In recent years， in order to improve the classification accuracy of ground objects， break through the technical system of single sensor， and make up for the limitations of single data source application， multi-source remote sensing data fusion has become a research hotspot concerned by many scholars in the field of remote sensing. The fusion technology of optical image and LiDAR point cloud data of hyperspectral remote sensing technology provides a feasible scheme to improve the accuracy of ground object recognition and classification at the technical level， breaks the technical upper limit of single sensor， and provides a new solution for the integrated acquisition of target three-dimensional space-spectral information. At the same time， it lays a foundation for the research of hyperspectral LiDAR imaging technology. This paper reviews the development history of LiDAR and hyperspectral imaging data fusion， discusses the main fusion methods and research progress at the feature level and decision level， introduces the commonly used feature level fusion and decision level fusion methods in detail， summarizes the latest research algorithms and discusses their challenges and future development and application prospects. Finally， the future development of LiDAR and hyperspectral imaging data fusion is prospected systematically.

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.

Namcha Barwa region is located in the core tectonic deformation zone of the Eastern Himalayan Syntaxis with a complicated geological-tectonic environment and frequent geohazards. Therefore， it is of great significance to strengthen the research of surface deformation monitoring in this area for local disaster prevention and mitigation and sustainable economic development. This study aims to monitor surface deformation using Sentinel-1 SAR images in this region. Using PS-InSAR technique， the surface deformation rates distribution and deformation time series on LOS （Line-Of-Sight） were acquired. Then the status of surface deformation distribution and coseismic deformation caused by Mainling M6.9 earthquake in 2017 were discussed. It is revealed that the deformation in Namcha Barwa is greatly affected by Cenozoic tectonic deformation. Tectonic deformation in the study area mainly included coseismic deformation， postseismic relaxation deformation and thrust deformation in plate boundary. The deformations were quite different on both sides of the Yarlung Zangbo River. A slow negative deformation trend is shown on the north side， while the south side is positive deformed at a high rate caused by thrust faults. The coseismic deformation of Mainling earthquake showed a spatial distribution trait of negative deformation on the southeast side of the epicenter， positive deformation on the northeast side， positive and a larger deformation on the southwest side. This study demonstrated that， InSAR can provide high spatial and temporal resolution surface deformation data for hazard monitoring and scientific research on Qinghai-Tibet Plateau.

To explore the capabilities of a set of machine learning methods for vegetation classification in typical humid mountainous areas of south China， four types of the machine learning models， including Decision Tree （DT）， Random Forest （RF）， Support Vector Machine （SVM） and Adaptive Boosting （AdaBoost）， were used to build the vegetation classification methods based on the UAV remote sensing images， field observation data， and digital elevation models. A suit of performance matrics such as classification accuracy， kappa coefficient， mean square error， user accuracy， and producer precision were selected to quantify the performance of the four machine learning methods. The results show that the AdaBoost model has the highest classification accuracy for identifying the forest vegetation types indicating that the AdaBoost model has an obvious advantage for distinguishing the forest vegetation types. Regarding the classification of non-forest types， the performances of the four methods differ relatively large with the highest accuracy in the RF model. In general， the four models can achieve good classification results in the typical humid mountainous areas of south China. The AdaBoost model has the highest classification accuracy and Kappa coefficient， while the SVM model has the relatively lowest accuracy. Auxiliary feature information such as topographic factors and texture features provide important information for improving the classification accuracy.

Existing Terrestrial Laser Scanning research on understory vegetation filtering has low precision and low degree of automation. In view of the shortcomings of the current research， the study takes the cloud data of two natural forest sample plots of Terrestrial Laser Scanning as the research object， an automatic filtering method of understory vegetation based on the main direction of the point cloud is proposed. First， after preprocessing the data such as cropping， denoising， filtering and height normalization， according to the growth height of understory vegetation in the sample plot， the data is divided into upper and lower layers at a certain height. Among them， the upper layer is a tree point cloud， and the lower layer is a point cloud containing understory vegetation. Then， the features in the spatial neighborhood of the lower layer point cloud are calculated to extract the main direction of the point cloud， and the understory main trunk is extracted according to the angle between the main direction and the normal vector of the Z-axis direction， so as to filter out a large number of understory vegetation point clouds. Finally， the Euclidean distance clustering method is used to cluster the extraction results of the understory main trunks， and the understory main trunks are finely extracted to achieve complete filtration of the understory vegetation. According to the above methods， two natural forest plots were tested. The results showed that when the neighborhood value k of plot 1 was 100 and the included angle threshold t was 30°， the neighborhood value k of plot 2 was 150 and the included angle threshold t was 30°. The number of understory trunk in the two plots achieved 100% complete extraction， indicating that the filter results of understory vegetation were good. Through the discussion of the neighborhood value k and the threshold of the included angle t， it is considered that 100~150 is appropriate for k value and 30° is appropriate for t value. This method has few parameters and high degree of automation， which can provide a certain technical reference for the study of shrubs or trees.

Compared with the traditional manual field survey method， the use of UAV tilt photogrammetry technology for multiangle photography to extract individual tree crown information has the advantages of high efficiency， accuracy and low cost. In this study， an individual tree crown extraction method combined with a visible vegetation index and watershed algorithm was proposed by taking a larch near-mature forest in the Wangyedian forest farm in southwestern Karaqin Banner， Chifeng city， Inner Mongolia， as the research object and using UAV images obtained by tilt photogrammetry as the data source. First， the Excess Green minus Excess Red （ExGR） in the visible light band was calculated by a digital orthophoto model. The median filter was used to denoise the tree crown area map， and a reasonable threshold was selected to binarize the image to separate the vegetation and non-vegetation areas. Vegetation areas were used to mask the canopy height model. Finally， the accuracy of individual tree crowns was verified by the watershed segmentation algorithm. In the process of extracting the crown area， vegetation and non-vegetation areas are successfully separated based on the ExGR index and threshold method. Through median filtering， speckle and noise caused by uneven brightness， shadow and texture in the non-vegetation area are effectively removed， the integrity of the crown edge and the connectivity of the crown are ensured， and the over segmentation phenomenon of the watershed algorithm is reduced. At the individual tree scale， the accuracy rate of crown parameter information extraction was 88.72% and 79.38%， the recall rate was 93.29% and 88.60%， and the F-score was 90.59% and 83.74%. On the sample plot scale， the relative errors are 15.45% and 22.92% respectively. The results show that the visible vegetation index based on Digital Orthophoto Image can effectively eliminate the influence of bare land and other background factors in the forest， and the watershed segmentation algorithm based on the canopy height model can accurately distinguish individual tree information. The combination of the two data sources based on the UAV tilt photogrammetry technology gives full play to their respective advantages. The method of extracting the single tree crown information based on the UAV tilt photogrammetry technology is feasible and can extract the single tree crown information of the forest with high canopy density efficiently and accurately.

Studying the topographic differentiation characteristics and driving mechanism of land use landscape pattern is of great significance for land use optimization and landscape dynamic management. Yongding District of Zhangjiajie City， with complex terrain， various types of coverage and tourism interference， was selected as the research object. The landscape type maps of multiple years in the study area were superimposed one by one with elevation， slope and aspect classification maps， and classified and counted. Eight landscape indices such as Patch Density（PD）， Aggregation Index（AI）， and contagion index（CONTAG） were selected from the landscape level index and type level index to calculate the annual change of the index and explore its topographic differentiation law and driving mechanism. The results show that ： （1） The land use landscape types in the study area have obvious altitude gradient characteristics. More than 80 % of the land area is concentrated in the area with an altitude of 300 ~ 800 m and a slope of 6 ° ~ 35 °. （2） Whether the landscape level index or the type level index， the topographic differentiation characteristics are obvious， and the differentiation of elevation and slope is significantly higher than that of slope direction. （3） The evolution of land use landscape pattern in the area with large terrain gradient （ high altitude steep slope area ） is dominated by natural ecological evolution， while the evolution of the area with small terrain gradient （ low altitude gentle slope area ） is obviously disturbed by social and economic factors.

The high spatial and temporal resolution Sentinel-2 images are increasingly becoming the primary remote sensing data source for surface water extraction.A comparative study of the extraction effects of various water index methods based on this satellite image is a significant reference value for improving surface water’s remote sensing monitoring capability. In this study， the seven water indexes （NDWI， MNDWI， AWEInsh， AWEIsh， WI2015， CDWI and MNDWI_VIs） are used to extract surface water from four sample areas with different combinations of surface water types in North China， Northeast China， the middle and lower reaches of the Yangtze River and Northwest China.The water indexes’ accuracy is quantified using Sentinel-2 MSI images on the GEE （Google Earth Engine） platform. The results show that， all seven water indexes generally can identify surface water well， but there are some differences in performance when extracting different types of surface water bodies； the NDWI index underestimate the distribution of surface water in transient water bodies （e.g.， paddy fields， floodplains， etc.） and have a high miss-score speed； while the AWEInsh， AWEIsh and WI2015 indexes have an overall tendency to overestimate and have a high miss-score rate； the MNDWI_VIs water index maintains the highest extraction accuracy in areas with complex water index； in the field of monitoring water changes in long time series， the performance of the seven water bodies is generally consistent with the conclusions obtained based on single-view imagery. This study provides an essential scientific basis for carrying out surface water monitoring in different water bodies.

As an important part of the intertidal ecosystem， tidal flats have unique environmental regulation service functions and ecological benefits such as maintaining coastline stability， accelerating material exchange and promoting carbon cycle. Accurate and timely assessment of the status of intertidal wetlands is essential to achieving sustainable management goals. With the help of Google Earth Engine （GEE） cloud computing platform， this paper uses the 2020 Sentinel-2 dense time series remote sensing images， integrates the Maximum Spectral Index Composite algorithm （MSIC） and the Otsu algorithm （Otsu） to construct a multi-layer decision tree classification model， so as to realize the rapid and automatic extraction of Australian intertidal tidal tidal flats. After vectorization， the spatial distribution dataset of high-resolution intertidal flats in Australia in 2020 was obtained， and the extracted tidal flats area was 10 708.22 km²， with an overall accuracy of 95.32% and a Kappa coefficient of 0.94. The dataset is stored in.shp format， with a temporal resolution of years， a spatial resolution of 10 m， and a data volume of 154 m. This data is suitable for coastline management， marine ecological research， environmental protection and monitoring， etc. The data can promote and manage coastal ecosystems， such as mangrove afforestation and control of alien species invasion such as Spartina alterniflora， and can also be used as basic data for scientific research， such as biodiversity， carbon storage estimation and sea level rise caused by sea level erosion etc.

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.

In order to reduce flood related disaster risks， it is necessary to quickly and accurately obtain the flood area， and use relevant data and information of flood events to analyze flood susceptibility areas， which can provide a scientific basis for flood prevention decision-making and management. Based on the Sentinel-1 SAR data， we analyzed the flood inundation status and susceptibility of the concentrated areas of the flood storage and detention areas in the middle and lower reaches for the Yangtze River in July 2020. The research results showed that the water body area during the flood period in 2020 reached 3 747 km² in the flood storage and detention area. Compared with the normal water period， increased about 1 301 km² water body in the flood period， accounting for 19% of the new water body area in the entire study area. In all flood storage detention area the largest new water body area is Honghu and Huayang River storage flood detention area. Judging from the changes of land use since 2010 within the new water bodies area， the area of cultivated land and man-made land has expanded， the area of wetland， woodland grassland， and bare land has decreased. So the shrinking area of wetland may reduce the capacity of water storage and flood reception. The area of high flood susceptibility in the study area accounts for 23.33% of the entire study area， and the extremely high level of flood susceptibility accounts for 22.55% of the entire study area； the area of high flood susceptibility in the flood storage and detention area accounts for 38.97%， the extremely high level accounts for 52.05% of the entire flood storage and detention area. The research results can provide scientific basis and theoretical reference for flood control， planning and construction in flood storage and detention areas.

Solar-Induced Chlorophyll Fluorescence（SIF） is closely related to vegetation photosynthesis and can reveal the true physiological status of vegetation. Accurate acquisition of SIF information is of great significance for terrestrial ecological carbon cycle and global vegetation monitoring. In this study， taking the SIF retrieval results of the 3FLD algorithm and NIRvR as references， the performance evaluation of data-driven SIF retrieval algorithm based on tower-based platform was carried out. Firstly， the SIF retrieval effect of the SVD algorithm in different atmospheric windows was analyzed by using the tower-based spectral observation data. Secondly， using the measured data before and after atmospheric correction， we explored the influence of atmospheric factor on the retrieval of SIF by SVD algorithm. Finally， the measured data are distinguished according to the lighting conditions， and the stability of the SIF retrieval results based on the SVD algorithm is compared under the conditions of stable weather and fluctuating weather. The results show that：（1）The SVD algorithm has higher SIF retrieval accuracy in the 735~759 nm（excluding the atmospheric absorption band） and 745~780 nm（including the atmospheric absorption band） windows.（2） The SIF inversion accuracy of the SVD algorithm is much less affected by the atmosphere than the 3FLD algorithm.（3）When the illumination conditions change drastically， the use of SVD algorithm can effectively overcome the dependence of FLD-type SIF retrieval algorithm on synchronous solar spectrum observation； even if the illumination changes rapidly， a stable and reliable SIF retrieval result can still be obtained based on the SVD algorithm.In summary， the SVD algorithm has great application potential for tower-base SIF retrieval.

As the capital of Xinjiang and central Asia regional economic center in "One Belt and One Road"， Urumqi is particularly important for its rational use of land resources and healthy development of urban form. Firstly， this study analyzed the spatial and temporal evolution of the land use based on the land use classification of the main urban area of Urumqi from 1990 to 2018. The main urban area of Urumqi expanded rapidly from 1990 to 2018. Since 2010， the transportation network has been laid and the main urban area has expanded rapidly. Surrounding farmland， forest land and grassland have shrunk by 95.12 km2， 6.49 km2 and 52.37 km2. The center of gravity has been expanding eastward and northward. Then， the driving factors affecting the expansion of the main urban area， such as natural， social and economic factors， were selected. Combined with the historical characteristics of the expansion of the main urban area of Urumqi， the scenarios of the priority of the primary industry， the priority of the secondary and tertiary industries and the priority of ecology were designed. The GeoSOS-FLUS model was used to simulate and predict the scenarios of the expansion of the main urban area under different scenarios. The study found that the construction land increased by 1 142.94 km2 under the priority scenario of secondary and tertiary industries. Under the ecological priority scenario， the areas of forest land， grassland and water area with high ecological benefits were significantly increased by 281.59 km2，651.38 km2 and 7.29 km2. Under the primary priority scenario， the area of construction land and cultivated land expanded by 617.14 km2 and 611.71 km2. It is not only helpful to re-examine the rationality of Urumqi's urban expansion， but also to point out the direction of its rational and scientific urban planning and development in the future.

Land Surface Temperature （LST） plays an important role in the study of land atmosphere energy exchange. LST changes rapidly with time， and the local solar time of LST obtained by polar orbit remote sensing satellite is different among pixels. Time normalization is needed to improve the application value of LST remote-sensing products. For MODIS LST products， a Temporal-effect Normalization Model of land surface temperature Based on Diurnal variation information （BDTNM） is proposed after the Diurnal Temperature Cycle model （DTC） is introduced and the coarse and fine resolution conversion registration method is constructed based on FY-4A high time resolution LST products. The effects of time window， normalized time and null value on the model are discussed. The normalized results of the INA08_2 model and BDTNM model are verified and evaluated by using the measured and simulated data of stations in Zhangye area. The proposed model has the following characteristics： （1） It can realize seamless reconstruction of FY-4A LST data with different loss rates； （2） Only FY-4A and MODIS LST product data are used to normalize the MODIS LSTs based on retaining the original precision and characteristics of MODIS LSTs； （3） Experiment and evaluation with simulated data， the RMSE and MAE of time normalization of BDTNM model are 0.45 k and 0.32 k， which are higher than those of INA08_2 model （RMSE is 1.36 k and MAE is 1.15 k） ； （4） The BDTNM model is not affected by the data quality and missing values of the other three observations when it normalizes the MODIS observation data at a certain time， and has a certain ability of null value reconstruction. According to the site simulation data， the model reconstructs the MODIS LST data and normalizes it to the standard time， RMSE is 0.53 k， MAE is 0.48 k. The model established in this study can also be used for reference to other remote-sensing satellite LST time normalization.

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.

The distribution of local surface heat flux in mountainous cities is quite different from that in plain cities because of complex terrain and changing climate. In order to explore the spatio-temporal evolution law of surface heat flux during the urbanization process in new mountainous development city， the spatio-temporal evolution law of heat balance process of different land use types and heat flux process before， during and after urbanization in Yuelai New City were analyzed by satellite remote sensing image data， LUMPS and SEBAL model， the effect of land use/vegetation cover on surface heat flux was also discussed. The results show that， （1） the net radiation flux and difference of various land use types in Yuelai New City reached the maximum in July and the minimum in January， vegetation coverage was one of the factors affectied sensible heat flux of different land use types. the order of latent heat flux was forest land > farmland > unused land > residential land， and the order of soil heat flux was unused land > residential land > forest land > farmland. （2）The urbanization process increased the area of low net radiation value in Yuelai New City， and sensible heat flux showed an increasing trend and accounted for the largest proportion in the energy output. The low value area of latent heat flux gradually expanded to the north and south. Soil heat flux and sensible heat flux were higher in the area with low latent heat flux. The distribution rules of soil heat flux and sensible heat were basically consistent， and both showed an increasing trend. （3）The correlation between soil heat flux and land use area was the best among the energy output factors. FVC had a greater impact on heat fluxes than land use area. Residential combined land was most closely related to FVC， and latent heat flux was most affected by FVC.

Clarifying the response relationship between landscape pattern or land use and water quality parameters is an important prerequisite for protecting and improving water quality. The Danjiangkou Reservoir is the water source for the South-to-North Water Diversion Project. Agricultural planting and industrial production activities will affect the water quality of the reservoir area in various ways. Using Sentinel-2B remote sensing image data to classify the land use status， based on the GIS spatial analysis technology to calculate the landscape development intensity index of the comprehensive effect of land use types， combined with the water quality data of the automatic monitoring station， using redundancy analysis （RDA）， a preliminary discussion of Danjiangkou the response relationship between reservoir land use and water quality changes. The results show that the nutrients produced by fertilization and animal husbandry in agricultural planting areas， and the nutrients that enter the lake through surface runoff are the main sources of non-point source pollution of Danjiangkou Reservoir. Land use in a 500 m buffer zone has the greatest impact on water quality. In this buffer zone， cultivated land and construction land are concentrated and have a high degree of connectivity and agglomeration. The pollution to rivers is relatively high. There are relatively more woodlands and overall connectivity and the degree of aggregation is relatively high， which has a certain inhibitory effect on water pollution. In the adjacent areas around the reservoir， on the one hand， it is necessary to increase forest coverage so as to increase the intensity of vegetation to improve river surface source pollution； on the other hand， it is necessary to prevent the impact of nitrogen and phosphorus from agricultural production on water quality to reduce the entire reservoir area of non-point source pollution.

Aiming at the problem that dark feature information such as water bodies affects the accuracy of terrain shadow extraction in mountainous areas， this paper proposes a terrain shadow extraction method based on the first principal component features and spectral features of ground objects. Firstly， the spectral features and the first principal component features of four typical ground features including topographic shadows were analyzed， and the shadow component （PCA1） and the water component （NDMBWI） were established to construct the Normalized Shadow Index （NSI）. Then， the dynamic threshold was constructed by analyzing the two-dimensional spatial distribution between NSI and NDVI. Finally， the image information is segmented to obtain the terrain shadow area. The test results show that： （1） Compared with other methods， the dynamic threshold method based on NSI has the highest overall accuracy and Kappa coefficient （about 0.893 and 0.759）. The three statistics （Range， Standard Deviation， and Coefficient of Variation） of the reflectance in the shadow area are the lower， indicating that the method can effectively remove the influence of water and other dark ground objects， and accurately extract the shadow； （2） The dynamic threshold method based on NSI can extract topographic shadows in different phases and different study areas with good results. The topographic shadows are highly distinguishable from water bodies， dark features and buildings， and can suppress the influence of cloud shadows to a certain extent. The algorithm has good stability and applicability.

The use of UAV hyperspectral remote sensing data technology to quickly and accurately extract typical grassland vegetation types is of great significance for dynamic monitoring of grassland ecological security.In the typical grassland area of Baiyinxile pasture with severe degradation， hyperspectral images with a spatial resolution of 1.8 cm and a spectral resolution of 4 nm， with a total of 125 bands （450 nm to 950 nm） were collected. The main degradation indicator species， Artemisia cholerae， was selected as the identification target， and after differential transformation， envelope removaland other spectral transformations， the differences in spectral characteristics were analyzed. There are obvious spectral differences at 500 nm、550 nm、670 nm， so the above three bands were selected as characteristic bands， and the degradation indicator species identification model of Support Vector Machine （SVM） and Random Forest （RF） was constructed， and the accuracy was verified. The results show that the recognition accuracy of SVM and RF are 96.92%和97.34%， respectively， and the Kappa coefficients are 0.95 and 0.96， respectively. It can be seen from the results that the identification accuracy of the random forest model is higher， and the pixel spatial distribution of degraded indicator species is closer to the natural state， which can provide technical support for monitoring typical grassland degradation indicator species.

The existing detection research of abnormal water bodies is usually carried out for specific regions， specific data sources and specific time phases. Anomaly recognition algorithm testing is often a backtracking of the water body anomaly events that have occurred， rather than real-time monitoring of the anomaly events， which cannot serve the requirements of rapid detection and identification of water body anomalies. In this paper， a method of extracting water body abnormal information based on unsupervised isolated forest plus decision rule （U-IForest-SD） is proposed. We selected Landsat and Sentinel as the test data， and tested the accuracy of U-IForest-SD with the black and smelly water body of Qingdao Enteromorpha， Songya lake and the oil spill in the Gulf of Mexico as research cases. We also compared U-IForest-SD with SVM and supervised isolated forests. The results show that the overall accuracy of the proposed method for the three types of anomalies is above 90%， and the kappa coefficient is above 0.8. The overall accuracy is higher than that of supervised isolated forest but slightly lower than that of SVM. This algorithm only needs to input single phase images， and does not need training samples. It has the advantages of good portability， strong universality and high automation. In addition， it can effectively avoid the occurrence of "wrong alarm" and "false alarm". Therefore， the newly proposed method has a good application prospect in the rapid detection and identification of abnormal water bodies.

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.

The Gaofen-6 medium resolution wide-width camera （GF6-WFV） is designed with two red-edge bands， which has the potential to monitor chlorophyll a concentration in water. In this study， six typical lakes in eastern China， including Guanting Reservoir， Luhun Reservoir and Baiyangdian Lake， were selected as the study area， and measured spectrum and chlorophyll a concentration data were obtained from 141 sampling points. Based on the measured data， the parameters of four kinds of commonly used semi-empirical inversion models of chlorophyll a concentration were optimized and the model accuracy verified， and the optimal inversion model was selected. The results show that the red edge band Ⅰ （B5：710 nm） and red band （B3： 660 nm） are newly added in GF6-WFV data. Which construct a two-band ratio 2BDA model with high inversion accuracy， correlation coefficient square （R²） is 0.89， the Mean Relative Error （MRE） is 34.71 %， and the Root Mean Square Error （RMSE） is 13.29 mg/m³. The results show that the chlorophyll a concentration in water body can be effectively retrieved by using GF6-WFV image data. The inversion model of chlorophyll a concentration in water body established in this paper based on multi-lake and multi-temporal data has good applicability in typical lake repositories in eastern China.

Mangrove forests are among the ecosystems with the highest net primary productivity in the world， and they play an important role in the study of global climate change and the evolution of coastal zone geography. Rapid and accurate acquisition of the spatial distribution of mangroves on a large scale is vital for effectively managing and exploiting mangrove resources. Landsat satellite images have become an important data source for extracting large-scale and long-period mangrove distribution information. Yingluo Bay and Pearl Bay along the coast of Guangxi， China are selected as the study sites in this study. Landsat-8 OLI images are used to construct five indices to extract the distribution of mangroves， including Normalized Difference Mangrove Index （NDMI）， Combined Mangrove Recognition Index （CMRI）， Modular Mangrove Recognition Index （MMRI）， Mangrove Index （MI） and Mangrove Vegetation Index （MVI）. This study compared the efficiency of different indices used for mangrove extraction to determine the optimal mangrove extraction index. Optimizing the mangrove distribution information extraction is proposed by combining Normalized Difference Water Index （NDWI） index. The aim is administrator improve the remote sensing classification accuracy of mangroves. It is also applied to the extraction of coastal mangroves in Guangxi. The results showed that： Mangrove distribution can be effectively extracted based on Landsat-8 OLI satellite images and index method. By comparing the extraction accuracy of five indices of mangroves， we found that the MVI has the best extraction effect and the CMRI has the worst extraction effect. The combination of NDWI can better optimize the extraction accuracy of mangroves， and the optimized MVI applied to Guangxi coastal mangroves showed the best extraction results with an overall accuracy of 97.10%. The research strategy and the range of mangrove index thresholds in this paper can provide reference and decision support for large-scale mangrove distribution extraction.

With the continuous development of the aerospace industry around the world， the satellite imaging business has developed towards the goal of multi-satellite collaboration covering large areas. In this process， multiple objective functions such as maximum coverage area and minimum satellite resource utilization need to be optimized simultaneously. Focusing on the whole process of regional coverage scheduling and data transmission planning of Earth observation satellites， the typical regional decomposition technology is firstly summarized， which plays an important role in satellite scheduling as a preparatory step for satellite regional coverage and makes the solving of combinatorial optimization problems possible. Then， the representative studies of Multi-Objective Evolutionary Algorithm （MOEA） in the field of multi-satellite joint regional coverage scheduling and data transmission planning in recent years are analyzed and reviewed. Common optimization goals include maximizing coverage rate， minimizing overlap ratio， minimizing the number of strips and so on. Finally， we summarize and put forward some prospects for future research， to provide a reliable reference for the application of multi-objective algorithms in related tasks.

Quickly and accurately obtaining information on the area of special plant planting plots is of great significance for drug production estimation and prevention of drug criminal activities. Aiming at the problem that existing special plant planting plot detection algorithms in high-resolution remote sensing images cannot quickly obtain location information and area information at the same time， this paper proposes an improved PSPNet semantic segmentation model suitable for quickly and accurately extracting certain special plant planting plots. . By introducing the channel attention SE module， the problem of holes in the segmentation of a certain special plant planting plot is solved. The Dice Loss loss function is added to improve the problem of imbalance of positive and negative samples. The encoder-decoder structure is introduced to make the extracted special plant planting Lot outline boundaries are more precise. By using the MobileNetv2 backbone network， the model prediction speed is increased by 90%. The improved I-PSPNet model achieved 95% and 84% MPA and 84% MIoU in the extraction of a special plant planting plot， and the detection efficiency reached 84 fps. Comparative experiments between I-PSPNet and UNet， Deeplabv3+， and PSPNet show that the prediction accuracy and speed of the improved model are better than the above three models. Among them， MPA increased by 24%， 7.4%， and 7.7%， and MIoU increased by 24%， 7.4%， and 7.7%. 19%， 4.3% and 4.9%， predicted speed improvements of 57 fps， 56 fps and 40 fps. At the same time， the improved model also has good applicability to RGB band data sets and GF-2 images. The improved model proposed in this article can be used to quickly and accurately obtain the location information and area information of a special plant planting plot， and help the anti-drug department quickly discover the illegal planting of a special plant planting plot， objectively assess the scale of illegal planting， and implement precise crackdowns on illegal drug and criminal activities. Provide technical support.

CFOSAT（China-France Oceanography Satellite） scatterometer is the first fanbeam conical scanning scatterometer in the world， which can obtain the observation data of multiple incident angles of targets. The CFOSAT scatterometer ground preprocessing service software generates level-1 data， including L1A （level-1 A） and L1B （level-1 A）data. According to the requirements of CFOSAT scatterometer level-1 data tracking test， a CFOSAT scatterometer data test software system is designed and developed， which focuses on testing and analyzing Sigma0 data. Data test system is based on MySQL database technology， combined with OpenMP parallel processing technology and developed by MATLAB and VC++ mixed programming. It can completely and efficiently obtain the test and evaluation results of CFOSAT scatterometer Sigma0 data quality. The test and analysis of CFOSAT scatterometer Sigma0 data shows the correctness of CFOSAT scatterometer data preprocessing algorithm， and also shows that CFOSAT scatterometer has high observation accuracy. The software is helpful to fully master the status of CFOSAT scatterometer business data， and provides an important reference for the optimization of CFOSAT scatterometer data preprocessing algorithm.

Unmanned Airborne Vehicle （UAV） based multispectral remote sensing has the advantages of low cost and flexible time in monitoring small water bodies. However， the common multispectral cameras have the problems of few pixels and lack of characteristic bands of inland water bodies， which limit the advantages of UAV based multispectral remote sensing in monitoring the water environment. In order to solve these problems， this study customized the bands for inland water quality monitoring for the Aerospace ShuWei KP-8 multispectral camera with high pixel， including 670 and 700 nm bands for inland water chlorophyll a retrieval； Then， a flight experiment was carried out to obtain the multispectral image of the turbid and eutrophic Luhun Reservoir. And the synchronously obtained water quality parameters from the water surface experiment were used to build the retrieval models of the typical water quality parameters， including the Secchi-disk depth， turbidity， suspended solids and chlorophyll a concentration； The retrieval models were applied to the multispectral image， and the typical water quality parameters in Luhun Reservoir were retrieved and their spatial distribution rules were analyzed. The results show that the UAV based high pixel multispectral camera has important potential in the operational monitoring of inland water environment.

As the functions of urban areas become more and more complicated， it is of great significance to identify the specific function types of urban blocks scientifically and accurately. This paper presents a time-series dynamic urban functional area recognition scheme. Taking the area within the Sixth Ring Road of Beijing as the research area， the high incidence area of travel mode is extracted from the massive travel data by using taxi trajectory data and Dynamic Topic Model （DTM）. Urban blocks are clustered based on topic model feature. The research use POI semantic annotation clustering results to identify urban functional areas. This paper studies and evaluates the change trend and distribution of topic blocks during six years， and discusses the dynamic changes of semantics of blocks： （1） The dynamic topics distribution has spatial diffusion， and the distribution of block semantic intensity shows obvious circle expansion. （2） The spatial boundary of clusters based on travel activities gradually coincides with the administrative divisions of the study area over time， and the function labeling results are highly matched with the specific functions of the area. （3） The high value of topic variation value is mainly distributed in the outer ring area， and has a negative correlation with the proportion of construction land. This research shows that the dynamic topic model is applicable in the travel data mining scenario， providing a new reference direction for the application of dynamic topic model in the field of mobile data mining.

Based on Sentinel-2 remote sensing data， we selected three methods， including Supervised Classification （Maximum Likelihood Classification）， Machine Learning Classification （Random Forest Classification） and Phenological Feature Classification based on time-series NDVI， to extract Potamogeton crispus L.community in Nansi Lake in early May 2021. By using the measured area and distribution data of the Potamogeton crispus L. community in Nansi Lake， we analyzed the classification accuracy of the three methods during the same period， and analyzed the extraction effects of the three methods for Potamogeton crispus L. in combination with the Fractional Vegetation Cover （FVC）. The results showed that （1） there was a significant difference in the total area of the Potamogeton crispus L. extracted by three methods. The areas of the Potamogeton crispus L. community extracted by both Supervised Classification and Random Forest Classification were less than 100 km²， which were 98.97 km² and 75.92 km² respectively. While the area extracted by the time-series NDVI method was 207.44 km²， which was closest to the measured area of Potamogeton crispus L. （2） Both the whole lake and the core area， the extraction accuracy of Supervised Classification and Random Forest Classification was just about 75%， the Mean Relative Error （MRE） was about 0.5， and Mean Error （ME_area） was about 20~30 km²， while the accuracy of the time-series NDVI method was above 90% and the MRE and ME_area were also the lowest. （3） Comparing the fractional vegetation cover， we found that Supervised Classification and Random Forest Classification could only extract the Potamogeton crispus L. with high fractional vegetation cover near the lake shore and poorly with low cover in the lake core area， while the time-series NDVI method was more sensitive to the low fractional vegetation cover Potamogeton crispus L. community and could extract it well in different areas of the whole lake， which is a potential method for Potamogeton crispus L. remote sensing extraction. This study has some theoretical value for innovative remote sensing extraction methods of submerged vegetation and guiding remote sensing monitoring of lake ecological environment.

The concentration of suspended matter in water body is an important parameter to describe the optical characteristics of water body. Satellite remote sensing has the advantages of a large range， fast and high-frequency word dynamic monitoring， which helps to strengthen the monitoring of water environment quality of Qinghai Lake and reduce the monitoring cost. And ZY1-02D satellite hyperspectral camera with high spatial resolution and high spectral resolution provides the possibility of high-precision monitoring of water quality in Qinghai Lake. In order to verify the applicability of the ZY1-02D hyperspectral camera in the application of remote sensing monitoring of water quality， this paper uses the ZY1-02D hyperspectral camera as the remote sensing data source， and also assists the actual measurement data to construct an inversion model of the suspended matter concentration in Qinghai Lake， and conducts accuracy verification to evaluate the accuracy of the inversion results. The results show that the average relative error of the Qinghai Lake suspended concentration inversion model is 21.1%， and the root mean square error is 0.296 mg/L. The accuracy is good， and the inversion results of Qinghai Lake suspended concentration show the characteristics of low in the center of the lake and high on the shore， compared with the retrieval results of Sentinel-2 and Landsat-8 in the same period， the retrieval results of Sentinel-2 and Landsat-8 in the same period， the inversion results remain consistent， results remain consistent， which indicates that the ZY1-02D hyperspectral image can retrieve the water quality parameters.

Crop residue burning is one of the important sources of air pollution. Due to its randomness and dispersion， it is hard to obtain the information of Crop Residue Burning Fire Points （CRBFPs） quickly， accurately and comprehensively， and regulatory authorities face great difficulties in supervision and law enforcement. In order to improve the monitoring accuracy and efficiency of straw burning， high-precision fire detection algorithms were developed in this study and used to monitor the CRBFPs based on multi-source satellite remote sensing data， and UAV and manual verification schemes were formulated to verify the CRBFPs’ information. The system platforms of WEB and mobile APP were developed to directional push and display the fire points information. A space-air-ground integration supervision system for crop residue burning was established with the functions of detection and verification. Relevant platforms were applied to monitoring of the CRBFPs in Shandong Province in the spring and autumn of 2020， and 58 fire points， which include 53 true fire points after verification， monitored by remote sensing images， with an accuracy rate of 91.38%. The results show that the system can extract high-precision fire point information quickly and provide new ideas and methods for the supervision of crop residue burning.

Coupled with the physical environment of the city and human social activities， the study of the spatial and temporal evolution characteristics of the city's spatial structure can clarify the current needs of urban development and provide references for the layout of territorial spatial planning. Based on multi-source geographic big data， the spatio-temporal evolution characteristics of urban spatial structure are portrayed in terms of both hierarchical structure and circle distribution from the perspective of coordination of urban static-dynamic system， taking the main urban area of Wuhan city as an example， using night light remote sensing data， POI data， land use data and road data， and drawing on the concept of coupled coordination degree. The results show that： （1） in terms of temporal changes， the spatial structure tends to be perfect in the main urban area of Wuhan between 2010 and 2020， and the scope and number of urban centers also appeared to extend and increase. （2） The distribution of advantages and disadvantages of spatial structure is unbalanced in the main city state for ten years， forming an overall pattern with the city center and gradually decreasing along the circle gradient towards the periphery from the perspective of space. （3） The portrayal of characteristics of urban spatial structure coulping urban static-dynamic system based on multi-source geographic big data is highly consistent with the actual development of the city， which is helpful to deepen the understanding of urban spatial structure and provides a reference for urban planning.

Building is the basic unit of urban refined management， the rapid and accurate extraction of urban building footprints based on high-resolution remote sensing images is of great significance for urban planning and management. Based on the high-resolution （0.8 m） remote sensing data of Beijing-2， a sample library of building footprints in Beijing was established. We used multiple semantic segmentation models， U-Net， DANet， UA-Net （U Attention Net） and instance segmentation models， Mask R-CNN， Mask R-CNN FPN， Mask R-CNN RX FPN to extract building footprints， performed accuracy evaluation and compare the extraction effects of different types of buildings （such as buildings， villas and village buildings， etc.）. Finally， we selected the U-Net model with the highest overall accuracy and the best extraction performance to extract all building footprints in the Beijing area. The results show that the classification accuracy of U-Net， DANet， UA-Net， Mask R-CNN， Mask R-CNN FPN and Mask R-CNN RX FPN models are 79.37%， 65.59%， 71.03%， 61.82%， 52.53% and 59.70%， respectively. And the U-Net model training time is relatively short. The U-Net has a good performance for the extraction of building footprints. Comparing the recognition effects of different models， it is found that the semantic segmentation model is more advantageous for the recognition of bungalow buildings， while the instance segmentation model is suitable for single-family buildings and villas in urban and surrounding areas. The study provides a scientific basis for model selection for typical building footprints extraction tasks and our achievement solves the problem of lack of fine-scale research data in cities to a certain extent.

In recent years， the abundance of hydro fluorocarbons （HFC） has been increasing， which has huge greenhouse potential value. It has an impact on global warming and also indirectly causes the destruction of the ozone layer. Scholars at home and abroad have carried out a wide range of in-situ ground measurements to obtain global abundance. At the same time， remote sensing technology can monitor the changes of HFC gas in a large range， for a long time and quickly， and has become an important means for the inversion of the gas concentration. The contents of in-situ measurement method， tracer ratio method， satellite inversion sensor development and satellite inversion method are described， and the advantages and disadvantages of different inversion methods are compared in combination with load characteristics analysis. Finally， discusses and prospects the existing problems and future development trend of current inversion.

Rapid and accurate winter wheat acreage extraction using remote sensing technology is of great importance for crop yield estimation and food security. Due to problems such as the difficulty of obtaining medium and high resolution time-series images due to revisit cycles， cloud and rain， and the low accuracy of low resolution remote sensing data in extracting crop planting information. In this study， taking Changge City， Henan Province as an example， Landsat 8 and MODIS images were obtained as the dataset during 2015~2020， and the 2 data were fused based on an optimized convolutional neural network spatio-temporal fusion model to construct a 30 m resolution NDVI time series set， and S-G （Savitzky-Golay） filtering was used to denoise the time series set， and finally The area planted with winter wheat was extracted using the RF method. The results show that the optimised fusion model is robust and the R² of both the predicted and real images is above 0.92. The agreement between wheat area extraction and statistical area in the study area was 97.3% and the results were reliable. Therefore， the optimised model can better fuse the medium and high resolution images， which is an effective technical means to supplement the missing images， and the constructed time series set can more accurately extract the wheat planting area in the county.

This study， focused on the area of Beibu Gulf， explores the remote sensing inversion method for chlorophyll concentration based on the Sentinel-3A satellite's OCLI water color sensor. The study partitions the Beibu Gulf by using measured spectral data and then combines the measured chlorophyll-a concentration with Sentinel-3A remote sensing data of which aims to build the remote sensing inversion model for chlorophyll-a concentration. The results show that （1） the remote sensing reflectance curves exhibit distinct partition characteristics， dividing the area into nearshore， transitional， and offshore water types based on the spectral features； （2） Different water types require different inversion factors for model construction， and all of them got relatively good fitted result. Among them， the fitted inversion factor is R_rs（764.375）/R_rs（681.25） that could be used in the nearshore water， for the transitional water， ［1/R_rs（620）-1/R_rs（708.75）］/R_rs（753.75） is the most suitable， and for the offshore water， R_rs（708.75）-R_rs（764.375） achieves the best fitting performance， with corresponding R² values of 0.67， 0.80， and 0.8， respectively； （3） The partitioning method effectively improves the applicability and accuracy of the remote sensing inversion model for chlorophyll concentration in the Beibu Gulf. This study successfully realizes the remote sensing inversion of chlorophyll concentration in the Beibu Gulf by using a partitioning model based on Sentinel-3A satellite's OCLI data. The result provides the important scientific support for the remote sensing monitoring of chlorophyll concentration in the Beibu Gulf， and enhances the management and protection of marine ecological environments.

The Tibetan Plateau （TP），with its unique climate characteristics and geographical pattern， plays an important role in global climate change. As an important part of the earth atmosphere system， cloud is key to affecting climate change. Cloud cover can more directly reflect the change of cloud. Therefore， it is of great significance to reconstruct a cloud cover product with longer time series and higher accuracy in the TP. In this paper， Considering the complex underlying surface types and geographical elevations in the TP， We select the cloud cover of MOD06， ERA5 and CRA40 from 2001 to 2020. We take the cloud cover of MOD06 from March to November as the true value and evaluate the applicability of the two reanalysis data in the TP through methods such as climate tendency rate and correlation coefficient.Based on ERA5 and MOD06， the improved auto-encoder model is used to reconstruct the cloud cover of the plateau from March to November of 1950 to 2020. The results show that the cloud cover of ERA5 is higher than that of MOD06， while that of CRA40 is lower than that of MOD06， and the correlation between ERA5 and MOD06 is obviously better than that between CRA40 and MOD06；The improved auto-encoder model evaluated by four evaluation indicators of correlation coefficient（R）， bias， Mean Absolute Error（MAE） and Root Mean Square Error（RMSE） has a good effect on cloud amount reconstruction. The correlation coefficient between cloud amount reconstructed by the improved autoencoder model and MOD06 cloud amount data increases by more than 20% on average from March to November， and can simulate the change trend of cloud amount over the TP. The results provide reliable long time series data for studying the temporal and spatial evolution of cloud cover over the TP.

Groundwater quality is becoming increasingly polluted and monitoring the content of groundwater ionic compounds is beneficial for dynamic groundwater management and accurate prevention. Little is known about the weak spectral response and inversion mechanisms of ionic compounds， and most existing studies have performed simple qualitative analyses of ionic compounds， with less use of mathematical and statistical methods for comprehensive estimation of their content. Based on the spectral mechanism of ionic compounds and the redundant nature of hyperspectral data， the spectral response mechanism of three ionic compounds in water， the optimal pre-processing method and the algorithm of feature band selection were investigated by measuring the visible-near infrared reflectance spectra （400~1 000 nm） of three ionic compound standard solutions with different concentrations of sodium， potassium and calcium in the laboratory. And based on the characteristic spectral bands， a BP neural network model is constructed to quantitatively invert the ionic compound content. It was found that （1） The overall reflectance of the three ionic compounds is inversely proportional to the content at wavelengths from 400 to 1 000 nm and proportional to the charge number and radius of the ions； （2） Compared with the continuous projection method， the multiple linear regression model constructed based on the characteristic spectral bands extracted by principal component analysis can better infer the content of ionic compounds in water bodies； （3） The preprocessing of the KCl optimal inversion model by SG filtering and the preprocessing of the CaCl₂ and NaCl optimal inversion models by SG filtering followed by reflectance normalization； （4） Compared with the traditional linear inversion model， the PCA-BPNN nonlinear model achieves the best inversion results， among which the inversion results of potassium ion compound content are the best， with the R² and RMSE of the training set reaching 0.996 4 and 248.77， respectively， the R² and RMSE of the test set reaching 0.998 8 and 156.89， respectively. This study can provide important theoretical and technical support for groundwater ionization inversion.

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.

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.