In the context of the growing maturity of on-road navigation, this study proposed a cross-disciplinary research direction-off-road navigation-based on the demands for navigation services in complex and unstructured environments. First, the development of on-road navigation and the demand scenarios of off-road navigation were introduced. Based on four specific aspects of vehicle trafficability, scientific issues in the transition from on-road to off-road navigation were presented, including refining remote sensing detection of geographical and geological trafficability elements, remote sensing-based retrieval of soft soil parameters in off-road areas, and quantitative mechanisms behind the impacts of climatic change on ground characteristics. Accordingly, the research direction of off-road navigation was clarified. Then, key technologies like vehicle trafficability calculation and characterization, digital road network construction in off-road areas, and intelligent path planning for off-road navigation were summarized. The technical approach of roadization for off-road areas and the concept of a digital road network for off-road areas were introduced, followed by the establishment of a comprehensive technology system for off-road navigation. Finally, in combination with practical applications, the potential of off-road navigation was confirmed. Research on off-road navigation will further enrich the connotation of navigation and expand its application boundaries.

Soil salinization is identified as a major cause of decreased soil fertility, productivity, vegetation coverage, and crop yield. Optical remote sensing monitoring enjoys advantages such as macro-scale, timeliness, dynamics, and low costs, rendering this technology significant for the dynamic monitoring of soil salinization. However, there is a lack of reviews of the systematic organization of multi-scale remote sensing data, multi-type remote sensing feature parameters, and inversion models. This study first organized the optical remote sensing data sources and summarized the remote sensing data sources and scale platforms utilized in current studies on saline soil monitoring. Accordingly, this study categorized multi-source remote sensing data into three different platforms: satellite, aerial, and ground. Second, this study organized the mainstream characteristic parameters for modeling and two typical inversion methods, i.e., statistical regression and machine learning, and analyzed the current status of research on both methods. Finally, this study explored the fusion of remote sensing data sources and compared the pros and cons of various modeling methods. Furthermore, in combination with current hot research topics, this study discussed the prospects for the application of data assimilation and deep learning to soil salinization monitoring.

This study aims to evaluate and analyze the geology of mines in Ili Valley and investigate the countermeasures for ecological restoration therein. Utilizing the mining development status derived from remote sensing data and the remote sensing survey results of geological environment, as well as multi-source geological, socio-economic, and meteorological data, this study built a hierarchy structural model using analytic hierarchy process (AHP) and assessed the geological environment of mines in the Ili Valley, The results indicate that the severely affected areas are relatively concentrated, accounting for 4.61% of the total area of Ili Valley. The moderately severely affected areas present a continuous distribution. These areas overlap with each other, exhibiting indistinct boundaries. The generally affected areas are primarily distributed in extremely high mountain areas, medium to high mountain areas, and low mountain and hilly areas. The unaffected areas are primarily distributed in the alluvial plain area in the central part Ili River Valley and the plain area of the Zhaosu Basin. The areas with high ecological carrying capacity are mainly concentrated in the central region except for the south of Zhaosu County and Tekes County, the eastern edge of Nilka County, and the northern area of Khorgos. This study proposed corresponding ecological restoration and management measures and countermeasures against major geological issues. The findings of this study can provide basic data and technical support for the sustainable development of the ecology and the rational exploitation of mine resources in the Ili Valley. Additionally, these findings can serve as a case study for monitoring and assessing the geology of mines in arid and semi-arid areas.

The geological exploration in Qinghai Province is conducted in typical highland desert areas characterized by significant terrain cutting, low vegetation cover, extensive bedrock outcrops, and challenging surface investigations. To investigate the application of hyperspectral data from a satellite in altered mineral mapping and ore prospecting in desert areas along the northern margin of the Qaidam Basin, this study utilized hyperspectral remote sensing images with high spatial and spectral resolutions obtained from the domestic ZY1-02D satellite. Following fine-scale radiometric calibration, atmospheric correction, data restoration, and orthorectification, high-quality hyperspectral remote sensing images were acquired for desert areas, such as Saibagou, along the northern margin of the Qinghai Province. After comprehensive field survey sampling and spectral testing using a FieldSpec Pro FR spectrometer, a standard laboratory spectral dataset of the surveyed areas was established, containing 13 major altered minerals such as chlorite. Finally, the extraction of altered minerals and ore prospecting were conducted. The results indicate that the primary altered minerals in the areas include 13 types: epidote, chlorite, albite, sericite, silicification, kaolinite, carbonate, limonite, actinolite, serpentine, hematite, pyrite, and malachite. The distribution of these altered minerals is closely associated with the strata, lithology of intrusions, and ductile shear structures. The analysis of the alteration characteristics of typical mineral deposits such as Saibagou reveals that malachitization, limonitization, pyritization, sericitization, and silicification are closely related to mineralization, being indicative of ore prospecting. Partial potassium feldspathization, chloritization, epidotization, and sodium feldspathization are also somewhat related to mineralization, serving as a valuable reference for ore prospecting. As a result of applying the above methods, combined with the 1:25 000 geochemical survey data, one copper-gold mineralized point was discovered in the Tuomoerrite area. Therefore, hyperspectral data-based alteration mineral mapping compensates for the limitations of traditional geological surveys and ore prospecting methods in the western desert areas of China with harsh natural conditions. This allows for rapid assessment of the regional geological setting and mineralization conditions, enabling the extraction of mineral spectral information. The hyperspectral data-based alteration mineral mapping provides abundant, fine-scale information for large-scale geological prospecting, offering broad application potential.

Sandstone-type uranium deposits emerge as important uranium resources, while remote sensing is identified as a vital method for mineral resource exploration. Since sandstone-type uranium deposits typically occur underground and tend to be covered by sediments, whether remote sensing can be effectively applied to the exploration of such deposits merits investigation. This study investigated the Yingen area in the Bayingobi basin. Utilizing multi-source remote sensing data from Sentinel2, Landsat7 ETM+, ASTER, ALOS DEM, and airborne radioactivity measurements, this study performed terrain visualization, structural interpretations, K-T transformation, NDVI index calculation, alteration mineral extraction, and Th/U ratio calculation. The results were then comprehensively analyzed from the perspective of the metallogenic model, metallogenic conditions, and ore-controlling factors of secondary reduced sandstone-type uranium deposits. The analytical results indicate that the Yingen area consists of an uplift zone in the center, a depression zone in the southeast, and a slope zone between them. The granitic rocks in the uplift zone are identified as significant sources of uranium. Multiple EW-trending faults in the slope zone facilitate the migration of uranium-bearing oxidized water underground. Additionally, the water-rich areas in the depression zone, combined with strong surface evaporation, create favorable conditions for the drainage and evaporation of uranium-bearing oxidized water, further promoting groundwater circulation. Therefore, the uplift zone, slope zone, and depression zone in the Yingen area form a complete circulation system for uranium-bearing oxidized water. In combination with previous data, this study holds that the slope zone might serve as a favorable area for the formation of secondary reduced sandstone-type uranium deposits. This study also demonstrates that even in seriously overburden areas, remote sensing can provide valuable guidance for uranium exploration by identifying metallogenic conditions and ore-controlling factors.

High-resolution remote sensing images have been widely applied to classification of ore deposits. However, there is a lack of studies on the information extraction and dynamic monitoring of open-pit lateritic nickel deposits. Using high-resolution remote sensing images from the Pleiades and GF-2 satellites, this study investigated the famous open-pit Tagaung Taung nickel deposit in Myanmar. First, information about surface features was extracted using object-oriented classification based on hierarchical multi-scale segmentation. Then, the dynamic changes in the nickel deposit were analyzed. Finally, qualitative and quantitative assessments of the classification accuracy were carried out. The results indicate that the hierarchical multi-scale segmentation technology exhibited encouraging classification and identification effects, with overall classification accuracy of 94.24% and 89.02% and the Kappa coefficients of 0.889 and 0.816, respectively for images from the Pleiades and GF-2 satellites. Therefore, the proposed method is suitable for the information extraction of open-pit lateritic nickel deposits. The dynamic change analysis reveals that the Tagaung Taung nickel deposit experienced continuous expansion of mining at high mining speeds from 2015 to 2017. It can be inferred that this deposit has great potential and broad prospects for resource development. The results of this study can provide technical support for the dynamic monitoring of the Tagaung Taung nickel deposit in Myanmar.

Shanxi Province is recognized as a significant coal base in China. The extensive and intensive coal mining activities have adversely affected the local ecology, rendering the research on ecological evolution in Shanxi Province highly significant. Utilizing the Google Earth Engine (GEE) platform, this study calculated the 2000-2022 remote sensing ecological index (RSEI) of Shanxi Province using images from Moderate Resolution Imaging Spectroradiometer (MODIS). The Mann-Kendall trend analysis method was employed to analyze the evolutionary trends of RSEI, while Pettitt mutation tests were conducted to identify RSEI mutations. Furthermore, the Pearson correlation coefficient was used to analyze the correlation between RSEI and climatic factors. The results indicate that Shanxi Province exhibited relatively high ecological quality on average during the period. However, the coal mining areas in the province displayed moderate ecological quality overall. The spatial distribution of precipitation and temperature can effectively account for the spatial distribution of RSEI. Most regions in Shanxi Province showed an upward trend in RSEI, with areas with reduced RSEI predominantly located in coal mining areas and basin areas with high population density and a developed economy. During the 23 years, the ecological quality in Shanxi Province has evolved from poor to moderate and then to relatively good, increased while fluctuating from 2000 to 2006, kept stable from 2006 to 2012, regressed after increase from 2012 to 2019, and continuously increased from 2019 to 2022. Similarly, the ecological quality in coal mining areas has shifted from relatively poor to relatively good, increased while fluctuating from 2000 to 2006, kept stable from 2006 to 2012, decreased while fluctuating from 2012 to 2019, and continuously increased from 2019 to 2022. The year 2010 is identified as a pivotal point for the ecological quality of Shanxi Province, with the ecological quality trending upward from 2000 to 2010 and comprehensively improving after 2010 across the province and its coal mining areas. The interannual variations in precipitation generally produce positive impacts on the ecological quality, while the variations in interannual temperature exert insignificant impacts.

Remote sensing data with a high spatial resolution can be used to quickly ascertain the current status of the geological environment of mines in China, yielding objective and accurate results. By comparing 2020—2021 remote sensing images of 169 districts, counties, and cities in Hebei Province, 728 patches signaling suspected lands destroyed in mines were identified through interpretation. In 2021, the newly rehabilitated areas of the mining environment in Hebei Province reached 1 305.51 hm², while the newly increased lands destroyed by mining were 932.13 hm², resulting in a net increase of 373.38 hm² in lands attributed to the geological environment rehabilitation. These results indicate the general achievement of mining while rehabilitating. Based on a preliminary analysis of the current status of the geological environment of mines in Hebei Province and existing primary issues, this study proposed countermeasures and recommendations for future rehabilitation efforts. The results of this study will provide foundational data and technical support for managing the geological environment of mines in Hebei Province and evaluating the effectiveness of mine greening initiatives.

Hyperspectral image data are characterized by high dimensionality, sparse data, and rich spatial and spectral information. In spatial-spectral joint classification models, convolution operations for hyperspectral images can lead to computational spatial redundancy when processing large regions of pixels of the same category. Furthermore, the 3D convolution fails to sufficiently extract the deep spatial texture features, and the serial attention mechanism cannot fully account for spatial-spectral correlations. This study proposed an improved 3D Octave convolution-based model for hyperspectral image classification. First, the input hyperspectral images were divided into high- and low-frequency feature maps using an improved 3D Octave convolution module to reduce spatial redundancy information and extract multi-scale spatial-spectral features. Concurrently, a cross-layer fusion strategy was introduced to enhance the extraction of shallow spatial texture features and spectral features. Subsequently, 2D convolution was used to extract deep spatial texture features and perform spectral feature fusion. Finally, a 3D attention mechanism was used to focus on and activate effective features through interactions across latitudes, thereby enhancing the performance and robustness of the network model. The results indicate that, due to the adequate extraction of effective spatial-spectral joint features, the overall accuracy (OA), Kappa coefficient, and average accuracy (AA) were 99.32%, 99.13%, and 99.15%, respectively in the case where the Indian Pines (IP) dataset accounted for 10% in the training set and were 99.61%, 99.44%, and 99.08%, respectively when the Pavia University (PU) dataset represented for 3% of the training set. Compared to five mainstream classification models, the proposed method exhibits higher classification accuracy.

Soil moisture products based on remote sensing are crucial for investigating climatic change and hydrological effects on a regional scale. However, there is a lack of verification and application of long-term soil moisture datasets in China due to factors such as inconsistent observation standards and instrument upgrades. Using the agro-meteorological dataset from the China Meteorological Administration and soil moisture data from the International Soil Moisture Network (ISMN), this study constructed a monthly dataset of soil moisture in eastern China covering the period from 1981 to 2013. Accordingly, this study analyzed and compared the performance of four microwave remote sensing-based soil moisture products developed by the European Space Agency’s Climate Change Initiative (ESA CCI): active, passive, combined, and combined adjusted products. The results indicate that active and passive products underestimated and overestimated soil moisture in eastern China, respectively. The maximum deviations from active products were found in the northern and northwestern regions, with relative deviations of -30.9% and -29.6%, respectively. In contrast, the passive products showed relative deviations of 39.1% and 26.5%, respectively for soil moisture in northeastern and northwestern regions. The combined products mitigated the underestimation of the active products and the overestimation of the passive product in these regions, reducing the relative deviations to 24.3% and 3.7%, respectively. Regarding the variation characteristics of regional monthly average soil moisture, both the active and combined products performed best for soil moisture in the Yangtze-Huaihe (YH) region, with the highest correlation coefficient of 0.66. The passive and combined products yielded correlation coefficients of 0.44 and 0.47, respectively for soil moisture in the northeastern region and performed poorly for soil moisture in the northern and northwestern regions. The analysis of the variance sources of the remote sensing-based products indicates that the active products enjoyed more advantages in describing the evolutionary characteristics of soil moisture, the passive products demonstrated greater accuracy, and the combined products yielded the highest accuracy overall. Additionally, this study investigated the impacts of changes in the integrated satellite equipment of CCI on product performance. The results indicate that the active products exhibited consistent performance for soil moisture in the northeastern and northwestern regions in different periods. However, passive sensors still exhibited gaps in reproducing the variations in soil moisture. The combined products exhibited better overall variance than both active and passive products. However, these products yielded comparable correlation coefficients with the active products for soil moisture in the northeastern and northwestern regions. The combined products presented no notable improvement after correction, proving that it is feasible to conduct long-term research using the combined products of CCI. The results of this study contribute to a deeper understanding of the error structures and characteristics of various satellite product datasets, providing evidence for researchers to select appropriate data products and conduct research on long time series.

Accurately extracting building information from high-resolution remote sensing images faces challenges due to complex background transformations and the diversity of building shapes. This study developed a high-resolution building semantic segmentation network-building mining net (BMNet), which integrated a hybrid attention mechanism with multi-scale feature enhancement. First, the encoder utilized VGG-16 as the backbone network to extract features, obtaining four layers of feature representations. Then, a decoder was designed to address the issue of detail loss in high-layer features within multi-scale information. Specifically, a series attention module (SAM), which combined channel attention and spatial attention, was introduced to enhance the representation capabilities of high-layer features. Additionally, the building mining module(BMM) with progressive feature enhancement was designed to further improve the accuracy of building segmentation. With the upsampled feature mapping, the feature mapping post-processed using SAM, and initial prediction results as input, the BMM output background noise information and then filtered out background information using the context information exploration module designed in this study. Optimal prediction results were achieved after multiple processing using the BMM. Comparative experiment results indicate that the BMNet outperformed U-Net, with accuracy and intersection over union (IoU) increasing by 4.6% and 4.8%, respectively on the WHU Building dataset, by 7.9% and 8.9%, respectively on the Massachusetts buildings dataset, and by 6.7% and 11.0%, respectively on the Inria Aerial Image Labeling Dataset. These results validate the effectiveness and practicality of the proposed model.

The rapid survey and accurate mapping of the spatial distribution of crops using remote sensing are fundamental to modern precision agriculture. However, limitations in the acquisition, processing, and analysis of remote sensing images impact the mapping accuracy of traditional crop planting structures. Therefore, there is an urgent need to conduct spatial modeling and feature analysis for the uncertainty in crop classification. Using the Ningxia Yellow River irrigation area as a trial area and farmland parcels as the basic spatial units, this study classified crops on a parcel scale utilizing multi-source remote sensing data and machine learning algorithms. Then, an uncertainty calculation model was constructed based on mixed entropy, yielding the spatial distribution of the uncertainty of crop types in farmland parcels. Afterward, multi-source auxiliary data were employed to build a regression model for the uncertainty, and the potential impacts of related geographical variables on the uncertainty were explored. The experiment results indicate that 1.49 million vector units were constructed for the farmland parcels during the farmland extraction and classification session, yielding an overall crop classification accuracy of 0.80. The mapping results aligned well with the actual agricultural management units, and the classification results proved more better than the traditional pixel-based methods. The uncertainty in the parcel-scale crop classification was generally lower, with significant differences among crop types. The uncertainty was low for rice, vegetable plots, and alfalfa, relatively higher for wheat of single- and double-cropping patterns, and moderate for maize. The uncertainty in parcel-scale crop classification is influenced by various environmental factors such as planting structure and resource conditions, exhibiting the most significant correlations with crop type and water accessibility.

This study aims to overcome the challenges of precise mapping of open beach areas using stereo satellite imagery. Based on the complementary characteristics of stereo optical satellite imagery and light detection and ranging (LiDAR) point cloud data in geometric positioning, this study developed a high-precision mapping method that employed high-precision LiDAR point cloud data for generalized control. First, the LiDAR depth map was matched with the optical satellite images to extract corresponding point pairs for generalized control. Then, the images and control points were used as inputs for adjustment to achieve an accurate geometric positioning of the images. Finally, guided by LiDAR point cloud data and in combination with multi-baseline and multi-primitive matching algorithms and the geomorphologic refined matching (GRM) algorithm, a high-precision digital surface model (DSM) for open beach areas was automatically extracted. The results of this study indicate that the combined use of laser point clouds and stereo satellite imagery, along with photogrammetric technology, allows for the quick and accurate preparation of high-precision topographic maps of open beach areas. This study provides valuable guidance for the precise mapping of open beach areas.

Traditional water-body index models exhibit high susceptibility to sediments in the shallow water areas at the boundaries of water bodies. This susceptibility leads to challenges such as water misclassification and omissions during water information extraction. Focusing on the Tanghe Reservoir, Tonghu Lake, and shallow offshore areas, this study developed a new multi-band water index (NMBWI) based on the spectral information of typical surface features derived from Landsat images. The comparison with traditional water-index models, including NDWI, MNDWI, EWI, and RNDWI, reveals that NMBWI can significantly enhance the detection effects of shallow water areas at water body boundaries, resulting in more comprehensive extraction results of water areas. NMBWI outperforms traditional water index models in terms of overall accuracy and Kappa coefficient. Furthermore, NMBWI demonstrates high universality and stability in the information extraction of shallow water areas across various water body boundaries.

The key to the high performance of semantic segmentation models for high-resolution remote sensing images lies in the high domain consistency between the training and testing datasets. The domain discrepancies between different datasets, including differences in geographic locations, sensors’ imaging patterns, and weather conditions, lead to significantly decreased accuracy when a model trained on one dataset is applied to another. Domain adaptation is an effective strategy to address the aforementioned issue. From the perspective of a domain adaptation model, this study developed an adversarial learning-based unsupervised domain adaptation framework for the semantic segmentation of high-resolution remote sensing images. This framework fused the entropy-weighted attention and class-wise domain feature aggregation mechanism into the global and local domain alignment modules, respectively, alleviating the domain discrepancies between the source and target. Additionally, the object context representation (OCR) and Atrous spatial pyramid pooling (ASPP) modules were incorporated to fully leverage spatial- and object-level contextual information in the images. Furthermore, the OCR and ASPP combination strategy was employed to improve segmentation accuracy and precision. The experimental results indicate that the proposed method allows for superior cross-domain segmentation on two publicly available datasets, outperforming other methods of the same type.

The accurate and timely detection of landslides is of great significance for reducing the threats to human life and properties, along with relevant losses, caused by landslides. This study proposed a landslide detection method using feature fusion based on convolutional neural networks (CNNs) and Segmentation Transformer (SETR). The CNN-based models utilized a fully convolutional network (FCN), U-Net, and Deeplabv3+, while the Transformer-based models used SETR. First, the landslide detection effects of the CNN-based models were evaluated. Then, SETR was introduced into the encoders of the CNN-based models, and the output of SETR was fused into the CNN decoder structure as the final output of the models. The experiments using the LandSlide4Sense dataset indicate that the fusion of typical CNNs with SETR can effectively improve the landslide detection effects. After SETR fusion, the FCN, U-Net, and Deeplabv3+ models exhibited higher F1-scores, which increased from 0.672 6, 0.727 3, and 0.687 3 to 0.686 9, 0.743 0, and 0.705 5, respectively. Given the close relationship between landslides and terrain, a digital elevation model (DEM) was incorporated into the U-Net model, which outperformed other models. As a result, the F1-score of the model increased from 0.732 5 to 0.750 3.

Integrated imaging spectrometers can effectively improve the monitoring capability of terrestrial ecosystems. Imaging simulation in the design and development of spectrometers is identified as an important means to improve their efficiency. To overcome the shortcomings of current imaging simulation in scene modeling and radiation transport models, this study developed a full-link hyperspectral imaging simulation model. Using this model, this study conducted preliminary assessments of load efficiency. First, heterogeneous modeling for large-scale scenes was conducted according to the observation targets of loads. Then, a surface radiation transport model containing fluorescence radiation and thermal radiation was derived for undulating terrain with uneven surface feature distribution (also referred to as unevenly undulating surface). Finally, by integrating the imaging model of grating spectrometers, this study established a full-link imaging simulation model. To determine the impacts of the adjacency effect of the unevenly undulating surface on the spatial distribution of solar-induced chlorophyll fluorescence (SIF), this study compared the radiance of red and far-red SIF derived with and without considering the adjacency effect of terrain under the spatial resolution of the used integrated imaging spectrometer. For data on significantly undulating terrain with uneven surface feature distribution, the SIF radiance exhibited differences of up to maxima of 22% and 52%, respectively, and ignoring the adjacency effect led to significant errors in the high-resolution SIF simulations. The imaging modeling method developed in this study can be used for hyperspectral imaging simulation of unevenly undulating surfaces, thus allowing for analyzing the efficiency of integrated imaging spectrometers for composite applications in ecological monitoring.

The Taiwan Strait holds a significant strategic position and great value for research. Investigating the spatiotemporal variations in sea surface temperature (SST) in the Taiwan Strait and its surrounding sea areas helps enhance the understanding of the marine-continental environmental interactions and changes in ocean currents in this region. Such investigation is particularly significant for comprehensively understanding the complex marine frontal systems within the Taiwan Strait. This study investigated the Taiwan Strait and its surrounding sea areas. Using 2016—2020 Himawari-8 satellite data, this study determined the annual, seasonal, and ten-day averages of SST remote sensing data. Based on these data, this study examined spatiotemporal variations in the SST and, accordingly, explored correlations between SST and inland precipitation and coastal fog in Fujian. The results indicate that the annual mean SST in the Taiwan Strait and surrounding sea areas exhibited a zonal distribution, increasing gradually from northwest to southeast. Seasonally, the SST exhibited two distribution patterns: a winter pattern, with isotherms approximately parallel to the coast, and a summer pattern, with isotherms more uniformly distributed. The ten-day SST data allowed for more fine-scale characterization of the spatiotemporal variations in the SST of the Taiwan Strait. The inland monthly precipitation generally exhibited a weak negative correlation with monthly mean SST, with this correlation strengthening with an increase in the distance from open sea areas. Additionally, a strong negative correlation was observed between the SST and coastal fog, with the coastal fog occurrence number trending downward with increasing SST.

Analyzing the carbon stock in a terrestrial ecosystem is a key link for research on the global and regional carbon cycle. Assessing the long-time-series carbon stock in the West Dongting Lake National Nature Reserve will provide scientific data for regional ecological monitoring and management. Based on the land use data from 2000 to 2020, this study explored the spatiotemporal changes in the carbon stock of the nature reserve based on the carbon stock estimated using the InVEST model and identified key areas of carbon stock changes. The results indicate that in the past two decades, the carbon stock in the nature reserve exhibited a fluctuating upward trend, ranging from 113.5×10⁴ to 125.7×10⁴ tons. The carbon stock presented relative changing rates of less than 2% during this period, except for 2003, when the changing rate was 3.2%. Over the past two decades, the core zone of the nature reserve ranked first in carbon stock among subregions every year, followed by the pilot zones. The carbon stock in most areas of the nature reserve remained unchanged or changed slightly. Nevertheless, there still existed some areas with significant changes in the carbon stock. The key areas of carbon stock changes featured diverse spatial distribution patterns of carbon stock, such as concentrated, linear, and scattered patterns, with land use types in these areas exhibiting corresponding change intensities of carbon stock. The changes in the carbon stock in the pilot zones were greatly affected by human interference, while those in the core area were primarily related to precipitation. The results of this study will assist in scientifically promoting carbon neutrality and peak carbon dioxide emissions in the West Dongting Lake National Nature Reserve.

The rapid assessment of building damage following destructive earthquakes serves as a critical foundation for decision-making and technical guarantee in post-earthquake scientific evaluations, holding great significance in humanitarian aid and emergency response. This study aims to overcome the challenge in rapidly quantifying the number of buildings affected. Considering that most existing post-earthquake building damage assessments based on remote sensing images rely on pre- and post-disaster image segmentation, this study, by using the U-Net deep convolutional neural network as the main model, introduced a three-stage convolutional neural network for building damage assessment (BDANet) framework that integrates assessment and prediction for post-earthquake building damage information. First, the encoder-decoder network structure of U-Net was used to extract building location information. Second, building damage was assessed using pre- and post-disaster images to localize and grade damage in the image segmentation results. Finally, the number of buildings damaged at various levels was predicted to support post-disaster rescue and reconstruction efforts. The study evaluated and quantified the levels of post-earthquake building damage in the M7.1 earthquake in Morelos State, central Mexico in 2017 and the M7.8 earthquake in Türkiye in 2023, confirming the accuracy and reliability of the proposed method. The experimental findings provide timely and precise data and technical support for post-disaster risk assessment.

The dam detection is crucial for urban planning, ecological environment assessment, and other purposes. Currently, research on remote-sensing-based dam detection mainly focuses on algorithm improvements using sample sets or small-scale localized detections, with a significant gap in practical applications over large-scale geographical regions. In large-scale regions, the sparse distribution of dams, along with the presence of more surface features such as bridges, significantly interferes with dam detection. To address this issue, this study explored the Daqing River basin as a case study to investigate remote sensing methods for dam detection in large-scale regions. This study consisted of two main phases. In the first stage, bridges, which are easily confused with dams, are considered hard negative samples (i.e., samples prone to false positives) for training. The neural network structure suitable for dam detection was improved based on the DIOR open dataset. In the second phase, the detection model was developed through fine-scale tuning using the optimized network alongside multi-source sample data from the large Daqing River basin. Concurrently, dams within the Daqing River region were detected. The optimized model yielded dam detection F1 of 0.783 in the first phase of tests and identified 330 dams in the Daqing River basin during the second phase. These results align with the existing publicly available dam spatial distribution dataset GRandD, even providing more details. The results of this study indicate that the model, optimized using bridge samples, can effectively mitigate the incorrect extraction of bridges, thereby improving detection accuracy.

In the management of modern agriculture production, the spatial distribution of different crop types is identified as important information about agricultural conditions. Identifying crop types from satellite remote sensing imagery serves as a fundamental method for acquiring such information. Although there exist various algorithms for identifying surface features from remote sensing imagery, reliable farmland classification remains challenging. This study selected three representative semantic segmentation-orientated deep convolutional models, i.e., UNet, ResUNet, and SegNext, and compared their performance in crop classification using remote sensing images of the Hetao irrigation district from the Gaofen-2 satellite. Using the three algorithms, nine network models with varying complexities were developed to analyze the differences in the performance of various network structures in classifying crops in farmland based on remote sensing imagery, thus providing optimization insights and an experimental basis for future research on relevant models. Experimental results indicate that the six-layer UNet achieved the highest identification accuracy (88.74%), while the six-layer SegNext yielded the lowest accuracy (84.33%). The ResUNet displayed the highest complexity but serious over-fitting with the dataset used in this study. Regarding computational efficiency, ResUNet was significantly less efficient than the other two model types.

Synthetic aperture radar (SAR), allowing for all-weather and all-day imaging, can provide essential data for large-scale flood inundation monitoring. However, limitations such as the revisit period of SAR images make it challenging for single-source SAR data to meet the high temporal requirements for dynamic flood inundation monitoring, which is crucial for disaster relief and decision-making support. Combining multi-temporal SAR data for dynamic flood inundation monitoring is of significant practical value. Nevertheless, SAR images from different sensors exhibit significant spatiotemporal heterogeneity, rendering direct comparisons difficult. Additionally, previous studies frequently extracted flood inundation extents using single-pixel or local spatial neighborhood features while neglecting the application of spatiotemporal non-local features pre- and post-flooding. Therefore, this study first proposed a feature space alignment method for multi-source SAR data based on backscatter characteristics. Then, differential information pre- and post-flooding was extracted using the progressive non-local theory, and flood inundation maps were prepared. Finally, dynamic flood inundation monitoring results were obtained through logical operations of the time-series flood inundation maps. This method was validated using the flood disaster in August 2023 in Zhuozhou, during which five multi-source SAR datasets were acquired from Sentinel-1, Gaofen-3 (GF-3), and Fucheng-1. The results indicate that compared to six commonly used flood monitoring methods, the proposed method exhibited the optimal performance, yielding a Kappa coefficient and F1 score of 0.85 and 0.88, respectively. The dynamic monitoring results of the flood inundation in Zhuozhou reveal that the floodwater in the main urban area largely receded by August 3, and the water levels then gradually decreased, with the inundated areas shifting to the Baigou River in the lower reaches.

Understanding the spatiotemporal characteristics of vegetation growth in the Yellow River basin and their influencing factors is crucial for the conservation and development of the ecology. However, existing studies rarely focus on the latest spatiotemporal characteristics of different vegetation types in the basin and their relationships with their influencing factors. Using the 2000-2020 time series remote sensing data of MODIS normalized difference vegetation index (NDVI), along with methods including trend analysis, correlation analysis, partial correlation analysis, and residual analysis, this study investigated the spatiotemporal characteristics of various vegetation types in the Yellow River basin. Accordingly, this study clarified the mechanisms behind the impacts of temperature and precipitation on annual and monthly scales and explored the influence of human activities on the spatiotemporal characteristics of different vegetation types. The results indicate that from 2000 to 2020, the NDVI of different vegetation types in the Yellow River basin trended upward overall, particularly in cultivated land and forest land. However, the increasing trends trended downward at different degrees with increasing elevation. Over the 21 years, various vegetation types were improved in most areas in the basin. However, a few areas exhibited degraded vegetation types, primarily including grassland and cultivated land. The proportion of areas with anti-continuous future trends in various vegetation types notably increased. Temperature and precipitation produced positive impacts on the growth of various vegetation types in the Yellow River basin. Nevertheless, various vegetation types exhibited greater responses to precipitation than to temperature, and the responses featured notable time lags. Furthermore, grassland and shrub growth were more sensitive to precipitation and temperature. Human activities had positive impacts on the vegetation of the Yellow River basin overall. However, some negative effects were also observed in grassland and cultivated land, warranting attention in future planning. Overall, most areas exhibited improved vegetation in the Yellow River basin in the 20 years. Given that partial grassland and cultivated land experienced degradation, it is necessary to protect typical degradation areas. The findings of this study will provide scientific data and theoretical support for ecological construction and economic development in the Yellow River basin.

Based on 1985-2020 Landsat data, this study estimated eight phases of annual vegetation fractional cover (VFC) of the Beijing-Tianjin-Hebei region. Using the Theil-Sen Median and Mann-Kendall trend analyses, this study comprehensively analyzed the spatiotemporal variation characteristics of VFC in four major functional areas for the coordinated development of the Beijing-Tianjin-Hebei region. Furthermore, employing geodetectors, this study explored the degrees and mechanisms of the impacts of climatic, natural, and anthropogenic factors, along with their interactions, on the regional VFC from both static and dynamic perspectives. The results indicate that from 1985 to 2020, the Beijing-Tianjin-Hebei region exhibited sound vegetation coverage overall, which decreased in the order of the southern functional expansion area (SFEA), the northwestern ecological conservation area (NECA), the central core functional area (CCFA), the eastern coastal development area (ECDA). The VFC of the Beijing-Tianjin-Hebei region trended upward while fluctuating, with an increasing rate of 0.097%/10a. The VFC exhibited a spatial distribution pattern of high values in the west and low values in the east. Specifically, areas with elevated VFC were primarily distributed in the Yanshan, Damaqun, and Taihang mountains within the NECA, while those with reduced VFC were principally found in the built-up areas and their surrounding areas of cities and counties in the CCFA, ECDA, and SFEA. At the single-factor level, the primary and secondary factors controlling VFC across the four functional areas differed greatly, with land-use and soil types exhibiting higher interpretability. Regarding the influencing elements, the main factors driving spatial differentiation of VFC in the CCFA and SFEA included anthropogenic factors, those in ECDA comprised anthropogenic and natural factors, and those in NECA were dominated by climatic and natural factors. For the VFC of the four functional areas in all these years, the land use type manifested high interpretability, which trended upward overall. The q values of soil types were higher in ECDA and NECA, trending downward in the NECA. Secondary factors controlling the VFC exhibited different interannual interpretability in various functional areas. All influencing factors exhibited enhanced influence to varying extents, with no mutual independence or weakened influence observed. Additionally, the meteorological factor emerged as the primary interacting variable.

On April 1, 2023, China’s first satellite constellation-L-band differential interferometric Synthetic Aperture Radar (L-SAR)-began to test the distribution of interferometric SAR data for natural resource applications. To evaluate the coherence and effectiveness of deformation monitoring using the L-SAR satellite for areas with high vegetation coverage, complex terrain, and long-term baseline, this study conducted potential landslide hazard identification in the Ankang area in the eastern Qinba Mountain. The deformation information of the study area was extracted using L-SAR data. Using such information, combined with high-resolution optical images for comprehensive remote sensing identification, this study identified seven potential landslide hazards in the study area through interpretation. Field investigation confirmed that the observed deformation signs in potential landslide hazard areas were consistent with the InSAR monitoring results. The study indicates that the L-SAR satellite enjoys a high interference imaging ability, high imaging quality, and effective deformation monitoring, meeting the demand for deformation monitoring in areas with high vegetation coverage. For mountainous areas with high vegetation coverage, the use of L-band SAR data through DInSAR technology, combined with comprehensive remote sensing identification using high-resolution optical imagery, allows for the effective identification of potential landslide hazards.

The changes in land cover types will affect the amount of solar radiant energy absorbed by the land surface and then influence the radiative equilibrium of the surface ecosystem. Under the background of dramatic changes in land cover, the patterns and changes of the solar radiation absorptivity of land exert significant influence on the thermal equilibrium of the land surface. Based on the MODIS MCD12Q1 land cover data, the MCD43A3 surface albedo data, and the MCD43A2 solar zenith angle data from 2001 to 2020, along with two adjacent phases of spatiotemporal changes in the surface cover types and the solar radiant energy absorbed by land surface across northwestern China, this study analyzed and explored the impacts of the changes in land cover types on solar radiation absorption. The results indicate that the changes in land cover types in the study area are primarily characterized by reduced bare land area and the expansion of other land cover types, with the largest areal change occurring in the shift from bare land to grassland. Different types of land cover display varying solar radiation absorptivities. Water bodies exhibit the greatest solar radiation absorptivity, followed by woodland, cultivated land, grassland, and construction land, with bare land and permanent ice and snow presenting the poorest solar radiation absorptivities. The conversion of land cover types will lead to different radiation absorptivities. Specifically, the transfer from grassland, cultivated land, bare land, and permanent ice and snow primarily exhibits an increasing trend in solar radiation absorptivity, while that of water bodies and forest land largely displays a decreasing trend. The same land cover type differs in the time series of solar radiation absorption, which primarily increases for construction land, grassland, cultivated land, bare land, and water bodies but decreases for woodland and permanent ice and snow. The results of this study will provide a scientific basis and reference for research on climatic change, ecological construction, and sustainable development in northwestern China.

To resolve the limitations of traditional economic data such as the lack of spatial information and the difficulty in capturing the spatial disparities and dynamic patterns of regional economic development, this study integrated nighttime light data with land use and socio-economic data to develop a spatialized gross domestic product (GDP) model for the Chengdu-Chongqing region. Using trend analysis and a modified gravity model, this work analyzes the economic development characteristics of the region at the pixel level and in terms of inter-city economic relationships. The results indicate that the spatialized GDP model, constructed from multiple data sources, demonstrated high accuracy, with errors not exceeding 1.1%. The areas with the fastest GDP density growth in the Chengdu-Chongqing region are mainly concentrated around the core urban areas of Chengdu and Chongqing, accounting for approximately 73.9% of the total. These areas also show pronounced economic agglomeration characteristics. The inter-city economic relationships in the Chengdu-Chongqing region are continually strengthening, and the overall quality of urban development is steadily improving. Chengdu, in particular, has the closest economic ties with its neighboring cities. Overall, the Chengdu-Chongqing regional economy exhibits a spatial pattern of “dual-core driven development”, with the intensity of inter-city economic relationships continuing to strengthen. This study will provide valuable data support and methodological insights for promoting the high-quality economic development of the Chengdu-Chongqing urban agglomeration.

The interferometric synthetic aperture radar (InSAR) technique is widely applied to surface deformation monitoring, providing all-weather, all-time, and high-precision measurements over large areas. However, due to the limitations of the single deformation observation method, significant uncertainties inevitably arise during the monitoring process, leading to potential misinterpretations. Using the SBAS-InSAR (small baseline subset) two-dimensional solution technique based on ascending and descending SAR data, this study analyzed the surface deformations of the upper reaches of the Huangdeng Hydropower Station from April 2020 to August 2022. A total of 34 scenes of ascending and descending data from the Sentinel-1 satellite were used to derive the two-dimensional deformations of the upper reaches, with six potential landslide hazard sites there being identified. The results indicate that the study area displayed a predominance of horizontal surface deformations, with the highest two-dimensional deformation rates of up to 158 mm/a horizontally and 81 mm/a vertically observed in the Cheyiping area. Additionally, by correlation analysis between the distance from the Lancang River bank, rainfall, and the time-series deformations, this study identified the distribution of two-dimensional deformations in the upper reaches and its seasonal variations.

The inversion of aerosol optical depth (AOD) above the bright surface holds great significance for monitoring urban atmospheric environment and investigating urban pollution islands. This study proposed an AOD inversion method using the data field method (DFM) by incorporating both spectral and spatial characteristics of pixels. Using the Land OLI data, this study conducted AOD inversion over Nanjing and analyzed the impacts of different spatial structures, data field intensity, and underlying surface features on the DFM. This method was then compared with the traditional Deep Blue algorithm and MOD04 products through a detailed comparative analysis. The results indicate that the DFM inversion results had a correlation coefficient of 0.936 with observations obtained using aCE318 sun photometer, a root mean square error of 0.151, a mean absolute error of 0.120, an average relative error of 22.7%, a relative mean deviation of 1.139, and an error ratio of 72.7%, demonstrating high consistency with ground-based measurements. In areas with surface data field intensity exceeding 12, the DFM algorithm exhibited superior inversion performance and proved more effective in the spring and summer than in autumn and winter. Particularly, this algorithm achieved enhanced inversion results in rural and industrial areas characterized by rapid changes in their underlying surfaces.

Net ecosystem productivity (NEP) is recognized as an important characteristic quantity of ecosystems and a physical quantity of carbon exchange between terrestrial ecosystems and the atmosphere. Utilizing MODIS NPP and meteorological data, this study estimated the vegetation NEP in Hunan Province from 2000 to 2020 using a soil microbial respiration model. Furthermore, this study analyzed the dynamic characteristics of vegetation carbon sink through trend analysis, variation coefficient, and standard deviation ellipse methods, followed by a quantitative assessment of the impacts of natural factors on vegetation carbon sink using geographical detectors and correlation analysis. The results indicate that the annual multiyear average of vegetation carbon sink in Hunan Province was 603.01 gC·m^-2·a^-1. The vegetation carbon sink presented a spatial distribution pattern of higher values in the south and west and lower values in the north and east, decreasing gradually from southwest to northeast. From 2000 to 2020, the average trend coefficient of vegetation carbon sink was 2.97 gC·m^-2·a^-1, trending upward overall. The coefficient of variation was primarily characterized by small fluctuations and fairly small fluctuations, while areas of great fluctuations are mainly scattered around certain cities, which are more susceptible to natural or anthropogenic disturbances. The variations in vegetation carbon sink in Hunan Province result from multiple factors, with the explanatory power of various factors decreased in the order of altitude, slope, temperature, precipitation, and slope. Both altitude and slope exhibited strong explanatory power regarding the spatiotemporal distribution of vegetation carbon sink in Hunan Province, while temperature and precipitation demonstrated weaker explanatory power. Areas where vegetation carbon sink was positively correlated with temperature and precipitation accounted for 75.13% and 73.11% of the total vegetation area, respectively.

During the melt season, supraglacial lakes are widely distributed across polar ice sheets, storing large amounts of surface meltwater. When some of these supraglacial lakes rupture at the bottom, the released meltwater infiltrates ice sheets, affecting their movement and stability. Therefore, accurate bathymetry retrieval of supraglacial lakes and estimating the volume of supraglacial lakes are significant for understanding the hydrological processes of polar ice sheets. However, field measurement of supraglacial lake depth is difficult, costly, and small-scale. Meanwhile, the bathymetry models derived from optical satellite images with low to medium spatial resolutions are insufficiently accurate. Given these, this study conducted the bathymetry retrieval of supraglacial lakes based on eight-band remote sensing images from the small-size optical satellite PlanetScope SuperDove (spatial resolution: 3 m) and ICESat-2 laser altimetry data. First, the ICESat-2 laser altimetry point clouds data for the lake surface and bottom were separated and modeled using adaptive kernel density estimation to derive lake depth observations. Second, Optimal Band Ratio Analysis (OBRA) was used to examine the correlations between various bands of PlanetScope images (and combinations thereof) and ICESat-2 bathymetry data, leading to the development of four kinds of empirical formulas for the bathymetry retrieval of supraglacial lakes: quadratic, exponential, power, and logarithmic functions. Then, four supraglacial lakes covered by concurrent PlanetScope and ICESat-2 data were selected to test the retrieval accuracy. The results indicate that the Green I band of PlanetScope is the most favorable for the bathymetry retrieval, demonstrating the strongest correlation with the ICESat-2 derived depths (R²=0.94) and the highest inversion accuracy (RMSE=1.0 m, RRMSE=0.15). The study reveals that integrating active and passive satellite data has great potential for analyzing hydrological processes in polar ice sheets.

In recent years, geological hazard identification based on integrated remote sensing has been widely carried out, featuring wide surveyed areas, high time pressure, and heavy tasks. To meet the demand for the effective utilization of multi-source remote sensing data, multi-person collaboration, and quick result integration, along with the requirements of geological hazard identification tools, this study established a 3D geological hazard identification information platform. This platform, adopting a C/S architecture, allows for the effective organization and management of multi-source optical and radar remote sensing data, vector data, and 3D models and possesses functions such as the loading of multi-source data, multi-person collaboration, quick interpretations and identification, and result expression and output. This platform has successively supported fine-scale identification of hidden hazards and multiple emergency security efforts in national key areas with high geologic hazard susceptibility, such as Gansu, Yunnan, and Sichuan provinces. The application results indicate that this platform allows for rapid data supply and assists in improving work efficiency.