Porphyry copper ore is a vital strategic mineral resource. It is often associated with significant hydrothermal alteration, which alters the original mineralogical properties of the rock. Extracting alteration information from remote sensing data is crucial for porphyry copper exploration. However, the current method of extracting hydrothermal alteration information from ASTER remote sensing data does not consider the influence of disturbing factors, such as topography, and ignores the weak report of surface minerals, which has significant limitations. Therefore, this paper selects the Gondwana region of the East Tethys–Himalayan tectonic domain as the study area, combines waveform calculation with principal component analysis methods, proposes a spectral feature-enhanced principal component analysis (EPCA) method, and constructs a model to complete the automatic selection of principal components for each scene image. The results show that the etching information extracted by the EPCA method is significantly better than the traditional Crosta method in terms of etching area and spatial aggregation and discovers several prospective mineralization areas that have not yet been explored and exploited, such as Sakya and Xietongmen counties in Rikaze, providing theoretical support for subsequent mineralization exploration and large-scale mineral extraction. Meanwhile, obtaining the alteration information of the whole area can help to understand the distribution of mineralizing elements from a macroscopic perspective in the future, which is of great scientific significance in order to deeply analyze the formation process of metal deposits in mineralizing areas and improve the theory of porphyry mineralization.

This study utilized TROPOMI remote sensing data, MODIS remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei (BTH) region using the Geographic Temporal Neural Network Weighted Regression (GTNNWR) model. Through this model, we obtained the daily distribution of ground-level nitrogen dioxide (NO2) concentrations in the Beijing–Tianjin–Hebei region at a resolution of 500 m for the period of 2019–2022. The research results exhibited higher accuracy and more detailed features compared to other models, enabling a more accurate reflection of the spatial distribution and temporal variations of ground-level NO2 concentrations in the region, while retaining more details and trends and excluding the influence of noisy data. Furthermore, we conducted an evaluation analysis considering important events such as public health incidents and the Winter Olympics. The results demonstrated that the GTNNWR model outperformed the Random Forest (RF), Convolutional Neural Network (CNN), and Geographic Neural Network Weighted Regression (GNNWR) models in performance metrics such as R2, RMSE, MAE, and MAPE, showcasing greater reliability when considering spatiotemporal heterogeneity and spatiotemporal non-stationarity. This study provides crucial data support and reference for atmospheric environmental management and pollution prevention and control in the Beijing–Tianjin–Hebei region.

Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. This study was carried out based on a potato nitrogen and potassium fertilizer gradient experiment in the year 2022 at Keshan Farm, Qiqihar Branch of Heilongjiang Agricultural Reclamation Bureau. Leaf hyperspectral and leaf chlorophyll content data were collected at the potato tuber formation, tuber growth, and starch accumulation periods. The PROSPECT-4 radiative transfer model was employed to construct a look-up table (LUT) as a simulated data set. This was accomplished by simulating potato leaves’ spectral reflectance and chlorophyll content. Then, the active learning (AL) technique was used to select the most enlightening training samples from the LUT based on the measured potato data. The Gaussian process regression (GPR) algorithm was finally employed to construct the inversion models for the chlorophyll content of potato leaves for both the whole and single growth periods based on the training samples selected by the AL method and the ground measured data of the potatoes. The R² values of model validation accuracy for the potato whole plantation period and three single growth periods are 0.742, 0.683, 0.828, and 0.533, respectively with RMSE values of 4.207, 4.364, 2.301, and 3.791 µg/cm². Compared with the LCC inversion accuracy through LUT with a cost function, the validation accuracies of the GPR_PROSPECT-AL hybrid model were improved by 0.119, 0.200, 0.328, and 0.255, and the RMSE were reduced by 3.763, 2.759, 0.118, and 5.058 µg/cm², respectively. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato LCC estimation, which provides a valid tool for accurately monitoring crop growth and growth health.

This article examines three spatiotemporal methods used for analyzing of infectious diseases, with a focus on COVID-19 in the United States. The methods considered include inverse distance weighting (IDW) interpolation, retrospective spatiotemporal scan statistics and Bayesian spatiotemporal models. The study covers a 12-month period from May 2020 to April 2021, including monthly data from 49 states or regions in the United States. The results show that the spread of COVID-19 pandemic increased rapidly to a high value in winter of 2020, followed by a brief decline that later reverted into another increase. Spatially, the COVID-19 epidemic in the United States exhibited a multi-centre, rapid spread character, with clustering areas represented by states such as New York, North Dakota, Texas and California. By demonstrating the applicability and limitations of different analytical tools in investigating the spatiotemporal dynamics of disease outbreaks, this study contributes to the broader field of epidemiology and helps improve strategies for responding to future major public health events.

The location and development of porphyry copper deposits is a key issue for the mining industry. In this study, the Gondwana metallogenic belt was chosen as the study area to compare multiple methods for extracting multi-source geological elements to maximize the accuracy of the datasets used for mining evaluation and to use them to assess porphyry copper mineability. By comparison, a support vector machine (SVM) with an overall classification accuracy of 97.6573% and a Kappa coefficient of 0.9806 was used to extract the lithological distribution of the study area. Spectral feature-enhanced principal component analysis (EPCA) was combined with ASTER images to extract alteration information, with significant improvements in spatial aggregation and overall area compared to other alteration extraction methods, while a hierarchical alteration interpolation method was proposed to overcome the limitations of relying solely on remote sensing images to obtain surface alteration information and qualitatively extend deep alteration information. In addition, by overlaying various geoscientific factors affecting copper mineralization and mining, a Pearson correlation analysis is carried out in conjunction with currently proven or mined copper occurrences, and a weight of evidence approach is used to classify the study area into four mineability classes, which is important for narrowing down potential target areas for mineral exploration and assessing their mining value while contributing to an in-depth understanding of the role of geological elements in mineralization and development.