Search Results (61)

Plateau–orogenic belts host a substantial share of global gold resources, yet quantitative prospectivity mapping is challenged by complex mineralization and strongly heterogeneous, multi-scale datasets. Using the Mayoumu area (Tibet) as a representative orogenic gold district, we develop an integrated multi-source workflow that fuses remote-sensing alteration information with regional geochemical and structural constraints within an ensemble-learning framework. Alteration anomalies were mapped from GF-5 hyperspectral imagery using mixture-tuned matched filtering (MTMF) and from Sentinel-2 multispectral imagery using the iCrosta method to extend alteration signals across scales. Geochemical anomalies were extracted from 1:200,000 stream-sediment data through isometric log-ratio (ILR) transformation and robust principal component analysis (RPCA). At the same time, ore-controlling structures were quantified using Euclidean-distance-to-fault layers. Three Boosting-based ensemble models—gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)—were trained to predict mineral prospectivity. Performance was evaluated using confusion matrix metrics and ROC–AUC, and key predictors were interpreted using SHAP. All three models achieved AUC values > 0.90, with LightGBM performing best (AUC = 0.94) and delineating high-prospectivity zones that coincide with known occurrences and highlight additional targets. The proposed workflow provides a practical, transferable reference for gold prospectivity mapping in complex orogenic belts worldwide. Full article

The Hatu region in the Western Junggar, Xinjiang, is one of the most significant gold metallogenic concentration areas in China. Gold mineralization is primarily controlled by several parallel NE-trending strike-slip faults and Late Paleozoic granitic plutons, accompanied by multiple stages of hydrothermal activity. To enhance the objectivity and accuracy of mineral prospecting prediction, this study develops an integrated forecasting framework that combines multi-source remote sensing datasets with machine learning techniques. Alteration anomalies related to iron staining and hydroxyl-bearing minerals are extracted from ASTER data, alteration mineral mapping is performed using GF-5 hyperspectral imagery, and Landsat-9 data is used for structural interpretation to refine the regional metallogenic framework. On this basis, these multi-source remote sensing products are then integrated to delineate five prospective metallogenic areas (T1–T5). Subsequently, a Random Forest (RF) model optimized by the Grey Wolf Optimizer (GWO) algorithm is employed to quantitatively integrate key evidence layers, including alteration, structure, and geochemistry, for estimating mineralization probability. The results show that the GWO-RF model effectively concentrates anomalous areas and identifies two high-confidence targets, Y1 and Y2, both with mineralization probabilities exceeding 0.8. Among them, the Y1 target is associated with the Bieluagaxi pluton and exhibits strong montmorillonitization, chloritization, and iron-staining alteration, typical for magmatic–hydrothermal controlled mineralization. In contrast, the Y2 target is strictly controlled by the Anqi Fault and its subsidiary faults, primarily characterized by linear chloritization and iron-staining anomalies indicative of structure–hydrothermal mineralization. Field verification confirms the significant metallogenic potential of both Y1 and Y2, demonstrating the effectiveness of integrating multi-source remote sensing and machine learning for predicting orogenic gold systems. This approach not only deepens the understanding of the diverse gold mineralization processes in the Western Junggar but also provides a transferable methodology and case study for improving regional mineral exploration accuracy. Full article

The Qulong–Jiama polymetallic ore concentration area, located in the eastern segment of the Gangdese metallogenic belt, is one of China’s most significant copper resource production zones. With the growing demand for copper resources, this area has become a key target for mineral exploration. The current study aims to explore the application potential of multispectral and hyperspectral remote sensing technologies in porphyry copper deposit prospecting, establish a hyperspectral remote sensing prospecting model tailored to this region, and provide technical support for prospecting prediction and resource exploration of similar deposits. Sentinel-2 and Landsat 8 data were used to outline major alteration anomalies at the regional scale, while GF-5 hyperspectral data enabled precision mineral mapping. Results show clear porphyry-style alteration zoning. Hyperspectral mineral identification reveals 33 mineralization- and alteration-related minerals, including muscovite, biotite, pyrophyllite, dickite, chlorite, epidote, and limonite. The ore concentration area exhibits a well-developed inner–middle–outer alteration sequence: (1) an inner potassic–silicic zone locally accompanied by skarn; (2) a middle phyllic and argillic zone dominated by quartz–sericite–pyrite assemblages; and (3) an outer propylitic zone of chlorite–epidote–carbonate with supergene iron oxides. These alteration patterns spatially coincide with known deposits and metallogenic structures such as faults, annular features, and intrusive contacts. Based on these spatial relationships, a hyperspectral remote sensing prospecting model was constructed. The model defines diagnostic mineral assemblages for each zone, highlights structurally altered overlapping areas as priority targets, and effectively predicts the distribution of ore-related alteration belts. The strong correspondence between remote sensing-derived anomalies and existing deposits demonstrates that hyperspectral alteration information is a reliable indicator of ore-forming systems. The proposed model not only provides a scientific basis for further prospecting and exploration in the Qulong–Jiama area but also serves as a reference for copper exploration in the Gangdese metallogenic belt and other similar porphyry–epithermal metallogenic systems. Full article

Sabina vulgaris is a keystone shrub species endemic to arid northwestern China, renowned for its exceptional drought tolerance, sand fixation capabilities, and critical role in desert ecosystem stability. This study investigates the impact of coal mining activities on the spatiotemporal dynamics of S. vulgaris shrublands in the ecologically fragile Mu Us Sandy Land, focusing on the Longde Coal Mine adjacent to the Shenmu S. vulgaris Nature Reserve. Utilizing seven periods (2013–2025) of 2 m resolution Gaofen-1 (GF-1) satellite imagery spanning 12 years of mining operations, we implemented a deep learning approach combining UAV-derived hyperspectral ground truth data and the SegU-Net semantic segmentation model to map shrub distribution via GF-1 data with high precision. Classification accuracy was rigorously validated through confusion matrix analysis (incorporating the Kappa coefficient and overall accuracy metrics). Results reveal contrasting trends: while the S. vulgaris Protection Area exhibited substantial expansion (e.g., Southern Section coverage grew from 2.6 km² in 2013 to 7.88 km² in 2025), mining panels experienced significant degradation. Within Panel 202, coverage declined by 15.4% (58.4 km² to 49.5 km²), and Panel 203 showed a 18.5% decrease (3.16 km² to 2.57 km²) over the study period. These losses correlate spatially and temporally with mining-induced groundwater depletion and land subsidence, disrupting the shrub’s shallow-root water access strategy. The study demonstrates that coal mining drives fragmentation and coverage reduction in S. vulgaris communities through mechanisms including (1) direct vegetation destruction, (2) aquifer disruption impairing drought adaptation, and (3) habitat fragmentation. These findings underscore the necessity for targeted ecological restoration strategies integrating groundwater management and progressive reclamation in mining-affected arid regions. Full article

Hyperspectral unmixing aims to extract pure spectral signatures (endmembers) and estimate their corresponding abundance fractions from mixed pixels, enabling quantitative analysis of surface material composition. However, in geological mineral exploration, existing unmixing methods often fail to explicitly identify informative spectral bands, lack inter-layer information transfer mechanisms, and overlook the physical constraints intrinsic to the unmixing process. These issues result in limited directionality, sparsity, and interpretability. To address these limitations, this paper proposes a novel model, CResDAE, based on a deep autoencoder architecture. The encoder integrates a channel attention mechanism and deep residual modules to enhance its ability to assign adaptive weights to spectral bands in geological hyperspectral unmixing tasks. The model is evaluated by comparing its performance with traditional and deep learning-based unmixing methods on synthetic datasets, and through a comparative analysis with a nonlinear autoencoder on the Urban hyperspectral scene. Experimental results show that CResDAE consistently outperforms both conventional and deep learning counterparts. Finally, CResDAE is applied to GF-5 hyperspectral imagery from Yunnan Province, China, where it effectively distinguishes surface materials such as Forest, Grassland, Silicate, Carbonate, and Sulfate, offering reliable data support for geological surveys and mineral exploration in covered regions. Full article

Multi-source hyperspectral data are increasingly applied in environmental monitoring, precision agriculture, and geological exploration, yet differences in sensor characteristics hinder interoperability. This study presents a systematic cross-validation of surface reflectance between the German EnMAP mission and the Chinese GF5-02 Advanced Hyperspectral Imager (AHSI) across four representative land cover types: minerals in the East Tianshan Mountains, tropical grasslands in Hainan Danzhou, desert in Dunhuang, and inland salt lakes in Qinghai. Using EnMAP Level-2A products as reference, we evaluated GF5-02 reflectance with spectral angle (SA), root mean squared error (RMSE), relative RMSE (RRMSE), and correlation coefficient (R). Results show strong consistency for high- and medium-reflectance surfaces (R > 0.96, SA < 0.08 rad), while water bodies exhibit larger discrepancies (R = 0.82, SA = 0.34 rad), likely due to atmospheric correction and sensor response differences. Additional ground validation in the East Tianshan region confirmed the reliability and stability of GF5-02 data. Overall, GF5-02 demonstrates high consistency with EnMAP across most land cover types, supporting quantitative applications, though further improvements are needed for low-reflectance environments. Full article

Mixed pixels remain a central obstacle to reliable endmember extraction from hyperspectral imagery. We present AMEE–PPI, a hybrid method that embeds the Pure Pixel Index (PPI) within morphological structuring elements and propagates spectral purity via dilation/erosion, thereby coupling spatial context with spectral cues while avoiding a user-fixed number of projections. On GaoFen-5 (GF-5) AHSI data from a geologically complex outcrop region, we benchmark AMEE–PPI against four widely used algorithms—PPI, OSP, VCA, and AMEE. The pipeline uses HySime for noise estimation and signal-subspace inference to set the endmember count prior to extraction and applies morphological elements spanning 3 × 3 to 15 × 15 to balance spatial support with local heterogeneity. Quantitatively, AMEE–PPI achieves the lowest spectral angle distance (SAD) for all outcrop types—purple–red: 0.135; yellow–brown: 0.316; gray: 0.191—surpassing the competing methods. It also attains the lowest spectral information divergence (SID)—purple–red: 0.028; yellow–brown: 0.184; gray: 0.055—confirming superior similarity to field reference spectra across materials. Visually, AMEE–PPI avoids the vegetation endmember leakage observed with several baselines on purple–red and gray outcrops, yielding cleaner, more representative endmembers. These results indicate that integrating spatial morphology with spectral purity improves robustness to illumination, mixing, and local variability in GF-5 imagery, with direct benefits for downstream unmixing, classification, and geological interpretation. Full article

Rapid evaluations of farmland soil quality can provide data support for farmland protection and utilization. This study focuses on the soda–saline soil region of Da’an City, Jilin Province, covering an area of 4879 km²; it proposes a framework for evaluating farmland soil quality based on multi-source remote sensing data (Sentinel-2 MSI, GF-5 AHSI hyperspectral and field hyperspectral data). Soil organic matter content, salt content, and pH were selected as indicators of cultivated land soil quality in soda–saline soil areas. A threshold of 20% crop residue cover was set to mask high-cover areas, extracting bare soil information. The spectral indices SI1 and SI2 were utilized to predict the comprehensive grade of soil organic matter + salinity based on the cloud model ($M{E}_c$ = 0.74 and $M{E}_v$ = 0.68). The pH grade was predicted using the red-edge ratio vegetation index (RVIre) ($M{E}_c$ = 0.95 and $M{E}_v$ = 0.98). The short-board method was used to construct a soil quality evaluation system. The results indicate that 13.73% of the cultivated land in Da’an City is of high quality (grade 1), 80.63% is of medium quality (grades 2–3), and 5.65% is of poor quality (grade 4). This study provides a rapid assessment tool for the sustainable management of cultivated land in saline–alkali areas at the county level. Full article

Rapid and large-scale monitoring of soil copper levels enables the quick identification of areas where copper concentrations significantly exceed safe thresholds. It allows for selecting regions that require treatment and protection and is essential for safeguarding environmental and human health. Widely adopted monitoring models that utilize ground- and uncrewed-aerial-vehicle-based spectral data are superior to labor-intensive and time-consuming traditional methods that rely on point sampling, chemical analysis, and spatial interpolation. However, these methods are unsuitable for large-scale observations. Therefore, this study investigates the potential of utilizing spaceborne GF-5 hyperspectral data for monitoring soil copper content. Ninety-five soil samples were collected from the Kalatage mining area in Xinjiang, China. Three-band indices were constructed using fractional derivative spectra, and estimation models were developed using spectral indices highly correlated with the copper content. The results show that the proposed three-band spectral index accurately identifies subtle spectral characteristics associated with the copper content. Although the model is relatively simple, selecting the correct fractional order is critical in constructing spectral indices. The three-band spectral index based on fractional derivatives with orders of less than 0.6 provides higher accuracy than higher-order fractional derivatives. The index with spectral wavelengths of 426.796 nm, 512.275 nm, and 974.245 nm with 0.35-order derivatives exhibits the optimal performance (R² = 0.51, RPD = 1.46). Additionally, we proposed a novel approach that identifies the three-band indices exhibiting a strong correlation with the copper content. Subsequently, the selected indices were used as independent variables to develop new spectral indices for model development. This approach provides higher performance than models that use spectral indices derived from individual band values. The model utilizing the proposed spectral index achieved the best performance (R² = 0.56, RPD = 1.52). These results indicate that utilizing GF-5 hyperspectral data for large-scale monitoring of soil copper content is feasible and practical. Full article

This study combined field survey data with Gaofen 5 (GF-5) satellite hyperspectral images of the Yuncheng Basin (China), considering 15 environmental variables. Random forest (RF) was used to select the optimal satellite hyperspectral model, sequentially introducing natural and farmland management factors into the model to analyze the spatial distribution of farmland soil organic carbon (SOC). Furthermore, RF factorial experiments determined the contributions of farmland management, climate, vegetation, soil, and topography to the SOC. Structural equation modeling (SEM) elucidated the driving mechanisms of SOC variations. Integrating satellite hyperspectral data and environmental variables improved the prediction accuracy and SOC-mapping precision of the model. The integration of natural variables significantly improved the RF model performance (R² = 0.78). The prediction accuracy enhanced with the introduction of crop phenology (R² = 0.81) and farmland management factors (R² = 0.87). The model that incorporated all 15 variables demonstrated the highest prediction accuracy (R² = 0.89) and greatest spatial SOC variability, with minimal uncertainty. Farmland management activities exerted the strongest influence on SOC (0.38). The proposed method can support future investigations on soil carbon sequestration processes in river basins worldwide. Full article

In recent years, heavy metal contamination in agricultural products has become a growing concern in the field of food safety. Copper (Cu) stress in crops not only leads to significant reductions in both yield and quality but also poses potential health risks to humans. This study proposes an efficient and precise non-destructive detection method for Cu stress in oilseed rape, which is based on hyperspectral false-color image construction using principal component analysis (PCA). By comprehensively capturing the spectral representation of oilseed rape plants, both the one-dimensional (1D) spectral sequence and spatial image data were utilized for multi-class classification. The classification performance of models based on 1D spectral sequences was compared from two perspectives: first, between machine learning and deep learning methods (best accuracy: 93.49% vs. 96.69%); and second, between shallow and deep convolutional neural networks (CNNs) (best accuracy: 95.15% vs. 96.69%). For spatial image data, deep residual networks were employed to evaluate the effectiveness of visible-light and false-color images. The RegNet architecture was chosen for its flexible parameterization and proven effectiveness in extracting multi-scale features from hyperspectral false-color images. This flexibility enabled RegNetX-6.4GF to achieve optimal performance on the dataset constructed from three types of false-color images, with the model reaching a Macro-Precision, Macro-Recall, Macro-F₁, and Accuracy of 98.17%, 98.15%, 98.15%, and 98.15%, respectively. Furthermore, Grad-CAM visualizations revealed that latent physiological changes in plants under heavy metal stress guided feature learning within CNNs, and demonstrated the effectiveness of false-color image construction in extracting discriminative features. Overall, the proposed technique can be integrated into portable hyperspectral imaging devices, enabling real-time and non-destructive detection of heavy metal stress in modern agricultural practices. Full article

Vegetation is a critical factor influencing the identification of rock outcrops using hyperspectral remote sensing data. When mixed pixels containing both vegetation and rock are formed, the spectral signatures of vegetation can partially or fully obscure the diagnostic absorption features of rocks. Based on GaoFen-5 (GF-5) Advanced Hyperspectral Imager (AHSI) data, this study employs a linear spectral mixture model to simulate sparse vegetation–rock mixed pixels. The potential of high-frequency components derived from discrete wavelet transform (DWT) to enhance lithological discrimination within sparse vegetation–rock mixed spectra was analyzed, and the findings were validated using image spectra. The results show that andesite spectra are the most susceptible to vegetation interference. Absorption features in the 2.0–2.4 μm wavelength range were identified as critical indicators for distinguishing lithologies from mixed spectra. High-frequency components extracted through the DWT of the simulated mixed spectra using the Daubechies 8 wavelet function were found to significantly improve classification performance. As vegetation content (including green grass, golden grass, bushes, and lichens) increased from 5% to 60%, the average overall accuracy improved by 15% (from 0.51 to 0.66) after using high-frequency features. The average F1-scores for granite and sandstone increased by 0.12 (from 0.68 to 0.80) and 0.20 (from 0.48 to 0.68), respectively. For AHSI image spectra, the use of high-frequency features resulted in F1-score improvements of 0.48, 0.11, and 0.09 for tuff, granite, and limestone, respectively. Although the identification of andesite remains challenging, this study provides a promising approach for improving lithological mapping accuracy using GF-5 hyperspectral data, particularly in humid and semi-humid regions. Full article

Methane is the second largest greenhouse gas. The detection of methane super-emitters and the quantification of their emission rates are necessary for the implementation of methane emission reduction policies to mitigate global warming. High-spectral-resolution satellites such as Gaofen-5 (GF-5), EMIT, GHGSat, and MethaneSAT have been successfully employed to detect and quantify methane point source leaks. In this study, a matched filter (MF) algorithm is improved using data from the EMIT instrument and applied to data from the Advanced Hyperspectral Imager (AHSI) onboard the Ziyuan-1 (ZY-1) satellite. Validation by comparison with EMIT′s L2 XCH₄ products shows the good performance of the improved MF algorithm, in spite of the lower spectral resolution of AHSI/ZY-1 in comparison with other point source imagers. The improved MF algorithm applied to AHSI/ZY-1 data was used to detect and quantify methane super-emitters in global methane hotspot regions. The results show that the improved MF algorithm effectively suppresses noise in retrieval results over both land and ocean surfaces, enhancing algorithm robustness. Sixteen methane plumes were detected in global hotspot regions, originating from coal mines, oil and gas fields, and landfills, with emission rates ranging from 0.57 to 78.85 t/h. The largest plume was located at an offshore oil and gas field in the Gulf of Mexico, with instantaneous emissions nearly equal to the combined total of the other 15 plumes. The findings demonstrate that AHSI, despite its lower spectral resolution, can detect sources with emission rates as small as 571 kg/h and achieve faster retrieval speeds, showing significant potential for global methane monitoring. Additionally, this study highlights the need to focus on methane emissions from marine sources, alongside terrestrial sources, to efficiently implement reduction strategies. Full article

Carbonate-hosted clay-type lithium deposits have emerged as strategic resources critical to the global energy transition, yet their exploration faces the dual challenges of technical complexity and environmental sustainability. Traditional methods often entail extensive land disruption, particularly in ecologically sensitive ecosystems where vegetation coverage and weathered layers hinder mineral detection. This study presents a case study of the San Dan lithium deposit in central Yunnan, where we propose a hierarchical anomaly extraction and multidimensional weighted comprehensive analysis. This comprehensive method integrates multi-source data from GF-3 QPSI SAR, GF-5B hyperspectral, and Landsat-8 OLI datasets and is structured around two core parts, as follows: (1) Hierarchical Anomaly Extraction: Utilizing principal component analysis, this part extracts hydroxyl and iron-stained alteration anomalies. It further employs the spectral hourglass technique for the precise identification of lithium-rich minerals, such as montmorillonite and illite. Additionally, concealed structures are extracted using azimuth filtering and structural detection in radar remote sensing. (2) Multidimensional Weighted Comprehensive Analysis: This module applies reclassification, kernel density analysis, and normalization preprocessing to five informational layers—hydroxyl, iron staining, minerals, lithology, and structure. Dynamic weighting, informed by expert experience and experimental adjustments using the weighted weight-of-evidence method, delineates graded target areas. Three priority target areas were identified, with field validation conducted in the most promising area revealing Li₂O contents ranging from 0.10% to 0.22%. This technical system, through the collaborative interpretation of multi-source data and quantitative decision-making processes, provides robust support for exploring carbonate-clay-type lithium deposits in central Yunnan. By promoting efficient, data-driven exploration and minimizing environmental disruption, it ensures that lithium extraction meets the growing demand while preserving ecological integrity, setting a benchmark for the sustainable exploration of clay-type lithium deposits worldwide. Full article

Lithology identification provides a crucial foundation for various geological tasks, such as mineral exploration and geological mapping. Traditionally, lithology identification requires geologists to interpret geological data collected from the field. However, the acquisition of geological data requires a substantial amount of time and becomes more challenging under harsh natural conditions. The development of remote sensing technology has effectively mitigated the limitations of traditional lithology identification. In this study, an interpretable dual-channel convolutional neural network (DC-CNN) with the Shapley additive explanations (SHAP) interpretability method is proposed for lithology identification; this approach combines the spectral and spatial features of the remote sensing data. The model adopts a parallel dual-channel structure to extract spectral and spatial features simultaneously, thus implementing lithology identification in remote sensing images. A case study from the Tuolugou mining area of East Kunlun (China) demonstrates the performance of the DC-CNN model in lithology identification on the basis of GF5B hyperspectral data and Landsat-8 multispectral data. The results show that the overall accuracy (OA) of the DC-CNN model is 93.51%, with an average accuracy (AA) of 89.77% and a kappa coefficient of 0.8988; these metrics exceed those of the traditional machine learning models (i.e., Random Forest and CNN), demonstrating its efficacy and potential utility in geological surveys. SHAP, as an interpretable method, was subsequently used to visualize the value and tendency of feature contribution. By utilizing SHAP feature-importance bar charts and SHAP force plots, the significance and direction of each feature’s contribution can be understood, which highlights the necessity and advantage of the new features introduced in the dataset. Full article