Diseases and pests have become one of the biggest constraints on rice yield. Traditional plant protection technology mainly relies on the vision and experience of plant protection personnel， which is subjective， time-consuming and laborious， and difficult to meet the needs of large-scale real-time monitoring. The development of remote sensing technology provides a large-area， all-weather， multi-directional data acquisition method， which can provide crop and environmental information for the identification and classification of diseases and pests， it is an important means to monitor and forecast rice diseases and pests in a large area. On the basis of expounding the mechanism of remote sensing monitoring and prediction of rice diseases and pests， this paper summarizes the research progress of rice diseases and pests monitoring and prediction from many aspects， such as multi-scale remote sensing monitoring method， forecasting method， construction of rice disease and pest monitoring and prediction models， monitoring and forecasting system， etc. ， the existing problems and future development trends of rice disease and pest monitoring and prediction are prospected. With the development of information agriculture and the fusion of multi-source data， the accurate and intelligent remote sensing monitoring and forecasting of rice diseases and pests will become more and more mature.

Terrain effect will distort the surface reflectance of remote sensing images， which in turn affects the accuracy of Leaf Area Index（LAI） estimation from reflectance data. To attenuate or eliminate the influence of topography on LAI inversion， a data set of slope reflectance and LAI was constructed as train data based on the three-dimensional radiative transfer model DART （Discrete Anisotropic Radiative Transfer）. With reflectance as the input and LAI as the output， a mountain LAI inversion model was subsequently obtained using random forest algorithm. Then the estimation of LAI in mountain area was realized by combining remote sensing image data in the study area， and the accuracy of LAI estimation was evaluated by the ground measured data. Meanwhile， a flat surface inversion model was constructed based on the DART model and random forest algorithm as a comparison to evaluate the effectiveness of the method proposed. The results indicated that the mountain LAI inversion model considering the influence of topography showed a good performance， with the determination coefficient （R²） of 0.57 and Root Mean Square Error （RMSE） of 0.77 m²/m²， which was better than that of the flat surface model （R² = 0.46 and RMSE = 0.86 m²/m²）.The mountain inversion model based on DART model can capture the influence of slope and aspect on the surface reflectance， and the inversion results can better restore the spatial distribution of LAI in the study area. This study proves that the mountain LAI inversion method based on coupling the DART model and random forest algorithm can partly reduce the terrain effect and effectively improve the estimation accuracy of mountain LAI， which can provide reference for the remote sensing inversion research of vegetation parameters over mountainous areas.

Global warming results that glaciers retreat rapidly. Monitoring and mapping glacier boundary are extremely significant for research on global climate change and predicting related disasters. However， snow covering is the main barrier all the time. Selecting Karakoram subregion as study area， the Landsat 8 OLI， and Senitnel-1 images and DEM data in spring （March 24th， 2019） were utilized. The spectral reflectance of green， red， near-infrared and short-wave infrared bands in Landsat 8 OLI images were selected as the optical image features. The backscattering coefficient of VH polarization channel， the coherence coefficient of VV polarization channel， local incident angle， polarization entropy H and scattering Angle α after polarization decomposition were gained from SAR data and used as SAR features. Topographic features included DEM and slope. These characters were employed as input of models. First， based on U-Net model， experiments compared the accuracies using different-size samples. The 256×256-pixel-size samples were imported to U-Net network model based on different backbone networks （MobileNetv2， VGGNet， ResNet and EfficientNet） and DeepLabv3+ model. Finally， the best one among the above networks was employed to import samples with different feature combinations. Results show： ①Using the bigger training sample with the richer spatial context information can obtain the higher segmentation accuracy and the glacier terminal boundary is more accurate. ②Among the different backbone networks， VGG19 backbone network exhibits the highest accuracy， which is higher than that of DeepLabv3+. Its F1-value is 0.899 6， and the mean intersection over union（mIoU） is 0.875 4， and the overall accuracy is 0.948 4. The recognition effect of shadow， snow melt-water， mist covering and frozen lake area is comparatively good. ③With the decrease in the number of training features， the accuracy also drops. Topographic features can improve the precision rate， while SAR features can increase the recall rate by 4% or so. This study proves the feasibility of the deep learning methods on the identification of mountain glaciers covered by a large amount of snow and provides reliable basis on model selection and parameters setting for rapid and large-scale mountain glaciers mapping.

The surface Evapotranspiration （ET） is an important controlling factor to water cycle and energy transmission in the biosphere， atmosphere and hydrosphere. Satellite provides an unprecedented spatial distribution of ET in the past decades. In this paper，the estimation methods of evapotranspiration using remotely sensed data were summarized，and the existing issues that should be further studied were discussed. In the future research，we should strengthen the improvement of the evapotranspiration regarding scale effect， nighttime ET， the general validation method of different ET products， remotely sensed data in China， the ET products with higher spatial-temporal resolution， and the new ET model using the machine learning methods.

Landslides are powerfully explosive and destructive， and are one of the natural disasters with high frequency in the world， causing serious damage to people's lives and properties. Accurate and rapid extraction of landslides and obtaining the distribution range of landslides after a disaster are extremely important for landslide disaster investigation and hazard assessment. The method of landslide monitoring based on high-resolution satellite remote sensing images is investigated. Firstly， the decoding characteristics of landslides on high-resolution satellite remote sensing images are introduced， while the research progress of landslide extraction methods and accuracy evaluation and analysis methods are discussed， and finally the advantages and shortcomings of current methods are summarized， as well as the development direction of future research. The results show that the deep learning method has greater potential， and the combination of deep learning and other automated interpretation methods should be strengthened in landslide monitoring in the future to solve the influence of sample size on the model results， realize the migrability of the model， and improve its automation.

Sentinel-2 is a high-resolution optical Earth observation mission within the GMES （Global Monitoring for Environment and Security） programme， which is renamed Copernicus in 2012， jointly implemented by the EC （European Commission） and ESA （European Space Agency） for global land observation with high revisit capability to provide enhanced continuity of data so far provided by SPOT and Landsat. Copernicus is the most ambitious Earth Observation programme to date. It provides accurate， timely and easily accessible information to improve the management of the environment， understand and mitigate the effects of climate change and ensure civil security. At present， Sentinel-2 is one of the most important data source for remote sensing monitoring and application research， and has been widely used in monitoring natural disasters such as floods，forest fires， landslides， volcanic eruptions， and emergency response and humanitarian crises around the globe，and there are also great potentials in detecting glacier and ice and supporting relief efforts for cryospheric disaster.In this study， the glacier and ice avalanches occurred in Arutso Lake basin in northwestern Tibet and Sedongpu basin in southeastern Tibet in 2016 and 2018 were investigated using Sentinel-2 images and field surveys， and the evolution process of two events were reproduced， which has important reference significance for monitoring cryospheric hazard， emergency relief and management in other mountain regions on the world.Study shows that Arutso No. 53 glacier avalanche completely melted away in July 2018 after lasting for two years from occurrence to final disappearance， while the area of Arutso No. 50 glacier avalanche is 0.58 km2 left on June 22，2021 because of more thickness compared to Arutso glacier No. 53. Four large-scale ice-rock ava lanche and debris flow events in the Sedongpu basin in 2017 and 2018 not only had significant impacts on the river flow， landscape and landform in the basin， but also caused great disasters in the basin and downstream.Two glacier and ice avalanche events were caused by climate warming and local heavy precipitation， acting on specific topographic and geomorphic structure of glacier properties in high mountains. Specifically， Arutso glacier avalanche was caused by climate- and weather-driven external forcing， acting on specific polythermal and soft-bed glacier properties and is an unprecedented large catastrophic instability of low angle mountain glaciers. Glacier and snow melting caused by climate warming and heavy rainfall are main triggering factors for ice and rock avalanche in the Sedongpu basin， which is a typical hazard cascades originating from cryosphere， followed by rock fall， debris flow， dammed lake， and lake outburst flood disaster. It often occurs in the Sedongpu basin and will continue to occur for a long time in the future， and the high mountain ridge covered with ice and snow in the right side of back of the basin is still a high-risk area for ice and rock avalanches in the future.

Using the new generation of Geostationary Meteorological Satellite Himawari-8 data， a new adaptive threshold decision tree low-temperature fire spot detection algorithm is proposed. The algorithm is based on the localization recognition results of clear sky pixels and background pixels， using data at channel 2.3 μm and channel 0.86 μm. Taking Shanxi Province as the research area， the identification results were verified using the data on April 24，2020 and February 20，2021. The results of the new algorithm show： （1） the new algorithm can identify small fire spots earlier （sampling point 40 minutes in advance）； （2） the new algorithm has better accuracy on identifying the fire spot with small range and low temperature on grassland and cultivated land； （3） the new algorithm solves the problem of false alarms and misses of fire spot， providing a new idea for identifying fire spot as soon as possible and monitoring fire disaster effectively.

Threshold segmentation method based on SAR image is one of the commonly used methods for effective extraction of water information. In view of the problem of low accuracy and high noise for water extraction on SAR image by Otsu algorithm， a new method based on Otsu algorithm is proposed using C-band Sentinel-1 SAR as the data source. This method constructs natural exponential function based on dual-polarization data to optimize the histogram distribution of pixels in original Sentinel-1 image firstly， and then combines Otsu algorithm to extract water information from image， at last removes the wrongly extracted hill shade based on DEM. The accuracy is evaluated by using optical images of Landsat 8 as the real water information. The results show that the accuracy of water extraction for the proposed method is superior to traditional Otsu algorithm in the case of different water proportions， accuracy of which increased by about 20—60% while water proportion less than 10%. Moreover， this proposed method has low noise and wide applicability features， which can be used for obtaining water information of large area quickly and efficiently.

Google Earth Engine （GEE） is a comprehensive application platform that integrates remote sensing image storage and analysis. It can conveniently and quickly call remote sensing images and information extraction. Therefore， GEE has attracted more and more scientific researchers' attention. With the continuous expansion and upgrade of GEE， the system platform has become more and more complex. For ordinary users， it is becoming more and more difficult to quickly understand its architecture and functional algorithms. In response to this problem， this article systematically introduces the technical architecture， data resources， model algorithms and computing resources of GEE， and summarizes the application results of GEE in various fields， hoping to provide GEE users with a quick understanding of the platform Window to help them make better use of the GEE platform to carry out their own application research.

Soil erosion， as one of the main environmental problems in the world， is increasingly affected by human activities. It is of great significance to study the relationship between human activities and soil erosion at regional scale for soil erosion control planning. In this paper， the relationship between human activities and soil erosion was analyzed on grid scale by introducing human activity index. Then， the driving mechanism of soil erosion caused by human activities was explored from the comprehensive perspectives of population change， land use change and the impact of human activities on vegetation cover. It was found that the average soil erosion modulus of Jiangxi province in 1990 was 841 t/（km²·a）， but decreased to 338 t/（km²·a） in 2018. During the past more than 20 years， the occurrence of soil erosion has a tendency of shifting from the mountainous and hilly areas with relatively low human activities to the cities and surrounding areas with moderate slope. The index of human activity increased from 0.005 to 0.014， and the increasing areas concentrated around the cities with gentle slope and low altitude. In remote mountainous areas， the degree of human activity does not change much. In remote mountainous and hilly areas far from cities， the reduction of rural population pressure and appropriate land use transformation promoted vegetation restoration and soil erosion mitigation. The degree of soil erosion in the city and its surrounding areas with gentle slope is intensified due to the significant enhancement of human activities.

Mineral resources are important production materials for human survival and development， and the monitoring of mine environment is crucial for mineral resources exploitation and protection. Due to the advantages including large-scale， multi temporal and comprehensive， remote sensing technology has become the main means of mine monitoring. Aiming to the requirements of mine development and utilization， geological disasters， ecological environment monitoring and quality evaluation， we systematically summarized data sources， methods and models used in remote sensing monitoring of mine environment. Especially， data sources adopted in remote sensing monitoring of mine have tended to diversify and involve in all aspects of mine monitoring. Along with the rapid development of cloud computing platform and artificial intelligence technology， methods such as big data analysis and deep learning have gradually played an important role in remote sensing monitoring of mine environment， while multi-source data fusion， intelligent extraction of features， three-dimensional deformation monitoring and quantitative inversion are the main problems and challenges.

Accurate extraction of ground elevation and vegetation canopy height is of great significance for the study of topography， ecology and so on. The new generation of Global Ecosystem Dynamics Investigation （GEDI） launched in December 2018 provides an unprecedented opportunity for accurate extraction of ground elevation and vegetation canopy height over large areas. The purpose of this paper is to verify the accuracies of ground elevation and canopy height extracted by GEDI using airborne LiDAR data， and to explore the influence of geographic positioning error， terrain slope， aspect， vegetation cover， azimuth， acquisition time， beam type and vegetation type on the estimation accuracy. The results show that the estimation accuracies of ground elevation and canopy height can be significantly improved by correcting the geolocation error of GEDI data. The main factor that affects the extraction accuracy of canopy height is vegetation cover， followed by slope； while the extraction accuracy of ground elevation is significantly affected by the aspect and slope. Additionally， the results also indicated that the estimation accuracy is high when the vegetation cover is more than 25%， and the accuracies of ground elevation and canopy height are the highest in gentle slope area with slope 0~5°. Overall， this study will provide a basis for the screening and application of GEDI data.

The low temperature during the growth period of paddy will greatly reduce the grain yield. Remote sensing monitoring of yield loss under low temperature stress is of great significance for variety improvement， field management and agricultural insurance claims. The study aimed to monitor yield loss of multiple cropping paddy using multi-temporal remote sensing images. with the support of the field samples， the model of monitoring yield loss of multi-cropping paddy was developed. The results showed that the growth period of paddy in which low temperature injury occurred was different，and the effect on rice yield was quite different.The effect of cold injury on middle rice in the middle filling stage is relatively small， with an average yield of about 6 637 kg/ha， with a yield reduction of nearly 20%. The yield of early late rice is significantly lower than that of middle rice after suffering from low temperature and cold injury at heading stage， with an average yield of 4 143 kg/ha and a yield reduction of about 45%. The yield of late-maturing late rice was most affected by continuous low temperature at jointing stage， with an average yield of only 1 541 kg/ha， which was much lower than that of previous years. The regression model was constructed by using the actual cut sample yield data and Sentinel data in several key phenological periods （NDVI）. The R² was more than 0.75. the precision was cross-verified by the measured sample yield data， and the MAPE was less than 10%. With the help of a small amount of ground data， this method can accurately calculate the yield of multi-cropping rice under the condition of low temperature and cold injury， which provides a new idea for the calculation of rice yield under complex conditions.

The central urban area of Tianjin is taken as the research object. Based on the abundant OSM road network data and POI big data， functional area identification is carried out at the fine scale. The road space generated by OSM road network data is used to divide the central urban area of Tianjin into 1960 research units. The density distribution and functional area distribution characteristics are analyzed by combining the POI data with weight assignment. The research results show that： （1） In the distribution of urban function density， except for the concentrated distribution of industrial functions in the periphery of the central city， the distribution of other urban functions shows the characteristics of gradual dispersion from the center to the periphery； （2） In a single functional area， commercial areas and public management and public service areas account for a relatively large proportion， while the other four single functional areas account for a small proportion； （3） Among the mixed functional areas， the mixed functional area mainly composed of business-public management and public services has the largest proportion； （4） Comparing the recognition results of functional areas with the Amap， it is found that the accuracy of the recognition results of urban functional areas is relatively high.

Water storage is a critical component of the global and regional hydrological cycle， which can be used to analyze the spatio-temporal evolution of regional water resources and drought. Traditional methods to monitor water storage are usually based on in-situ groundwater level data. However， challenges arise due to the limited placement and distribution of monitoring stations in large-scale research and exploration. The Gravity Recovery and Climate Experiment （GRACE） satellite have provided large-scale monthly data on Earth's gravity field variation. Several scholars have applied the water storage anomalies data retrieved by GRACE in hydrology research， which has facilitated the progress and development of hydrology. However， the current systematic elaboration of research on inversion of water storage based on GRACE data is not comprehensive enough， and few studies have systematically summarized the status of monitoring drought， interpolation， and reconstruction based on GRACE data. Firstly， this study briefly introduces the application fields of GRACE data， and discusses the advantages and disadvantages of the two data processing methods. Then， the application status and existing problems of GRACE data in the verification and uncertainty of inversion results， terrestrial water storage anomalies， groundwater storage anomalies， drought evolution and response， and interpolation and reconstruction were analyzed and summarized. Finally， further research about GRACE was suggested to carry out in the aspects of exploring the impact of changing environments on water storage anomalies， reducing the uncertainty of GRACE data， constructing a suitable drought index for drought monitoring， improving the accuracy of interpolation and reconstruction， and improving spatio-temporal resolution. The study is aiming to provide reference and insight for related research using GRACE data.

Based on the TensorFlow framework， which has the characteristics of GPU or CPU parallel acceleration calculation on tensor （multidimensional arrays）. The WGS84 latitude and longitude projection is selected， combined with FY-3D MERSI L1 data with high-precision and same-resolution positioning data， to generated tensors （multidimensional array） to align latitude and longitude data according to resolution， and calculated the new image pixel mapping position information. According to the position index information， the MERSI data can be geometric corrected point by point， and the BowTie effect caused by the scanning observation and earth curvature of medium-resolution polar orbit satellites can be eliminated at the same time. Finally， the convolution is used to calculate the inverse distance weighted interpolation point values and fill the pixels with no data after geometric correction. Using this method， the author implemented geometric correction of all 25 channels of FY-3D MERSI data in Python under the TensorFlow framework. Compared with the geometric correction results with ENVI software as the standard. The error and correction precision are calculated， and the overall processing speed of geometric correction is also tested. The results show that the algorithm proposed in this paper has a high consistency with the geometric correction of ENVI software， and the accuracy below 5% absolute error percentage is greater than 0.92， and the structural similarity SSIM index is around 0.95. Speedup is more than 36 times to complete geometric correction for all channels using GPU parallel acceleration. In summary， the geometric correction method adopted in this paper is fast and efficient in processing， and ensures the accuracy of correction.

LiDAR is now widely used in many fields. Massive discrete point cloud data is obtained by scanning objects， which not only include accurate three-dimensional coordinates， but also intensity information. The point cloud intensity information reflects the reflection characteristics of objects to a certain degree. Due to the influence of many factors， the original intensity data has a large variability， which needs to be corrected in order to improve its value. First， in this paper， the application of intensity in forestry remote sensing， surveying and mapping engineering， ground feature classification and Marine environment survey were reviewed in detail. Second， the main factors affecting the intensity were discussed from the perspectives of atmospheric attenuation effect， scanner characteristics， target surface parameters and data acquisition. Then， the basic theoretical basis of intensity correction was discussed， the theoretical and empirical correction methods were summarized. The intensity correction methods of intensity normalization and radiometric calibration were also briefly introduced. Finally， some urgent problems of intensity correction technology were pointed out.

Xisha Islands locate in the tropical zone which frequently suffers from cloud cover. Optical systems are vulnerable to bad weather which results in data gaps or low data quality， resulting in difficulties in tropical surface monitoring. To solve this problem， a study on analyzing Xisha vegetation was conducted based on a low-altitude platform. Multi-spectral images were obtained via the DJI Phantom 4 UAV and four vegetation indices from five spectral bands were derived， including the Normalized Difference Vegetation Index （NDVI）， Grassland Chlorophyll Index （GCI）， Green Normalized Difference Vegetation Index （GNDVI）， and Normalized Green-red Difference Index （NGRDI） to analyze the vegetation growth in North island during May 2020. Combined with key meteorological parameters and Worldview2 optical images， the vegetation growth changes between 2020 and 2018 as well as their potential attribution were analyzed. Results showed that the average NDVI， GCI， GNDVI and NGRDI in North Island were 0.30， 0.84， 0.26 and 0.05 in May 2020， reflecting a low vegetation coverage and health status， which was consistent with the ground monitoring results. In 2020， the index difference between artificially managed and natural vegetated region increased from -23%—15% in 2018 to 15%—40%， indicating that the growth of natural vegetation is significantly worse than that of artificially managed vegetation in 2020 which demonstrates strong environmental stress. Meteorological data in this region showed that from April to May 2020， the average daily temperature and wind speed increased while the cumulative precipitation decreased compared with the same period of previous years， leading to increased soil water deficit. This may be the main reason for the deterioration of vegetation growth. These results demonstrated that DJI Phantom 4 images could effectively and quantitatively reflect the vegetation growth which will greatly support the ecological environmental monitoring over tropical islands.

Vegetation phenology information is a key indicator for evaluating climate-vegetation interaction， land coverage， and interannual productivity changes in ecosystems. Traditional phenological monitoring methods are based on visual observation， the monitoring range is limited and requires a lot of manpower and resources. As a new monitoring method in recent years， remote sensing technology has the characteristics of large monitoring range， convenient information acquisition and saving manpower and material resources. Its application has promoted the development of vegetation phenology dynamic monitoring research. Firstly， this paper combs the process of vegetation phenology remote sensing monitoring in recent years， and clarifies the existing remote sensing phenology monitoring system； The remote sensing data sources that can be used to establish vegetation growth curve are summarized， and the application scenarios of different data sources are discussed； The existing curve noise reduction algorithms and application processes are summarized， and the causes of errors in different methods are analyzed； The main vegetation phenology extraction methods are summarized； Finally， the remaining uncertainties in remote sensing monitoring of vegetation phenology， such as data resolution， vegetation phenology stage definition， and monitoring timeliness， were discussed， and the main directions for future research on remote sensing monitoring of vegetation phenology were prospected.

Quick and accurate access to the spatial distribution of forests is of great significance for assessing the status of forest resources and ecological environment protection.Taking Pu'er City in Yunnan Province as the research area， Based on the Google Earth Engine （GEE） platform and Sentinel-2 image data，combined with the field survey data， airborne remote sensing data and terrain auxiliary data， the spectral features， texture features and topographic features were extracted. Through feature screening， the optimal feature set suitable for forest classification was obtained.Combining Simple Non-Iterative Clustering （SNIC） superpixel segmentation algorithmto explore the influence of different classification methods and characteristic variables on the classification accuracy.The results showed that the classification accuracy of the object-oriented classification method was higher than that of the pixel-based classification method， with an overall classification accuracy of 88.21% and the Kappa coefficient of 0.87. which can accurately map the forest cover of Pu 'er City. The object-oriented method can effectively alleviate the “salt and pepper phenomenon”， and feature optimization avoids the influence of redundant information on classification results and effectively improves classification efficiency. The combination of GEE platform and object-oriented method can provide large-area， high-precision forest cover remote sensing rapid mapping.

Rice is one of the main grain crops in China， and rice yield is related to people's well-being. Timely and accurate acquisition of rice planting area information and its spatial distribution is of great significance for regional agricultural development planning and yield assessment. To solve the problems of rice mixing easily with other crops and optical data being susceptible to cloud and rain weather， taking the Sanjiang Plain in northeast China as an example， a complete rice phenological growth curve was constructed by using time-series water index SDWI and vegetation index NDVI， respectively， based on sentinel-1 microwave data and Sentinel-2 optical data. The spectral differences of four important growth stages of rice were analyzed， including transplanting stage， tillering stage， heading stage and maturity stage， and the planting area of rice in different phenological stages was extracted by threshold segmentation and combination of data of different stages， and compared with the traditional method based on single optical data. The results show that the proposed method can accurately extract the planting area of rice in several key growth stages in Sanjiang Plain， and is superior to the method using optical data alone. At the same time， the overall accuracy of rice area extraction from single growth period images such as transplanting period images can also reach 87.08%. With the completeness of growth period data， the overall accuracy of rice area extraction based on the whole growth period is also as high as 91.88%， and the Kappa coefficient is 0.834， which can meet the requirements of practical application. Therefore， the rice planting area extraction method combined with multi-source data can accurately and efficiently extract the rice planting area in different phenological periods in Sanjiang Plain， and provide a basis for short-term agricultural situation investigation and management and regional agricultural sustainable development.

Frequent forest fires have caused extensive vegetation destruction in the Amazon tropical rain forest. It’s of great importance to obtain the fire influence range and vegetation destruction in different years to understand the spatio-temporal evolution of fire in this area， study the interaction between fire and vegetation， and then explore the fire development mechanism， so as to provide a scientific basis for disaster forest and reduction. To this end， the MODIS vegetation index products and surface temperature products range from 2015 to 2019 were used in this paper to construct the MODIS Global Disturbance Model （MGDI）， combined with fire point data （hereinafter collectively referred to as MOD14A1） and Vegetation Continuous Field （VCF）to extract combustion scope and intensity at 1 000 m resolution， and the spatial and temporal law of burned area within 5 years of the study area was analyzed. The results revealed that ：（1） Burned area are mainly distributed in the central part of Brazil and the border between Brazil and Bolivia， accounting for about 67% of the total burning area；（2） The information of burned area and burned intensity comprehensively `indicated that the fire showing a “rise-drop-rise” trend；（3） The fire mainly occurred in shrub grassland（more than 50%） and broad-leaved forest（30%）， and most of them took place during the dry season； under the global warming circumstance， the fire frequency increased a lot；（4） The expansion of human activities， unreasonable agricultural reclamation and deforestation lead to serious grassland degradation in the study area， and agricultural land and construction land are increasing year by year， which provides good conditions for the occurrence and conduction of fire to a certain extent.

As one of the geological disasters causing huge economic losses and casualties， landslides have attracted more and more attention from society. In order to accurately identify landslide disasters in mountainous woodland areas， the Leijiashan landslide， which occurred on July 6， 2020 in Panping Village， Nanbei Town， Shimen County， Changde City， Hunan Province， was taken as the research object. Different fusion methods such as Principal Component Analysis （PCA）， Gram-Schmidt （GS） and Nearest-Neighbor Diffusion （NNDiffuse） are used to fuse the images of Sentinel-1A Interferometric Wide Swath （IW） Ground Range Detected （GRD） image after non-decibelization and decibelization with Sentinel-2A MSI2A image. Through the quality evaluation of the fused image， the PCA fusion method effect of the VV polarization image of Sentinel-1A after decibelization and Sentinel-2A image is the optimal， that is， the optimal fusion image is PCA-VV-DB. The Support Vector Machine （SVM） method was used to identify the landslide of the optimal fusion image （PCA-VV-DB） and the original optical image Sentinel-2A， respectively. Finally， the Sentinel-2A landslide visual interpretation results were used as the inspection standard to evaluate and compare the accuracy of SVM landslide identification results. At the same time， the Shaziba landslide in Mazhe Village， Tunbao Township， Enshi City， Hubei Province， on July 21， 2020， was used as a case to verify the feasibility of this scheme. The results show that compared with the single use of optical image for landslide recognition in the study area， the accuracy of landslide recognition using the optimal fusion image is increased from 95.24% to 96.65%， and the quality of landslide extraction also increased from 87.18% to 91.84%. The leakage recognition and excessive recognition of landslides are reduced， and the research scheme is popularized. It shows that the fusion of optical image and Synthetic Aperture Radar （SAR） image can improve the accuracy of landslide recognition in mountainous woodland areas， and provide valuable information for landslide risk assessment， disaster emergency investigation and disaster recovery and reconstruction.

Aiming at the problem of low precision of abandoned land extraction caused by complex land cover and broken land， a method of abandoned land information extraction based on multi temporal collaborative change detection was proposed. Taking Luquan District， Shijiazhuang City， Hebei Province as the research area， the Normalized Difference Vegetation Index （NDVI） of various types of cultivated land cover was analyzed by using sentinel 2a and Landsat 7 multispectral images and supported by field samples Based on the classification system of seasonal abandonment， perennial abandonment， winter wheat and perennial garden， a multi temporal collaborative change detection model was constructed to carry out remote sensing monitoring of cultivated land abandonment in the study area. The results show that the classification accuracy of seasonal and perennial abandoned farmland based on Sentinel 2A image is 95.83% and 96.55% respectively； the classification accuracy of seasonal and perennial abandoned farmland based on Landsat 7 image is 91.67% and 93.10% respectively； the seasonal abandoned farmland accounts for 4.7% and perennial abandoned farmland accounts for 7.1% in Luquan District in 2019. This method can quickly and accurately obtain the spatial distribution and area information of cultivated land in the study area， and has good extraction accuracy for abandoned land in different resolution images.

The quantitative relationship between FPAR（Fraction of Absorbed Photosynthetically Active Radiation）and vegetation indices has certain reference value for improving FPAR inversion accuracy and guiding production practice. Based on the three-dimensional radiative transfer model LESS， a module named LESS1D （formally released with LESS though www.lessrt.org） with advantages of simplicity of 1D model and accuracy of 3D model is proposed. Based on this model， the influences of vegetation canopy， coverage and other factors on the relationship between FPAR_green and 6 vegetation indices were explored in random homogeneous scenes and 3D heterogeneous scenes. The results showed that in homogeneous scenarios， NDVI， SAVI and EVI fit FPAR_green best in homogeneous scenarios， while NDVI and RVI fit FPARgreen best in heterogeneous scenarios. In heterogeneous scenes， the fitting accuracy of FPAR_green and vegetation index under different crown shapes is cylindrical > ellipsoidal > conical； When the vegetation coverage is low， the fitting accuracy of vegetation indices to FPARgreen is poor； As the solar zenith angle increases， the relationship between RVI and FPAR_green changes from linear to exponential. Canopy volume and canopy geometry are the key factors affecting the size of FPAR_green with different crown shapes， while leaf aggregation， vegetation coverage and vegetation index type are the relevant factors affecting the saturation effect of vegetation index.

Sand and its surrounding vegetation types play an important role in fixing dunes， preventing soil erosion and environmental management for sandy land. Identification of Sand and its surrounding vegetation types can objectively reflect the vegetation growth environment of sandy land and its surrounding areas， so as to provide a valuable reference for ecological restoration and the control policies formulating of sandy land. With huge amount of long-term earth observation data and powerful cloud computing capabilities， Google Earth Engine （GEE） cloud platform provides a convenient way for the identification of vegetation types in a large areas. In this study， based on the Sentinel-2 time series data of 2019 stored in the GEE cloud platform， the applied potentialities of GEE cloud platform in vegetation types identification was explored by combining the RF algorithm and vegetation phenology information in Hulunbuir sandy land and its surroundings. Results showed that： ① The spectral information of Sentinel-2 image and the texture information obtained from the near-infrared band have limited ability to identify the main vegetation types in the study area， but the phenological characteristics effectively make up for this shortcoming； ② Accuracy of the vegetation types identification method achieved by the RF algorithm and considering the phenological characteristics extracted from the long time series remote sensing data is 84.37% （with the Kappa coefficient of 0.8）， which is 10.01% higher than that identification result acquired based on single-phase data； ③Phenological characteristics of the main vegetation types in the Hulunbuir sandy land and its surroundings show significant differences， which is helpful for the identification of the vegetation types， especially to improve the recognition accuracy of shrubs and grassland.The research shows that the use of Sentinel-2 data and GEE cloud platform to identify vegetation types in large areas such as sandy land has great potential and broad application prospects.

In the field of earth observation， cloud detection is not only an important part in the quantitative application of remote sensing， but also a key step in the application of satellite meteorology. In recent years， remote sensing image cloud detection based on machine learning has gradually become a research hotspot in this field， and a series of research achievements have been obtained. Systematically describes the research progress of remote sensing image cloud detection based on machine learning at home and abroad in recent 10 years， dividing the algorithm models into traditional machine learning model and deep learning model. Moreover， the specific models of two categories are introduced in detail， and the advantages， disadvantages and applications of different models are compared and analyzed. This paper focuses on the Support Vector Machine （SVM）， random forest and other methods in traditional machine learning， and the neural network models in deep learning， including Convolutional Neural Network （CNN）， improved U-Net network and so on. On this basis， the existing problems in the research of remote sensing image cloud detection based on machine learning are analyzed， and the potential development direction in the future is discussed.

Compared to the change detection of homologous remote sensing images， heterogeneous images can integrate the advantages of different satellite sensor data features and temporal relevance， better satisfying application requirements. To address the issues of spectral differences and inconsistent feature spaces in change detection of heterogeneous remote sensing images， this study proposes an aligned generative adversarial network for high-precision change detection of heterogeneous images. Considering the differences in channels and data types between heterogeneous images， it is difficult to maintain the consistency of spatial structures before and after reconstruction. The study incorporates autoencoders and constructs alignment loss to constrain the spatial structure changes of encoder output features， ensuring consistency in spatial structures between the reconstructed images and reducing information loss effectively. In the cross-domain mapping process， to minimize the color differences between source and target domain images， a cycle-consistent adversarial generative network is used for color transfer in the absence of paired images， enabling mutual cross-domain mapping between two temporally distinct heterogeneous images， generating color-preserving reconstructed images that can be directly compared with the original images. By utilizing designed change probability weights， the network automatically selects samples during the training process， effectively extracting land cover change information. Experimental results demonstrate that compared to methods such as CGAN and SCCN， the proposed method can more fully extract image features and reduce the randomness of cross-domain mapping functions. The detection accuracies on four publicly available datasets reach 0.93， 0.96， 0.97， and 0.88， with the highest accuracy achieved. The consistency between the change detection results and the reference maps， as well as the quality of the difference maps， is optimal. This method enables high-precision change detection in heterogeneous remote sensing images.

Based on the GOME-2 satellite SIF dataset， we analyzed the spatial and temporal changes of SIF from 2007 to 2018 in China， and investigated the response of SIF to climate changes， such as temperature， precipitation， and radiation. The results showed that： （1） The SIF in China's vegetation region generally shows a decreasing distribution from southeast to northwest. The average annual SIF increases by 20.2% in last 12 years， with an amplitude of 0.034 mW/m²/sr/nm， and the increase area accounts for 80.3% of the whole China. The area with significant growth of SIF accounts for 25.7%， which were mainly distributed in eastern， southern and northeastern China. （2） The SIF increase in summer season during last twelve years is the largest with an amplitude of 0.065 mW/m²/sr/nm； the area with increased summer SIF accounts for 82.1% of the whole China， and the area with significant increase accounts for 19.4%. （3） The response of SIF to climate change was investigated using the partial correlation method. temperature is the main factor affecting the interannual variation of SIF； precipitation is the main driven factor for SIF in warm temperate and temperate vegetation regions； human activities are more likely to affect the growth of SIF in the green broad-leaved forest area； radiation is the driven factor for tropical monsoon rain forest areas located in low latitudes. The above results reveal the temporal and spatial changes of vegetation fluorescence in China from 2007 to 2018 and its response to climate change， which can provide important support for global carbon cycle research.

Backpack Laser Scanning （BLS） is a potential tool in forest resource survey， but shows much uncertainty for the extraction accuracy of single-tree volume and forest stand volume in complex topographic circumstances. Using BLS point cloud data from the Gaofeng Forest Farm in Guangxi Province， this study implemented the estimation of single-tree volume and sample plot volume by random forest approach. First， individual tree segmentation was conducted using the BLS point cloud data， 8 characteristic parameters were extracted including Diameter at Breast Height （DBH）， Tree Height （Htree）， Crown Diameter （CD）， Crown Area （CA）， Crown Volume （CV）， Canopy Cover （CC）， Gap Fraction （GF）， and Leaf Area Index （LAI）， and 56 stratification height indicators were calculated （height percentage， cumulative height percentage， coefficient of variation， canopy undulation rate， etc.）. Then， an individual treee volume estimation model was developed using the random forest technique， and the prediction accuracy of various parameter combinations was investigated. The results showed that： （1） modeling with only 8 characteristic parameters of an individual tree structure indicated an estimated accuracy of R2=0.83、RMSE=0.097 m³； （2） modeling estimation accuracy was improved with the addition of the layered height index： R2=0.87、RMSE=0.087 m³； （3） the Boruta algorithm for variable screening reduced the input parameters from 64 to 52， with little difference in estimation accuracy： R²=0.87， RMSE=0.087 m³； （4） the estimation accuracy of sample plot volume was R²=0.97， RMSE=0.703 m³·ha^-1. The results suggested the application potential to use the BLS point cloud for individual tree volume estimation and the sample volume by random forest algorithm.

As an important index for monitoring urban ecological environment， the extraction of impervious surface is of great significance. Due to the complexity of urban surface and the need of detailed urban management， it is urgent to extract high-precision urban impervious surface. However， it is difficult to extract high precision impervious surface based on traditional methods. Deep learning method has gradually become a new method of remote sensing image feature extraction because of its characteristics of automatic image feature extraction. Based on this， this paper uses the U-Net deep learning method based on multi-scale feature fusion to improve the semantic segmentation accuracy， and carries out the research on the accurate extraction of impervious surface from high resolution remote sensing images.The residual module is introduced instead of convolution to deepen the network and extract more image features， the pyramid pooling module is added to enhance the network's ability to resolve complex scenarios. It is beneficial to recover spatial information by combining different scale features with jump connection. In this paper， aerial orthophoto images of Guangzhou were taken as the data source. Through convolutional neural network， the remote sensing image is segmtioned into five types of features： background， others， vegetation， road and building. Verify it with the ground truth value of manual visual interpretation， finally， the impervious surface of the study area was extracted. Experiments show that the overall accuracy and Kappa coefficient of the U-NET model are 87.596% and 0.82， respectively. It is superior to traditional supervised taxonomy， object-oriented taxonomy and classical U-Net model in both qualitative and quantitative aspects. The results show that the model can effectively improve the segmentation accuracy of complex scene images by using the multi-dimensional image feature information， and the segmentation effect is good， which is suitable for the extraction of impervious water from high resolution remote sensing images. The research results in this paper can provide data support for urban environmental monitoring.

Land Surface Emissivity （LSE） is a key parameter that measures the ability of the object surface to release energy in the thermal radiation. And it plays an important role in Land Surface Temperature （LST） retrieval from the thermal remote sensing data. To evaluate the effect of Land Surface Emissivity （LSE） on the retrieval of Land Surface Temperature （LST）， firstly three groups of Gaussian distribution randoms with different mean and standard deviation values are generated to present the noises of the LSE products. Secondly the well-known Split Window Algorithm （SWA） is selected to retrieve LST with the Advanced Himawari Imager （AHI） data and LSE products added the Gaussian distribution noises. Finally LST difference between retrieved by inputting LSE with noises and that without noise under different conditions （single temporal LST， multi-temporal LST， averaged LST， LST of different water vapor contents and different sensor zenith angles， LST of different land covers） are analyzed. Our study shows that the retrieved LST will be smaller when LSE with noises is input into the SWA； The bigger the noise’s standard deviation is， the bigger the LST difference’s standard deviation will be； When the noise’s standard deviation is 0.01， the standard deviation of the LST difference in day， night and daily average is 0.48 K、0.52 K and 0.34 K relatively. While when the noise’s standard deviation is 0.03， the standard deviation of the LST difference in the three different time is 1.46 K、1.57 K and 0.88 K. At conditions of different water vapor contents and different sensor zenith angles， the results show that the correlation coefficient between the LST retrieved with LSE added noise and that without noise will be smaller with the bigger of the added noise， while the root mean squared error and standard deviation will be bigger with the bigger of standard deviation of the added noise. The bias volue is less than 0， and its absolute will be smaller with the bigger of standard deviation of the added noise. As for different land covers， when the noise’s standard deviation of LSE is 0.01， the LST difference’s standard deviation for woody savannas， open shrubland and savannas is 0.52 K、0.51 K and 0.53 K separately； When the noise’s standard deviation is 0.03， the LST difference’s standard deviation for them is 1.58 K， 1.53 K and 1.6 K.

Burned area is one of the main parameters required for research such as global changes and carbon cycles. Accurate monitoring of burned area is of great significance for improving the accuracy of fire risk warning and risk assessment. This research used three MODIS satellite data products to assess their accuracy in estimating the annual and multi-year extent （2001 to 2016） of burned areas of the eastern Mongolian Plateau. The analysis used 30 m Resolution Global Annual Burned Area Map （GABAM） product as a reference dataset to evaluate monitoring accuracy of three MODIS burned area products referred to as MCD45A1， MCD64A1， and FireCCI51. Respectively， these products recorded 327， 160， and 71 fires in 2015. Only 40 fires were jointly monitored by three products. Monitoring areas of 27 082.46 km2， 17 227.62 km2， and 19 526.47 km2 overlapped to give a cumulative area of 6 896.99 km2. Compared with reference data， the three products gave a composite accuracy F1 score ranging from 0.96 to 0.02 indicating relatively uneven monitoring rates. Over a three-year time scale （2013~2015）， the data products gave average composite accuracy scores of 0.70， 0.62， and 0.60 so as to rank the products as MCD45A1>FireCCI51>MCD64A1. On the multi-year （2001~2016） time scale， monitoring rates of the three products were 61%， 59%， and 50% ranking products as MCD64A1>MCD45A1>FireCCI51.

Carrying out coastal erosion vulnerability assessment of Hainan Island is of great significance to the protection of ecological resources and disaster prevention in coastal areas. In this paper， the coastal vulnerability index EI （Exposure Index） of the Integrated Valuation of Ecosystem Services and Trade-offs （InVEST） model is used to evaluate the coastal erosion vulnerability of Hainan Island. Then the evaluation system of coastal characteristics-coastal dynamics-economic and social indicators was established for the typical study area. Suitable evaluation factors for coastal zone characteristics were selected including coastal erosion rate， coastal type， coastal habitat， etc. The vulnerability index was quantified using the integrated index method. Finally， the vulnerability of the coast of Hainan Island under different scenarios， as well as the coastal erosion rate and erosion vulnerability class of the key areas were obtained. The study shows that： （1） The spatial distribution of erosion vulnerability on Hainan Island is low in the east and high in the west， with the highest vulnerability in the southwestern cities and counties， the lowest vulnerability in the southeastern cities and counties， and moderate vulnerability in the remaining areas. The coastal vulnerability in the habitat-free scenario is much higher than in the habitat-protected scenario. （2） The sandy shore of the east and west coast of Haikou in the typical study area is subject to more erosion from 2016 to 2020， with the most places exceeding 20 m/a. Coastal erosion vulnerability is high in the main urban areas of Haikou such as Longhua and Meilan District， followed by the west coast and east coast， respectively， and the lowest vulnerability in the Dongzhai Port area. （3） The study found that the coast under the protection of mangroves and other habitats can be effectively protected with very low vulnerability， while the degraded sandy shoreline shows high vulnerability， so it is necessary to protect coastal habitats and prevent coastal sediment loss.

Leaf Area Index （LAI） is an important index for crop growth monitoring and yield estimation. Accurate and efficient LAI retrieval plays an important role in the macroscopic management of farmland economy. This study explored the potential of combining UAV LiDAR and hyperspectral data to retrieve maize leaf area index， studied the effects of different sampling size， height threshold and point density of LiDAR data on LAI inversion accuracy， and determined the optimal values of the three parameters. In this study， LiDAR variables and vegetation indices were extracted from resampled LiDAR data and hyperspectral imagery respectively. Then， based on Partial Least Squares Regression （PLSR） and Random Forest （RF） regression， LiDAR variables， vegetation indices， combined LiDAR variables and vegetation indices were used to construct prediction models， and the optimal prediction model for LAI inversion of maize was determined. The results show that the optimal sampling size， height threshold and point density of maize LAI inversion are 5.5 m， 0.55 m and 18 points/m² respectively. We found that the highest point density （420 points/m²） did not obtain the optimal LAI inversion accuracy of maize. Therefore， it is not reliable to improve the inversion accuracy of LAI by increasing point density alone. The LAI inversion accuracies based on LiDAR variables （PLSR： R² = 0.874， RMSE = 0.317； RF： R² = 0.942， RMSE = 0.222） were higher than those based on vegetation indices （PLSR： R² = 0.741， RMSE = 0.454； RF： R² = 0.861， RMSE = 0.338）， and the inversion accuracies of the prediction model constructed using combination variable （PLSR： R²=0.885， RMSE=0.304； RF： R²=0.950， RMSE=0.203） were better than using single variable， in which the random forest regression model established by using combined LiDAR variables and vegetation indices is the best prediction model. Therefore， the fusion of the two data sources has a certain potential in improving the accuracy of vegetation LAI retrieval.

The level 2 （L2） soil moisture data of SMAP satellite is a direct retrieval result， which can reflect its comprehensive ability of soil moisture retrieval from models， algorithms， parameters and other aspects. At this level， SMAP designed soil moisture data at multiple scales including L2_SM_P（36 km）、L2_SM_P_E（9 km） and L2_SM_SP（3 km and 1 km），the soil moisture data can meet different experimental and application requirements. In this paper， the difference characteristics between SMAP L2 soil moisture data and ISMN measured data are studied and analyzed by using the ISMN ground measured soil moisture data as reference， Bias， root mean square error （RMSE）， unbiased root mean square error （ubRMSE） and correlation coefficient （R） as analysis indicators. The results show that under different static conditions （climate type， soil property and vegetation type）， vegetation has the largest impact on the difference， while soil property has the smallest impact； Under different dynamic conditions （surface soil moisture， vegetation optical depth and surface temperature）， vegetation optical depth and surface soil moisture have a greater impact on the difference， while surface temperature has a smaller impact； Among the four SMAP L2 soil moisture data with different spatial scales， the difference between the 9km data and the ISMN ground measured data is the smallest， followed by the 36km data， 3km data and 1km data scales； According to the static and dynamic conditions， the differences between the 36km and 9km scale data and the ISMN ground measured data are similar， and the differences between the 3km and 1km data are similar.

Typhoon disasters are important natural disasters that lead to casualties and property losses in Fujian Province， China. Based on the historic typhoon disasters data from 2009 to 2020， precipitation， wind field， terrain， soil texture， NDVI， population density and GDP， the spatiotemporal patterns were analyzed on the county scale. Besides， the influencing factors of typhoon disasters were also explored by Geodetector technique. The results show that there were 38 typhoons landfalling or affecting Fujian Province， 16 super typhoons of which caused the most serious losses， while the most serious region mainly located in coastal areas. The maximum three-day precipitation， maximum wind velocity and distance to typhoon center are the three dominant factors that influenced the affected population， deaths， -damage crops and direct economic losses. The study can be employed to quantify influencing factors and provide theoretical reference for disaster risk –reduction in Fujian Province.

In the context of global warming and urbanization， the recent decades have been witnessing intensifying Surface Urban Heat Island （SUHI） effect. Investigations on the spatiotemporal patterns of SUHI area （SUHIA） are crucial for better understanding the SUHI effect. By combining MODIS （Moderate-resolution Imaging Spectroradiometer） land surface temperature data， Gaussian model， and Diurnal Temperature Cycle （DTC） model， here we calculated the ratios of SUHI area to urban area （I_R） of 504 global major cities during 2000~2019. We further analyzed the hourly， seasonal， and inter-annual variations in I_R across different climate zones. The results show that： （1） In terms of the spatial patterns， the multi-year average daytime and nighttime I_R of global major cities are 0.85 and 0.75， respectively， with a significantly larger I_R in snow climate zone （0.94 and 0.86 for daytime and nighttime， respectively） than in arid， equatorial and warm climate zones. （2） On the hourly time-scale， the I_R patterns are very similar across different climate zones. The I_R firstly decreases and then increases after sunrise， reaching the minimum and maximum at 3 hours and 7 hours after sunrise， respectively； and it then decreases in volatility and finally becomes stable. （3） On the seasonal scale， the global mean I_R is larger in summer （0.86 and 0.76 for day and night， respectively） than in winter （0.81 and 0.72 for day and night， respectively）. The seasonal variations of I_R in arid， snow and warm climate zones are similar to those on a global scale， while the situation is reversed in equatorial climate zone. （4） On the inter-annual scale， the annual mean I_R shows an increasing trend in 54% of global cities during the daytime， while it shows a decreasing trend in 62% of global cities at night. This study reveals the spatial patterns of SUHI area at multiple time scales， and compares these temporal variations among different climate zones. Our findings contribute to a better understanding of the spatiotemporal patterns of SUHI effect.

Methane is the most representative component of the gaseous hydrocarbon in the marine hydrocarbon seepage. In order to detect the marine methane anomalies accurately，a methane spectra experiment was designed to obtain hyperspectral data of different methane content in seawater background. Based on the measured data， the spectral characteristics of methane are analyzed. The ratio derivative spectrum method is used to weaken the spectral interference of seawater background components for extracting the absorption characteristic band of methane. The results show that methane has spectral absorption in the wavelength range of 1 642—1 672 nm and 2 169—2 378 nm， and the absorption characteristics of methane in the range of 1 642—1 672 nm and 2 169—2 208 nm are significantly enhanced by ratio derivative treatment. Based on the methane index CH₄I， the ratio derivative parameter is added to establish the marine CH₄ content index MI for AVIRIS data. The correlation coefficient R² between MI and methane content is 0.994 2.MI index is applied to the identification of methane anomalies in the hydrocarbon seepage area of the Santa Barbara Channel Coal Oil Point （COP）， California， USA. Compared with the inversion results of CH₄I index and CH₄ index ζ （L2298/L2058）. The abnormal distribution of methane concentration indicated by MI is more consistent with the hydrocarbon leakage area， and the results is better than the inversion results of CH₄I index.

Using remote sensing technology to evaluate the social and economic development situation and differences between East and west China is of great significance for China to formulate development strategies and implement them. In this paper， we use remote sensing-derived nighttime light data to characterize the social and economic development， and analyze the development rate and gravity center transfer of East and West （on both sides of Hu Huanyong line） at the county level. Combined with remote sensing vegetation index， the ratio index of "light/vegetation" is introduced to analyze the dynamic trade-off between economic development and green space. The proportion of light in different distance buffer zone of coastal zone was compared with that in the West. Gini coefficient is used to measure the unbalanced development of the East and the West. The results show that： with the rapid development of social economy in the whole country， the East and the west， the lighting center is basically stable， drifting in a small range in Kaifeng City， Huaibei City and the south of Alashan； The coastal zone of China has gathered high-intensity social and economic activities， and the total amount of light in the 30 km buffer zone is almost equal to that in the West； The Gini coefficient in the East and West decreased year by year. The spatial correlation analysis of the ratio index shows that the areas along the Bohai Sea， Yellow Sea and East China Sea are high-intensity development areas tending to be saturated， the adjacent inland counties are potential high-intensity development areas， and the Qinghai Tibet and southwest regions are weak development areas. The results show that the internal economic development difference between the East and the west is still significant， but the balance is getting better. The eastern development should pay more attention to the maintenance of greening trend. Affected by the spillover of coastline development results， the inland areas will gradually enter the stage of rapid development. The conclusions of this study can be used for reference in accurate identification of key areas of Rural Revitalization and ecological restoration planning in China.