Soil moisture is closely associated with global climate change, the carbon cycle, and the water cycle, as well as agricultural production and ecological conservation and restoration. The detection of soil moisture has shifted from ground survey to remote sensing detection, achieving global- and regional-scale survey and monitoring. Given differences in data spectrum segments, radiative transfer mechanisms, and inversion algorithms, it is necessary to comprehensively analyze the mechanisms, advantages, and limitations of algorithms, with the purpose of laying a foundation for accuracy and algorithm improvement. From the aspects of optical remote sensing, microwave remote sensing, and optic-microwave cooperation, this study systematically analyzed the features and challenges of the following inversion techniques: inversion based on the T_s-VI spatial and T_s-NSSR temporal characteristics of optical remote sensing data, inversion using passive and active microwave data, joint inversion using active and passive microwave data and remote sensing data, and optical-microwave cooperative inversion based on accuracy improvement and spatio-temporal transformation. At present, the joint inversion of soil moisture using multi-source remote sensing data faces the following challenges: ① The data suffer missing and spatio-temporal mismatching; ② Different data sources exhibit varying degrees of surface penetration; ③ The joint inversion model relies on empirical parameters and numerous auxiliary parameters. These challenges can be addressed with the improvement in the satellite monitoring network, the increase in the surface detection depths of data sources, the clarification of the physical mechanisms of joint inversion, and the establishment of spatio-temporal continuous datasets of auxiliary parameters.

High-intensity coal mining leads to significant surface deformation and secondary geological disasters. Synthetic aperture Radar interferometry (InSAR), exhibiting high deformation monitoring capability, fails to detect enough target pixels in the mining core and surrounding low-coherence areas. This study intends to increase the density and coverage of deformation monitoring points in mining areas by combining distributed targets (DTs) and slowly-decorrelating filtered phase targets (SDFPTs). First, DT and SDFPT candidate pixels were selected using the fast statistically homogenous pixel selection (FaSHPS) method and the amplitude dispersion index method, respectively for phase optimization and stability analysis. Then, qualified DT and SDFPT pixels were screened out to constitute a fused pixel set, which was subjected to three-dimensional phase unwrapping, phase time series recovery, and spatio-temporal filtering. Consequently, the deformation time series and the annual average deformation rate were determined based on the fused pixel set. Finally, the method proposed in this study was applied to monitor the deformation in the Buertai coal mine using 60 scenes of Sentinel-1 images covering the coal mine from April 2018 to April 2020. The results reveal a significant increase in the density and coverage of deformation points through the integration of DT and SDFPT, thus allowing for the monitoring of higher levels of maximum deformation. Within the experimental area, five deformation cones were identified, with the maximum cumulative deformation amplitude reaching -309.76 mm. The influencing range of the deformations and the difference in the deformation amplitude of the time series in different years are closely related to mining activities.

Earthquake-induced surface deformations are characterized by large scales and extensive coverage, and the resultant secondary geological disasters significantly impact local infrastructure and engineering construction. Investigating the surface deformations caused by the Menyuan earthquake is critical for understanding the seismic deformation movement and identifying potential geological disasters. This study obtained the coseismic deformation field of the Menyuan earthquake using the differential interferometric synthetic aperture Radar (D-InSAR) technique. Based on the geometric relationships between the ascending descending passes, this study extracted the two-dimensional information of surface deformations induced by the Menyuan earthquake. The results show that the coseismic deformations occurred primarily at the intersection of Lenglongling and Tuolaishan faults. The line-of-sight (LOS) surface deformations from ascending and descending passes exhibited uplift of 0.40 m and 0.80 m and subsidence of -0.65 m and -0.70 m, respectively. As indicated by the analysis of two-dimensional deformation based on the ascending and descending LOS surface deformation results, the maximum amplitude of vertical deformations dominated by subsidence was -0.32 m and the maximum amplitude of horizontal deformation dominated by eastward motion was 0.87 m, suggesting significant horizontal seismic deformations and fault activity dominated by left-lateral strike-slip process. Based on the 21 scenes of Sentinel-1A SAR images covering the study area taken from the ascending pass, this study extracted the information on the surface deformations after the Mengyuan earthquake using the small baseline subset-interferometric synthetic aperture Radar (SBAS-InSAR) technique, determining the LOS time series and average deformation rates. The results show that from January 17, 2022 to September 26, 2022, the study area experienced relatively stable overall deformations and significant local deformations. The fault activity was identified as the primary factor affecting the surface deformations, with a maximum average deformation rate of 53 mm/a and a maximum deformation amplitude of 77 mm. The results of this study will provide technical support for earthquake disaster mitigation, emergency management, and sustainable socio-economic development.

This study aims to lay the scientific foundation for regional disaster prediction, prevention, and control, as well as urban planning, by analyzing the spatio-temporal distribution, evolutionary patterns, and driving factors of land subsidence in the Qinfang area, Guangxi Province, China. Using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique, this study extracted information on land subsidence in the study area during 2018—2021 from 45 scenes of Sentinel-1A SAR images. By combining the geological setting, precipitation, land use, and road data and using methods such as GIS spatial analysis, mathematical statistics, remote sensing image classification, and change detection, this study conducted visual and quantitative analyses of the overall characteristics, spatio-temporal evolutionary trends, and influencing factors of land subsidence in the study area. The results show that: ① In the spatial dimension, the ground deformations, at rates ranging from -114.37 to 58.55 mm/a within the study area, exhibited extensive but significantly nonuniform distributions during 2018—2021. Consequently, three primary subsidence centers emerged in the central and southern urban areas of Qinnan District, Qinzhou Port, and the port area, with subsidence areas expanding southward annually; ② In the temporal dimension, the subsidence centers displayed an overall uneven subsidence trend over time. Besides, they exhibited periodic rebounds, with a maximum rebound amplitude of 18.4 mm; ③ In terms of influencing factors, primary factors causing land subsidence in the study area included urbanization, road density, tectonic movement, stratigraphy, precipitation, and sea level rise, which play a predominant role in the expansion and intensification of land subsidence.

The Beijing Daxing International Airport, located in the Yufa—Lixian area of Daxing District, is one of Beijing’s five major land subsidence areas. Differential deformations pose risks to the airport’s safe and stable operation. By applying the time-series interferometric synthetic aperture Radar (InSAR) technique, this study obtained the spatio-temporal characteristics of the airport’s deformations from 39 scenes of high-resolution COSMO-SkyMed (CSK) SAR images taken from September 2019 to November 2021. The monitoring results, with high accuracy, are roughly consistent with level monitoring results. Findings indicate that the airport’s subsidence lasted from 2019 to 2021, with the highest subsidence rate measured at -47.5 mm/a and a maximum cumulative subsidence amount of -103.84 mm. Notably, all four runways exhibited varying degrees of differential subsidence. Furthermore, this study delved into the spatio-temporal characteristics of deformations in the runways, as well as deformations in other high-deformation zones such as terminal buildings, maintenance aprons, oil tank areas, and the business jet apron. By combining the foundation treatment, this study analyzed the factors influencing the airport’s subsidence, providing a reference for the airport’s safe and stable operation.

In high-resolution remote sensing images, missing areas feature intricate surface features and pronounced spatial heterogeneity, causing the image inpainting results to suffer texture blurring and structural distortion, particularly for boundaries and areas with complex textures. This study proposed a two-stage remote sensing image inpainting network combined with spatial semantic attention (SSA). The network comprised two networks in series: one for coarse image inpainting and one for fine-scale image inpainting (also referred to as the coarse and fine-scale networks, respectively). This network was designed to guide the fine-scale network to restore the missing areas using the priori information provided by the coarse network. In the coarse network, a multi-level loss structure was constructed to enhance the stability of network training. In the fine-scale network, a novel SSA mechanism was proposed, with SSA being embedded differentially in the encoder and decoder based on the distribution of network features. This ensured the continuity of local features and the correlation of global semantic information. The experimental results show that the network proposed in this study can further improve the image inpainting effects compared to other existing algorithms.

Based on data from multiple sources including Landsat8 OLI_TIRS remote sensing images, digital elevation model (DEM), road networks, and water system, as well as the corrected energy factor model and gravity model, this study improved the method for ecological network construction based on morphological spatial pattern analysis (MSPA) and the minimum cumulative resistance (MCR) model. Using the method, this study constructed and optimized a hierarchical ecological network for Zhongwei City, Ningxia. The results show that: ① The identification of ecological source areas in arid regions should be conducted using the data of woodland, grassland, and water bodies as foreground data and woodland and selecting ecological source areas based on ecological redlines; ② The ecological network of Zhongwei City presents a spatial structure mode characterized by four cores, three first-level ecological corridors, and multiple minor ecological source areas. Seventeen ecological source areas were identified, accounting for 22.33% of the study area. Among them, four grade-1 and -2 source areas exhibited significantly high energy factors, forming four cores. A total of 33 potential ecological corridors were identified, including three grade-1 ecological corridors; ③ Strategies for optimizing the ecological network, including improving ecological source areas’ quality, enhancing corridors, and restoring ecological breaking points. Except for the stable corridors connecting Nos. 1, 7, and 9 source areas, other source areas manifest poor connectivity, leading to low ecological network stability. Therefore, it is necessary to establish 24 ecological stepping stones. Furthermore, 38 ecological breaking points requiring urgent restoration were discovered; (4) The optimized ecological network demonstrates enhanced stability, with α, β, and γ indices elevated at 9.5%, 3.8%, and 4.2%, respectively. This network will promote the flow of ecological materials/information and biodiversity conservation.

The accurate acquisition of land cover/use changes and their types is critical to territorial space planning, ecological environment monitoring, and disaster assessment. However, most current studies on the change detection focus on binary change detection. This study proposed a multi-class change detection method using a multi-task Siamese network of remote sensing images. First, an object-oriented unsupervised change detection method was employed to select areas that were most/least prone to change in the new and old temporal images. These areas were used as samples for the multi-task Siamese network. Subsequently, the multi-task Siamese network model was used to learn and predict the new and old temporal land-use maps and binary change maps. Finally, the final multi-class change detection results were derived from these maps. The multi-task Siamese network was tested based on the images from the Third National Land Survey and corresponding land-use maps. The results demonstrate that the method proposed in this study is applicable to the change detection cases where changed and unchanged samples lack but there are available historical thematic maps.

The integration of multi-source remote sensing images and multi-feature parameters is effective in the accurate identification of target ground objects. However, excess feature parameters can cause data redundancy, reducing classification accuracy. Focusing on a sugarcane planting area with Karst landforms, this study extracted the spectral, index, texture, topographic, and polarization features of the ground objects in the study area from Sentinel-1/2 images and SRTM digital elevation data. The index features involved the red edge index calculated based on the red-edge band, which was scarce in data derived from remote sensing sensors, and the texture features included the Radar image textures. In the experiment, six schemes were designed to explore the effects of different image features and the random forest-based optimal feature association on sugarcane information extraction. The results show that for the classification of ground objects in the study area using spectral features combined with other feature types, the importance of the feature types ranked in descending order of spectral features, index features, texture features, topographic features, and polarization features. Among the six schemes, the scheme based on the random forest algorithm, integrating different feature variables, yielded the optimal information extraction effect for sugarcane, with both user and producer accuracy higher than 97%, overall accuracy of 95.49%, and a Kappa coefficient of 0.94.

Accurate mangrove forest distribution information is critical to the conservation and management of mangrove forests. Despite extensive studies on the remote sensing mapping of mangrove forests, it is necessary to improve their mapping accuracy by effectively utilizing multi-source remote sensing features. First, this study designed 15 feature associations using temporal features, including spectral, scattering, texture, and terrain features, which were extracted from multi-source remote sensing data. Then, using a random forest model, it analyzed the accuracy of different feature associations in mangrove forest identification, obtaining the optimal feature association. Finally, this study mapped the 10-m-resolution mangrove forest distribution of Guangdong Province in 2021 based on platform Google Earth Engine (GEE). The results show that spectral features in winter exhibited the highest importance, with richer feature types corresponding to higher mapping accuracy. The optimal feature association yielded overall accuracy of 92.25% and a Kappa value of 0.91. Overall, this study extracted information on mangrove forests in Guangdong based on multi-feature parameters and the optimal feature association. The results of this study will provide a scientific reference for accurate mapping of mangrove forests on a large scale.

Tailings ponds are considerable hazard sources with high potential energy. Ascertaining the number and distribution of tailings ponds in a timely manner through rapid identification and monitoring of their spatial extents is critical for the environmental supervision and governance of tailings ponds in China. Due to the lack of pertinence for potential targets, identifying tailings ponds based on solely remote sensing images is prone to produce confusion between tailings ponds and exposed surfaces, resulting in significant errors in practical applications. This study proposed an extraction method for tailings ponds, which integrated enterprise directory, multi-source geographic data (e.g., data from spatial distribution points, digital elevation model (DEM), and road networks), and high-resolution remote sensing images. The application of this method in Gejiu City, Yunnan Province indicates that integrating multi-source geographic data can effectively exclude the interferential areas without tailings ponds, with the precision and recall rates of the extraction results reaching 83.9% and 72.4%, respectively. The method proposed in this study boasts significant application prospects in high-frequency and automated monitoring of tailings ponds nationwide.

Every summer, the surface melting on the Greenland Ice Sheet (GrIS) results in a large amount of surface meltwater, which is transported via supraglacial rivers and stored supraglacial lakes and water-filled crevasses, forming a large-scale and complex hydrologic system. However, there is a lack of studies on the spatial distribution of surface meltwater on the GrIS. This study extracted the surface meltwater information of the GrIS during the peak melting period in 2019 using 134 scenes of 10-m-resolution Sentinel-2 satellite images. Furthermore, we compared the surface meltwater distribution derived from the remote sensing observation and the surface meltwater runoff simulated by the regional atmospheric climate model (RACMO). The results show that: ① During the peak melting period in 2019, the GrIS exhibited a surface meltwater area of 9 900.9 km² and a surface meltwater volume of 6.8 km³; ② The GrIS surface meltwater exhibited a significantly varying spatial distribution characterized by high volumes in the western and northern basins and low volumes in the eastern and southern basins; ③ The surface meltwater on the GrIS was primarily composed of supraglacial rivers, which accounted for 57.1% of the overall surface meltwater volume, followed by water-filled crevasses (25.6%) and supraglacial lakes (17.3%); ④ RACMO accurately simulated the surface meltwater runoff regions in most GrIS basins. This study enhanced the understanding of key hydrologic processes such as surface meltwater routing and storage, demonstrating the high application potential of high-resolution remote sensing images in the hydrologic research of the GrIS.

Identifying forest tree species can provide a valuable scientific reference for ascertaining forest resources. However, it is difficult to achieve accurate tree species classification even using hyperspectral data with high spatial resolution. Hence, there is an urgent need to meet this challenge. This study investigated the Genhe Forest Reserve in the Great Xing’an Range within Inner Mongolia. At spatial resolutions of 1 m and 3 m, two sample value scales were employed: sample points (i.e., the spectral values of pixels corresponding to sample plots) and sample planes (i.e., the average spectral values of pixels in a 3×3 window corresponding to sample plots). Then, this study explored the identification effects of predominant tree species using airborne hyperspectral images based on three machine learning algorithms: neural network (NN), three-dimensional convolution neural network (3DCNN), and support vector machine (SVM). Key findings include: ① Regardless of spatial resolution and sample value scales, the 3DCNN exhibited the highest classification accuracy, yielding the highest overall accuracy and Kappa coefficient of 95.42% and 0.94, respectively; ② Compared to a low spatial resolution (3 m), a high spatial resolution was more favorable to the identification of predominant tree species, with overall accuracy and Kappa coefficient increased by 30.97% and 54.24% at most, respectively; ③ In the case of NN/SVM-based classification, sample points outperformed sample planes in improving the accuracy of tree species identification. In contrast, sample planes outperformed sample points for 3DCNN-based classification at a spatial resolution of 3 m. Overall, spatial resolution, sample value scales, and classification algorithms manifested varying degrees of effects on the identification accuracy of predominant tree species. High-spatial-resolution images, small-sample data, and deep-learning algorithms can be combined to enhance the accuracy of predominant tree species identification using airborne hyperspectral images.

Zayu County, located in the southeastern portion of the Qinghai-Tibet Plateau, is characterized by a large area, significantly varying topographic and climatic characteristics, and frequent landslides. The hazard identification and early warning of landslides are critical to disaster prevention and mitigation in the county. Based on the data acquired from January 2020 to November 2022, including 162 scenes of Sentinel-1A Radar remote sensing images taken on ascending and descending passes and high-resolution optical remote sensing images, this study conducted hazard identification, cataloging, mapping, analysis, and assessment of active landslides in Zayu County using the integrated remote sensing (IRS) technique on the Google Earth platform. A total of 237 active landslide hazards were identified primarily along the Gongrigabuqu River (the western tributary of the Zayu River), Zayu River, both sides of the Nujiang River, and the eastern Zayu River to the western Nujiang River. As revealed by the statistical analysis of the interpretation results combined with quantitative factors such as topography (elevation, slope, lithology) and natural environment (rainfall, temperature), the landslides in Zuobu and Azha villages pose high disaster risks, necessitating further mitigation measures. With relatively accurate results, this study can serve as a reference for disaster prevention and mitigation in Zayu County.

This study aims to explore the origin of the excellent ecology in Fujian Province in the past 15 years. First, a land use intensity system with a five-year time interval was constructed using the 2005—2020 MODIS images and land use data of Fujian as data sources. Then, the coupling relationship between the remote sensing ecological index (RSEI) and land use intensity was analyzed based on a coupled coordination model. Finally, the spatio-temporal evolution analysis was conducted for the ecological health of Fujian from 2005 to 2020. The results show that: ① The ecological environment of Fujian manifested an improvement-degradation-degradation trend, with an average RSEI value of 0.704 8 in 2020, suggesting a sound ecological environment; ② The land use intensity of Fujian displayed an increasing trend, with a growth rate of 26.00%. Most especially, Sanming City demonstrated a maximum increase of 160.91% in land use intensity; ③ The coupled coordination degree of Fujian increased by 0.729 0, suggesting high coordination. All cities in Fujian exhibited increased coupled coordination degrees, except for Xiamen City, where the coupled coordination degree decreased by 0.131 0, implying a slight imbalance. This study fills the gap in the research on the interactions between ecosystem health and land use intensity. It also provides a new perspective for ecological civilization construction and ecosystem health assessment in Fujian and even China.

The validation and analysis of latent heat flux products are critical for research on climate change and energy circulation. High-resolution global land surface satellite evapotranspiration (Hi-GLASS ET) products, which integrate five traditional evapotranspiration algorithms, can produce high-precision products for land surface latent heat flux. However, these products are yet to be validated. This study obtained multiple sets of valid validation data by comparing the latent heat flux observed values from Ameriflux flux observation sites with the corresponding estimated values of Hi-GLASS land surface latent heat flux products. The validation results yielded a squared correlation coefficient (R²) of 0.6, a root mean square error (RMSE) of 34.4 W/m², an average bias of -13.4 W/m², and Kling-Gupta efficiency (KGE) of 0.49. These suggest that Hi-GLASS latent heat flux products boast high precision and that their algorithms enjoy satisfactory fitting results. In addition, spatial distributions imply that Hi-GLASS latent heat flux products conform to normal natural laws. Due to data acquisition limitations, the validation of this study was conducted based on data from only 18 sites in the U.S., and further validation using data from other areas is required.

Conventional remote sensing monitoring techniques, constrained by data availability and computational capacity, often fall short of the research requirements of extensive landslide disaster monitoring. This study established a dynamic assessment model for landslide hazards in the Three Gorges Reservoir area based on cloud computing platform Google Earth Engine (GEE), achieving dynamic assessment of landslide hazards in the area under the support of the massive data storage and robust computational capabilities of GEE. First, based on factors such as slope, slope aspect, normalized difference vegetation index (NDVI), normalized differential water index (NDWI), and geological structures, a landslide susceptibility zone map was established using a weighted gradient boosting decision tree (WGBDT) model. Then, the rainfall threshold inducing landslides in the Three Gorges Reservoir area was determined based on the Global Precipitation Measurement (GPM) data from the National Aeronautics and Space Administration (NASA). Subsequently, the rainfall classification criteria and a landslide hazard assessment model were established by combining rainfall and landslide susceptibility. Finally, focusing on the rainfall on August 31 in the Three Gorges Reservoir area, the daily distribution maps of landslide hazards in the Three Gorges Reservoir area were plotted, yielding the spatio-temporal variation trend of landslide hazards. In sum, the data processing and analysis tools of GEE allow for the analysis of landslide-related data of the Three Gorges Reservoir area, thus providing nearly real-time monitoring and early warning information for landslide hazards and offering a basis for the formulation of disaster prevention and mitigation policies.

The urban heat island effect is closely linked with the well-being of urban residents. Rapid urbanization has further accelerated urban expansion. This is accompanied by an increasingly significant urban heat island effect, especially in cities of central and western China in the past 20 years. To explore the relationship between the expansion of cities and urban agglomerations and the changes in the urban heat island effect, this study analyzed the expansion and spatial form variation of cities in the Changsha-Zhuzhou-Xiangtan urban agglomeration using the Boyce-Clark shape index. The land surface temperatures were derived through inversion using the practical single-channel algorithm based on the Google Earth Engine (GEE) platform. The temperature zones with different grades were determined using the mean-standard deviation method, followed by the definition and extraction of the range of the urban heat island effect. The urban center and heat island center were extracted, and the variation trends of the relationship between urban expansion and urban heat island effect were analyzed using the center shift method. The results show that the changes in the urban heat island effect were consistent with the expansion of the urban agglomeration and its cities. The results lead to the following conclusions: ① After 2015, the Changsha-Zhuzhou-Xiangtan urban agglomeration entered a critical period of rapid development; ② Urban expansion is the primary cause of the increase in the area of urban heat island effect; ③ The urban heat island center roughly shares the same variation trend with the urban center, and the urban heat island range increases in the direction roughly consistent with the urban expansion direction.

A vital task for the current economic construction of China is to consolidate poverty alleviation achievements, implement rural revitalization strategies, and prevent poverty relapse. The 2022 Central Rural Work Conference emphasized that China must firmly prevent any large-scale relapse into poverty. The multidimensional poverty theory underlines the significant role of the dynamic and objective monitoring of the spatio-temporal evolution of rural poverty at the county level in China’s poverty-returning prevention. Rapid progress in remote sensing satellites has enabled the gradual enrichment and extensive application of high-quality remote sensing images that contain massive information. Compared to traditional statistical data, the remote sensing data of nighttime light exhibit a high correlation with socio-economic factors, pronounced objectivity, and a relatively long time span. This study constructed a model to describe the relationship between the nighttime light intensity index extracted from the DMSP/OLS and NPP/VIIRS remote sensing data, as well as the multidimensional poverty alleviation index. Using this model, this study explored the spatio-temporal evolution of rural poverty at the county level in Guangxi from 2010 to 2020 and analyzed the existing poverty-returning causes, providing a scientific reference and preventive measures for rural revitalization and poverty alleviation.

Since the Dongting Lake basin is a significant ecological zone in the middle and lower reaches of the Yangtze River, quantitative monitoring and evaluation of its ecological environment serve as a prerequisite for regional ecological conservation, restoration, and governance. Using MODIS products involving 2010—2019 remote sensing data, this study constructed the remote sensing ecological index (RSEI) for the Dongting Lake basin based on four ecological indices: greenness, humidity, dryness, and heat. Furthermore, this study explored the spatio-temporal dynamic characteristics of the ecological environment in the basin and their influencing factors. The results show that: ① From 2010 to 2019, the Dongting Lake basin exhibited an elevated greenness index, a reduced humidity index, and relatively stable dryness and heat indices; ② The ecological environment of the Dongting Lake basin was generally satisfactory, with a mean annual RSEI of 0.58, indicating a fluctuating growth. In terms of spatial distribution, the ecological environment in the western and surrounding areas was superior to that in the eastern and central areas; ③ There were strong correlations between RSEI and precipitation, air temperature, elevation, and land cover. The RSEI was the highest (0.65) for forest land and the lowest (0.31) for construction land. As for the two primary land conversion types (grassland → forest land, arable land → grassland) in the basin, the former type could improve the regional ecological environment (ΔRSEI=0.002 5, a contribution rate of 46.3%), whereas the latter type might lead to ecological environment deterioration (ΔRSEI=-0.000 4, contribution rate: 44.44%). The results of this study, assisting in deeply understanding the spatio-temporal characteristics of the ecological environment in the basin and their internal driving mechanisms and facilitating scientific land planning and ecological environment governance, hold critical theoretical and practical significance.

Discerning the spatial pattern and driving mechanism of urban expansion will contribute to the sustainable development of the Central Plains urban agglomeration (CPUA). Based on the DMSP/OLS and NPP/VIIRS nighttime light images, this study extracted the built-up area of the CPUA from 1993 to 2018 through statistical data comparison. Furthermore, this study delved into the spatial-temporal evolutionary characteristics of the urban expansion in this period on scales of the entire urban agglomeration and prefecture-level cities. Accordingly, this study investigated the driving force behind the spatial-temporal expansion using a driving force model. The results show that: ① In terms of the spatial evolution, with Zhengzhou as the center and the northeast to southwest as the reference direction, the built-up areas and expansion scales of cities in the CPUA were generally large in the central part but small on both sides. With 2010 as the point of division, the expansion type shifted from edge expansion to exclave expansion, and the expansion mode transitioned from planar expansion to multi-center dotted expansion and linear expansion along main traffic routes; ② Regarding the temporal evolution, different cities exhibited significantly distinct expansion area, speed, and intensity. The expansion speed and intensity were both positive, roughly manifesting W-shaped fluctuations. The center of the built-up areas shifted from southwest to northeast, then northeast, then west, then northwest, and finally southeast, wandering between Zhengzhou and Kaifeng cities; ③ The main driving force behind the urban expansion resulting from economic factors, followed by social, transportation, and environmental factors. The top five driving force indicators affecting the urban expansion comprised general public budget revenue, GDP, actually utilized foreign capital, education expenditure, and population density.

Land surface albedo (LSA) directly affects the radiation balance and surface energy balance of the earth-atmosphere system. Stable forest land exhibits integrated ecological vegetation, a relatively stable regional microclimate, and an intricate relationship with LSA. Based on the MODIS LSA (MCD43A3), enhanced vegetation index (EVI,MOD13Q1), land use (MOD12Q1), soil moisture, air temperature, and precipitation data, this study investigated the spatio-temporal variations in LSA of stable forest land in southeastern Guizhou Province, as well as their correlation with various factors and driving factors, through Theil-Sen (T-S)/Mann-Kendall (M-K) trend analysis, correlation analysis, and multiple regression analysis. The results show that: ① The stable forest land exhibited LSAs varying between 0.102~0.112, 0.110~0.113, and 0.099~0.102, respectively in the interannual period, growing season, and dormant season. These suggest an overall stable trend and a spatial distribution pattern characterized by low values in the central portion and high values in surrounding areas; ② The LSA was significantly negatively correlated with soil moisture in the inter-annual period and the growing season, with correlation coefficients of -0.951 and -0.943, respectively. In the dormant season, the LSA was significantly positively correlated with EVI, with a correlation coefficient of 0.933; ③ The LSA was subjected to the negative driving by EVI and air temperature and positive driving by soil moisture in the interannual period, growing season, and dormant season, with standardized coefficients of -9.168, -11.332, and 1.319, respectively. The results of this study can assist in accurately understanding the driving mechanism behind the LSA of stable forest land in southeastern Guizhou Province, thereby providing a reference for studying the climate change of forest land in small areas at low latitudes.

Forest stock volume (FSV), a critical indicator in forestry surveys, plays a significant role in evaluating the health and carbon sequestration capacity of forests. Cooperative inversion using active and passive remote sensing data is an essential method for FSV inversion of large areas. Focusing on forests in Shangri-La, Yunnan Province, this study extracted feature variables from ICESat-2/ATLAS and Sentinel-2A images and then screened them through correlation analysis and collinearity diagnostics. Using the selected feature variables, this study constructed a Sentinel-2A variable set, an ICESat-2/ATLAS variable set, and a combined variable set. Based on the measured data of sample sites and the three feature variable sets, this study built linear and nonlinear regression models for FSV inversion using stepwise linear regression and the random forest method, respectively. Finally, this study performed accuracy verification and comparative analysis of the results: ① For the three variable sets, the random forest method yielded higher accuracy than the stepwise linear regression; ② The ICESat-2/ATLAS variable set exhibited higher inversion accuracy than the Sentinel-2A variable set under both regression methods; ③ Combining Sentinel-2A and ICESat-2/ATLAS variable sets, the random forest method yielded the highest inversion accuracy, with its coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE) of 0.7034, 84.78 m³/hm², and 36.46%, respectively. Overall, compared to Sentinel-2A data, the inversion models based on ICESat-2/ATLAS data and multi-source remote sensing data can effectively improve the accuracy of FSV inversion and model stability.

The Delong area, located in the eastern segment of the Eastern Kunlun gold-polymetallic metallogenic belt in Qinghai, is recognized as an area with significant exploration potential. However, its remote geographical location and rugged terrain pose challenges to large-scale geochemical explorations and conventional geological surveys. Based on ASTER and GF-2 data, this study identified the linear and circular structures by analyzing the color tones, geometric structures, and textures of remote sensing images with varying resolutions. Through the spectral analysis of primary alteration minerals, this study extracted the information on ferrugination, Al-OH, and Mg-OH alterations from visible light to near-infrared bands and short-wave infrared bands of ASTER using the mask + principal component analysis technique. Then, this study established a remote sensing-based prospecting prediction model for gold deposits in the study area by combining geoscience information and field survey results and comprehensively analyzing the association between the tectonic alteration information derived from remote sensing image interpretation and the gold mineralization of the study area. Using the prediction model, this study delineated three prospective areas for mineral exploration. The field verification revealed several new gold ore bodies in the Delong prospective area. The results show that the integration of remote sensing data and GIS technology can effectively identify surface hydrothermal alterations and tectonic spatial structures. The integration can serve as a guide for subsequent prospecting prediction of the study area.

This study aims to provide a guide for the optimal management of land use and ecological environment in the Bayin River basin or similar areas by revealing the comprehensive evolution characteristics of the ecological environment in the Bayin River basin. Based on the 12 scenes of remote sensing image data from 2005 to 2020, this study quantitatively explored the critical factors influencing the ecological environment in the study area using a geographical detector. By combining the model for integrated valuation of ecosystem services and trade-offs (InVEST), this study established an ecological environment quality assessment model through statistical analysis, overlay analysis, and analytic hierarchy process, revealing the comprehensive evolutionary characteristics of the ecological environment in the basin. The results show that: ① The critical factors influencing the ecological environment quality in the basin included population size, GDP, elevation, and rainfall. The comprehensive assessment value of the ecological environmental quality in the basin increased from 0.455 to 0.533, suggesting an overall upward trend; ② The ecological environmental quality in the basin exhibited significant regional differences. Specifically, 14.9% of the basin manifested degraded ecological environmental quality, primarily distributed in the vicinity of the Bayin River basin and the surrounding area of Delingha City. In contrast, 33.6% displayed improved ecological environmental quality, spreading in areas to the south of lakes in the middle and lower reaches of the Bayin River basin. This study indicates that the future ecological environment protection and planning in the Bayin River basin should focus on the balance between agricultural land and other ecological and construction land during urbanization, thereby achieving coordinated development of economy and ecology through scientific planning of spatial framework.

With continuously accelerated urbanization, urban expansion-induced farmland occupation and rural hollowing have gradually aggravated arable land abandonment, posing challenges to China’s food security. Hence, accurately determining the distribution of abandoned arable land is critical to arable land protection and food security. This study investigated a county in the major crop production area in a hilly region in southern China. Based on the phenological characteristics of local rice planting, this study adopted six phases of satellite remote sensing images and aerial images acquired in 2020 and 2021 were used as the data source and selected paddy fields determined based on the Third National Land Survey and field ridges in 1∶2 000 digital line graphics (DLGs) as the minimum information extraction unit. Then, the rice planting patches were extracted through time-series normalized difference vegetation index (NDVI) analysis and the improved SVM method. Suspected abandoned areas were screened out through the difference calculation for two consecutive years and then further identified in the unmanned aerial vehicle (UAV)-based sampling aerial survey. Consequently, the abandoned arable land areas were determined, with monitoring accuracy exceeding 85%, as revealed by on-site verification. The results of this study show that the space-air-ground integrated remote sensing monitoring method can provide scientific and effective data support for agricultural management departments to control and manage cultivated land abandonment.

Sea ice disasters, significant marine disasters in the Bohai Sea in winter, severely threaten oil exploitation, marine transportation, and fishery. Hence, it is particularly critical to monitor and predict the formation and melting of sea ice. Based on MODIS data, reanalysis grid data, and meteorological observation data from 2001 to 2020, this study derived the daily sea ice area of the Bohai Sea through the inversion of satellite data. Then, this study conducted a statistical analysis of changes in the sea ice area, including inter-annual variations and the variations during the freezing, severe ice, and melting periods in different years with sea ice disasters. Moreover, this study delved into the differences in the atmospheric circulation of the Bohai Sea between years with severe and mild sea ice disasters. The results show that: ① Over the past 20 years, higher sea ice grades in the Bohai Sea corresponded to longer ice periods, and the sea ice area experienced a decrease-increase-decrease process, which was opposite to the changes in accumulated temperature; ② Intra-annual sea ice formation and melting processes exhibited a single peak or multiple peaks, with the multi-peak type corresponding to a longer ice period and large-scale ice-bound time; ③ During the initial ice formation period, the sea ice in the Bohai Sea was primarily distributed in the Liaodong Bay, with the sea ice area in a year with severe sea ice disasters more than twice that in a year with mild sea ice disasters. During the ice period, the sea ice covered almost all three bays, completely covering the three bays in years with severe sea ice disasters. During the ice melting period, the sea ice still spread primarily in the Liaodong Bay, manifesting an EN-WS-directed distribution; ④ Compared to years with mild sea ice disasters, years with severe sea ice disasters showed more favorable atmospheric circulation for sea ice formation, accompanied by stronger and cooler cold air behind the upper-level trough along the area from Lake Baikal to Northeastern China. Furthermore, there is a strong negative correlation between the sea ice situation in the Bohai Sea and the 500 hPa geopotential height, with the latter determining the former.

Quantitative post-flood recovery assessment based on vegetation and lighting indices is critical for assessing economic reconstruction and ecological restoration in afflicted areas. This study investigated the “7.20” rainstorm disaster area in Henan. Based on the daily and monthly NPP-VIIRS data, Sentinel-NDVI and MODIS-EVI data, and statistical yearbook data, this study characterized the spatial intricacies within urban areas by constructing a normalized difference urban index (NDUI). Then, it simulated the population and GDP distributions by employing a regression model. Finally, this study assessed the post-flood recovery from two distinct aspects: nighttime light data and vegetation cover data. The results are as follows: ① High- and medium-risk zones covered an area of 1 429.04 km², accounting for 6.06% of the total study area. High-risk zones were primarily distributed in western Zhengzhou, eastern Xinxiang, eastern Anyang, and northern Hebi, with Zhengzhou suffering the most severe impact; ② In terms of the vegetation cover recovery rate (VCRR), low overall vegetation recovery was observed in Weihui and Linzhou cities and Qixian and Huaxian counties, with VCRRs mostly below 0. This indicates a deteriorating vegetation cover trend; ③ The fitting between NDUI and socio-economic statistical data yielded accuracy exceeding 0.8, suggesting that the NDUI can be applied to precise location-based rescue and targeted post-disaster reconstruction in the aftermath of floods. Additionally, the assessment results based on NPP-VIIRS and MODIS-EVI data were highly complementary, implying that the flood research based on the integration of the two types of data enjoys high application value for post-disaster rescue and recovery assessment.

China boasts long shorelines and abundant marine resources. Xiamen, a coastal city in China, has seen significant changes in its shorelines in recent years due to an increase in activities including port construction, coastal industry, and coastal tourism development. Hence, efficiently and accurately determining the spatio-temporal changes in Xiamen's shoreline holds critical research value and practical significance. This study extracted information on the shorelines from the Landsat remote sensing images in 2005, 2010, 2015, and 2020 using the object-oriented method. Then, it delved into the spatio-temporal evolution of shoreline length, shoreline morphology, and land area changes. The results reveal significant spatio-temporal changes in Xiamen’s shoreline over the 15 years. The shoreline length changed quickly and then moderately, peaking from 2010 to 2015, with an average rate as high as 4.1 km/a, primarily in the vicinity of Dadeng Island and Haixiang Pier. The shoreline morphology tended to be flattened year by year, with the most pronounced changes observed in Huli District. From 2005 to 2020, the land area of Xiamen increased approximately 24.5 km², with the most significant increase occurring in Xiang’an District. The changes in the shoreline were influenced by many factors, predominantly including population growth, economic development, and the introduction and change of policies.

To investigate the anthropogenic influence on air pollution before and after the COVID-19 pandemic, this study analyzed the variations and causes of air pollutants in Tangshan City from 2019 to 2021. It derived the changes in their concentrations and their influencing factors in each time period using an estimation model for emission reduction effects and an inverse distance interpolation method. Accordingly, this study proposed reasonable suggestions for the atmospheric environment control in Tangshan. The results show that: ① From 2019 to 2021, the proportions of primary pollutants in Tangshan remained relatively stable, with PM_2.5 ranking first and photochemical pollution represented by O₃ increasing significantly in recent years; ② The results calculated using the formula for anthropogenic emission reduction show that the control over anthropogenic emissions plays a significant role in constraining air pollution; ③ The analysis based on the inverse distance interpolation method indicates that the peak concentration of PM_2.5 gradually decreased, with the high-concentration distribution shifting toward the city center.