Remote sensing data provide significant information about surface geological features, but they have not been fully investigated as a tool for delineating mineral prospective targets using the latest advancements in machine learning predictive modeling. In this study, besides available geological data (lithology, structure, lineaments), Landsat-8, Sentinel-2, and ASTER multispectral remote sensing data were processed to produce various predictor maps, which then formed four distinct datasets (namely Landsat-8, Sentinel-2, ASTER, and Data-integration). Remote sensing enhancement techniques, including band ratio (BR), principal component analysis (PCA), and minimum noise fraction (MNF), were applied to produce predictor maps related to hydrothermal alteration zones in Hamissana area, while geological-based predictor maps were derived from applying spatial analysis methods. These four datasets were used independently to train a random forest algorithm (RF), which was then employed to conduct data-driven gold mineral prospectivity modeling (MPM) of the study area and compare the capability of different datasets. The modeling results revealed that ASTER and Sentinel-2 datasets achieved very similar accuracy and outperformed Landsat-8 dataset. Based on the area under the ROC curve (AUC), both datasets had the same prediction accuracy of 0.875. However, ASTER dataset yielded the highest overall classification accuracy of 73%, which is 6% higher than Sentinel-2 and 13% higher than Landsat-8. By using the data-integration concept, the prediction accuracy increased by about 6% (AUC: 0.938) compared with the ASTER dataset. Hence, these results suggest that the framework of exploiting remote sensing data is promising and should be used as an alternative technique for MPM in case of data availability issues.

PM_2.5 pollution presents a significant risk to urban habitability. The urban landscape pattern (ULP) serves as a crucial regulator that profoundly influences the spatiotemporal distribution features of PM_2.5. Analysis of the driving mechanisms of the ULP is therefore essential for optimizing urban ecological spatial planning. However, the driving mechanism is dynamic and exhibits seasonal variations. This study selected four landscape metrics and four control variables, developed a geographically and temporally weighted regression (GTWR) model, and examined the spatiotemporal and seasonal effects of ULP on PM_2.5 concentrations in the central urban area of Urumqi (CUA) from 2003 to 2023. The results show the following: (1) Over the past two decades, the four ULP metrics have shown an increasing trend in the CUA. (2) The spatial distribution of PM_2.5 concentrations follows a latitudinal gradient, with higher concentrations observed in the northern regions and lower concentrations in the southern regions, initially increasing and then declining over time. (3) The driving mechanisms of ULP on PM_2.5 exhibited significant variations across different locations and time scales. (4) Seasonal variations arise from pronounced meteorological contrasts and intensified pollution from central heating, which is particularly evident in central CUA.

To evaluate the application potential of convolutional neural network (CNN)-based Local Climate Zone (LCZ) mapping in urban thermal environment studies, this study employed a lightweight convolutional neural network model (Light-model) to classify LCZs within the area enclosed by the Fourth Ring Road of Xuzhou City. ECOSTRESS data obtained from summer (June to September) at different times were integrated to analyze the temporal and spatial changes of surface temperature (LST) and surface urban heat island intensity (SUHII). The classification results demonstrate that the Light-model achieved an overall accuracy of 84.46%, which is markedly higher than that of the random forest model (72.07%). It also outperformed random forest in built-up area identification (built-up overall accuracy: 69.41% vs. 46.91%) and non-built-up area identification (natural overall accuracy: 91.85% vs. 84.43%), as well as in Kappa coefficient and mean F1-score. Time-series analysis based on ECOSTRESS observations revealed a typical diurnal LST pattern characterized by the lowest temperatures before dawn, a peak in the afternoon, and a decline at night. High-density built-up zones (LCZ1–LCZ3) and large impervious areas (LCZ8) exhibited the highest daytime temperatures and the slowest nocturnal cooling, whereas bare soil areas (LCZF) showed the largest diurnal temperature range and the greatest fluctuations. Vegetation-covered and bare land zones (LCZA and LCZD) generally maintained lower temperatures, while water bodies (LCZG) functioned as persistent cooling sources throughout the day due to their high specific heat capacity. Overall, the findings suggest that CNN-based LCZ classification, when integrated with high-temporal-resolution LST observations, provides a reliable technical framework for urban thermal environment monitoring and regulation at the regional scale.