods were used in this research leading to the identification of a series of alteration minerals (including muscovite [sericite], kaolinite, alunite, epidote, chlorite, and calcite) from ZY1-02D data. The alteration mineral maps derived from ZY1-02D data match well with the known deposits and field inspections. Twenty-three new targets were identified as potential porphyry Cu mineralization targets for further exploration in the study area. Three targets, north of Saindak, Koh-i-Sultan, and Durban Chah, and six alteration sites in the southwest of Durban Chah, were inspected in the field, and Cu-Au mineralization was confirmed in all these inspected areas. As the ZY1-02D hyperspectral data covers most of the land area of the earth, this study provides new insights for mineral exploration and lithologic mapping in remote regions.

The adverse combination of excessive mining practices and the resulting land subsidence is a significant obstacle to the sustainable growth and stability of regions associated with mining activities. The Lakhra coal mines, which contain some of Pakistan’s largest coal deposits, have been overlooked in land subsidence monitoring, indicating a considerable oversight in the region. Subsidence in mining areas can be spotted early when using Interferometric Synthetic Aperture Radar (InSAR), which can precisely monitor ground changes over time. This study is the first to employ the Small Baseline Subset (SBAS)-InSAR and stacking-InSAR techniques to identify land subsidence at the Lakhra coal mines. This research offers critical insights into subsidence mechanisms in the study area, which has never been previously investigated for ground deformation monitoring, by utilizing 150 Sentinel-1A (ascending) images obtained between January 2018 and September 2023. A total of 102 deformation spots were identified using SBAS-InSAR, while stacking-InSAR detected 73 deformation locations. The most extensive cumulative subsidence in the Lakhra coal mine was −114 mm, according to SBAS-InSAR, with a standard deviation of 6.63 mm. In comparison, a subsidence rate of −19 mm/year was reported using stacking-InSAR with a standard deviation of 1.17 mm/year. The rangeland covered 88.8% of the total area and exhibited the most significant deformation values, as determined by stacking and SBAS-InSAR techniques. Linear regression showed that there was not a strong correlation between subsidence and topographic factors. As detected by optical remote sensing data, the subsidence locations were near or above the mines in the research area, indicating that widespread mining in Lakhra coal mines was the cause of subsidence. Our findings suggest that SAR interferometric time series analysis is helpful for proactively identifying and controlling subsidence difficulties in mining regions by closely monitoring activities, hence reducing negative consequences on operations and the environment.