Vehicle trajectory is a time series geospatial location sampling data. The traditional vehicle trajectory-map matching methods are mainly computed by ways of global or local incremental optimization, which limited the relative independence in matching process of the trajectory data in spatial temporal situation. To address this problem, this paper proposes the method of computing matching relationships between vehicle trajectory and road map based on the Particle Filter (PF) method. First, construct the road network from the road dataset, and search the neighboring nodes from the road network based on the vehicle sampling locations along the moving direction that are detected from the vehicle trajectory. Then, construct the motion model based on the vehicle trajectories, randomly generate particles on the road arcs that are related to the searched nodes, and move the particles along the sampled road segments according to the trajectory motion model. Second, compute the motion states of the particles according to the motion model in each time state, get the distance errors between the particles and the vehicle position sampling locations, obtain the particle weights based on the Gaussian probability density function, resample particles based on the random resampling method, and then update the motion states of particles iteratively. Finally, compute the accumulated weights of the particles in each of the topologically related road arcs, which are searched by the neighboring nodes, and calculate the matching relations between the vehicle trajectories and the map based on the accumulated weights of the particles. With this method, the experiments were conducted based on the vehicles' trajectories, which were two long sequenced trajectories with the total length > 102 km. The results showed that 85.51% and 93.01% correctness rates of vehicle trajectory-map matching experiments had been achieved for each of the vehicle trajectories. Besides, the motion of the vehicle sampling locations could be reflected by the spatial-temporal movements of the particles, where particles started to follow the motion of the vehicle sampling locations after a few time states. The results showed that it could achieve the accurate matching relations between the vehicle trajectories and the road map.

Existing filtering algorithms based on airborne LiDAR data have many disadvantages, e.g., they fail to make full use of all the information of the LiDAR data, and their data structures are complex or suffer from information loss. In this paper, we proposed an airborne LiDAR 3D filtering algorithm based on the grayscale voxel structure segmentation model. The proposed algorithm ?rst regularizes a given LiDAR point cloud into a grayscale voxel structure to comprehensively utilize the elevation and intensity information of the LiDAR data, in which the voxel's grayscale denotes the discretized mean intensity of LiDAR points within the voxel. Then, the constructed grayscale voxel structure is segmented into multiple 3D connected regions depending on the spatial connectivity and grayscale similarity among voxels. Finally, the 3D connected regions corresponding to ground objects are detected based on elevation differences. The proposed algorithm has the following advantages. (1) It is designed based on the grayscale voxel structure and is a 3D filtering algorithm. (2) The average completeness, correctness and quality of the proposed filtering algorithm for dataset 1 with 0.67 points per square meter were 0.9611, 0.9248, and 0.8934. The average completeness, correctness and quality of the proposed filtering algorithm for dataset 2 with 4 points per square meter were 0.8490, 0.8531, and 0.7404. (3) Its filtering result is in the form of the 3D ground voxel, which can be directly used for creating a 3D model of ground. The sensitivity of "spatial adjacency size" parameter in the model and the validity of the proposed algorithm is analyzed by using two airborne LiDAR benchmark test datasets of different densities provided by the International Society for Photogrammetry and Remote Sensing (ISPRS), and the accuracy of the proposed filtering algorithm is compared with that of the other classical filtering algorithms. Our experimental results of quantitative evaluation show that: (1) The 56-adjacency was the optimal adjacent size. (2) The average completeness, correctness and quality of the proposed filtering algorithm for dataset 1/2 with 0.67/4 points per square meter were 0.9611/0.8490, 0.9248/0.8531, and 0.8934/0.7404, respectively. (3) Compared with other classical filtering algorithms, the proposed algorithm performed better in high-density point cloud data filtering.

Establishing a user preference recommendation model suitable for thematic map product search is one of the effective ways to improve the quality of the thematic map products. In the thematic map product recommendation scenario, there are serious problems of content cold-start and sparse comment data. The existing recommendation algorithms cannot recommend thematic map products with different features for specific types of users, resulting in users' limited preference for obtaining preference information from the thematic maps. Hence, this paper presents a user preference recommendation method based on the combination of CBOW with Negative Sampling and Iten2Vec based on Word2Vec. Firstly, calculating implicit ratings of the interaction behavior data in the user behavior log, to replace sparse user ratings in thematic disaster scenarios; Secondly, extracting context-aware feature information of central thematic map based on CBOW model with Negative Sampling. By controlling the ratio of positive and negative samples to 1:2, the prediction accuracy of the potential score of the target thematic map is improved; Finally, mapping Thematic CMaps with user behavior characteristics information to vector space via Item2Vec, calculating the user's similarity matrix to the thematic map and completing recommendations based on user preference. Test results on thematic map scoring experiment dataset Thematic CMaps and four validation dataset MovieLens show that, compared with the four traditional recommendation algorithm of LFM, Personal Rank, Content Based, and SVD, this proposed method can effectively improve the precision potential scoring, and the highest recommending performance is 27.85%. Compared with Item2Vec with Huffman sampling method and YouTubeNet two neural network recommendation algorithms, the score prediction accuracy has improved to a certain extent, and the recommendation performance has been continuously improved, reaching the maximums of 2.97% and 5.78%. Taking the singular value decomposition (SVD) of the classic algorithm as an example, in the increasing data subset after the segmentation of MovieLens-20M dataset, the score prediction accuracy and performance of the method used in this paper are better than SVD method.

Rapid urban expansion has brought disorder and low efficiency to the socioeconomic development and the land utilizatopm. However, due to a large number of scale-free phenomena in the urban complex system, it is difficult to measure its morphological characteristics effectively. In essence, fractal is a hierarchical system which is related to the complex network cascade structure. Fractal structure can be used to measure the spatial cycle subdivision of urban geographic system, which plays an important role in the exploration on the law of urban morphology evolution, and provides an effective mathematical tool for the implementation of territorial spatial planning. Based on the urban fractal theory, this paper forms a logical frame for the evolution of urban functional land, through the dual analysis of the radial dimension and the grid dimension representing urban spatial form. By calculating and analyzing the empirical case of Zhengzhou, a typical representative of urban growth and evolution in China, the validity of using the second derivative to automatically identify the scaling range in the radius method is verified. Finally, the paper discusses the structural and functional problems hidden in the urban evolution process, and provides theoretical reference and method enlightenment for the exploration of integrated optimization scheme of the fractal urban system. The results show that: (1) Using the second derivative method to automatically identify the boundary of scaling range in the radius method can significantly improve the feasibility of the fractal model. The fitting accuracy R² is increased from 0.920 to above 0.996. This method makes a breakthrough attempt on the application bottleneck in the fractal model of radius method, which is difficult to improve the fitting accuracy. This method has obvious advantages in simulating the urban growth, especially the evolution of urban spatial structure, and can judge the expansion speed and mode, so as to evaluate the city's health. (2) The double scale phenomenon exists in the urban spatial structure of Zhengzhou. The radius method model only fits effectively in the circular range of "the edge of urban center-inside the built-up area", which may have the growth characteristics of self-affine or random multi-fractal. (3) From 1982 to 2020, the urban form of Zhengzhou evolves into a central sprawl mode, and the urban center and the periphery present a dualistic trend. The interaction between the systems is insufficient, the system efficiency is lower, and the urban spatial structure can be further refined and upgraded.

Operational application of an appropriate model to estimate evapotranspiration (ET) and the components evaporation (E), transpiration (T) at a range of space and time scales is very useful for managing water resources. The Two Source Energy Balance (TSEB) and Dual Temperature Difference (DTD) models have been applied to estimate land surface evapotranspiration under various landcover types and environment conditions. The DTD model requires twice radiometric temperature observations as inputs while the TSEB model only use single observation. This may reduce the uncertainty of DTD model which introduced from the observation or remotely sensed based radiometric temperatures. However, the two models may perform inconsistent under various land surface, which mainly associated with the different theoretical mechanisms in the models. In this study, the two models were evaluated using the long time ground observation data collected from three total different landcover types and environment conditions, including alpine grassland, semi-arid irrigated farmland and arid riparian forest in Heihe River Basin in Northwest China. The results showed that the latent heat flux estimated by the DTD model had a better agreement with half an hour ground measurements at the alpine grassland site, with the RMSE value of 62.00 W/m^2, while TSEB model showed a higher RMSE value of 75.49 W/m². But the performance of the two models was associated with the variation of the vegetation coverage. While, in the semi-arid farmland site, the performances of two models were more consistent where they produced a closer RMSE values, but in the arid riparian forest site, both of the models significantly underestimated the latent heat flux. The DTD model showed a worse agreement with the ground measurements in both sensible and latent heat fluxes. The DTD modeled latent heat flux had a higher RMSE values of 136.74 W/m² where the TSEB model had a RMSE value of 86.40 W/m². The better agreement of the TSEB model latent heat fluxes may associate with greater underestimation of modeled sensible heat flux which can partly compensate underestimation of net radiation. However, at the daily scale the performances of the DTD model and TSEB model were more similar. Additionally, the ratio of plant transpiration to evapotranspiration partitioned by the two models were in good agreement with results simulated from water use efficiency (uWUE) model with ground measurements, while the DTD model also performed better than the TSEB model. Finally, the TSEB model was more sensitive to the model input of land surface temperature. Therefore how to improve the accuracy of remote sense land surface temperature products is vital to the model application. Meanwhile, future researches can focus on optimizing model for extending applications over heterogeneous surface and different meteorological conditions.

Earthquake is one of the most serious natural disasters for our human existence, and often causes huge losses. Disaster assessment based on similar historical cases is useful for disaster analysis and quick policy making when detailed real disaster data are not available. Based on the framework of the Assessment Method of Natural Disaster Based on Similarly Historical Cases (SHC model), this paper constructs the Spatial Reasoning of Similarly Historical Cases (SRSHC) model for earthquake casualty assessment, which considers the spatial relevance of historical cases to current disasters. We select earthquake magnitude, focal depth, and time of earthquake to describe the characteristics of an earthquake and use a similarity assessment model based on Manhattan distance to evaluate its similarity to historical cases. Besides, this paper introduces the distance of earthquake fault to quantify the spatial correlation between historical cases and current disaster. In this study, earthquake disasters with a magnitude of 4.0 or above in China from 2000 to 2013 are selected as historical cases. Three representative cases are chosen for accuracy verification, including the Yushu earthquake in Qinghai in 2010, the Lushan earthquake in Sichuan in 2013, and the Ludian earthquake in Yunnan in 2014. Results show that: (1) For the three verification cases, the accuracy of the SRSHC model is all above 95%, indicating that the model has certain feasibility and applicability in assessment of earthquake casualty; (2) Compared with the SHC model, the SRSHC model requires fewer participating cases but higher accuracy. For example, for the Yushu earthquake, when the number of participating cases is three, the accuracy of the SRSHC model reaches highest (97.92%). While the SHC model requires six participating cases to reach the highest accuracy (35.49%). It reveals that the spatial correlation between historical cases and the current disaster has a great impact on the evaluation of results; and (3) The accuracy of the disaster assessment model is related to the number of participating cases. When there are more than two participating cases, the accuracy of the model assessment decreases with increasing number of participating cases. When the number of participating cases is three or four, the accuracy of the SRSHC model is the highest. In conclusion, the advantages of the method developed this study are low cost, high efficiency, timely effectiveness, its simplicity, less constraint, and easy to implement, which has certain practical value and prospects in disaster assessment．

The prevalence of elderly hypertension and type 2 diabetes diseases have a strong positive correlation with regional socioeconomic development. As nighttime light images can reflect the regional socio-economic development directly, the application of nighttime light data to study of diseases in the elderly become very significant. Selecting Changning City as the study area, this paper analyzed the difference in spatial distributions of the prevalence of elderly hypertension and type 2 diabetes among 26 townships based on the Luojia1-01 nighttime light data and the prevalence rate data of these two diseases in the study area. The spatial distribution of the prevalence of these two diseases in the study area was simulated by linear regression models. Results show that: (1) The correlation between mean nighttime light values and prevalence of hypertension or type 2 diabetes was stronger than that between total nighttime light values and prevalence of hypertension or type 2 diabetes. The relationship between mean or total nighttime light values and prevalence of hypertension was weaker than that between mean or total nighttime light values and prevalence of type 2 diabetes in the elderly; (2) The impacts of mean nighttime light on the distribution of the both diseases were larger than that of total nighttime value. And both the mean and total nighttime light had larger impacts on the spatial distribution of type 2 diabetes; (3) The risk of the elderly living in areas with high nighttime light was 6.493 times higher than those living in areas with low nighttime light, with the OR value for type 2 diabetes was 8.556; and (4) The linear regression model between the prevalence of either elderly hypertension or type 2 diabetes and mean nighttime light showed a high accuracy, which could accurately predict the spatial distribution of the prevalence of hypertension or type 2 diabetes of the elderly in the study area. Our research results can provide reference for the application of nighttime light data in disease researches and the analysis of the causes of regional hypertension and type 2 diabetes diseases in the elderly, as well as the investigation and prediction of similar diseases.

As a new driver of economic growth, urban-rural integration is a key way to build an efficient energy economic system and balance economic and social development with ecological environmental protection. Analyzing the effect of urban-rural integration on energy efficiency is important to promote sustainable economic and social development. In this paper, we took the provinces of the Yangtze River Economic Belt as the research object. We used the DEA values to measure the energy efficiency and analyzed its differentiation characteristics over time and space. The level of urban-rural integration was evaluated by a comprehensive index system of economy, society, and infrastructure. Based on these, Moran's I index was used to determine the spatial correlation of energy efficiency, urban-rural integration level, and other influencing factors. Finally, the urban-rural integration and three control variables were estimated by mixed geographically weighted regression to analyze the spatial effect. Based on spatial correlation analysis, the urban-rural integration level of the Yangtze River Economic Belt had a positive effect on energy efficiency. Economy development level and industrial structure also had positive impacts on energy efficiency. However, technological progress was negatively correlated with energy efficiency. Based on estimated regression coefficients, the impact of urban-rural integration on energy efficiency was smaller than technological progress and industrial structure, and economic development level had the least impact on energy efficiency. The positive impact of urban-rural integration level on energy efficiency showed a trend of first increasing and then decreasing with the increasing of energy efficiency, and an spatial pattern of increasing from east to west. The energy efficiency of the Yangtze River Economic Belt presented significant spatial correlation and spatial heterogeneity. Our study demonstrates a significantly increasing energy efficiency result from the process of urban-rural integration in the Yangtze River Economic Belt.

High-precision spatially-explicit population data performs a quantitative reference for evaluating urban resources and environment pressure and promoting a rational population distribution. This study first classified and ranked street blocks of Beijing based on land use categories and VANUI index. Based on this, a hierarchical population spatialization model was built to generate the spatial distribution of population at 100 m resolution. In addition, Beijing permanent resident demographic information of 2012 and 2017, NPP/VIIRS nighttime lights data, land use, road networks, and other auxiliary data were also used as model inputs. In our study, the model simulation error against the verified data was less than 10%. Compared with other published results, the population distribution result generated in this study had a higher overall accuracy and local accuracy. We further analyzed the spatio-temporal pattern of population in Beijing and its impact factors. Results show that the population of Beijing in each 100 m grid varied from -2564 to 1904, with -500~500 being the main change level. The spatial patterns of population in 2012 and 2017 both demonstrated that central Beijing was densely populated while Beijing suburb was sparsely populated. Between these two years, population of Beijing declined by approximately 210,000, which mainly happened in six main districts. The core functional area of Beijing had a remarkable reduction in population, accounting for 62% of the total population decline within the six districts of the city. In addition, population between the second and third ring of Beijing decreased the most, with nearly 110 000 people moved out, accounting for 52% of the population decline within the six districts. On the contrary, the population increased in the surrounding street blocks at the border of the six districts, which might form new population centers in the future. The spatial and temporal dynamics of Beijing's population were closely related to factors, such as the functional orientation of the capital, industrial upgrading and transformation, and the implementation of population redistribution policies. This study provides a scientific reference for the rational layout of Beijing's population space and formulation of Beijing's population redistribution policies in the future.

The delimitation of urban growth boundaries is of great significance for ensuring the rational use of resources and promoting the orderly development of cities and towns. Most existing studies mainly focus on the technical exploration of planning practice, while ignoring the quantitative assessment of the ecological environment to some extent. In addition, few studies have been carried out at the metropolitan area scale. This study takes Wuhan Metropolitan Area as the study case, and proposes to combine multiple factors to build a "dual environment evaluation" system. The FLUS model is used for land growth simulation and delineation of urban growth boundaries. Furthermore, the results are analyzed using the landscape metrics. The results show that: (1) the FLUS simulated urban area expansion demonstrates an applicable accuracy. The simulation results reveal that the urban built-up areas keep expanding, so it is necessary to delimit the development boundary to restrict urban development; (2) The delineated urban growth boundaries can prevent from urban construction occupying areas with high ecological or agricultural value, and improve the urban spatial layout of the Wuhan Metropolitan Area based on the optimization of spatial forms with strong applicability; (3)The evaluation of urban expansion driving factors shows that compared to a single factor library, the "dual environment evaluation" factor library has a higher accuracy, can optimize the landscape pattern, promote the development of construction land, and fill the gaps in the built-up area better, which is corresponded with regional development requirements; and (4) The evaluation of expansion analysis shows that the results are consistent with the expected development pattern of Wuhan Metropolitan Area, and government departments might pay attention to the potential value of the expansion in the airport region, the Yangluo region, the Optics Valley-Future City region, and the Zhifang region. The above shows the effectiveness of the FLUS model in the Wuhan metropolitan area, which also provides great reference for planning management and construction land optimization.

The lack of gain recording and cross-calibration during the OLS sensor navigation makes DMSP nighttime lights image oversaturated in the city center, which affects the accuracy of using night light data to evaluate human activity intensity. In order to suppress the occurrence of saturation, the radiometric calibration night light data developed by Elvedge have been widely used. The radiometric calibration data products have a high accuracy and strong reliability. However, the calibration process is complex, and the required data is usually difficult to obtain. At present, only a few results have applied the calibration data to the continuity analysis. In recent years, many scholars found that NDVI can desaturate DMSP/OLS night light images and enhance the heterogeneity of urban center. Based on this, non-radiation calibration method has been applied to correct the saturation effect and shown a good correction result. On the basis of summarizing the idea by VANUI that the difference between night light intensity and vegetation coverage shows a decreasing trend from the city center to the suburb, this paper considers that the population density increases exponentially with the increase of rural-urban distance. We proposed a correction of nighttime light index based on compound exponential model (CEANI). Results show that (1) compared with VANUI, CEANI showed better details and spatial heterogeneity when characterizing the saturated regions of the city. In addition, CEANI not only identified areas where human activity was concentrated, such as stations, airports, and business areas with high traffic and people flow, but also clearly identified the areas with high vegetation coverage and low DN values such as forests and parks with sparse road network; (2) in the correlation analysis using 25 random samples, CEANI showed a higher correlation (R²mean = 0.79) with radiometric calibration products than VANUI (R²mean = 0.68); (3) CEANI had a stronger correlation with the number of permanent residents and significantly estimated population indicators better than VANUI, which suggests the better calculation index for describing the intensity of human activity. In summary, the CEANI can be used to correct the saturation problem in DMSP/OLS luminous data products. It better shows the internal details of the city and its spatial heterogeneity, and thus can derive more accurate results for the evaluation of human activity intensity.

Due to the fact that optical remote sensing image and SAR image have obvious nonlinear intensity differences, and that SAR image has speckle noise, it is difficult to register them. Feature-based image registration and region-based image registration are the two most common methods of optical and SAR image registration. One advantage of feature-based image registration is that it can solve the problem of rotation, scale, and translation differences between images. Another advantage is the small amount of calculation. However, this method usually has the disadvantages of low registration accuracy and instability. Region-based image registration can achieve high-precision registration of heterogeneous images. However, it performs poorly for images with large rotations, scale differences, and it has heavy computation task. For these problem, this paper combines the advantages of feature-based and region-based image registration methods into a hybrid model and proposes an automatic registration algorithm for optical and SAR images. The optical remote sensing image is the reference image while the SAR image is the one to be registered. The SAR-SIFT based on the feature points is used to complete the coarse registration and then the ROEWA-HOG based on the region is used to complete the fine registration. Firstly, the SAR-SIFT algorithm, robust to nonlinear intensity differences and speckle noise, is used to perform feature point detection and feature matching to calculate the affine transformation model of the image to eliminate the obvious rotation, scale and translation difference between the optical image and the SAR image. This is the coarse image registration. Secondly, we use the block Harris corner detection method to obtain a certain number of evenly distributed feature points on the reference image. We determine the search area of the corresponding points on the image to be registered according to the feature points, calculate the ROEWA gradient of the image, and then use a fast calculation strategy to construct the HOG feature vector in the template area with the feature points as the center. Then, we use SSD as the similarity measure to search the corresponding points on the image to be registered. This is the high-precision image registration. Finally, we carry out the image registration and perform visual inspection and quantitative evaluation of the registration results. It is demonstrated that the algorithm in this paper can combine the advantages of feature-based and region-based image registration methods to better resist the noise effect of SAR images and the nonlinear intensity, rotation, scale, and translation differences between optic and SAR images. The final registration accuracy of our high-precision automatic registration method is 1 pixel. High-precision automatic registration for SAR images can meet subsequent comprehensive applications of optical and SAR images.

The Traditional Range Doppler (RD) algorithm has become the most classic method in Synthetic Aperture Radar (SAR) image processing because of its advantages of easy implementation and high efficiency. However, its low imaging quality is unable to meet the needs of practical applications nowadays. To resolve this problem, we propose a high-performance imaging procesing algorithm (FrFT-RD) in this paper. The expression of optimal order of SAR range signals using fractional Fourier transform is deduced, and the corresponding formula of the azimuth direction is also given. The theoretical analysis shows that the optimal orders of the range and azimuth direction both depend on the SAR imaging parameters and are unique. Thus, the engineering practicability of FrFT-RD algorithm are large without iteration approaches. The construction of the FrFT-RD algorithm includes: Firstly, the FrFT-RD algorithm based on the traditional range Doppler algorithm is established in the fractional Fourier transform domain using the calculated optimal orders of the range and azimuth direction; Secondly, the fractional Fourier transform of corresponding order is used to process the range signals and the reference function of the range direction to complete the range pulse compression and the range cell migration correction (RCMC). The range signals are reconstructed by the inverse fractional Fourier transform (IFrFT) with the order of 1; Thirdly, the fractional Fourier transform of the corresponding order is used to process the azimuth signals and the reference function of the azimuth direction to complete the azimuth pulse compression. The azimuth signals are finally reconstructed by the inverse fractional Fourier transform (IFrFT) with the order of 1. By comparing results of airborne SAR simulation with spaceborne SAR measurement data, we find that the FrFT-RD algorithm significantly improves the imaging performance on resolution, and peak side lobe ratio (PSLR) compared to traditional RD algorithm. The resolutions of the range and azimuth directions are increased by 45.92% and 48.06%, respectively, and the PSLR and ISLR of the range and direction are decreased by 1.45 dB and 2.59 dB, respectively. While the Frft-RD algorithm almost has the same imaging performance as the traditional RD algorithm in PSLR and ISLR on the azimuth direction.

Based on the demographic data, nighttime light remote sensing images and Landsat8 images of streets and communities in Gulou district, Fuzhou city, Fujian province, combined with the kernel density and regression equation are integrated to draw a 30 m spatial resolution population density map and conduct spatial autocorrelation analysis. Firstly, the population density distribution map of 69 communities are calculated by kernel density method. Based on a quantile-quantile plot between the population density and nighttime light remote sensing of 786 residential community points, we find that the population density has a large error in wufeng street and hongshan town. Secondly, the binary quadratic regression equation is established to correct the population density error in these two regions. This equation expresses the relationship between population density, and the impervious surface image of Landsat 8 using linear unmixing and nighttime light remote sensing. Thirdly, Getis-Ord General G, Getis-Ord Gi*, and Anselin local Moran I are used to obtain the high clustering attributes of population in Gulou district to show the largest business circle area, the largest population density residential area in the city, and the local spatial pattern of population clustering. In this study, the population spatialization technique integrates two spatialization methods: kernel density and regression equation. The population density map with 30 m spatial resolution is generated finally. The mean population density of Gulou district is divided into three types: 11 000 people/km², 25 000 people/km², and 50000 people/km². The population density approximately obeys a normal distribution. When the mean population density of Gulou district is greater than 33 000 people/km², the correlation between the impervious surface gray value and population density is stronger. Otherwise, the correlation between nighttime light remote sensing image and population density is stronger.

Spatial and temporal characteristics assessment of cloud coverage for optical satellite images is a prerequisite for evaluating its potential as an important remote sensing monitoring data source. Sentinel-2 A/B images have been highly valued in the aspects of land surface vegetation and ecological monitoring at different spatial scales due to their advantages of free access, multi-spectral bands (especially the introduction of red edge bands), and finer spatial (10 m/20 m) and temporal (5-day) resolutions. Compared with Landsat and other similar satellite products, cloud coverage analysis of Sentinel-2 A/B has not been reported. In this paper, the cloud of 5288 Sentinel-2 A/B images (Granule/Tile) over northern Laos from 2016 to 2018 were used to determine the appropriate threshold of cloud coverage for image acquisition probability analysis under different cloud coverage thresholds (0~100%) based on GIS, aiming to reveal the spatial-temporal difference in acquisition probability. The main conclusions are as follows: (1) Sentinel-2 A/B imagery was highly appropriate for land surface remote sensing monitoring with a cloud coverage threshold of 20% (i.e., cloud coverage is ≤ 20%). This threshold resulted in the largest monthly cumulative probability (~27.41%) of Sentinel-2 A/B images in northern Laos. (2) Using the threshold of 20% cloud coverage, the differences in monthly cumulative average acquisition probabilities of Sentinel-2 A/B images in northern Laos were consistent with the temporal distributions of dry season (November to April) and wet season (May to October). The acquisition probability was 42.91% in the dry season, with the largest in March (50.27%), followed by April and February. The fact that Sentinel-2 is featured by larger acquisition probability during the peak of dry season greatly facilitates the monitoring of dynamics in swidden agriculture and rubber plantations. The corresponding probability in the wet season was merely 11.81%, with the lowest in June (~1.26%). (3) Huge differences in monthly cumulative average acquisition probabilities of Sentinel-2 A/B images between the east and west of northern Laos were revealed. In the dry season, the image acquisition probabilities of the western provinces (e.g., Luang Namtha) were much larger than those of the eastern ones, while the situation was just the opposite in the wet season. This study can provide important reference for the large-scale (e.g. global) cloud coverage analysis of Sentinel-2 A/B images and the selection of Sentinel-2 images for monitoring land use change due to United Nations Collaborative Programme on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (UN-REDD) in the tropics, including swidden agriculture transformation and rubber plantation expansion.

The Qinghai-Tibet Plateau is sensitive to climate change. At present, relevant researches mostly focus on the dynamic changes of ice and snow in the Qinghai-Tibet Plateau, and seldom pay attention to the dynamic changes of the rocky desert left by the melting ice and snow. Through the earth-atmosphere interaction, rocky desert may change the regional heterogeneity of climate at a large scale. This paper sorted out the extraction methods of remote sensing monitoring of ice and snow melting and rocky desert dynamic changes in the Qinghai-Tibet Plateau, and analyzed the advantages, disadvantages and applicability of various remote sensing data and extraction methods. We also summarized the data and research methods of the dynamic monitoring of ice and snow and the dynamic changes of the rocky desert in the Qinghai-Tibet Plateau. At present, the remote sensing monitoring data of the snow and ice dynamic changes in the Qinghai-Tibet Plateau are diverse and the research methods are mature. However, the remote sensing monitoring of the rocky desert dynamic changes left by the melting ice and snow has not yet formed a systematic study. Besides, under the condition of insignificant human disturbance, the dynamic changes of the rocky desert in the ice and snow melting area can also be used as a supplement to remote sensing monitoring of ice and snow dynamic changes.

Soil moisture is a key parameter to connect the land surface water cycle and the land surface energy cycle. Accurate soil moisture is great important to understand the climate change process, the land surface hydrological process, the mechanism of energy exchange between the earth and the atmosphere and so on. Due to its relatively suitable detection depth and strong theoretical foundation, microwave remote sensing has great advantage in observing land surface soil moisture. Combined with retrieval method, microwave remote sensing can obtain spatial continuous land surface soil moisture information easily, which is helpful to comprehend the spatiotemporal evolution mechanism of soil moisture more objectively. With the gradual enrichment of microwave remote sensing data, various soil moisture microwave remote sensing retrieval methods have been proposed one after another. In order to better investigate these soil moisture microwave retrieval methods, this paper summarizes the current satellite microwave remote sensing data which is commonly used in soil moisture retrieval research and analysis the development of these data source at first. Then the principles, development process, advantages and disadvantages of various soil moisture inversion methods are sorted out systematically from the three aspects of active microwave soil moisture retrieval, passive microwave soil moisture retrieval and multi-source collaboration soil moisture retrieval. Finally, three development trends of soil moisture microwave remote sensing retrieval method are summarized as follow. First, the space-time universality of soil moisture microwave remote sensing inversion method is gradually increasing. Second, soil moisture microwave cooperative retrieval methods for high spatial and temporal resolution are developing rapidly. Third, the intelligent level of soil moisture microwave inversion method is improving continuously.

Multi-scale of geospatial data is the cornerstone of cartography, and plays a key role in supporting geographic element analysis and feature recognition. Multi-scale vector data can be generated by selecting, simplifying, aggregating, or other processing of geographic element vector data of a certain scale obtained from remote sensing images. However, a variety of comprehensive processing models and methods will also lead to various levels of information loss in multi-scale vector data. The global coastline is a geographic information element with a wide coverage area, complex curves, various island combinations, and complicated structures of land and water regions. The variation of coastline vector data attributes shows different properties at different scales. For the special coastline vector data, there are multiple influencing factors, and the relationships between them are ambiguous. Therefore, it is impossible to judge the attributes of the elements only based on the combinations of a single or a small number of characteristics of the node or line elements. Meanwhile, using a single mathematical model or algorithm for simplification, the drawing effect often has a large deviation from the actual situation, and it cannot meet the drawing needs of different regions and different scales. Thus, we used Geographic Information System (ArcGIS 10.6) technology to support the automatic comprehensive function of geospatial data mapping, integrated different embedded automatic algorithms and models, and combined human-machine collaboration to build a systematic scale-up method system to achieve different scales of coastline data. Based on fractal theory, the concept of line vector data complexity index was first proposed to characterize the coastline geographic elements and to compare the degree of declination of their information. With the m-scale coastline data interpreted by manual visual interpretation, the scale-up is used to generate coastline data on the scales of 30 m, 250 m, and 1 km, respectively. The information loss assessment was performed on the obtained 30 m, 250 m and 1 km coastline vector data, and the results showed that the mapping integration caused changes in the spatial attributes of land and water. There are significant differences in the fineness of geographic element information represented by different scales. Compared with the m-scale coastline data, the loss of the number of islands on the scales of 30 m, 250 m, and 1 km is 32.07%, 90.46%, and 98.61%, respectively, the information loss of the coastline length is 6.32%, 49.26%, and 75.47%, respectively, and the information granularity of the vector data of the coastline of South America is reduced by 1.97%, 25.33%, and 45.39%, respectively. With the processes of the up-scale of the coastline, it has an increasing trend of the median, mean of the islands area and their complexity index from the m-level to 30 m, 250 m, and 1 km scales. The scale-up method constructed in this paper to combine the computer automatic synthesis model with the artificial processing of the coastline vector data has the potential to efficiently realize the scale-up of the coastline vector data, and describe the information loss of vector data at different spatial scales.

With the rapid growth of remote sensing data, greater challenges arise in raster data efficient processing and value mining. Traditional map services focus on content sharing and visualization, but lacking real-time image analysis and processing functions. In this study, the real-time analysis and processing capabilities of raster tile data are realized in the form of map service. The cloud optimized GeoTIFF (Cloud Optimized GeoTIFF, COG) is used as the data organization method. The distributed collaborative prefetching strategy is designed to realize the raster tile loading in a cold or hot way, which optimizes the efficiency of reading image data from the cloud. Based on the efficient raster tile data loading, an expression-based raster tile processing model is proposed. By converting the expression into a calculation workflow, the raster tile is processed in the request of the map service in real time. The massive remote sensing data stored in the cloud is quickly analyzed to realize the direct visual conversion from raw data to products. For scenarios where full data are involved, use appropriate resampling data to simplify calculations to meet the real-time performance of map services. Three types of different complexity models, NDVI, ground object classification, and fractional vegetation cover, are used to perform real-time calculation and analysis on Landsat 8 images in the map service. Experimental results show that the processing model can effectively analyze raster tiles, and can be extended in a distributed manner. It can provide stable map service capabilities in high-concurrency scenarios, adapt to calculations at various levels and scales, and contribute a new idea to the future development of map service.

Map generalization is in essence a spatial similarity transformation of maps. Studying the Douglas-Peucker algorithm and its parameter setting is in essence studying the relationship between the optimal distance threshold of the algorithm and map scale change. However, the quantitative relationship between them is still unknown, which leads to strong subjectivity in parameter setting and selection of simplification results. Therefore, in order to realize the automated simplification of polyline based on DP algorithm, this paper proposes to take the spatial similarity evaluation model of multi-scale polylines as the coincidence point, and determine the quantitative relationship between them using the principle of threshold parameter optimization. The results indicate that quadratic function is the optimal function to describe the quantitative relationship between the optimal distance threshold and map scale change. It is feasible to use the same optimal distance to automatically simplify the polylines from the same geographical feature area based on the Douglas-Peucker algorithm, such as the polylines from the Lower Yangtze River plain. The simplification results match well with the existing target scale data. However, it is unreasonable to use the same optimal distance threshold to simplify the polylines from different geographical feature areas, such as polylines from the Lower Yangtze River plain and the Jianghuai plain. Therefore, different optimal distance thresholds should be selected to realize full automated simplification of DP algorithm for polylines from different geographical feature areas.

The national land survey is a major component of evaluating national conditions and strength. Its main purpose is to master the detailed national land use status and natural resource changes. It is of great significance to cultivated land protection and sustainable social and economic development. With the development of remote sensing technology, investigating the status, quantity, and distribution of land resources has always been the focus of remote sensing applications. This article reviews the application of remote sensing in national land survey over the past four decades. Until now, remote sensing technology has shown broad prospects in national land survey. However, the remote sensing information extraction in national land survey still mainly relies on visual interpretation and is not automated enough. In recent years, the remote sensing data tend to have the characteristics of high-resolution, large-scale, multi-temporal, and multi-sensor. However, the existing automated information extraction technology does not fully integrate those characteristics, hindering the application in national land survey. This article first introduces the relevant progress in national land survey from four aspects: feature extraction using very-high-resolution images, samples acquisition from large-scale images, transfer learning in multi-temporal/multi-sensors images, and multi-source heterogeneous data fusion. Then, four challenges in the existing remote sensing information extraction technology in the national land survey are summarized: ① Image feature is the key to image classification. There are questions on how to define and select features. In addition, high-resolution images put forward higher requirements for advanced feature extraction; ② Remote sensing data in the national land survey are usually large in scale, and there are inter-class imbalance and intra-class diversity. Therefore, it is a challenge to obtain sufficient, balanced, and diverse sample sets from such complex data set; ③ Generally, the efficiency of sample collection cannot catch up with the accumulation speed of remote sensing data, thus the labeled samples are relatively small compared with the data. For multi-sensor/multi-temporal imagery, how to realize land use classification in a low-cost and timely manner is a question worth considering; ④ There is a semantic gap between land cover and land use. Since remote sensing images mainly reflect land cover information, how to properly introduce semantic information to bridge the semantic gap and realize land use classification is a problem. Finally, the future development and application of remote sensing technology in national land survey are prospected, such as transformation from visual interpretation to artificial intelligence technology, accuracy and consistency assessment of remote sensing classification products in land survey, crowdsourcing methods for large-scale land use production, and update of large-scale land use data.

Thermal radiation directionality refers to the phenomenon that thermal radiation values measured from different observation directions are different for a certain surface object, which is usually reflected in different directional radiance or different brightness temperature. With the emergence of high spatial resolution remotely sensed data and the demand for high-precision surface temperature products, the effect of thermal radiation anisotropy cannot be ignored. Now it has become one of the hottest issues concerned widely in the thermal infrared field. The thermal anisotropy is more obvious for the urban surface with diverse surface features and complex geometric structure. This article describes three observational experiments, including ground observation experiment, airborne observation experiment, and space observation experiment. These three methods have their own advantages and disadvantages and can be used in different situations. The observation data that represents the reality of urban radiation directionality often shows obvious thermal radiation anisotropy in urban areas during the daytime. In addition, a series of forward models of thermal radiation anisotropy carried out in urban areas are categorized and analyzed. These models can be divided into three categories: geometric three-dimensional model, radiative transfer model, and parameter model. According to existing academic papers, in-situ observation data are usually used to estimate the coefficients and verify the simulation accuracy of forward models. By combining these two approaches, observations and models, some scholars have made some achievements in this field. The purpose of studying thermal radiation anisotropy in urban areas is to obtain land surface parameters with higher accuracy. So, the exploration of true values of urban surface temperature are also included in this study. Furthermore, the impact factors of thermal radiation anisotropy are summarized, such as observation season and time, surface geometry, physical properties of surface materials, observation angle, FOV of sensor, etc. which influence the spatial and temporal patterns of intensity of thermal radiation anisotropy. At last, for the ultimate goal of improving the retrieval accuracy of urban surface temperature, five prospects are put forward: using high-resolution thermal infrared sensors to get the data of urban thermal background field, carrying out more thermal infrared multi-angle remote sensing experiments from different platforms, improving understanding of the mechanism of thermal radiation of non-isothermal heterogeneous pixels, performing validation of urban surface temperature, applying the research results into practice such as angle correction of satellite temperature products.

The simplification of contours of natural continuous polygons is an important step of automatic cartographic generalization of natural polygons in topographic map and natural patches in general survey of geographical conditions. Most of the existing simplification algorithms of polygonal contours are based on the line simplification algorithms, which cannot effectively simplify bending features, maintain the area balance, and meet the requirements of graphic visual clarity. Moreover, there are topological problems in the simplification result, such as inconsistent shared contour, self-intersection of contour and intersection between contours. Therefore, combined with the expression characteristics and simplification requirements of natural continuous polygons, a synergistic simplification method is proposed for the contours of natural continuous polygons. First, the natural continuous polygons are transformed into topological data structure, and the constrained Delaunay triangulation is constructed based on the arc segment to be simplified and its adjacent arc segments to identify the simplified region. Second, the arc segment bilateral hierarchical multiple tree model is used to gradually remove or partially remove narrow bends and simplify small bends. Third, the narrow regions are adaptively exaggerated to avoid unclear details on the map. The simplification experiment of Vegetation and Soil polygons in 1:50000 scale topographic map of a region in Henan, China was carried out. Compared with the reference methods, our proposed method can effectively maintain the topological consistency and area balance among natural continuous polygons before and after the simplification and fully simplify the invisible details under the target map scale, and the position accuracy of our simplification results meet the requirement. Therefore, the proposed method has better superiority in terms of topological consistency, visual clarity, and area balance.

Due to the advantages of isotropy, adjacency equivalence, and high fitting accuracy, regular hexagonal grid is used as the grid unit of regular grid DEM data structure and has been applied to digital terrain analysis such as flow direction analysis and valley line extraction. However, its quality detection and evaluation has not been well studied, and the quality of DEM directly affects the correctness and reliability of subsequent data analysis results and related decisions. Conventional methods such as checkpoint method and profile method can only evaluate the error of DEM locally, and cannot comprehensively evaluate the quality of DEM. Contour lines can reflect the overall situation of topography. Therefore, contour playback method is a relatively comprehensive and accurate method to evaluate the quality of DEM by analyzing the quality of playback contour lines and then detecting and evaluating the quality of DEM. Therefore, this paper applies the vertex height difference marking method to the grid structure of hexagonal DEM, proposes a contour generation algorithm for regular hexagonal grid DEM, and evaluates and analyzes the data quality of regular hexagonal grid DEM. Firstly, this paper uses three indexes: the topological correctness of the generated contour, the fit with the original contour, and the maintenance of bending features to evaluate the contour tracked by the vertex height difference marking method under the hexagonal grid structure. It has no topological errors such as self-intersection, fits well with the original contour, and maintains the bending features well, which proves the feasibility of the algorithm. In addition, this generation method is applied to the quality comparison of DEM with different regular grids, that is, the contour lines of quadrilateral DEM and hexagonal DEM are generated respectively based on the vertex height difference marking method, and the quality of contour lines generated by hexagonal DEM and quadrilateral DEM is compared based on the above three indexes, so as to compare the quality difference between hexagonal DEM and quadrilateral DEM. The experimental comparison shows that under the same resolution, the contour played back by hexagonal DEM has a higher fit with the original contour, and the bending feature is maintained better, and with the decrease of resolution, the decrease of fit is smaller, the loss of bending feature is less, there is no sharp angle, excessive shape deformation, etc. Therefore, the quality of hexagonal DEM is better than that of quadrilateral DEM, and with the decrease of resolution, the accuracy loss of hexagonal DEM is smaller.

Landform recognition is of great significance to human construction, geological structure research, environmental governance and other related fields. Traditional recognition methodology is mainly based on pixel unit or object-oriented recgnition, which existed limitations. Landform recognition based on the watershed unit has become a new hotspot in this field because of its surface morphology integrity and clear geographical significance. However, the traditional methods of landform recognition based on terrain factors are often simple or repeatable in the geological description, which cannot be used to describe the spatial structure and quantify the topological relationship characteristics of the watershed unit. The slope spectrum method was used to solve the problem that it was difficult to determine the stable area of watershed unit, and 181 small watersheds were extracted through hydrological analysis. Based on the theory of complex network and geomorphology, the concept of watershed weighted complex network and 8 quantitative indexes were put forward to simulate and quantify the spatial structure of the watershed. Finally, XGBoost machine learning algorithm is adopted for landform recognition. XGBoost machine learning algorithm based on decision tree is used for landform recognition. The experiment shows a well performance on the landform recognition of the main landform types on the Loess Plateau, with the Kappa coefficient of 86.00% and the overall accuracy of 88.33%. Compared with the landforms having obvious morphological features, the complex network method considers the characteristics of spatial structure and topological features, resulting in higher recognition accuracy and kappa coefficient of 90%~100%. Compared with previous studies, the recognition results show high accuracy, which verifies that the method based on watershed weighted complex network is an effective method with high accuracy for landform recognition based on watershed.

As a key issue of sustainable development, scientific assessment of sustainable development potential at county scale provides a solid support for policy making of regional planning. The existing studies have mostly evaluated development potentials of counties using the aggregation of multi-dimensional indicators based on actual development conditions, but rarely focused on the evolution of development potentials in future. Here, we construct an indicator system for the evaluation of sustainable development potential at county scale based on the 2030 Sustainable Development Goals (SDGs), and project the changes in evaluation indicators based on the integration of System Dynamics model (SD) and Future Land Use Simulation model (FLUS). The Zhaoyuan City in Shandong Province, one of China's top 100 economic counties and famous of its gold mining, was selected as a case study to explore the potential of its transition from the mining-dependent to the sustainable development mode. To examine the impacts of different development modes on sustainable development potentials of the study area, we designed five simulation scenarios based on multiple Shared Socioeconomic Pathways (SSPs), i.e., business-as-usual scenario, SSP1, SSP2, SSP3, and SSP5, and performed the evaluation under different pathways from a simulation perspective. The results show that: (1) A majority of indicators on economic and social dimensions are likely to be improved under all scenarios, while ecological indicators, e.g. carbon sequestration, forest, grass, water shape index, and number of forest, grass, and water patches, will be significantly declined; (2) The changing rate of development potentials during the period of 2018-2030 will be less than that from 2009 to 2018 due to the development transition from extensive to the high-quality mode; (3) Compared with the year of 2018, the development potential on average in 2030 under SSP1 and SSP2 scenarios will be increased by 17.36% and 9.8%, respectively, while those under SSP3 and SSP5 will be decreased by 0.5% and 4.20%, respectively. The SSP1 can maximize the development sustainability of the study area, but SSP5 may exert significantly negative impact; (4) future development of Zhaoyuan City should focus on the promotion of SSP1 scenario and cope with backward indicators such as the labor force proportion in different industries, aging population, and carbon sequestration. Overall, we aim to clarify the mapping relationship between 2030 Sustainable Development Goals and development potentials at county scale and provide a comprehensive evaluation framework for development potentials under multiple simulation scenarios. Our work is expected to provide scientific guidance for development policy making and high-quality development transition of Zhaoyuan City.

The migrant population is an important part of the population structure of large or super large cities. Studying the migration characteristics and influencing factors of the migrant population in a particular city will not only help to discover the pattern of population migration targeting a particular city from the perspective of the migration place, but also affect new towns. The construction and development of urbanization in the context of urbanization also has important practical significance. Taking Beijing as an example, this paper collects the migrant population registration data of the public security organs from 2005 to 2018, studies the spatial distribution pattern of the migrant population in different years in the city-level emigration areas, and uses the spatial regression model to analyze the factors that affect population migration. The following findings are obtained: ① The emigration area of Beijing's migrant population shows obvious spatial agglomeration effect at the municipal scale, and the aggregation effect is increasing year by year. The spatial distribution of migrant population emigration area is generally stable. The hot spot emigration places is mainly concentrated in two main clusters: Hebei-Tianjin and southern Henan Province-Northern Hubei Province; ② The main variables affecting population migration from various places to Beijing are the population size of the emigration area, transportation time, per capita income, and education level. The impact of population size and per capita income on population migration is relatively stable, while the effects of education level and population density only began to appear after 2010 and 2014, respectively. Transportation time has an negative effect on population migration. Although the transportation time has decreased in recent years, its impact on population migration has not changed much; ③ The spatial error continues to be significant, indicating that the population migration volume of a given emigration area may be affected by other variables such as the social culture of neighboring cities.

Different urban land surface covers and spatial structures lead to different heat island effects and different urban spatial thermal environment. Local Climate Zones (LCZ) have been widely applied in the study of urban heat island. Reasonable division of LCZ and scientific formulation of LCZ classification standards are the key technical problems in the study of urban heat island based on LCZ. In this study, the LCZ of Jinan city was divided by the urban road network, Digital Elevation Model (DEM), and big data of buildings, and the quantitative classification standard of LCZ was determined by the building height and the building density. The land surface temperature was retrieved by Landsat 8 remote sensing image, and the Kriging method was used for air temperature spatial interpolation. The urban thermal environment was expressed by land surface temperature and air temperature. Based on this, the spatial differentiation characteristics of urban thermal environment and the differences of thermal environment in the same type of LCZ were studied by the method of variance analysis, and the factors of urban thermal environment were studied by the method of correlation analysis. The results show that: (1) There were obvious differences in the spatial distribution pattern of land surface temperature and air temperature at 4:00 a.m., 8:00 a.m., and 14:00 p.m. in Jinan city. Among the four types of temperature, the number of LCZ with high temperature outliers respectively accounted for 0.25%, 1.60%, 4.05%, and 3.96% of the total LCZ in the city. The area with higher land surface temperature was located in the area with dense buildings, which includes scattered areas with higher air temperature, showing heat island effect; (2) There were obvious differences in land surface temperature and air temperature at different times of a day in different types of LCZ. The number of high air temperature outliers in the LCZ of high height and low density, the LCZ of high height and medium density, and the LCZ of medium height and low density respectively accounted for 47.37%, 33.33%, and 9.65% of the total high temperature outliers, the intraclass heat island effect of these LCZ was obvious; (3) Different types of LCZ had different intraclass heat island effects. LCZ types such as low height and low density, medium height and low density, high height and low density, and high height and medium density had significant differences in heat island effect, the p values of their variance analysis were less than 0.05; (4) The impact of building spatial distribution index on urban thermal environment was different due to different location and elevation of LCZ. Overall, the negative correlation between land surface temperature and the average values of building height reached the significant level of more than 0.05, and the positive correlation between the air temperature and average building height reached the significant level of 0.001. The average values and standard deviations of building base area and building volume, building density, and floor area ratio were significantly (p<0.001) positively correlated with urban thermal environment, which indicated a significant positive impact on the urban thermal environment.

The MODIS Enhanced Vegetation Index (EVI) time-series data has been widely used in many research fields such as vegetation observation, ecological environment, and global meteorological changes. However, even though the EVI time series data has undergone strict preprocessing, there are still some noises in it. Therefore, this paper develops a simple and effective method to reconstruct EVI time-series data and eliminate the noise in EVI time-series data, especially some noise caused by atmospheric clouds and snow cover. The theory of the new method is derived from graph theory, using the relationship of the Laplacian matrix to assign the weight of the pixel of the selected neighborhood window in EVI to get the fitting of the center pixel. The new method has been applied to MODIS MOD13A1 products from 2016 to 2018 and compared with the S-G filtering method, Harmonic Analysis of Time Series method, Double Logistic function method, and Asymmetric Gaussian model function method. The results show that in the desert, grassland, and woodland, the absolute difference of the leave-one verification test of the new method is the smallest, which is better than other methods; when fitting EVI time-series data of different vegetation types, the graph theory neighbor method presents a better detailed fitting curve; the RMSE values of the new method in the five vegetation types are 200.59, 46.58, 63.48, 165.47, and 40.95 respectively, which are the smallest values among the five methods and are more effective in obtaining high-fidelity and high-quality EVI time-series data. The method research in this article can provide a useful reference for the denoising of vegetation remote sensing time-series data and the study of the ecological environment.