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Vegetation is an important type of land cover. Long-term, large-scale, and high-precision vegetation monitoring is of great significance for ecological environment investigation and regional sustainable development in protected areas. This paper develops a long-term remote sensing monitoring method for vegetation by calculating the normalized difference vegetation index (NDVI) based on the Google Earth Engine (GEE) cloud platform and Landsat satellite remote sensing images. First, based on Landsat long-term satellite images and GEE, the spatiotemporal distribution map of the NDVI is accurately drawn. Subsequently, the NDVI is accurately classified, and the time trend analysis of the NDVI is conducted based on the NDVI mean trend graphs, transition matrices, etc. Then, combined with Moran's I, high/low clusters, and other methods, the spatial pattern characteristics of the NDVI are analyzed. Finally, climate factors, terrain factors, and anthropologic factors are considered comprehensively. An analysis of the factors affecting the evolution of the NDVI is performed. Taking Zhoushan Island, China, as an example, an experiment is conducted, and the results reveal that (1) the average NDVI exhibits a decreasing trend from 1985 to 2022, decreasing from 0.53 in 1985 to 0.46 in 2022. (2) Regarding vegetation index transitions, the high NDVI areas (0.6–1) exhibit the most substantial shift toward moderately high NDVI values (0.4–0.6), covering an area of 83.10 km2. (3) There is an obvious spatial agglomeration phenomenon in the NDVI on Zhoushan Island. The high-high NDVI clusters and the significant hot spots are predominantly concentrated in the island's interior regions, while the low-low NDVI clusters and the significant cold spots are mainly situated along the coastal areas. (4) The DEM, slope, and temperature have a greater influence among the single factors on the spatial pattern distribution of the NDVI in 2015. There are significant differences in the spatial pattern distribution of the NDVI between the temperature and DEM, temperature and slope, DEM and precipitation, slope and precipitation, aspect and population, and aspect and gross domestic product (GDP). The DEM and slope, DEM and temperature, and DEM and population are three sets of factors with a strong influence on spatial pattern interaction. This study provides data support for the scientific management of vegetation resources on Zhoushan Island and is of great significance to the sustainable development of the island region. [ABSTRACT FROM AUTHOR]

The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 μm, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 μm in northeast China, 2001–2019. The mean value was 441.78 μm, with an annual increase of 0.26 μm/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 μm, followed by January and February with 450.77 μm and 417.78 μm, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 μm to 497.141 μm. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow–ice albedo and their radiative forcing effect. [ABSTRACT FROM AUTHOR]

Landslides pose significant and serious geological threat disasters worldwide, threatening human lives and property; China is particularly susceptible to these disasters. This paper focuses on Pengyang County, which is situated in the Ningxia Hui Autonomous Region of China, an area prone to landslides. This study investigated the application of machine learning techniques for analyzing landslide susceptibility. To construct and validate the model, we initially compiled a landslide inventory comprising 972 historical landslides and an equivalent number of non-landslide sites (Data sourced from the Pengyang County Department of Natural Resources). To ensure an impartial evaluation, both the landslide and non-landslide datasets were randomly divided into two sets using a 70/30 ratio. Next, we extracted 15 landslide conditioning factors, including the slope angle, elevation, profile curvature, plan curvature, slope aspect, TWI (topographic wetness index), TPI (topographic position index), distance to roads and rivers, NDVI (normalized difference vegetation index), rainfall, land use, lithology, SPI (stream power index), and STI (sediment transport index), from the spatial database. Subsequently, a correlation analysis between the conditioning factors and landslide occurrences was conducted using the certainty factor (CF) method. Three landslide models were established by employing logistic regression (LR), functional trees (FTs), and random subspace functional trees (RSFTs) algorithms. The landslide susceptibility map was categorized into five levels: very low, low, medium, high, and very high susceptibility. Finally, the predictive capability of the three algorithms was assessed using the area under the receiver operating characteristic curve (AUC). The better the prediction, the higher the AUC value. The results indicate that all three models are predictive and practical, with only minor discrepancies in accuracy. The integrated model (RSFT) displayed the highest predictive performance, achieving an AUC value of 0.844 for the training dataset and 0.837 for the validation dataset. This was followed by the LR model (0.811 for the training dataset and 0.814 for the validation dataset) and the FT model (0.776 for the training dataset and 0.760 for the validation dataset). The proposed methods and resulting landslide susceptibility map can assist researchers and local authorities in making informed decisions for future geohazard prevention and mitigation. Furthermore, they will prove valuable and be useful for other regions with similar geological characteristics features. [ABSTRACT FROM AUTHOR]

The simulation and prediction of coastline changes are of great significance for the development and scientific management of coastal zones. Coastline changes are difficult to capture completely but appear significantly periodic over a long time series. In this paper, the transformer model is used to learn the changing trend of the coastline so as to deduce the position of the coastline in the coming year. First, we use the distance regularization level set evolution (DRLSE) model for instantaneous waterline extraction (IWE) from preprocessed Landsat time-series images from 2010–2020 in Weitou Bay, China. Then, tidal correction (TC) is performed on the extracted instantaneous waterline dataset to obtain coastlines projected to a single reference tidal datum. Finally, the coastline datasets from 2010–2019 are used for model training, and the coastline in 2020 is used for accuracy assessment. Three precision evaluation methods, including receiver operating characteristic curve matching, the mean offset, and the root mean square error, were used to verify the predicted coastline data. The receiver operating characteristic curve was specifically designed and improved to evaluate the accuracy of the obtained coastline. Compared with the support vector regression (SVR) and long–short-term memory (LSTM) methods, the results showed that the coastline predicted by the transformer model was the closest to the accurate extracted coastline. The accuracies of the correct values corresponding to SVR, LSTM, and transformer models were 88.27%, 94.08%, and 98.80%, respectively, which indicated the accuracy of the coastline extraction results. Additionally, the mean offset and root mean square error were 0.32 pixels and 0.57 pixels, respectively. In addition, the experimental results showed that tidal correction is important for coastline prediction. Moreover, through field investigations of coastlines, the predicted results obtained for natural coastlines were more accurate, while the predicted results were relatively poor for some artificial coastlines that were intensely influenced by human activities. This study shows that the transformer model can provide natural coastline changes for coastal management. [ABSTRACT FROM AUTHOR]

Remote-sensing images of high spatial resolution (HSR) are valuable sources of fine-grained spatial information for various applications, such as urban surveys and governance. There is continuing research on positional errors in remote-sensing images and their impacts in geoprocessing and applications. This paper explores the combined use of multi-point geostatistics (MPS), machine learning—in particular, generalized additive modeling (GAM)—and computer-image correlation for characterizing positional errors in images—in particular, HSR images. These methods are employed because of the merits of MPS in being flexible for non-parametric and joint simulation of positional errors in X and Y coordinates, the merits of GAM in being capable of handling non-stationarity in-positional errors through error de-trending, and the merits of computer-image correlation in being cost-effective in furnishing the training data (TD) required in MPS. Procedurally, image correlation is applied to identify homologous image points in reference-test image pairs to extract image displacements automatically in constructing TD. To cope with the complexity of urban scenes and the unavailability of truly orthorectified images, visual screening is performed to clean the raw displacement data to create quality-enhanced TD, while manual digitization is used to obtain reference sample data, including conditioning data (CD), for MPS and test data for performance evaluation. GAM is used to decompose CD and TD into trends and residuals. With CD and TD both de-trended, the direct sampling (DS) algorithm for MPS is applied to simulate residuals over a simulation grid (SG) at 80 m spatial resolution. With the realizations of residuals and, hence, positional errors generated in this way, the means, standard deviation, and cross correlation in bivariate positional errors at SG nodes are computed. The simulated error fields are also used to generate equal-probable realizations of vertices that define some road centerlines (RCLs), selected for this research through interpolation over the aforementioned simulated error fields, leading to error metrics for the RCLs and for the lengths of some RCL segments. The enhanced georectification of the RCLs is facilitated through error correction. A case study based in Shanghai municipality, China, was carried out, using HSR images as part of generalized point clouds that were developed. The experiment results confirmed that by using the proposed methods, spatially explicit positional-error metrics, including means, standard deviation, and cross correlation, can be quantified flexibly, with those in the selected RCLs and the lengths of some RCL segments derived easily through error propagation. The reference positions of these RCLs were obtained through error correction. The positional accuracy gains achieved by the proposed methods were found to be comparable with those achieved by conventional image georectification, in which the CD were used as image-georectification control data. The proposed methods are valuable not only for uncertainty-informed image geolocation and analysis, but also for integrated geoinformation processing. [ABSTRACT FROM AUTHOR]

Road extraction is crucial in urban planning, rescue operations, and military applications. Compared to traditional methods, using deep learning for road extraction from remote sensing images has demonstrated unique advantages. However, previous convolutional neural networks (CNN)-based road extraction methods have had limited receptivity and failed to effectively capture long-distance road features. On the other hand, transformer-based methods have good global information-capturing capabilities, but face challenges in extracting road edge information. Additionally, existing excellent road extraction methods lack validation for the Chinese region. To address these issues, this paper proposes a novel road extraction model called the global–local information fusion network (GLNet). In this model, the global information extraction (GIE) module effectively integrates global contextual relationships, the local information extraction (LIE) module accurately captures road edge information, and the information fusion (IF) module combines the output features from both global and local branches to generate the final extraction results. Further, a series of experiments on two different Chinese road datasets with geographic robustness demonstrate that our model outperforms the state-of-the-art deep learning models for road extraction tasks in China. On the CHN6-CUG dataset, the overall accuracy (OA) and intersection over union (IoU) reach 97.49% and 63.27%, respectively, while on the RDCME dataset, OA and IoU reach 98.73% and 84.97%, respectively. These research results hold significant implications for road traffic, humanitarian rescue, and environmental monitoring, particularly in the context of the Chinese region. [ABSTRACT FROM AUTHOR]

Heritage corridors are methods to effectively protect and utilize linear cultural heritage based on the concept of regional conservation. The construction of a heritage corridor system is extremely important to preserve the natural environment of the heritage corridor area as well as the history and culture alongside. The majority of the research on the construction of heritage corridors heretofore focused on the generation of corridors, whereas studies on the classification of corridors are relatively limited, without a complete system for the construction of heritage corridors. Therefore, this paper aimed to (1) establish a comprehensive system for the construction of heritage corridors, (2) provide new ideas for the construction of heritage corridors, and (3) guide the scientific development of heritage corridors combining conservation and tourism. In the first place, the minimum cumulative resistance (MCR) model was applied to analyze the spatial structure of the study area and explore site selection of the heritage corridors; secondly, spatial syntax was used to measure the heritage corridors and determine the level of the heritage corridors; last but not least, the kernel density analysis was used to classify the types of heritage corridors. The present study shows that the heritage corridor system is built in a scientific approach, covering all aspects including construction, protection, and development. [ABSTRACT FROM AUTHOR]

It is normally difficult to identify the ground deformation of potential landslides in highly vegetation-covered areas in terms of field investigation or remote sensing interpretation. In order to explore a methodology to effectively identify potential landslides in highly vegetation-covered areas, this paper established an integrated identification method, including sliding prone area identification based on regional geological environment analysis, target area identification of potential landslides in terms of comprehensive remote sensing methods, and landslide recognition through engineering geological survey. The Miaoyuan catchment in Quzhou City, Zhejiang Province, southeastern China, was taken as an example to validate the identification methods. Particularly, the Shangfang landslide was successfully studied in terms of comprehensive methods, such as geophysical survey, drilling, mineral and chemical composition analysis, and microstructure scanning of the sliding zone. In order to assess the landslide risk, the potential runout of the Shangfang landslide was evaluated in a quantitative simulation. This paper suggests a methodology to identify potential landslides from a large area to a specific slope covered by dense vegetation. [ABSTRACT FROM AUTHOR]

X-band dual-polarization phased-array weather radars (X-PARs) have been used in South China extensively. Eliminating the attenuation and system bias of X-band radar data is the key to utilizing the advantage of X-PAR networks. In this paper, the disdrometer raindrop-size distribution (DSD) measurements are used to calculate the radar polarimetric variables and analyze the characteristics of precipitation attenuation. Furthermore, based on the network of S-band dual-polarization Doppler weather radar (S-POL) and X-PARs, an attenuation-correction method for X-PAR reflectivity is proposed with S-POL constraints in view of the radar-mosaic requirements of a multi-radar network. Linear programming is used to calculate the attenuation-correction parameters of different rainfall areas, which realizes the attenuation correction for X-PAR. The results show that the attenuation-correction parameters simulated based on the disdrometer DSD vary with different precipitation classification; the attenuation-corrected reflectivity of X-PARs is consistent with S-POL and can realize a more precise observation of the evolution of the convective system. Compared with previous attenuation-correction methods with constant correction parameters, the improved method can reduce the deviation between X-PAR reflectivity and that of S-POL in heavy rainfall areas and areas of strong attenuation. The method proposed in this paper is stable and effective. After effective quality control, it is found that the X-PAR network deployed in South China observes data accurately and is consistent with S-POL; thus, it is expected to achieve high temporal–spatial resolution within a radar mosaic. [ABSTRACT FROM AUTHOR]

The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ours can more accurately eliminate landslides not triggered by the Jiuzhaigou earthquake. While using the image block strategy to ensure extraction efficiency, it also improves the extraction accuracy of wide-area coseismic landslides in complex backgrounds. [ABSTRACT FROM AUTHOR]

From 19 July 2022 to 31 August 2022, a rare persistent drought and heat event occurred in the middle of the Yangtze River basin (MYRB). Normalized difference vegetation Index (NDVI) over 25% of the area decreased more than 0.05 compared with the climatology, causing extremely agricultural drought disaster and economic losses to China. Previous studies have shown that the occurrence of compound drought and heat events (CDHEs) in the MYRB was associated with intra-seasonal oscillations (ISOs) from different latitudes. Nevertheless, what was the role of ISOs at different latitudes in the formation of the CDHE? To address this question, this paper designed a numerical simulation experiment of partial lateral forcing to investigate the changes in meteorological elements by removing the signals of ISOs on different lateral boundaries. We found that the wave series formed in the upper troposphere at 200 hPa played a significant role in the occurrence of the CDHE in the northern part of the MYRB in this progress. It was found that the ISO component of the northern boundary caused the mean temperature to rise by 2.4 °C and aggravated the drought in 53.7% of the region. On the other hand, the anticyclone anomaly in the lower troposphere at 800 hPa had a continuous impact on the southern and eastern boundaries. It was found that the ISO component of these two boundaries can increase the average temperature by 1.93 °C in the MYRB and intensify the drought in 49.7% of the area. In the developing period of the CDHE, the South Asian high and the Western North Pacific subtropical high were coupled with each other and jointly controlled the MYRB, so that the significant positive geopotential height anomaly stayed above the MYRB for a long time, which was conducive to the development of local subsidence. The results of this paper will help to better understand the formation mechanism of CDHEs in the MYRB and assist meteorologists to prevent and forecast the occurrence of CDHEs in advance. [ABSTRACT FROM AUTHOR]

Using a shape model (SM) is a typical method to determine the phenological phases of crops with long-time-series satellite remote sensing data. The average AGDD-based shape model (AAGDD-SM) takes temperature into account compared to SM, however, the commonly used daily average temperature is not sufficient to determine the exact AGDD owing to the possibly significant changes in temperatures throughout the day. In this paper, a modified shape model was proposed for the better estimation of phenological dates and it is incorporated into the continuous AGDD (CAGDD) which was calculated based on temperatures from a continuous 24 h within a day, different from the calendar day or the average AGDD indicators. In this study, the CAGDD replaced the abscissa of the NDVI growth curve over a 5-year period (2014 to 2018, excluding 2015) for a test site of early rice in Jiangxi province of China. Four key phenological phases, including the reviving, tillering, heading and anthesis phases, were selected and determined with reference to the field-observed phenological data. The results show that compared with the AAGDD-SM, the method proposed in this paper has basically improved the prediction of each phenological period. For those cases where the average temperature is lower than the minimum temperatures (K1) but the effective accumulated temperature is not zero, more accurate AGDD can be calculated according to the method in this paper. [ABSTRACT FROM AUTHOR]

The new type of multi-temporal global land use data with multiple classes is able to provide information on both the different land covers and their temporal changes; furthermore, it is able to contribute to many applications, such as those involving global climate and Earth ecosystem analyses. However, the current accuracy assessment methods have two limitations regarding multi-temporal land cover data that have multiple classes. First, multi-temporal land cover uses data from multiple phases, which is time-consuming and inefficient if evaluated one by one. Secondly, the conversion between different land cover classes increases the complexity of the sample stratification, and the assessments with different types of land cover suffer from inefficient sample stratification. In this paper, we propose a spatiotemporal stratified sampling method for stratifying the multi-temporal GlobeLand30 products for China. The changed and unchanged types of each class of data in the three periods are used to obtain a reasonable stratification. Then, the strata labels are simplified by using binary coding, i.e., a 1 or 0 representing a specified class or a nonspecified class, to improve the efficiency of the stratification. Additionally, the stratified sample size is determined by the combination of proportional allocation and empirical evaluation. The experimental results show that spatiotemporal stratified sampling is beneficial for increasing the sample size of the "change" strata for multi-temporal data and can evaluate not only the accuracy and area of the data in a single data but also the accuracy and area of the data in a multi-period change type and an unchanged type. This work also provides a good reference for the assessment of multi-temporal data with multiple classes. [ABSTRACT FROM AUTHOR]

The global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions made from the studies. The spatio-temporal correlations between GNSS reference stations cannot be considered when using traditional interpolation methods. This paper examines the use of machine learning models to reflect the spatio-temporal correlation among GNSS reference stations. To form the machine learning problem, the time series to be interpolated are treated as output values, and the time series from the remaining GNSS reference stations are used as input data. Specifically, three machine learning algorithms (i.e., the gradient boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), and random forest (RF)) are utilized to perform interpolation with the time series data from five GNSS reference stations in North China. The results of the interpolation of discrete points indicate that the three machine learning models achieve similar interpolation precision in the Up component, which is 45% better than the traditional cubic spline interpolation precision. The results of the interpolation of continuous missing data indicate that seasonal oscillations caused by thermal expansion effects in summer significantly affect the interpolation precision. Meanwhile, we improved the interpolation precision of the three models by adding data from five stations which have high correlation with the initial five GNSS reference stations. The interpolated time series for the North, East, and Up (NEU) are examined by principal component analysis (PCA), and the results show that the GBDT and RF models perform interpolation better than the XGBoost model. [ABSTRACT FROM AUTHOR]

The Absorbing Aerosol Sensor (AAS) is carried on the Gao-Fen 5B (GF-5B) satellite, and it allows for the measurement of solar backscatter radiation by the atmosphere in the UV–Vis bands. The AAS is an imaging spectrometer that employs CCD for capturing both a continuous spectrum and the cross-track orientation with a 114° wide swath. The broad field of view provides daily global envelopment with a 4 km spatial resolution at the nadir. This paper mainly analyzes the initial working status of the instrument in orbit, including wavelength calibration, radiometric calibration, detector performance, and product availability. Preliminary observations indicate the ability of the AAS to monitor absorbing aerosols like dust, biomass burning, volcano ash, and some pollution aerosols and to identify the aerosol events in China and other regions with high spatial resolution. [ABSTRACT FROM AUTHOR]

With the comprehensive promotion of digital construction in China, cameras scattered throughout the country are of great significance in obtaining first-hand data. However, their potential role is limited due to the lack of georeference information on current surveillance cameras. Provided surveillance camera images and real scenes are combined and given georeference information, this problem can be solved, allowing cameras to generate significant social benefits. This article proposed an accurate registration method based on misalignment calibration and least squares matching between real scene and surveillance camera images to address this issue. Firstly, it is necessary to convert the navigation coordinate system from which cameras obtain data to the photogrammetric coordinate system and then solve for the misalignment and internal orientation elements of the camera. Then, accurate registration is achieved using the least squares matching on pyramid images. The experiment obtained surrounding image data of two common scenes with lens pitch angles of 45°, 55°, 65°, 75°, and 85° using the surveillance camera and obtained a 3D real scene model of each scene using a low-altitude aircraft. The experiment results show that the proposed method in this paper can achieve the expected goals of accurately matching real scene and surveillance camera images and assigning georeference information. Through extensive data analysis, the success rate and accuracy rate of registration are 98.1% and 97.06%, respectively. [ABSTRACT FROM AUTHOR]

Northeast China plays a pivotal role in producing commodity grains. The precipitation and temperature distribution during the growth season is impacted by geographical and climate factors, rendering the region vulnerable to drought. However, relying on a single index does not reflect the severity and extent of drought in different regions. This research utilised the random forest (RF) model for screening remote sensing indices. Relative soil moisture (RSM) was employed to compare seven commonly used indices: the temperature vegetation dryness index (TVDI), vegetation supply water index (VSWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), multi-band drought index (MBDI), and normalised difference drought index (NDDI). The effectiveness of these indices for monitoring drought during different developmental stages of spring maize was evaluated. Trend rates were employed to investigate the temporal changes in drought patterns of spring maize from 2003 to 2020, and the Sen + Mann–Kendall test was used to analyse spatial variations. The results showed the following: (1) The seven remote sensing indices could accurately track drought during critical growth stages with the TVDI demonstrating higher applicability than the other six indices. (2) The application periods of two TVDIs with different parameters differed for the drought monitoring of spring maize in different developmental periods. The consistency and accuracy of the normalised difference vegetation index (NDVI)-based TVDI (TVDIN) were 5.77% and 34.62% higher than those of the enhanced vegetation index (EVI)-based TVDI (TVDIE), respectively, in the early stage. In contrast, the TVDIE exhibited 13.46% higher consistency than the TVDIN in the middle stage, and the accuracy was the same. During the later stage, the TVDIE showed significantly higher consistency and accuracy than the TVDIN with consistency increases of 9.61% and 38.64%, respectively. (3) The drought trend in northeast China increased from 2003 to 2020, exhibiting severe spring drought and a weakening of the drought in summer. The southern, southwestern, and northwestern parts of northeast China showed an upward drought trend; the drought-affected areas accounted for 37.91% of the study area. This paper identified the most suitable remote sensing indices for monitoring drought in different developmental stages of spring maize. The results provide a comprehensive understanding of the spatial–temporal patterns of drought during the past 18 years. These findings can be used to develop a dynamic agricultural drought monitoring model to ensure food security. [ABSTRACT FROM AUTHOR]

Monitoring mine activities can help management track the status of mineral resource exploration and mine rehabilitation. It is crucial to the sustainable development of the mining industry and the protection of the geological environment in mining areas. To monitor the mining activities of shallow surface outcrops in the arid and semi-arid regions of northwest China, this paper proposes a remote sensing monitoring approach of mining activities based on deep learning and integrated interferometric synthetic aperture radar technique. This approach uses the DeepLabV3-ResNet model to identify and extract the spatial location of the mine patches and then uses object-oriented analysis and spatial analysis methods to optimize the mine patch boundaries. SBAS-InSAR technique is used to obtain the time-series deformation information of the mine patches and is combined with the multi-temporal optical imagery to analyze the mining activities in the study area. The proposed approach has a recognition accuracy of 95.80% for the identification and extraction of mine patches, with an F1-score of 0.727 at the pixel level, and the average area similarity for all patches is 0.78 at the object-oriented level. The proposed approach possesses the capability to analyze mining activities, indicating promising prospects for engineering applications. It provides a reference for monitoring mining activities using multisource satellite remote sensing. [ABSTRACT FROM AUTHOR]

In 2020, the BeiDou-3 global navigation satellite system (BDS-3) was officially completed and put into service. Currently, network real-time kinematic (RTK) technology is considered the main means through which to improve the positioning accuracy of the BeiDou navigation satellite system (BDS). This paper proposes a long-range undifferenced network RTK (URTK) algorithm, based on multi-frequency observation data of the BDS. First, the multi-frequency phase integer ambiguity resolution (AR) model considering atmospheric error parameters is designed, and the multi-frequency phase integer ambiguity of the long-range BDS reference station is determined. Then, the undifferenced integer ambiguity of each reference station is obtained, using linear variation based on the accurately determined phase integer ambiguity between reference stations, and the undifferenced observation error of each reference station is calculated. Considering the weakening spatial correlation of the observation errors between long-range stations, undifferenced classification error corrections of a reference station network are separated, according to different error characteristics. Finally, the inverse distance weighting method is employed to calculate the classification undifferenced error correction of the rover station. The rover station corrects the observation error through applying the undifferenced error correction to achieve high-precision positioning. The measured data of a long-range continuous operation reference station (CORS) network are selected for an experiment. The results show that the proposed algorithm can quickly and accurately realize the resolution of the BDS integer ambiguity of a reference station network and establish an undifferenced area error correction model in order to achieve accurate classification of undifferenced error correction values for a rover station. In China, the BDS-3 is superior to the global positioning system (GPS) in terms of the satellite number, position dilution of precision (PDOP) value, AR success rate, stability, and convergence time. The results show that the AR success rate, stability, and convergence time increase with the operational frequency, and the BDS-3 can achieve centimeter-level positioning of single-system rover stations without relying on the GPS. [ABSTRACT FROM AUTHOR]

Improving the carbon sequestration capacity (CSC) of an ecosystem by optimizing urban ecological networks is one of the effective ways to achieve the goal of "carbon neutrality" in the world. The contradiction between the irreplaceable ecological function and economic development of Yancheng City is prominent. Therefore, taking Yancheng City as an example, this paper adopted the morphological spatial pattern analysis–minimum cumulative resistance (MSPA-MCR) model to establish the ecological network of Yancheng City in 2020 and combined it with complex network theory to evaluate its ecological base, network quality, and CSC. The results show that the ecological network of Yancheng City has obvious characteristics of coastal cities. There is a significant positive correlation between CSC and the clustering efficiency of ecological sources, and improving the clustering efficiency of vegetation and water ecological nodes is conducive to enhancing the CSC of ecological networks. In terms of functional restoration of ecological networks, four types of 13 ecological stepping stones and 12 ecological corridors have been designed to strengthen the connectivity and balance of the network, and the improvement of network robustness before and after optimization verifies that the optimization scheme is reasonable and effective. This study improved the optimization method of ecological networks in Yancheng City based on enhancing the CSC of ecological nodes, which provided a theoretical framework and practical reference for the realization of global strategic goals of carbon neutrality. [ABSTRACT FROM AUTHOR]

The Qinghai–Tibet Plateau is the highest and largest permafrost area in the middle and low latitudes of China. In this region, permafrost thaw settlement is the main form of expressway subgrade disaster. Therefore, the quantitative analysis and regionalization study of permafrost thaw settlement deformation are of great significance for expressway construction and maintenance in the Qinghai–Tibet region. This paper establishes a thaw settlement prediction model using the thaw settlement coefficient and thaw depth. The thaw depth was predicted by the mean annual ground temperatures and active-layer thicknesses using the Radial Basis Function (RBF) neural network model, and the thaw settlement coefficient was determined according to the type of ice content. Further, the distribution characteristics of thaw settlement risk of the permafrost subgrade in the study region were mapped and analyzed. The results showed that the thaw settlement risk was able to be divided into four risk levels, namely significant risk, high risk, medium risk and low risk levels, with the areas of these four risk levels covering 3868.67 km2, 1594.21 km2, 2456.10 km2 and 558.78 km2, respectively, of the total study region. The significant risk level had the highest proportion among all the risk levels and was mainly distributed across the Chumar River Basin, Beiluhe River Basin and Gaerqu River Basin regions. Moreover, ice content was found to be the main factor affecting thaw settlement, with thaw settlement found to increase as the ice content increased. [ABSTRACT FROM AUTHOR]

External interference in ionospheric sounding seriously degrades the quality of echo signals and data; thus, it should be eliminated. This paper presents a method for suppressing interference using chaotic coding with a set of Bernoulli map sequences; compared with other commonly used coding methods such as Barker code, complementary code, and Barker-like codes, through simulation, the ambiguity function (AF) of Bernoulli map codes has better performance in terms of peak sidelobe level (PSL), integral sidelobe ratio (ISL), noise suppression (NS), and signal-to-noise ratio (SNR). Experimental tests were performed using a vertical ionosonde in Yinchuan, Ningxia Hui Autonomous Region, China, and the ionosonde was operated by alternating 40-bit Barker-like coding and 40-bit Bernoulli map coding each day to compare the effectiveness of interference suppression. The results showed that using Bernoulli map coding could remove interference and improve SNR significantly, thereby improving the data quality of the resulting ionograms. [ABSTRACT FROM AUTHOR]

The continuous increase in the volume of geospatial data has led to the creation of storage tools and the cloud to process data. Google Earth Engine (GEE) is a cloud-based platform that facilitates geoprocessing, making it a tool of great interest to the academic and research world. This article proposes a bibliometric analysis of the GEE platform to analyze its scientific production. The methodology consists of four phases. The first phase corresponds to selecting "search" criteria, followed by the second phase focused on collecting data during the 2011 and 2022 periods using Elsevier's Scopus database. Software and bibliometrics allowed to review the published articles during the third phase. Finally, the results were analyzed and interpreted in the last phase. The research found 2800 documents that received contributions from 125 countries, with China and the USA leading as the countries with higher contributions supporting an increment in the use of GEE for the visualization and processing of geospatial data. The intellectual structure study and knowledge mapping showed that topics of interest included satellites, sensors, remote sensing, machine learning, land use and land cover. The co-citations analysis revealed the connection between the researchers who used the GEE platform in their research papers. GEE has proven to be an emergent web platform with the potential to manage big satellite data easily. Furthermore, GEE is considered a multidisciplinary tool with multiple applications in various areas of knowledge. This research adds to the current knowledge about the Google Earth Engine platform, analyzing its cognitive structure related to the research in the Scopus database. In addition, this study presents inferences and suggestions to develop future works with this methodology. [ABSTRACT FROM AUTHOR]

The world's largest macroalgae bloom (also known as green tide) has been reported since the 29th Olympic Games in 2008, which is verified as the fast reproduction of floating green macroalgae (Ulva prolifera). It is helpful to assess the biomass of macroalgae for the government of marine environment protection. In this study, the synchronization cruise experiment was firstly introduced, which aimed to investigate the biomass evaluation of Ulva prolifera in the Yellow Sea of China. The Floating Algae Index by Polarimetric SAR image (FAIPS) was then proposed. Finally, the floating algae biomass evaluation model was demonstrated and verified, which showed an exponential relationship between FAIPS and wet biomass per area (kg/m2) of macroalgae. The model proposed in this paper can be used in the biomass assessment of floating algae in the presence of polarimetric SAR images, regardless of daylight and cloud coverage over the sea surface. [ABSTRACT FROM AUTHOR]

The analysis of marine environmental parameters plays a significant role in various aspects, including sea surface target detection, the monitoring of the marine ecological environment, marine meteorology and disaster forecasting, and the monitoring of internal waves in the ocean. In particular, for sea surface target detection, the accurate and high-resolution input of marine environmental parameters is crucial for multi-scale sea surface modeling and the prediction of sea clutter characteristics. In this paper, based on the low-resolution wind speed, significant wave height, and wave period data provided by ECMWF for the surrounding seas of China (specified latitude and longitude range), a deep learning model based on a residual structure is proposed. By introducing an attention module, the model effectively addresses the poor modeling performance of traditional methods like nearest neighbor interpolation and linear interpolation at the edge positions in the image. Experimental results demonstrate that with the proposed approach, when the spatial resolution of wind speed increases from 0.5° to 0.25°, the results achieve a mean square error (MSE) of 0.713, a peak signal-to-noise ratio (PSNR) of 49.598, and a structural similarity index measure (SSIM) of 0.981. When the spatial resolution of the significant wave height increases from 1° to 0.5°, the results achieve a MSE of 1.319, a PSNR of 46.928, and an SSIM of 0.957. When the spatial resolution of the wave period increases from 1° to 0.5°, the results achieve a MSE of 2.299, a PSNR of 44.515, and an SSIM of 0.940. The proposed method can generate high-resolution marine environmental parameter data for the surrounding seas of China at any given moment, providing data support for subsequent sea surface modeling and for the prediction of sea clutter characteristics. [ABSTRACT FROM AUTHOR]

In this paper, we forecasted the ionospheric total electron content (TEC) over the region of China using the bidirectional convolutional gated recurrent unit (BiConvGRU) model. We first generated the China Regional Ionospheric Maps (CRIMs) using GNSS observations provide by the Crustal Movement Observation Network of China (CMONOC). We then used gridded TEC maps from 2015 to 2018 with a 1 h interval from the CRIMs as the dataset, including quiet periods and storm periods of ionospheric TEC. The BiConvGRU model was then utilized to forecast the ionospheric TEC across China for the year 2018. The forecasted TEC was compared with the TEC from the International Reference Ionosphere (IRI-2016), Convolutional Long Short-Term Memory (ConvLSTM), Convolutional Gated Recurrent Unit (ConvGRU), Bidirectional Convolutional Long Short-Term Memory (BiConvLSTM), and the 1-day Predicted Global Ionospheric Map (C1PG) provided by the Center for Orbit Determination in Europe (CODE). In addition, indices including Kp, ap, Dst and F10.7 were added to the training dataset to improve the forecasting accuracy of the model (-A indicates no indices, while -B indicates with indices). The results verified that the prediction accuracies of the models integrated with these indices were significantly improved, especially during geomagnetic storms. The BiConvGRU-B model presented a decrease of 41.5%, 22.3%, and 13.2% in the root mean square error (RMSE) compared to the IRI-2016, ConvGRU, and BiConvLSTM-B models during geomagnetic storm days. Furthermore, at a specific grid point, the BiConvGRU-B model showed a decrease of 42.6%, 49.1%, and 31.9% in RMSE during geomagnetic quiet days and 30.6%, 34.1%, and 15.1% during geomagnetic storm days compared to the IRI-2016, C1PG, and BiConvLSTM-B models, respectively. In the cumulative percentage analysis, the BiConvGRU-B model had a significantly higher percentage of mean absolute error (MAE) within the range of 0–1 TECU in all seasons compared to the BiConvLSTM-B model. Meanwhile, the BiConvGRU-B model outperformed the BiConvLSTM-B model with lower RMSE for each month of 2018. [ABSTRACT FROM AUTHOR]

Space-based measurements, such as the Greenhouse gases Observing SATellite (GOSAT) and the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite, provide global observations of the column-averaged CH4 concentration (XCH4). Due to the irregular observations and data gaps in the retrievals, studies on the spatial and temporal variations of regional atmospheric CH4 concentrations are limited. In this paper, we mapped XCH4 data over monsoon Asia using GOSAT and TROPOMI observations from April 2009 to December 2021 and analyzed the spatial and temporal pattern of atmospheric CH4 variations and emissions. The results show that atmospheric CH4 concentrations over monsoon Asia have long-term increases with an annual growth rate of roughly 8.4 ppb. The spatial and temporal trends of XCH4 data are significantly correlated with anthropogenic CH4 emissions from the bottom-up emission inventory of EDGAR. The spatial pattern of gridded XCH4 temporal variations in China presents a basically consistent distribution with the Heihe–Tengchong Line, which is mainly related to the difference in anthropogenic emissions in the eastern and western areas. Using the mapping of XCH4 data from 2019 to 2021, this study further revealed the response of atmospheric CH4 concentrations to anthropogenic emissions in different urban agglomerations. For the urban agglomerations, the triangle of Central China (TCC), the Chengdu–Chongqing City Group (CCG), and the Yangtze River Delta (YRD) show higher CH4 concentrations and emissions than the Beijing–Tianjin–Hebei region and nearby areas (BTH). The results reveal the spatial and temporal distribution of CH4 concentrations and quantify the differences between urban agglomerations, which will support further studies on the drivers of methane emissions. [ABSTRACT FROM AUTHOR]

Land motions are significantly widespread in the Yellow River delta (YRD). There is, however, a lack of understanding of the delta-wide comprehensive deformation mode and its dynamic mechanism, especially triggered by groundwater extraction. This paper adopts an integrated analysis of multidisciplinary data of image geodesy, geophysics, geology and hydrogeology to provide insights into Earth surface displacement patterns and dynamics in the YRD. Delta-scale land motions were measured for the first time using L-band ALOS images processed using multi-temporal InSAR, illustrating multiple obvious surface sinking regions and a maximum annual subsidence velocity of up to 130 mm. Then, the InSAR-constrained distributed point source model with optimal kernel parameters, a smoothness factor of 10 and a model grid size of 300 m was established and confirmed to be rational, reliable and accurate for modeling analysis over the YRD. Remarkable horizontal surface displacements, moving towards and converging on a sinking center, were recovered by means of modeling and measured using InSAR, with a maximum rate of up to 60 mm per year, which can trigger significant disasters, such as ground fissures and building damage. In addition, the annual total water storage variation at the delta scale, the most meaningful outcome, can be calculated and reaches a total of approximately 12,010 × 103 m3 in Guangrao city, efficiently filling the gap of GRACE and in situ investigations for delta-wide aquifer monitoring. Finally, a comparative analysis of time series InSAR measurements, modeling outcomes, and fault and groundwater data was conducted, and the strong agreement demonstrates that faults control aquifer distribution and hence the spatial distribution of groundwater-withdrawal-related regional land subsidence. Moreover, the obvious asymmetric displacements, demonstrating a northeasterly displacement trend, further reveal that faults control aquifer distribution and Earth surface deformation. These findings are useful for understanding the land motion patterns and dynamics, helping to sustainably manage groundwater and control disasters in the YRD and elsewhere worldwide. [ABSTRACT FROM AUTHOR]

Landslides are a major concern in the mountainous regions of southwest China, leading to significant loss of life and property damage. Therefore, it is crucial to identify potential landslides for early warning and mitigation. stacking-InSAR, a technique used for landslide identification in a wide area, has been found to be faster than conventional time-series InSAR. However, the dense vegetation in southwest China mountains has an adverse impact on the coherence of stacking-InSAR, resulting in more noise and inaccuracies in landslide identification. To address this problem, this paper proposes an improved seasonal interferometry stacking-InSAR method. It uses Sentinel-1 satellite data from 2017 to 2022. The study area is the river valley section of the G213 road from Wenchuan County to Mao County. The study reveals the characteristics of seasonal decoherence in the steep mountainous region, and identifies a total of 21 potential landslides using the improved method. Additionally, optical satellite imagery and LiDAR data were used to assist in the identification of potential landslides. The results of the conventional stacking-InSAR method and the improved seasonal interferometry stacking-InSAR method are compared, showing that the latter is more effective in noise suppression caused by low coherence. Their standard deviations were reduced by 46%, 22%, 10%, and 14%, respectively, using the quantitative statistics for the four tested areas. The proposed method provides an efficient and effective approach for detecting potential landslides in the mountainous regions of southwest China. It can serve as a valuable technical reference for future landslide identification studies in this area. [ABSTRACT FROM AUTHOR]

Geomorphic parameters, which reflect the migration of drainage divide responses, are widely used to assess tectonic activity. There have been several large earthquakes in the history of the Hetao Basin, within which the Sertengshan Piedmont Fault is important. This study highlights certain references for regional seismic risk assessment. Few studies have been conducted on the Sertengshan area from the perspective of geomorphic parameters. In this paper, ksn, HI, and Vf were obtained to indicate the tectonic activity in the Sertengshan area, and χ and Gilbert metrics were extracted to explore the state of the drainage basin. The results show that the tectonic activity varies spatially and is strong in the western part of the southern Sertengshan region and the northern part corresponding to the turning point of the fault. Most of the Sertengshan area is in the prime and old stages of geomorphological evolution, whereas some areas are in the juvenile stage. The old stage was mainly concentrated in the northern region, and the southern part was younger than the northern region. Overall, the Sertengshan area is tectonically active and affected by the activity of the Sertengshan Piedmont Fault. The western part of the divide migrated northwest, while the central and eastern parts tended to move southward. We suggest that the divide migration is influenced by tectonic activity and tends to move towards the direction of lower tectonic activity. [ABSTRACT FROM AUTHOR]

The radio determination service system (RDSS), a navigation and positioning system independently developed by China, features services such as short-message communication, position reporting, and international search and rescue. The L-band pseudo-range and phase data are the primary data sources in precise orbit determination (POD) for geostationary Earth orbit (GEO) satellite in the BeiDou system, especially in the orbit manoeuvre period. These data are the only data sources in the POD for GEOs. However, when the pseudo-range and phase data is abnormal due to unforeseen reasons, such as satellite hardware failure or monitoring receiver abnormalities, the data abnormality leads to orbit determination abnormalities or even failures for GEOs, then the service performance and availability of the RDSS system are greatly degraded. Therefore, a new POD method for GEOs based on BeiDou short-message communication ranging data has gained research attention to improve the service reliability of the BeiDou navigation satellite system (BDS)-3, realising the deep integration of communication and navigation services of the BDS. This problem has not been addressed so far. Therefore, in this study, a new POD method for GEO satellites is investigated using high-precision satellite laser ranging (SLR) data and RDSS data. The SLR data are used as the benchmark to calibrate the time delay value of RDSS equipment, and RDSS data are only used in the orbit determination process by fixing the corrected RDSS time delay value, and the satellite orbit parameters and dynamic parameters are solved. Experimental analysis is conducted using the measured SLR and RDSS data of the BDS, and the orbit accuracy in this paper is evaluated by the precise ephemeris of the Multi-GNSS pilot project (MGEX) and SLR data. The results show that the orbit accuracy in the orbital arc and the 2-h orbital prediction arc for GEOs are 6.01 m and 6.99 m, respectively, compared with the ephemeris of MGEX, and the short-arc orbit accuracy after 4 h of manoeuvring is 11.11 m. The orbit accuracy in the radial component by SLR data is 0.54 m. The required orbit accuracy for GEO satellites in the RDSS service of the BDS-3 is 15 m. The orbit accuracy achieved in this paper is superior to that of this technical index. This method expands the application field of the RDSS data and greatly enriches the POD method for GEOs. It can be adopted as a backup technology for the POD method for GEOs based on RNSS data, significantly improving the service reliability of the BeiDou RDSS service. [ABSTRACT FROM AUTHOR]

In the context of global sustainable development, solar energy is very widely used. The installed capacity of photovoltaic panels in countries around the world, especially in China, is increasing steadily and rapidly. In order to obtain accurate information about photovoltaic panels and provide data support for the macro-control of the photovoltaic industry, this paper proposed a hierarchical information extraction method, including positioning information and shape information, and carried out photovoltaic panel distribution mapping. This method is suitable for large-scale centralized photovoltaic power plants based on multi-source satellite remote sensing images. This experiment takes the three northwest provinces of China as the research area. First, a deep learning scene classification model, the EfficientNet-B5 model, is used to locate the photovoltaic power plants on 16-m spatial resolution images. This step obtains the area that contains or may contain photovoltaic panels, greatly reducing the study area with an accuracy of 99.97%. Second, a deep learning semantic segmentation model, the U2-Net model, is used to precisely locate photovoltaic panels on 2-m spatial resolution images. This step achieves the exact extraction results of the photovoltaic panels from the area obtained in the previous step, with an accuracy of 97.686%. This paper verifies the superiority of a hierarchical information extraction method in terms of accuracy and efficiency through comparative experiments with DeepLabV3+, U-Net, SegNet, and FCN8s. This meaningful work identified 180 centralized photovoltaic power plants in the study area. Additionally, this method makes full use of the characteristics of different remote sensing data sources. This method can be applied to the rapid extraction of global photovoltaic panels. [ABSTRACT FROM AUTHOR]

Land-use–cover change (LUCC)/vegetation cover plays a critical role in Earth system science and is a reflection of human activities and environmental changes. LUCC will affect the structure and function of ecosystems and a series of other terrestrial surface processes, such as energy exchange, water circulation, biogeochemical circulation, and vegetation productivity. Therefore, accurate LUCC mapping and vegetation cover monitoring are the bases for simulating the global carbon and hydrological cycles, studying the interactions of the land surface and climate, and assessing land degradation. Based on field GPS surveys and UAV data, with cloud-free and snow/glacier algorithms and the SVM classifier to train and model alpine grassland, the alpine grassland and LUCC were extracted by using Landsat-8 OLI satellite images in Sanjiangyuan National Park in this paper. The latest datasets of vegetation types with 30 m × 30 m spatial resolution in the three parks were prepared and formed. The classification results show that the SVM classifier could better distinguish the major land-use types, and the overall classification accuracy was very high. However, in the alpine grassland subcategories, the classification accuracies of the four typical grasslands were relatively low, especially between desert steppes and alpine meadows, and desert steppes and alpine steppes. It manifests the limitations of Landsat-8 multispectral remote sensing imageries in finer-resolution grassland classifications of high-altitude alpine mountains. The method can be utilized for other multispectral satellite imageries with the same band matching, such as Landsat 7, Landsat 9, Sentinel-2, etc. The method described in this paper can rapidly and efficiently process annual alpine grassland maps of the source areas of the Yellow River, the Yangtze River, and the Lancang River. It can provide timely and high-spatial-resolution datasets for supporting scientific decisions for the sustainable management of Sanjiangyuan National Park. [ABSTRACT FROM AUTHOR]

Mountain areas in China account for 69% of the total land area; however, it is still an urgent that we grasp the special ecological structure of mountain areas and maximize the resource advantages of mountain areas under the principle of maintaining a certain ecological level. In this paper, Landsat 5, Landsat 8 and Sentinel-2A images were used as data sources to monitor and analyze land development and ecological change in Gui 'an in 2010, 2013, 2016, 2019 and 2021, so as to explore ecological maintenance mechanisms. Firstly, random forest classification based on multi-source remote sensing data was used to classify land, and the five phases of land-use change were assessed using quantitative analysis, in order to analyze the mountain region's land-use-change characteristics at different stages of development. The results show that Gui 'an has the "two-stage", rapid-development, rapid-recovery mode. Each stage includes a development-and-expansion period and a construction-and-protection period. In the construction period, ecological recovery construction will be emphasized, and the change intensity and rate of the second stage are lower than that of the first stage. Secondly, using a remote sensing ecological index, vegetation coverage, and a landscape index, an ecological evaluation model of the study area was constructed to analyze changes in the ecological environment and its protection in the process of land development. The ecological maintenance status of the five stages was quantitatively monitored using the analysis methods of difference change and coefficient of variation. The results showed that in the first stage of land development and expansion, the landscape pattern and ecological quality fluctuated greatly, and the proportion of ecological quality of an excellent grade decreased by 28.46%. However, in the second stage, the change slowed down and remained unchanged, and gradually moved to the middle and upper level. The results show that there is a close relationship between ecological maintenance and the land development mode, and new mountain towns can maintain ecological quality and achieve sustainable development through a reasonable land development mode. At the end of this paper, the factors affecting the ecological maintenance capacity of Gui 'an are discussed, providing effective reference material and development models for the development of mountainous areas. [ABSTRACT FROM AUTHOR]

Models for bathymetry retrieval from multispectral images have not considered the errors caused by tidal fluctuation. A rigorous bathymetric model that considers the variation in tide height time series, including the tide height calculation and instantaneous tide height correction at the epoch of satellite flight into the bathymetric retrieval model, is proposed in this paper. The model was applied on Weizhou Island, located in Guangxi Province, China, and its accuracy verificated with four check lines and seven checkpoints. A scene from the Landsat 8 satellite image was used as experimental data. The reference ("true") water depth data collected by a RESON SeaBat 7125 multibeam instrument was used for comparison analysis. When satellite-derived bathymetry is compared, it is found that maximum absolute error, mean absolute error, and RMSE have decreased 54, 45, and 30% relative to that of the traditional model in the entire test field. The accuracy of the water depths retrieved by our model increased 30 and 56% when validated using four check lines and seven checkpoints, respectively. Therefore, it can be concluded that the model proposed in this paper can effectively improve the accuracy of bathymetry retrieved from Landsat 8 images. [ABSTRACT FROM AUTHOR]

To improve the accuracy and reliability of precipitation estimation, numerous models based on machine learning technology have been developed for integrating data from multiple sources. However, little attention has been paid to extracting the spatiotemporal correlation patterns between satellite products and rain gauge observations during the merging process. This paper focuses on this issue by proposing an integrated framework to generate an accurate and reliable spatiotemporal estimation of precipitation. The proposed framework integrates Funk-Singular Value Decomposition (F-SVD) in the recommender system to achieve the accurate spatial distribution of precipitation based on the spatiotemporal interpolation of rain gauge observations and Convolutional Long Short-Term Memory (ConvLSTM) to merge precipitation data from interpolation results and satellite observation through exploiting the spatiotemporal correlation pattern between them. The framework (FS-ConvLSTM) is utilized to obtain hourly precipitation merging data with a resolution of 0.1° in Jianxi Basin, southeast of China, from both rain gauge data and Global Precipitation Measurement (GPM) from 2006 to 2018. The LSTM and Inverse Distance Weighting (IDW) are constructed for comparison purposes. The results demonstrate that the framework could not only provide more accurate precipitation distribution but also achieve better stability and reliability. Compared with other models, it performs better in variation process description and rainfall capture capability, and the root mean square error (RSME) and probability of detection (POD) are improved by 63.6% and 22.9% from the original GPM, respectively. In addition, the merged precipitation combines the strength of different data while mitigating their weaknesses and has good agreement with observed precipitation in terms of magnitude and spatial distribution. Consequently, the proposed framework provides a valuable tool to improve the accuracy of precipitation estimation, which can have important implications for water resource management and natural disaster preparedness. [ABSTRACT FROM AUTHOR]

The Local Climate Zone (LCZ) classification scheme is a vital method of building a category dataset for high-resolution urban land. For the development of urban meteorology, air pollution and related disciplines, the high-resolution classification data of urban buildings are very important. This study aims to create LCZ datasets with detailed architectural characteristics for major cities and urban agglomerations in China, and obtain more accurate results. We constructed 120 m resolution land use datasets for 63 cities (mainly provincial capitals, municipalities directly under the Central Government, important prefecture-level cities and special administrative regions) and 4 urban agglomerations in China based on the local climate zone (LCZ) classification scheme using the World Urban Database and Access Portal Tools method (WUDAPT). Nearly 100,000 samples were used, of which 76,000 training samples were used to provide spectral signatures and 23,000 validation samples were used to ensure accuracy assessments. Compared with similar studies, the LCZ datasets in this paper were generally of good quality, with an overall accuracy of 71–93% (mean 82%), an accuracy for built classifications of 57–83% (mean 72%), and an accuracy for natural classifications of 70–99% (mean 90%). In addition, 35% of 63 Chinese cities have construction areas of more than 5%, and the plateaus northwest of Chengdu and Chongqing are covered with snow all year round. Therefore, based on the original LCZ classification system, the construction area (LZC H) and the snow cover (LCZ I) were newly added as the basic classifications of urban LCZ classification in China. Detailed architectural features of cities and urban agglomerations in China are provided by the LCZ datasets in this study. It can be applied to fine numerical models of the meteorological and atmospheric environment and improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

The International Association of Geodesy (IAG) aims to establish the International Height Reference System (IHRS) as one of its primary scientific objectives. Central to the realization of the IHRS is global vertical datum unification, which requires the connection of existing local vertical height reference systems (VHS) robustly and consistently. However, conventional methods are not suitable for estimating the offsets between two distant local height systems. In this paper, we propose a framework for connecting two local VHSs using ultraprecise clock frequency signal links between satellites and ground stations, referred to as the satellite frequency signal transmission (SFST) approach. The SFST approach allows for the direct determination of the geopotential and height differences between two ground datum stations without any location restrictions between the two VHSs. The simulation results show that the VHSs of China and the US can be unified with an accuracy of several centimeters, provided that the stability of atomic clocks used on-board the satellite and at on-ground datum locations reaches 4.8 × 10 − 17 τ − 1 / 2 for an averaging time τ (in seconds). We conclude that the SFST approach shows promise for achieving centimeter-level accuracy in unifying the global vertical height datum and represents a new paradigm for the realization of the IHRS. [ABSTRACT FROM AUTHOR]

Using the Gravity Recovery and Climate Experiment (GRACE) satellite to monitor groundwater storage (GWS) anomalies (GWSAs) at the local scale is difficult due to the low spatial resolution of GRACE. Many attempts have been made to downscale GRACE-based GWSAs to a finer resolution using statistical downscaling approaches. However, the time-lag effect of GWSAs relative to environmental variables and optimal model parameters is always ignored, making it challenging to achieve good spatial downscaling, especially for arid regions with longer groundwater infiltration paths. In this paper, we present a novel spatial downscaling method for constructing GRACE-based 1 km-resolution GWSAs by using the back propagation neural network (BPNN) and considering the time-lag effect and the number of hidden neurons in the model. The method was validated in Alxa League, China. The results show that a good simulation performance was achieved by adopting varying lag times (from 0 to 4 months) for the environmental variables and 14 hidden neurons for all the networks, with a mean correlation coefficient (CC) of 0.81 and a mean root-mean-square error (RMSE) of 0.70 cm for each month from April 2002 to December 2020. The downscaled GWSAs were highly consistent with the original data in terms of long-term temporal variations (the decline rate of the GWSAs was about −0.40 ± 0.01 cm/year) and spatial distribution. This study provides a feasible approach for downscaling GRACE data to 1 km resolution in arid regions, thereby assisting with the sustainable management and conservation of groundwater resources at different scales. [ABSTRACT FROM AUTHOR]

Analysis of the spatial and temporal variation patterns of surface evapotranspiration is important for understanding global climate change, promoting scientific deployment of regional water resources, and improving crop yield and water productivity. Based on Landsat 8 OIL_TIRS data and remote sensing image data of the lower Yangtze River urban cluster for the same period of 2016–2021, combined with soil and meteorological data of the study area, this paper constructed a multiple linear regression (MLR) model and an extreme learning machine (ELM) inversion model with evapotranspiration as the target and, based on the model inversion, quantitatively and qualitatively analyzed the spatial and temporal variability in surface evapotranspiration in the study area in the past six years. The results show that both models based on feature factors and spectral indices obtained a good inversion accuracy, with the fusion of feature factors effectively improving the inversion ability of the model for ET. The best model for ET in 2016, 2017, and 2021 was MLR, with an R2 greater than 0.8; the best model for ET in 2018–2019 was ELM, with an R2 of 0.83 and 0.62, respectively. The inter-annual ET in the study area showed a "double-peak" dynamic variation, with peaks in 2018 and 2020; the intra-annual ET showed a single-peak cycle, with peaks in July–August. Seasonal differences were obvious, and spatially high-ET areas were mainly found in rural areas north of the Yangtze River and central and western China where agricultural land is concentrated. The net solar radiation, soil heat flux, soil temperature and humidity, and fractional vegetation cover all had significant positive effects on ET, with correlation coefficients ranging from 0.39 to 0.94. This study can provide methodological and scientific support for the quantitative and qualitative estimation of regional ET. [ABSTRACT FROM AUTHOR]

The detection of lava tubes beneath the surfaces of the Moon and Mars has been a popular research topic and challenge in planetary radar observation. In recent years, the Moon–based ground penetrating radar (GPR) carried by the Chinese Chang'e–3/–4 mission, the RIMFAX radar carried by the Mars mission Perseverance, and the RoSPR radar and MOSIR radar carried by China's Tianwen–1 orbiter have extensively promoted the exploration of the underground space of extraterrestrial bodies, which is crucial for the future utilization and development of these spaces. This paper expounds on the principles, methods, and detection results of using GPR to detect lava tubes on the Moon and Mars. First, lava tubes' formation mechanism and morphological characteristics are outlined, followed by an introduction to GPR's working principles and classification. The advantages, disadvantages, and prospects of different types of radar in detecting the lava tubes are analyzed. Finally, the distribution of lava tubes on the Moon and Mars is briefly summarized, and the potential utilization of lava tubes is discussed. We believe that the GPR technique is an effective geophysical method for exploring the underground structures of the Moon and Mars, and the lava tubes beneath the surface of extraterrestrial bodies can provide important references for selecting future Moon and Mars bases. [ABSTRACT FROM AUTHOR]

As an essential infrastructure that provides positioning, navigation, and timing services, China constructed the BeiDou Navigation Satellite System (BDS). The last BDS satellite was launched in June 2020, which represents the completion of BDS. BDS's constellation consists of Medium Earth Orbit (MEO), Inclined Geosynchronous Orbit (IGSO), and Geostationary Orbit satellites. The precise modeling of non-conservative forces for BDS satellites is a challenging task. As an independent observation, Satellite Laser Ranging (SLR) is an important validation method of GNSS orbit modeling. In this paper, we validated the precise orbit products of different Analysis Centers (ACs) by using SLR observations, focusing on the BDS orbit modeling. By comparing BDS precise orbit products generated by four ACs with respect to SLR observations for the period of February 2017 to March 2021, we proved that an obvious satellite signature effect exists in the SLR residuals of BDS observed by multi-photon stations. The result indicates that multi-photon stations have a root mean square (RMS) of SLR residuals about 5 mm lower than that of single-photon detectors. The slope of SLR residuals with regard to nadir angle of IGSO satellites for single-photon and multi-photon stations is −2.0 and −2.5 mm/deg, respectively, while the slope of MEO satellites for these stations is about −0.6 to −0.3 and −1.0 to −0.4 mm/deg, respectively. To assess the effect of non-conservative force modeling, we selected seven high-performing stations, including five single-photon and two multi-photon stations. By comparing the SLR residuals of four ACs' orbits, we analyzed the effect of the solutions of orbit processing, especially solar radiation pressure (SRP) models. We found that some centers may have modeling defects, including BDS-3 orbits of the Deutsches GeoForschungsZentrum and BDS-2 orbits of the European Space Agency, inferred from the large RMS of SLR residuals. Modeling the SRP of BDS satellites is challenging, while an appropriate prior box-wing model can improve the accuracy of SRP modeling and provide a more stable performance. [ABSTRACT FROM AUTHOR]

The LuTan-1(LT-1), known as the L-band differential interferometric synthetic aperture radar (SAR) satellite system, is an essential piece of civil infrastructure in China, providing extensive applications such as surface deformation monitoring and topographic mapping. To achieve high-resolution and wide-swath (HRWS) observation abilities, the LT-1 takes the dual receiving antenna (DRA) imaging mode as its working mode. However, amplitude and phase errors between channels lead to a mismatch between the reconstruction filter and the multichannel echo signal, worsen the reconstructed azimuth spectrum, and introduce ambiguity targets in the final imaging results, seriously affecting the final imaging quality. In order to better evaluate the channel error and azimuth ambiguity performance of the LT-1 system, this paper proposed an advanced channel consistency correction method and conducted many measured data experiments. The experimental results show that the proposed method is effective, and the LT-1 system has excellent channel error control and azimuth ambiguity performance. [ABSTRACT FROM AUTHOR]

In this paper, a method for joint sea surface rainfall intensity (RI), wind speed, and wave height retrieval based on spaceborne global navigation satellite system reflectometry (GNSS-R) data is proposed, which especially considers the effects between these two parameters. A method of rainfall detection (RD) according to different wind speed ranges is also proposed by mitigating the impact of swell and wind speed. The results, with data collected over the oceans near Southeast Asia, show that the RD method has a detection accuracy of up to 81.74%. The RI retrieval accuracy can reach about 2 mm/h by simultaneously correcting the effects of wind speed and swell. The accuracy of wind speed retrieval is improved by about 5% after removing rainfall interference through RD in advance. After considering the influence of wind speed and eliminating rainfall interference, the retrieval accuracy of significant wave height (SWH) is improved by about 18%. Finally, the deep convolutional neural network (DCNN) model is built to estimate the SWH of the swell. The results show that the retrieval accuracy of the swell height is better than 0.20 m after excluding rainfall interference. The proposed joint retrieval method provides an important reference for the future acquisition of multiple high-precision marine geophysical parameters by spaceborne GNSS-R technology. [ABSTRACT FROM AUTHOR]

While rapid urbanization promotes social and economic development, it exacerbates human outdoor thermal comfort, which increases the risks to human health. This paper uses four thermal comfort indices and multiple satellite observations to explore the urbanization effect on summer heat stress in Guangdong from 1979–2018, a coastal province of China. Two types of thermal comfort index are used here, namely the direct thermal comfort index (Heat Index, HI; Temperature–Humidity Index, THI; Discomfort Index, DI) and the physiological thermal comfort index (Universal Thermal Climate Index, UTCI). We compare the differences in the urbanization effects on the changes in the three direct thermal comfort indices (HI, THI, and DI) and a physiological thermal comfort index (UTCI). The results show that all four thermal comfort indices indicate an overall warming trend. Of them, urban sites show a higher warming trend than rural sites, indicating that heat stress changes are significantly influenced by urbanization from 1979–2018, which is consistent with the effect of urbanization on surface air temperature. However, except for the UTCI, this warming of direct thermal comfort indices affected by urbanization has become insignificant under the regional vegetation greening from 2004–2018 (also consistent with surface air temperature). This is primarily attributed to the different effects of wind speed on the physiological thermal comfort index in urban and rural areas: Decreasing wind speeds in urban areas lead to an increase in UTCI, while wind speeds in rural areas increase instead and decrease UTCI, thus widening the UTCI differences between urban and rural areas. Our results indicate that urbanization has a different effect on thermal comfort indices. When using the thermal comfort index, it is necessary to consider that different thermal comfort indices may bring different results. UTCI considers more factors that affect human heat perception, so it can better describe human outdoor thermal comfort. It also highlights the importance of urban ventilation and urban greenness in mitigating urban outdoor thermal comfort in the sustainable construction of future urbanization in coastal cities. [ABSTRACT FROM AUTHOR]

The technology of remote sensing-assisted tree species classification is increasingly developing, but the rapid refinement of tree species classification on a large scale is still challenging. As one of the treasures of ecological resources in China, Arxan has 80% forest cover, and tree species classification surveys guarantee ecological environment management and sustainable development. In this study, we identified tree species in three samples within the Arxan Duraer Forestry Zone based on the spectral, textural, and topographic features of unmanned aerial vehicle (UAV) multispectral remote sensing imagery and light detection and ranging (LiDAR) point cloud data as classification variables to distinguish among birch, larch, and nonforest areas. The best extracted classification variables were combined to compare the accuracy of the random forest (RF), support vector machine (SVM), and classification and regression tree (CART) methodologies for classifying species into three sample strips in the Arxan Duraer Forestry Zone. Furthermore, the effect on the overall classification results of adding a canopy height model (CHM) was investigated based on spectral and texture feature classification combined with field measurement data to improve the accuracy. The results showed that the overall accuracy of the RF was 79%, and the kappa coefficient was 0.63. After adding the CHM extracted from the point cloud data, the overall accuracy was improved by 7%, and the kappa coefficient increased to 0.75. The overall accuracy of the CART model was 78%, and the kappa coefficient was 0.63; the overall accuracy of the SVM was 81%, and the kappa coefficient was 0.67; and the overall accuracy of the RF was 86%, and the kappa coefficient was 0.75. To verify whether the above results can be applied to a large area, Google Earth Engine was used to write code to extract the features required for classification from Sentinel-2 multispectral and radar topographic data (create equivalent conditions), and six tree species and one nonforest in the study area were classified using RF, with an overall accuracy of 0.98, and a kappa coefficient of 0.97. In this paper, we mainly integrate active and passive remote sensing data for forest surveying and add vertical data to a two-dimensional image to form a three-dimensional scene. The main goal of the research is not only to find schemes to improve the accuracy of tree species classification, but also to apply the results to large-scale areas. This is necessary to improve the time-consuming and labor-intensive traditional forest survey methods and to ensure the accuracy and reliability of survey data. [ABSTRACT FROM AUTHOR]

As a particular type of alpine glacier, debris-covered glaciers are essential for local water resources and glacial disaster warnings. The Eastern Tomur Peak Region (EPTR) is the most concentrated glacier in Tien Shan Mountain, China, where the glaciers have not been studied in detail. This paper evaluates the delineation accuracy of Landsat8 OLI, Sentinel-1A, and GF images for debris-covered glaciers in the EPTR. Each image uses the most advanced delineation method for itself to minimize the error of inherent resolutions. The results show that the accuracy of these images for delineating debris-covered glaciers is very high, and the F1 scores are expressed as 96.73%, 93.55%, and 95.81%, respectively. Therefore, Landsat images were selected to analyze the area change of EPTR from 2000 to 2022 over a 5-year time scale. The results indicate that glaciers of the EPTR decreased by 19.05 km2 from 2000 to 2020, accounting for 1.9% (0.08% a−1), and debris increased by 10.8%, which validates the opinion that the presence of debris inhibits glacier melting. The most varied time was 2010–2022, but it was much less than other Tien Shan regions. The lower glacier ablation rate in this area results from the combined effect of decreased bare ice and increased debris. The main reason for the change in debris-covered glaciers is the increase in temperature. [ABSTRACT FROM AUTHOR]

The Wudaoliang–Changshagongma fault is one of the NW-trending faults located within the southern Bayan Har Block of the Tibetan Plateau in China. In this paper, we used high-resolution imagery and digital elevation model data to study the geomorphological and geological characteristics of the fault. Furthermore, the result also determined the fault trace and estimated the average horizontal slip rate of the fault since the late Quaternary to have been 2.6 ± 0.6 mm/a. This slip rate is approximately equivalent to that of the Awancang, Madoi–Garde, and Dari faults, which are also located within the block. Furthermore, the slip rates of these faults obtained by remote sensing and geological methods are consistent with GPS observations. It indicates that tectonic deformation within the block is continuous and diffuse. Using trenching study results and sedimentary radiocarbon dating, we identified four paleoearthquake events that occurred at 42,378–32,975, 33,935–20,663, 5052–4862, and after 673–628 cal BP, respectively. The recurrence intervals of large earthquakes on the faults within the block are much longer than those of the boundary faults, and the slip rates are also smaller, indicating that faults within the block play a regulatory role in the tectonic deformation of the Bayan Har Block. [ABSTRACT FROM AUTHOR]

The subtropical forests in China play a pivotal part in the global and regional carbon–water cycle and in regulating the climate. Ecosystem water-use efficiency (WUE) is a crucial index for understanding the trade-off between ecosystem carbon gain and water consumption. However, the underlying mechanisms of the WUE in forest ecosystems, especially the different subtropical forests, have remained unclear. In this paper, we developed a simple framework for estimating forest WUE and revealing the underlying mechanisms of forest WUE changes via a series of numerical experiments. Validated by measured WUE, the simulated WUE from our developed WUE framework showed a good performance. In addition, we found that the subtropical forest WUE experienced a significant increasing trend during 2001–2018, especially in evergreen and deciduous broadleaf forests where the increasing rate was greatest (0.027 gC kg−1 H2O year−1, p < 0.001). Further analysis indicated that the atmospheric CO2 concentration and vapor pressure deficits (VPD), rather than leaf area index (LAI), were the dominant drivers leading to the subtropical forest WUE changes. When summed for the whole subtropical forests, CO2 and VPD had an almost equal spatial impact on annual WUE change trends and accounted for 45.3% and 49.1% of the whole study area, respectively. This suggests that future forest management aiming to increase forest carbon uptake and protect water resources needs to pay more attention to the long-term impacts of climate change on forest WUE. [ABSTRACT FROM AUTHOR]

High-rise building areas (HRBs) provide significant social and environmental services and play a crucial role in modern urbanization. The large-scale and long-term spatial distribution of HRBs is of great interest to many fields, such as urban planning and local climate analysis. While previous studies have confirmed the value of Sentinel-2 images in extracting HRBs and their changes, current work is limited to relatively local areas and short-term analysis. One reason is due to the fact that the earliest Sentinel-2 image can only date back to 2015. To address this research gap, this paper proposes an efficient procedure to intelligently extract HRBs and their changes from multitemporal Landsat-7 and Sentinel-2 images, using a specifically designed fully convolutional network. To validate the proposed method, we selected four typical cities in China, namely, Beijing, Shanghai, Guangzhou, and Zhengzhou, as study areas. We utilized Landsat-7 images acquired in 2000 and 2010, along with Sentinel-2 images acquired in 2020, as experimental data. We extracted and analyzed three periods of HRBs and their changes in the four cities, along with urban rail terminal data and gross domestic product (GDP) data in the same period. The results show that the proposed method can efficiently extract HRBs and their changes in the four cities over the past 20 years, with an overall accuracy of more than 90%. HRBs changes are primarily driven by urban planning policies and geographical factors. There is a strong positive correlation between the increase in HRBs and the increase in rail terminals, both in terms of quantity and spatial distribution. Additionally, there is a positive correlation between HRBs increase and GDP increase in terms of quantity, but the trend varies in different cities due to their diverse developing modes. Overall, the results indicate that the proposed method can be a potential operational tool to extract large-scale and long-term HRBs and their changes in China. [ABSTRACT FROM AUTHOR]