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Covering the full texts of all papers published in MDPI's Remote Sensing between 2009 and 2021, in-depth scientometric analyses were conducted. Trends in publications show an increase in the overall number of papers. A relative increase in papers using SAR sensors and a relative decrease in papers using optical remote sensing can also be seen. The full-text analyses reveal distinctive styles and writing patterns for papers from different sub-fields of remote sensing and for different countries and even cities. While a slight increase in the readability of abstracts is detected over time, the overall readability of papers is decreasing. Institutional co-authorship analysis reveals the ongoing 'scientific decoupling' between China and the USA in remote sensing. Using scientometric full-text analysis, current trends and developments are revealed. [ABSTRACT FROM AUTHOR]

Spartina alterniflora (S. alterniflora) has grown rapidly in China since its introduction in 1979, showing the trend of alien species invasion, which has seriously affected the ecosystem balance of coastal wetlands. The temporal and spatial expansion law of S. alterniflora can be obtained through remote sensing monitoring, which can provide a reference and basis for S. alterniflora management. This paper presents a method for extracting and mapping S. alterniflora based on phenological characteristics. The coastal areas of the Yangtze River Delta Urban Agglomeration are selected as the research area, and the Landsat time series data from 1990 to 2022 on the Google Earth Engine (GEE) platform are used to support the experiment in this paper. Firstly, the possible growing area of S. alterniflora was extracted using the normalized differential moisture index (NDMI), normalized differential vegetation index (NDVI), and normalized differential water index (NDWI); Then, the time series curve characterizing the phenological characteristics of vegetation was constructed using the vegetation index to determine the difference phase of phenological characteristics between S. alterniflora and other vegetation. Finally, a decision tree was constructed based on the phenological feature difference phase data to extract S. alterniflora, and it is applied to the analysis of temporal and spatial changes of S. alterniflora in the study area from 1990 to 2022. The results show that the area of S. alterniflora increased from ~1426 ha in 1990 to ~44,508 ha in 2022. However, the area of S. alterniflora began to show negative growth in 2015 due to the construction of nature reserves and ecological management. The results of correlation analysis showed that the growth of C. japonicum was significantly affected by temperature stress and weakly affected by precipitation. This study verified that Landsat time series images can effectively extract vegetation phenological information, which has strong feasibility for extraction and dynamic monitoring of S. alterniflora and provides technical support for the management and monitoring of invasive plants in coastal wetlands. [ABSTRACT FROM AUTHOR]

Haiyang-2A (HY-2A) is the first marine dynamic environment satellite established by China, which has made significant contributions to the marine scientific research field. It carries the satellite-based GPS receiver named HY2, which was independently developed by China. It is an experimental satellite-borne GPS receiver for low earth orbit satellites, and during its operational period in orbit, the satellite-borne GPS data are not made accessible to the public. Therefore, this paper assesses the quality of HY-2A satellite-borne GPS data based on indicators such as satellite visibility, multipath effect, and ionospheric delay. The results indicate that the data acquired by the HY2 receiver are of high quality. The precise orbit determination (POD) uses the reduced-dynamic (RD) method. The adjustment effects of the pseudo-stochastic pulse time interval and a priori sigma on POD are analyzed, and the antenna phase center variation (PCV) is estimated using the direct method and residual method. Furthermore, this paper investigates the impact of PCV models with different resolutions (10° × 10° and 5° × 5°) on satellite orbit determination. To evaluate the orbit precision, three methods are used for validation, including carrier phase residual analysis, external precise science orbit (PSO) validation, and SLR three-dimensional (3D) validation, respectively. The results indicate that the highest orbit precision is achieved when the pseudo-stochastic pulse time interval is configured to 15 min, with the a priori sigma of 1 × 10−8 m/s2. The orbit carrier phase residuals reach the millimeter level. The 10° × 10°PCV model was estimated using the direct method and residual method, respectively; the root mean square of the external orbit validation for both methods show a millimeter-level improvement. The results obtained from the direct method and residual method are comparable. The resolution is increased from 10° to 5°, and the improvement in orbital precision is relatively small. The results obtained from the SLR 3D validation are consistent with those from the external PSO validation. The experimental results contribute valuable information for the POD of the HY2 series satellites. [ABSTRACT FROM AUTHOR]

Land desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images to extract desert vegetation data and quickly locate and accurately monitor land desertification in relevant areas according to changes in vegetation coverage. Due to the strong light and dry climate of deserts in Northwest China, which results in deeper vegetation shadow texture and mostly dry shrubs with fewer stems and leaves, the accuracy of the vegetation index commonly used in visible remote sensing image classification is not able to meet the requirements for monitoring and evaluating land desertification. For this reason, in this paper, we took the Hangjin Banner in Bayannur as an example and constructed a new vegetation index, the HSVGVI (hue–saturation–value green enhancement vegetation index), based on the HSV (hue–saturation–value) color space using channel enhancement that can improve the extraction accuracy of desert vegetation and reduce misclassification. In addition, in order to further test the extraction accuracy, samples of densely vegetated and multi-shaded areas were divided in the study area according to the accuracy-influencing factors. At the same time, the HSVGVI was compared with the vegetation indices EXG (excess green index), RGBVI (red–green–blue vegetation index), MGRVI (modified green–red vegetation index), NGBDI (normalized green–red discrepancy index), and VDVI (visible-band discrepancy vegetation index) constructed based on the RGB (red–green–blue) color space. The experimental results show that the extraction accuracy of the EXG and other vegetation indices constructed in RGB color space can only reach 70%, while the extraction accuracy of the HSVGVI can reach more than 95%. In summary, the HSVGVI proposed in this paper can better realize the extraction of desert vegetation data and can provide a reliable technical tool for monitoring and evaluating land desertification. [ABSTRACT FROM AUTHOR]

Microwave hyperspectral instruments represent one of the main atmospheric sounders of China's next-generation Fengyun meteorological satellites. In order to better apply microwave hyperspectral observations in the fields of atmospheric parameter retrieval and data assimilation, this paper analyzes the sensitivity of trace gases to five selected bandwidth channels using a radiative transfer model based on the simulated data of microwave hyperspectral radiances at 50–60 GHz. This method uses information entropy and a weighting function to select channels and analyze the impact of this on the retrieval accuracy of atmospheric profiles before and after channel selection. The experimental results show that channel selection can reduce the number of channels by approximately 74.05% while maintaining a large amount of information content, and this retrieval effect is significantly better than that of MWTS-III. After channel selection, the 10 MHz, 30 MHz, and 50 MHz bandwidths have the best retrieval results in the stratosphere, whole atmosphere, and troposphere, respectively. When considering the number of channels, computational scale, and retrieval results comprehensively, the channel selection method is effective. [ABSTRACT FROM AUTHOR]

The investigation of reactivated landslides in the alpine-canyon areas suffers the difficult accessibility of precipitous terrain. In particular, when reactivated landslides occur along the major roads, efforts are focused on measuring ground surface displacements during road construction. Nevertheless, the ancient landslide deposits may reactivate after several years of road operation, while they show a stable state during the road construction. The characterization of this type of reactivated landslides is challenging, due to their complex mechanism and the limited monitoring data. Appropriate multi-source data can provide insights into deformation fields and enhance the understanding of landslide mechanisms, ensuring the outperformance of remedial works. This paper reports a recent Tangjiawan reactivated landslide along the Wenchuan-Maerkang Highway in Li County, China. The outcomes, including satellite InSAR, in situ real-time monitoring, and detailed ground and UAV investigation, conducted at this landslide are presented. Early deformation of the reactivated landslide began from 2019, with an InSAR-derived velocity of −11.7 mm/year, furthermore, a significant subsidence of about 21.2 mm, which occurred within a span of only 12 days from 3 June 2020 to 15 June 2020, was observed. The deformation characteristics derived from in situ monitoring during the remedial works were likely firstly associated with the initial unreinforced slope condition and the heavy rainfall. Subsequently, the displacement evolution transformed into deformation induced by time-dependent reduction in slope strength under rainfall conditions. The existing of unconsolidated deposits derived from ancient landslides, along with a fragile geo-structure consisting of rock blocks and gravels interlayered with breccias, exacerbated by large relief created a predisposition for landslide reactivation. Furthermore, 13 days of antecedent cumulative rainfall totaling 224.5 mm directly triggered the occurrence of a landslide event. The significance and implications of integrating multiple monitoring techniques are emphasized. [ABSTRACT FROM AUTHOR]

Precipitation is the basic component of the Earth's water cycle. Obtaining high-resolution and high-precision precipitation data is of great significance. This paper establishes a precipitation retrieval model based on a random forest classification and regression model during the day and at night with FY-4B/AGRI Level1 data on China from July to August 2022. To evaluate the retrieval effect of the model, the GPM IMERG product is used as a reference, and the retrieval results are compared against those of the FY-4B/AGRI operational precipitation product. In addition, the retrieval results are analyzed according to different underlying surfaces. The results showed that compared with the FY-4B/AGRI operational precipitation product, the retrieval model can better identify precipitation and capture precipitation areas of light rain, moderate rain, heavy rain and torrential rain. Among them, the probability of detection (POD) of the day model increased from 0.328 to 0.680, and the equitable threat score (ETS) increased from 0.252 to 0.432. The POD of the night model increased from 0.337 to 0.639, and the ETS score increased from 0.239 to 0.369. Meanwhile, the precipitation estimation accuracy of the day model increased by 38.98% and that of the night model increased by 40.85%. Our results also showed that due to the surface uniformity of the ocean, the model can identify precipitation better on the ocean than on the land. Our findings also indicated that for the different underlying surfaces of the land, there is no significant difference in each evaluation index of the model. This is a strong argument for the universal applicability of the model. Notably, the results showed that, especially for more vegetated areas and areas covered by water, the model is capable of estimating precipitation. In conclusion, the precipitation retrieval model that is proposed herein can better determine precipitation regions and estimate precipitation intensities compared with the FY-4B/AGRI operational precipitation product. It can provide some reference value for future precipitation retrieval research on FY-4B/AGRI. [ABSTRACT FROM AUTHOR]

The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha. [ABSTRACT FROM AUTHOR]

Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds. [ABSTRACT FROM AUTHOR]

Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R2 of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R2 of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R2 of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation. [ABSTRACT FROM AUTHOR]

Lanzhou City is located in the semi-arid region of northwest China, which experiences serious desertification. Moreover, the high intensity of land development, with the accelerated industrialization and urbanization, causes increasingly aggravated conflict between humans and the environment. Exploring the response of the ecological environment quality to the natural environment and anthropogenic activities is important to protect the sustainable development of urban economic construction and the environment. Based on the Google Earth Engine (GEE) platform, this paper constructed a modified Remote Sensing Ecological Index (MRSEI) model which could reflect the ecological environment quality by integrating the desertification index (DI) into the Remote Sensing Ecological index (RSEI) model. This paper explores the spatiotemporal variation in the environmental quality from 2000 to 2020 in Lanzhou, China, and analyzes the natural and anthropogenic factors affecting the environment quality in terms of temperature, precipitation, gross domestic product (GDP), land use, night lighting, and population. The results showed that the mean value of MRSEI ranged from 0.254 to 0.400. The area undergoing fast growth in ecological quality was in the northwestern part of Lanzhou, and the area of decrease was in the central part. Various factors have different degrees of influence on the ecosystem, with temperature, precipitation, and land use having a greater impact, and GDP and population having a limited impact. Precipitation and temperature showed a strong impact when interacting with other factors, demonstrating that precipitation and temperature were also key factors affecting MRSEI. Overall, climate change and the implementation of ecological restoration projects have led to an improvement in the quality of the ecological environment in Lanzhou. This study provides a reference for understanding the spatiotemporal changes in the ecological environment in semi-arid Lanzhou and is conducive to formulating proper protection strategies. [ABSTRACT FROM AUTHOR]

In this paper, multi-source observation, such as aircraft, ground-based remote sensing, and satellite-retrieved data, has been utilized to compare and analyze the vertical characteristics of aerosol optical properties and the planetary boundary layer height (HPBL) over the North China Plain (NCP) region during May–June 2016. Aircraft observations show the vertical profiles of aerosol absorption coefficients (σabs), scattering coefficients (σsca), and extinction coefficients (σext) gradually decrease with altitude, with their maximum values near HPBL. The vertical profiles of σext depended most on the vertical distribution of measured σsca, indicating a significant contribution of scattering aerosols. In addition, the prominent characteristic of the inverse relationship between σext and moisture profile could serve as a reference for predicting air quality in the NCP region. The lower layer pollution during the field experiment was likely caused by the accumulation of fine-mode aerosols, characterized by the vertical distribution of the Ångström exponent and the Aerosol Robotic Network (AERONET) products. Typically, HPBL derived from aircraft and surface Micro Pulse Lidar (MPL) was approximate, while the predicted HPBL by meteorological data indicates an underestimation of ~192 m. Aerosol optical depth (AOD) calculated from aircraft and ground-based remote sensing (such as MPL and AERONET) experienced a strong correlation, and both of them exhibited a similar tendency. However, the AOD retrieved from satellites was significantly larger than that from aircraft and ground-based remote sensing. Overall, the inversion algorithm, cloud identification algorithm, representativeness of the space, and time of the observation may lead to an overestimation or underestimation of AOD under certain circumstances. This study may serve as a re-evaluation of AOD retrieved from multi-source observations and provide a reference to uncover the actual atmospheric environment in the NCP regions. [ABSTRACT FROM AUTHOR]

Mountainous terrains are typical over southeast China, with complex and diverse topography, large terrain undulations, rich geographic features, and meteorological variations. Previous studies show that ERA5 meteorological variables are generally accurate with respect to large plains or urban agglomerations, while their applicability to mountainous areas remains inconclusive. In this paper, using high-precision measurements probed by ground-based remote sensing instruments in May–July 2023 at a typical mountainous Shanghuang site in southeast China, the vertical accuracy of the ERA5 reanalysis datasets were comparatively evaluated. Our findings depict that the horizontal wind speeds of the ERA5 reanalysis data show a good performance compared to the Doppler lidar observations. In quantitative terms, ERA5 horizontal wind speeds are about 8% higher than the observed values below a height of 400 m, while above 400 m, an increasing negative bias is observed along as altitude increases. Differing from the horizontal wind speeds, there is a large discrepancy in the vertical wind speeds between the ERA5 and the observations, with a deviation of −150% to 40%. In terms of the thermal variables, the temperature extracted from ERA5 are consistent with the measurements in the low troposphere. Nevertheless, large systematic errors occur at 2000–3000 m, and the overall presentation shows that the errors gradually increase with the increase in altitude. Concerning the relative humidity, the general trend in ERA5 is similar to that observed by the microwave radiometer, but the relative errors from 500 to 2500 m range from 40% to 100%. This study also reveals that ERA5 is poorly representative and requires further improvements during extreme weather events such as rainstorms and typhoons. In particular, the horizontal wind speeds at the middle and lower levels deviate strongly from the observations. Given the importance of atmospheric thermodynamic stratifications in terms of both environmental and climatic issues, the results expand the application of the ERA5 reanalysis datasets in the mountainous areas of southeast China. More importantly, it provides credible reference data for the meteorological predictions and climate modelings in the southeast China mountainous region. [ABSTRACT FROM AUTHOR]

A trend in the development of spaceborne Synthetic Aperture Radar (SAR) technology is the shift from a single-satellite repeated observation mode to a multi-satellite collaborative observation mode. However, current multi-satellite collaborative geolocation algorithms face challenges, such as geometric model mismatch and poor baseline estimation accuracy, arising from highly dynamic changes among multi-satellites. This paper introduces a high-precision and efficient geolocation algorithm for a spaceborne bistatic interferometric SAR (BiInSAR) system based on an improved range–Doppler (IRD) model. The proposed algorithm encompasses three key contributions. Firstly, a comprehensive description of the spatial baseline geometric model unique to the bistatic configuration is provided, with a specific focus on deriving the perpendicular baseline expression. Secondly, IRD geolocation functions are established to meet the specific requirements of the bistatic configuration. Then, a novel BiInSAR geolocation algorithm based on the IRD's functions is proposed, which can significantly improve the target geolocation accuracy by modifying the range–Doppler equation to suit the bistatic configuration. Meanwhile, a low-coupling parallel calculation method is proposed, which can improve the calculation speed by two to three times. Finally, the accuracy and efficiency of the algorithm are demonstrated using experimental data acquired by the TH-2 satellite, which is China's first spaceborne BiInSAR system. The experimental results prove that the IRD algorithm exhibits geolocation accuracy with an average error of less than 1 m and a standard deviation of less than 2.5 m while maintaining computational efficiency at a calculation speed of 1,429,678 pixels per second. [ABSTRACT FROM AUTHOR]

In typical alteration extraction methods, e.g., band math and principal component analysis (PCA), the bands or band combinations unitized to extract altered minerals are usually selected based on empirical models or previous rules. This results in significant differences in the alteration of mineral mapping even in the same area, thus greatly increasing the uncertainty of mineral resource prediction. In this paper, an intelligent alteration extraction approach was proposed in which an optimization algorithm, i.e., a genetic algorithm (GA), was introduced into the PCA; this approach is termed GA-PCA and is used for selecting the optimized band combinations of mineralized alterations. The proposed GA-PCA was employed to map iron oxides and hydroxyl minerals using the most commonly adopted multispectral data, i.e., Landsat-8 OLI data, at the Lalingzaohuo polymetallic deposits, China. The results showed that the spectral characteristics of GA-PCA-selected OLI band combinations in the research area were beneficial for enhancing alteration information and were more capable of suppressing the interference of vegetation information. The mapping alteration zones using the GA-PCA approach had a higher agreement with known ore spots, i.e., 25% and 33.3% in ferrous-bearing and hydroxyl-bearing deposits, compared to the classical PCA. Furthermore, two predicted targets (not shown in the classical PCA results) were precisely obtained via analyzing the GA-PCA alteration maps combined with the ore-forming geological conditions of the mine and its tectonic characteristics. This indicated that the intelligent selection of mineral alteration band combinations increased the reliability of remote sensing-based mineral exploration. [ABSTRACT FROM AUTHOR]

Underground coal-mining-induced ground subsidence deformation is a common geological disaster impacting buildings, transportation and water supplies. Models predicting ground subsidence dynamically with high precision are important for the prevention of damage derived from ground subsidence. In this paper, the Hook function is utilized to develop a model describing the velocity of ground subsidence due to underground coal mining. Based on the subsidence velocity model, a dynamic subsidence model is established by taking an integral of the velocity model. Coefficients of the model, which depend on maximum subsidence, maximum subsidence velocity and the time corresponding to the maximum subsidence velocity, are related to the geological and mining conditions of the coal seam being investigated. A Levenberg–Marquardt-algorithm-based method is also proposed to calculate the optimal model coefficients based on subsidence velocity observations. Four continuously operating Global Navigation Satellite System (GNSS) stations were constructed above a typical longwall coal mining working face in the Jining mining area, China. These GNSS stations collected subsidence observations over two years, which were used to validate the developed prediction model. The results show that the root-mean-square (RMS) of the model-predicted ground subsidence error is 56.1 mm, and the maximum relative error is 2.5% for all four GNSS stations, when the ground subsidence is less than 6000 mm. [ABSTRACT FROM AUTHOR]

Long-distance water transfer is a critical engineering measure to rectify disparities in water resource distribution across regions. The effective operation and safety of such projects are paramount to their success, as localized issues can have cascading consequences, potentially disrupting the entire network. Conventional ground-based monitoring methods have limitations in measuring the deformation of large-scale structures. In this paper, InSAR is employed to monitor the deformation of the Shuangwangcheng (SWC) Reservoir, which features a long embankment dam as part of the South-to-North Water Diversion Project in China. We utilize data from both Sentinel-1 and TerraSAR-X satellites to derive 7-year deformation. Results reveal that the entire dam experiences continuous subsidence, with the maximum deformation in the line-of-sight direction measuring ~160 mm. While minor differential settlements are noted in different sections of the dam, the gradient is not significant due to the dam's substantial length. The InSAR deformation results from multiple geometries demonstrate good consistency, with the highest correlation observed between the Sentinel-1 ascending and descending datasets, exceeding 0.9. Validation against the GNSS observations of the three sites on the SWC Dam shows the accuracy of InSAR displacements is ~8 mm. Water level changes do impact deformation, but consolidation settlement appears to be the primary controlling factor during the monitoring period. This study underscores the potential of InSAR in long-distance water transfer projects and highlights that spatially continuous deformation is the most significant advantage. [ABSTRACT FROM AUTHOR]

Forest stock volume is the main factor to evaluate forest carbon sink level. At present, the combination of multi-source remote sensing and non-parametric models has been widely used in FSV estimation. However, the biodiversity of natural forests is complex, and the response of the spatial information of remote sensing images to FSV is significantly reduced, which seriously affects the accuracy of FSV estimation. To address this challenge, this paper takes China's Baishanzu Forest Park with representative characteristics of natural forests as the research object, integrates the forest survey data, SRTM data, and Landsat 8 images of Baishanzu Forest Park, constructs a time series dataset based on survey time, and establishes an FSV estimation model based on the CNN-LSTM-Attention algorithm. The model uses the convolutional neural network to extract the spatial features of remote sensing images, uses the LSTM to capture the time-varying characteristics of FSV, captures the feature variables with a high response to FSV through the attention mechanism, and finally completes the prediction of FSV. The experimental results show that some features (e.g., texture, elevation, etc.) of the dataset based on multi-source data feature variables are more effective in FSV estimation than spectral features. Compared with the existing models such as MLR and RF, the proposed model achieved higher accuracy in the study area (R2 = 0.8463, rMSE = 26.73 m3/ha, MAE = 16.47 m3/ha). [ABSTRACT FROM AUTHOR]

As a major grain-producing region in China, the North China Plain (NCP) faces serious challenges such as water shortage and land subsidence. In late 2014, the Central Route of the South-to-North Water Diversion Project (SNWD-C) began to provide NCP with water resources. However, the effectiveness of this supply in mitigating land subsidence remains a pivotal and yet unassessed aspect. In this paper, we utilized various geodetic datasets, including the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO), Global Navigation Satellite System (GNSS) and leveling data, to conduct a spatial-temporal analysis of the equivalent water height (EWH) and vertical ground movement in the NCP. The results reveal a noteworthy decline in EWH from 2011 to 2015, followed by a slight increase with minor fluctuations from 2015 to 2020, demonstrating a strong correlation with the water resources supplied by the SNWD-C. The GRACE-derived surface deformation rate induced by hydrological loading is estimated to be <1 mm/yr. In comparison, GNSS-derived vertical ground movements exhibit considerable regional differences during the 2011–2020 period. Substantial surface subsidence is evident in the central and eastern NCP, contrasting with a gradual uplift in the front plain of the Taihang Mountains. Three-stage leveling results indicate that the rate of subsidence in the central and eastern plains is gradually increasing with the depression area expanding from 1960 to 2010. Based on these geodetic results, it can be inferred that the SNWD-C's operation since 2014 has effectively mitigated the reduction in terrestrial water storage in the NCP. However, land subsidence in the NCP persists, as the subsidence rate does not turn around in sync with the change in EWH following the operation of SNWD-C. Consequently, it's necessary to maintain and enforce existing policies, including controlling groundwater exploitation and water resources supply (e.g., SNWD-C) to curtail the exacerbation of land subsidence in the NCP. Additionally, continuous monitoring of land subsidence by GRACE, GNSS, leveling and other geodetic techniques is crucial to enable timely policy adjustments based on monitoring results. [ABSTRACT FROM AUTHOR]

Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas. [ABSTRACT FROM AUTHOR]

Ponds constitute a pivotal component of aquatic ecosystems. The aquatic ecosystem of the Huai River Basin (HRB) in China was once damaged by severe pollution, and numerous ponds in the basin have not been secured. In this paper, Shenqiu County, a typical county in HRB with many ponds, is selected. Based on high-resolution images with ALOS in 2010, GF-2 in 2016, and GF-1 in 2022, we employed discriminant analysis (DA), classification and regression tree, support vector machine, and random forest to extract the ponds based on object-oriented and further analyzed the spatial-temporal variations of the ponds in this county. The results of the DA in these three years exhibited a higher kappa coefficient (>0.7), and overall accuracy (>75%), signifying superior performance when compared to the other three methods. There were 4625, 5315, and 4748 ponds in 2010, 2016, and 2022, with a total area of 12.87, 11.99, and 9.37 km2, respectively. The number of ponds had a trend of rising in the initial period (2010–2016) and falling later (2016–2022), while the total area revealed a continuous decline. Meanwhile, these ponds showed a clustering phenomenon with three main clustering areas, and the scope of the clustering areas also changed to a certain extent from 2010 to 2022. Our study offers valuable methodological support for the ecological monitoring and management of water environments in regions characterized by a dense concentration of ponds. The crucial data related to ponds in this study will help inform both environmental and social development initiatives within the area. [ABSTRACT FROM AUTHOR]

In recent years, numerous ancient landslides initially triggered by historic earthquakes on the eastern Tibetan Plateau have been reactivated by fault activity and heavy rainfall, causing severe human and economic losses. Previous studies have indicated that short-term heavy rainfall plays a crucial role in the reactivation of ancient landslides. However, the deformation behavior and reactivation mechanisms of seasonal rainfall-induced ancient landslides remain poorly understood. In this paper, taking the Dandu ancient landslide as an example, field investigations, ring shear experiments, and interferometric synthetic aperture radar (InSAR) deformation monitoring were performed. The cracks in the landslide, formed by fault creeping and seismic activity, provide pathways for rainwater infiltration, ultimately reducing the shear resistance of the slip zone and causing reactivation and deformation of the Dandu landslide. The deformation behavior of landslides is very responsive to seasonal rainfall, with sliding movements beginning to accelerate sharply during the rainy season and decelerating during the dry season. However, this response generally lags by several weeks, indicating that rainfall takes time to infiltrate into the slip zone. These research results could help us better understand the reactivation mechanism of ancient landslides triggered by seasonal rainfall. Furthermore, these findings explain why many slope failures take place in the dry season, which typically occurs approximately a month after the rainy season, rather than in the rainy season itself. [ABSTRACT FROM AUTHOR]

This paper comprehensively analyzed two consecutive tornado events associated with heavy precipitation during the Mei-yu season (a period of continuous rainy weather that occurs in the middle and lower reaches of the Yangtze River in China from mid-June to mid-July each year) and detailed the formation and development process of the tornadoes using Doppler weather radar, wind profiler radar, ERA5 reanalysis data, ground automatic station data and other multi-source data. The results showed that: (1) Small-scale vortices were triggered and developed during the eastward movement of the low vortex, forming two tornadoes successively on the eastern section of the Mei-yu front. (2) The presence of a gap on the front side of the reflectivity factor profile indicated that strong incoming airflow entered the updraft. Mesocyclones were detected with decreasing heights and increasing shear strengths. The bottom height of the tornado vortex signature (TVS) dropped to 0.7 km, and the shear value increased to 55.4 × 10−3 s−1. Tornado debris signatures (TDSs) could be seen with a low cross-correlation coefficient (CC) value area of 0.85–0.9 in the mesocyclone. The difference between the lowest-level difference velocity (LLDV) and the maximum difference velocity (MXDV) reached the largest value when a tornado occurred. (3) The continuously enhanced low-level jet propagated downward to form a super-low-level jet, and the strong wind direction and wind speed convergence in the boundary layer created a warm, moist and unstable atmosphere in Suzhou. With the entrainment of dry air, the northwest dry jet and the southeast moist jet stimulated the formation of a miniature supercell. (4) The low-level vertical wind shear of 0–1 km increased significantly upon tornado occurrence, which was more conducive to the formation and intensification of horizontal vorticity tubes. Encountering updrafts and downdrafts, the vorticity tubes might have been stretched and intensified. The first lightning jumps appeared 15 min and 66 min earlier than the Kunshan Bacheng tornado and the Taicang Liuhe tornado. The Liuhe tornado occurred during the stage when the lightning frequency reached its peak and then fell back. [ABSTRACT FROM AUTHOR]

This article takes the measured Total Electron Content (TEC) from the GPS points of the China Regional Crust Observation Network as the starting point to establish a regional ionospheric empirical model. The model's performance is enhanced by considering solar flux and geomagnetic activity data. The refinement function model of the ionospheric TEC diurnal variation component, seasonal variation component, and geomagnetic component is studied. Using the nonlinear least squares method to fit undetermined coefficients, MEFM-ITCR (Multi-parameter Empirical Fusion Model–Ionospheric TEC China Regional Model) is proposed to forecast the regional ionosphere TEC in China. The results show that the standard deviation of MEFM-ITCR residuals is 3.74TECU, and MEFM-ITCR fits the modeling dataset well. Analyses of geographic location variation, seasonal variation, and geomagnetic disturbance were carried out for MEFM-ITCR performance. The results indicate that in the Chinese region, MEFM-ITCR outperforms IRI2020 and NeQuick2 models in terms of forecast accuracy, linear correlation, and model precision for TEC measured using GPS points under different latitudes and longitudes, different seasons, and different geomagnetic disturbances. The empirical TEC model built for the Chinese region in this paper provides a new ionospheric delay correction method for GNSS single frequency users and is of great significance for establishing other new and improving existing ionospheric empirical models. [ABSTRACT FROM AUTHOR]

Against the background of global warming, permafrost areas are facing increasing thawing, and the threat to the surface of the Qinghai–Tibet Engineering Corridor (QTEC) is serious. It is imperative to understand the current surface deformation and analyze the changes spatiotemporal characteristics for future warnings. At present, observation of a long time series and overall coverage of vertical ground deformation in QTEC are lacking. This paper takes the permafrost deformation of the QTEC as its research object. It uses the pretreated LiCSAR product and combines it with the LiCSBAS package to obtain monitoring results of the long time series deformation of the engineering corridor's surface. The SAR image acquisition date is taken as the constraint, the results covering the whole processing area are selected, and then the vertical deformation information covering the entire engineering corridor area by ignoring the north–south displacement is calculated. The results show that the surface of the study area, as a whole, slightly subsided between May 2017 and March 2022, and the vertical deformation rate was mostly distributed at −27.068 mm/yr − 18.586 mm/yr, with an average of −1.06 mm/yr. Vertical deformation dominated at 52.84 percent of the study area, of which settlement accounted for 27.57 percent and uplift accounted for 25.27 percent. According to the statistics of the normal distribution of deformation velocity per pixel, a total of 77% of the engineering corridor was stable, with a vertical deformation rate between −6.964 mm/yr and −4.844 mm/yr, and 17.7% of the region was sub-stable, with a settling rate of −12.868 mm/yr − –6.964 mm/yr. The unstable regions included areas with settlement rates greater than 12.868 mm/yr and uplift rates greater than 10.748 mm/yr, representing 4.4 percent and 0.9 percent of the total area, respectively, for a total of 5.3 percent. The results of this paper can be used as the theoretical basis and as basic data for decision making and scientific research in various departments, and they are of great significance for surface stability assessment and early warnings along engineering corridors and traffic projects. [ABSTRACT FROM AUTHOR]

This paper utilizes high-resolution ERA5 hourly data from 1980 to 2020 and long-term normalized difference vegetation index (NDVI) time series obtained from remote sensing and applies trend analysis, correlation analysis, lag analysis, and other methods to study the spatiotemporal characteristics of extreme rainfall at daily and hourly scales in the Huang-Huai-Hai Plain. The paper explores the NDVI's variability and its relationship with extreme hourly precipitation and analyzes the main factors affecting it. The study made the following observations: (1) The extreme daily precipitation in the Huang-Huai-Hai Plain shows a decreasing trend, with a 13.6 mm/yr reduction rate. In contrast, the proportion of extreme rainfall to total precipitation generally exceeds 20%, and the intensity of extreme rain has gradually increased. The spatial distribution pattern of extreme rainfall follows the distribution pattern of China's rain belts, with the terrain being an important influencing factor. The high-incidence areas for extreme rainfall are the Huaihe River region and the Shandong Peninsula. (2) The observed significant increase in hourly extreme precipitation events in the Shandong and Henan provinces of the Huang-Huai-Hai Plain has led to an increased risk of flooding, while the corresponding events in the northwest region of the Plain have exhibited a gradual weakening trend over time. (3) The extreme hourly precipitation in the Huang-Huai-Hai plain shows a frequent and scattered pattern, with decreasing intensity over time. Extreme precipitation mainly occurs in the first half of the night, especially between 19:00 and 21:00, with extreme hourly rainfall intensity fluctuating between 0.2 and 0.25 and the proportion of rainfall to total precipitation reaching as high as 10%. The spatial distribution of extreme hourly rainstorms during the peak period (19:00–21:00) exhibits a high rainfall volume, intensity, and frequency pattern in the eastern region, while the western part exhibits low rainfall volume, intensity, and frequency. (4) The incidence of extremely heavy rainfall in an hour has exhibited a more significant increase compared to extreme daily events in the Huang-Huai-Hai Plain, primarily in the form of backward-type precipitation. Hourly extreme precipitation events in the Huang-Huai-Hai Plain are affected by terrain and land use/cover change (LUCC), with the micro-topography of hilly areas leading to a concentrated distribution of precipitation and LUCC suppressing extreme precipitation events in arid climates. (5) At the ten-day scale, the spatial distribution of the NDVI shows a gradually increasing trend from northwest to southeast, with the highest NDVI value reaching up to 0.6 in the southern part of the study area. For extreme hourly precipitation, there is no significant change observed at the multi-year ten-day scale; while the NDVI in the northern and central parts of the Huang-Huai-Hai Plain shows a significant decreasing trend, in contrast, it presents a significant increasing trend in the southern region. (6) Finally, the correlation between NDVI at the ten-day scale and extreme hourly precipitation exhibits a decreasing pattern from north to south, with a correlation coefficient decreasing from 0.48 to 0.08. The lagged correlation analysis of extreme hourly rainfall and NDVI for one, two, and three ten-day periods shows that the lagged effect of extreme hourly precipitation on NDVI is negligible. Analyzing the correlation between extreme hourly rainfall and NDVI for different months, the impact of extreme hourly precipitation on NDVI is predominantly negative, except for June, which shows a positive correlation (0.35), passing the significance test. This study offers a scientific foundation for enhancing disaster warning accuracy and timeliness and strengthening the research on disaster reduction techniques. [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]

Continuous evapotranspiration (ET) data with high spatial resolution are crucial for water resources management in irrigated agricultural areas in arid regions. Many global ET products are available now but with a coarse spatial resolution. Spatial-temporal fusion methods, such as the spatial and temporal adaptive reflectance fusion model (STARFM), can help to downscale coarse spatial resolution ET products. In this paper, the STARFM model is improved by incorporating the temperature vegetation dryness index (TVDI) into the data fusion process, and we propose a spatial and temporal adaptive evapotranspiration downscaling method (STAEDM). The modified method STAEDM was applied to the 1 km SSEBOP ET product to derive a downscaled 30 m ET for irrigated agricultural fields of Northwest China. The STAEDM exhibits a significant improvement compared to the original STARFM method for downscaling SSEBOP ET on Landsat-unavailable dates, with an increase in the squared correlation coefficients (r2) from 0.68 to 0.77 and a decrease in the root mean square error (RMSE) from 10.28 mm/10 d to 8.48 mm/10 d. The ET based on the STAEDM additionally preserves more spatial details than STARFM for heterogeneous agricultural fields and can better capture the ET seasonal dynamics. The STAEDM ET can better capture the temporal variation of 10-day ET during the whole crop growing season than SSEBOP. [ABSTRACT FROM AUTHOR]

Drought is a complex natural hazard that affects various regions of the world, causing significant economic and environmental losses. Accurate and timely monitoring and forecasting of drought conditions are essential for mitigating their impacts and enhancing resilience. Satellite-based drought indices have the advantage of providing spatially continuous and consistent information on drought severity and extent. A new drought product was developed from the thermal infrared observations of the Fengyun (FY) series of satellites. We proposed a data fusion algorithm to combine multiple FY satellites, including FY-2F, FY-2G, and FY-4A, to create a long time series of a land surface temperature (LST) data set without systematic bias. An FY drought index (FYDI) is then derived by coupling the long-term LST data set with the surface–atmospheric energy exchange model at 4 km spatial resolution over China from 2013 to present. The performance and reliability of the new FYDI product are evaluated in this study by comparing it with the Meteorological-drought Composite Index (MCI), one of the authoritative drought monitoring indices used in the Chinese meteorological services. The main objectives of this paper are: (1) to evaluate the performance of the FYDI in capturing the spatiotemporal patterns of drought events over China; (2) to quantitively analyze the consistency between the FYDI and MCI products; and (3) to explore the advantages and limitations of the FYDI for drought monitoring and assessment. The preliminary results show that the FYDI product has good agreement with the MCI, indicating that the FYDI can effectively identify the occurrence, duration, severity, and frequency of drought events over China. These two products have a strong correlation in terms of drought detection, with a correlation coefficient of approximately 0.7. The FYDI was found to be particularly effective in the regions where ground observation is scarce, with the capability of reflecting the spatial heterogeneity and variability of drought patterns more clearly. Overall, the FYDI can be a useful measure for operational drought monitoring and early warning, complementing the existing ground-based MCI drought indices. [ABSTRACT FROM AUTHOR]

Accurately grasping the distribution and area of cotton for agricultural irrigation scheduling, intensive and efficient management of water resources, and yield estimation in arid and semiarid regions is of great significance. In this paper, taking the Xinjiang Shihezi oasis agriculture region as the study area, extracting the spectroscopic characterization (R, G, B, panchromatic), texture feature (entropy, mean, variance, contrast, homogeneity, angular second moment, correlation, and dissimilarity) and characteristics of vegetation index (normalized difference vegetation index/NDVI, ratio vegetation index/DVI, difference vegetation index/RVI) in the cotton flowering period before and after based on GF-6 image data, four models such as the random forests (RF) and deep learning approach (U-Net, DeepLabV3+ network, Deeplabv3+ model based on attention mechanism) were used to identify cotton and to compare their accuracies. The results show that the deep learning model is better than that of the random forest model. In all the deep learning models with three kinds of feature sets, the recognition accuracy and credibility of the DeepLabV3+ model based on the attention mechanism are the highest, the overall recognition accuracy of cotton is 98.23%, and the kappa coefficient is 96.11. Using the same Deeplabv3+ model based on an attention mechanism with different input feature sets (all features and only spectroscopic characterization), the identification accuracy of the former is much higher than that of the latter. GF-6 satellite image data in the field of crop type recognition has great application potential and prospects. [ABSTRACT FROM AUTHOR]

The high-precision prediction of sea clutter reflectivity is helpful in improving the performance of marine radar and sea surface remote sensing capabilities. Under the same sea state, when the significant wave height, wave period, wind speed, and other marine environmental parameters are different, the backward reflectivity of the sea clutter corresponding to the wave structure is not the same. Due to the complex and variable nature of sea clutter characteristics across various wave structures, a meticulous classification of wave structures by integrating multiple marine environmental parameters enables the achievement of the high-precision prediction of sea clutter reflectivity. In this study, utilizing measured data of diverse marine environmental parameters in the Yellow Sea, China, we applied the Affinity Propagation algorithm to data clustering. Based on the clustering outcomes, we accomplished a refined classification of wave structures and developed a discriminant model to precisely classify the refined wave structure, facilitating the categorization of new data. In order to achieve more accurate predictions of sea clutter reflectivity, this paper proposes a deep neural network model named GIT-HYB-DNN, which combines the empirical models GIT and HYB. The GIT-HYB-DNN model is applied to predict the reflectivity for each wave structure category separately. The results demonstrate that the root mean square errors of sea clutter reflectivity predictions for different wave structure categories in this study range from 0.62 dB to 0.84 dB. The prediction errors are significantly reduced compared to the root mean square error of 1.08 dB, which was obtained without refined wave structure classification. This study holds theoretical significance and practical value for the investigation of sea clutter characteristics and the selection of radar parameters. [ABSTRACT FROM AUTHOR]

Precipitation datasets derived from reanalysis products play a crucial role in weather forecasting and hydrological applications. This paper aims to evaluate the performance of two distinct reanalysis precipitation products, i.e., the first-generation Chinese global land-surface reanalysis precipitation product (CRA40) and the fifth-generation European reanalysis precipitation product (ERA5), over mainland China. The evaluation is based on continuous and categorical statistical indicators with daily-scale gridded-point rain gauge data obtained from Chinese surface meteorological stations. The findings of this study can be summarized as follows: (1) CRA40 demonstrates a clear superiority over ERA5 in terms of the 13-year daily mean precipitation and seasonal daily precipitation. CRA40 exhibits better correlation coefficients (0.97), relative biases (5.25%), root mean square errors (0.34 mm), and fractional standard errors (0.05). (2) Both reanalyzed precipitation products generally exhibit an overestimation of precipitation in mainland China. The degree of overestimation is particularly pronounced in dry climatic regions (e.g., QZ, XJ), while wet regions (e.g., CJ, HN) demonstrate relatively less overestimation. (3) ERA5 shows better performance in the detection of daily precipitation. Neither CRA40 nor ERA5 can capture heavy precipitation events well. These findings are expected to advance our understanding of the strengths and limitations of the reanalysis precipitation products, CRA40 and ERA5, over China. [ABSTRACT FROM AUTHOR]

China accomplished a historic milestone in 2020 when the mission Chang'e-5 (CE-5) to the Lunar's surface was successfully launched. An extraordinary component of this mission is the "Lunar Regolith Penetrating Radar" (LRPR) housed within its lander, which currently stands as the most advanced payload in terms of vertical resolution among all penetrating radars employed in lunar exploration. This provides an unprecedented opportunity for high-precision research into the interior structure of the shallow lunar regolith. Previous studies have achieved fruitful research results based on the data from LRPR, updating our perception of the shallow-level regolith of the Moon. This paper provides an overview of the new advancements achieved by the LRPR in observing the basic structure of the shallow regolith of the Moon. It places special emphasis on the role played by the LRPR in revealing details about the shallow lunar regolith's structure, its estimated dielectric properties, the provenance of the regolith materials from the landing area, and its interpretation of the geological stratification at the landing site. Lastly, it envisions the application and developmental trends of in situ radar technology in future lunar exploration. [ABSTRACT FROM AUTHOR]

Since 2002, China has launched four Haiyang-1 (HY-1) satellites equipped with the Chinese Ocean Color and Temperature Scanner (COCTS), which can observe the sea surface temperature (SST). The planned new generation ocean color observation satellites also carry a sensor for observing the SST represented by the payload in this paper. We analyze the spectral brightness temperature (BT) difference between the payload and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra for the thermal infrared channels (11 and 12 µm) based on atmospheric radiative transfer simulation. The bias and standard deviation (SD) of spectral BT difference for the 11 µm channel are −0.12 K and 0.15 K, respectively, and those for the 12 µm channel are −0.10 K and 0.03 K, respectively. When the total column water vapor (TCWV) decreases from the oceans near the equator to high-latitude oceans, the spectral BT difference of the 11 µm channel varies from a positive deviation to a negative deviation, and that of the 12 µm channel basically remains stable. By correcting the MODIS BT observation using the spectral BT differences, we produce the simulated BT data for the thermal infrared channels of the payload, and then validate it using the Infrared Atmospheric Sounding Interferometer (IASI) carried on METOP-B. The validation results show that the bias of BT difference between the payload and IASI is −0.22 K for the 11 µm channel, while it is −0.05 K for the 12 µm channel. The SD of both channels is 0.13 K. In this study, we provide the simulated BT dataset for the 11 and 12 µm channels of a payload for the retrieval of SST. The simulated BT dataset corrected may be used to develop SST-retrieval algorithms. [ABSTRACT FROM AUTHOR]

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]

Weather forecasting requires a comprehensive analysis of various types of meteorology data, and with the wide application of deep learning in various fields, deep learning has proved to have powerful feature extraction capabilities. In this paper, from the viewpoint of an image semantic segmentation problem, a deep learning framework based on semantic segmentation is proposed to nowcast Cloud-to-Ground and Intra-Cloud lightning simultaneously within an hour. First, a dataset with spatiotemporal features is constructed using radar echo reflectivity data and lightning observation data. More specifically, each sample in the dataset consists of the past half hour of observations. Then, a Light3DUnet is presented based on 3D U-Net. The three-dimensional structured network can extract spatiotemporal features, and the encoder–decoder structure and the skip connection can handle small targets and recover more details. Due to the sparsity of lightning observations, a weighted cross-loss function was used to evaluate network performance. Finally, Light3DUnet was trained using the dataset to predict Cloud-to-Ground and Intra-Cloud lightning in the next hour. We evaluated the prediction performance of the network using a real-world dataset from middle China. The results show that Light3DUnet has a good ability to nowcast IC and CG lightning. Meanwhile, due to the spatial position coupling of IC and CG on a two-dimensional plane, predictions from summing the probabilistic prediction matrices will be augmented to obtain accurate prediction results for total flashes. [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]

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]

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]