To explore the application potential of airborne point cloud and UAV visible light image in tree species identification and classification， a single-tree scale tree species classification and recognition method based on UAV hybrid fusion of multi-modal features and decision was proposed. Firstly， Kendall Rank correlation coefficient method and Permutation Importance （PI） were used for feature selection， and Efficient Low-Rank Multi-Mode Fusion Algorithm （LMF） was used to fuse the selected point cloud and visible image features. Ensemble learning was introduced to input point cloud， image， and fusion features into eXtreme Gradient Boosting （XGBoost）， Light Gradient Boosting Machine （LightGBM）， and Random Forest （RF） base classifiers integrated by Stacking. Finally， the meta classifier， Naive Bayes， is used for decision fusion. The experimental data show that the independent test accuracy of the proposed algorithm is 99.4%， which improves 22.58% compared with the Random Forest classifier by traditional feature concatenate fusion. In addition， the Kappa coefficient also increased by 28.54%. The comparison experiment with Convolutional Neural Network（CNN） shows that the proposed algorithm has obvious advantages in small sample training and better generalization ability.

Speckle is an inherent property of SAR image， but its existence seriously interferes with the quality of SAR image and affects the high-quality application based on SAR image， so it is urgent to suppress it. The accuracy of the edge detection model of the traditional AD （Anisotropic Diffusion） filter still has room for improvement， and the noise suppression effect is often limited by the problem that it is difficult to accurately estimate the diffusion threshold. To solve the above problems， a novel AD filter based on Multidirectional Sobel （MSAD） is proposed. MSAD filter is an improved algorithm of SRAD. It builds a new edge detection model based on Multidirectional Sobel templates. Based on this， a new AD diffusion coefficient is established by integrating Gaussian kernel function， which can effectively solve the limitation of traditional AD diffusion coefficient by parameter estimation and improve the accuracy of speckle anisotropy suppression. Three real SAR images are selected for filtering experiments. In experiments， SRAD， DPAD， EnLee， and PPB filters are selected as the comparison algorithms； ENL， SSI， ESI， and M-Index are selected to evaluate the performance of proposed algorithms. Experiments show that MSAD filter can effectively improve the edge detection ability and obtain better speckle suppression effect.

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.

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.

Surface deformation is a geological phenomenon caused by natural or artificial factors， and its disaster-causing process is slow and irreversible. It is also a geological disaster with destructive solid power. Therefore， real-time and high-precision surface deformation monitoring is one of the most critical tasks in maintaining urban safety. However， due to the complex causes， long duration， wide range， and many triggering factors of surface deformation， there are many difficulties in monitoring surface deformation using single technology such as leveling， GNSS， INSAR， and optical remote sensing. Considering the characteristics and complementarities of InSAR and GNSS， the combination of InSAR and BeiDou/GNSS can improve the surface deformation monitoring capability in space and time at the same time. Unluckily， the traditional GNSS-InSAR data fusion method is simple to fuse and cannot dynamically reflect surface deformation characteristics， leading to insufficient data use and low accuracy of deformation features. A new fusion method is proposed based on the Kalman filter algorithm GNSS-InSAR correction values. The method mainly consists of two sequential processes， i.e.， the a priori processing of GNSS and INSAR data and the fusion process of GNSS-InSAR correction values based on the Kalman filter algorithm. The a priori processing of GNSS and INSAR data is to obtain the a priori deformation results using the fitted estimation model to correct the systematic errors in the InSAR observations. The fusion process of GNSS-InSAR correction values based on the Kalman filtering algorithm is to fuse the two data through Kalman filtering based on the spatial and temporal correlation between the time-series GNSS observations and the InSAR correction observations. The experiment was processed using 103 views of sentinel-1A data from November 15， 2018， to June 3， 2022， and 13 GNSS point data during the same period. The experimental results show that the fusion result of the corrected InSAR observations and GNSS observations by the Kalman filter is 45% more accurate than the fusion result of the uncorrected InSAR observations and the GNSS observations， which is 45% 57% higher than the accuracy of InSAR observations. Therefore， the fusion method model based on the Kalman filter algorithm of GNSS-InSAR corrected values proposed in this paper improves the accuracy of InSAR deformation monitoring and expands the breadth and depth of InSAR applications.

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.

Estimation of Aboveground Biomass （AGB） in Spartina alterniflora （S.alterniflora） can provide an important basis for ecosystem stability evaluation and regional carbon sink assessment in coastal wetlands. Using typical coastal wetlands in Zhejiang， China as an example， this study used 48 measured aboveground biomass data of S.alterniflora to extract vegetation index and band characteristics reflecting aboveground biomass information of vegetation based on Landsat8 OLI images， constructed a Univariate Regression （UR） model， a Multiple Linear Regression（MLR） model， a multi-scale geographically weighted regression model （MGWR）， and a Partial Least Squares Regression （PLSR） model to estimate the AGBof S.alterniflora from actual field measurement data. The results showed that： （1） The AGBof S.alterniflora was significantly correlated with the 29 selected remote sensing variables， and the correlation coefficients were all between 0.5 and 0.8（P<0.01）. （2） The model constructed by PLSR method was the optimal model of S.alterniflora AGB inversion in the Zhejiang coastal wetland （R²=0.767； RMSE=130.576 g/m²； MAE=100.801 g/m²）. （3） The average AGB of S.alterniflora in Zhejiang coastal wetland was 6 607.01 g/m²， and the total AGB was 1.36×10³ t. The distribution pattern of S.alterniflora AGB in the coastal area of Zhejiang Province was high in the south and low in the north. This study could provide the scientific basis for the rational development and utilization of coastal wetland resources， carbon sink monitoring， and ecosystem function evaluation.

As an important part of terrestrial ecosystems， vegetation is often used as an indicator to assess the effectiveness of climate change and ecological restoration. In this study， the Shiyang River Basin is taken as an example， Theil-Sen and Mann-Kendall models， and the Hurst index were used to analyze the change characteristics of vegetation cover. The correlation analysis， residual analysis and Geodetector were used to explore the influencing factors of vegetation cover change. The results showed that the vegetation NDVI demonstrated a fluctuating but upward trend from 2001 to 2020， with a rate of increase of 0.023/10 a. Areas with significant increased and significant decreased accounted for 72.32% and 2.4%， respectively. Areas with sustainability （Hurst>0.5） accounted for 63.84 % of the entire area， among which 47.37% showed continuously significant increasing trend. The correlation results between NDVI and climatic factors indicated that the impact of precipitation was particularly significant， and the impacts of temperature， solar radiation and saturated vapor pressure deficit were relatively weak. The area of NDVI_pre showed a significant increase trend accounted for 21.59%， while the area of NDVI_res showed a significant increase trend accounted for 60.07%， so interannual variation of NDVI in Shiyang River Basin was greatly affected by human activities. Geodetector results showed that the spatial distributation characteristics of water-heat conditions. It is noted that the spatial distribution of NDVI of cultivated land is greatly affected by population density. The results of this study are helpful to deepen the understanding of the driving factors of vegetation change and provide scientific reference for ecological protection and restoration of Shiyang River Basin.

The Eastern Tianshan regions of Xinjiang were characterized by severe climate and exposed bedrock. The lithological boundaries were not accurate enough in the existing 1∶200 000 geological map， and the application of high-resolution remote sensing technology was lacked. GF-1 and Landsat 8 satellite images were used for lithology identification and geological mapping in Changji Area， Eastern Tianshan， Xinjiang. Image processing technology such as IHS and Gram-Schmidt were executed to obtain high spatial resolution images. And the image processing process was based on image enhancement using False Color Composite （FCC）， Principal Component Analysis （PCA） and Minimun Noise Fraction （MNF）. Image interpretation markers were established by the combination of 3D image constructed from DEM data and available geological records. Additionally， an updated 1∶50 000 lithological map was generated for study area by fieldwork verification， sample thin section identification and reflectance spectrum characteristic analysis. The results showed that， in Changji area with good outcrop of bedrock， the integrated application of multi-source remote sensing data could identify the lithological units which were missed in the 1∶200 000 geological map and corrected the lithological boundaries. It improved the efficiency of geological mapping and guided the follow-up geological survey and geological prospecting.

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.

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 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.

The vegetation greening period is one of the important stages in the vegetation growth process， so it is of great significance to explore the change of vegetation greening period and its influencing factors. At present， there are few studies that combine snow cover with temperature and precipitation to explore the influencing mechanism of vegetation greening period. Therefore， based on MODIS NDVI， snow cover products and ERA-5 reanalysis data， this paper adopts the Logistic curve curvature extreme value method， trend analysis， correlation analysis and sensitivity analysis of accumulated NDVI. To explore the spatio-temporal variation of vegetation greening period in Mongolia Plateau from 2001 to 2018 and its response mechanism to climate， snow cover and soil water change. The main results show that the average greening period of the Mongolian plateau in recent 18 years is about 123d， and the overall trend is not significantly delayed. The earliest greening stage was found in the southwest region and the Greater Khingan Mountains， and the latest was found in the Sayan Mountains and Hangai Mountains. The regions with significant positive correlation with snow cover， snow cover date and snow cover total day were all >30%， while the regions with significant negative correlation with temperature during snowmelt period were >25%， and the correlations with precipitation， soil moisture and snow cover first day were weak. The results showed that snow cover had a significant effect on the greening stage of vegetation， and its factor sensitivity was ranked as follows： snow cover （0.467） > snow cover date （0.184） > temperature during snowmelt period （0.113） > snow cover day （0.028）.

Regional target decomposition is a key part of remote sensing satellite imaging mission planning for large area coverage， which is of great significance to quickly acquire effective data in large area. Based on the planning experience of satellite photographing in large area， the main influencing factors in the actual operation of large area photographing are summarized， including satellite orbit transit， regional cloud cover at transit window and real-time update of base image data， and a large area photographing decomposition method based on multi-factor superposition is proposed. In this method， the cloud cover factor is applied to the decomposition of the strips in each transit window， and the better photographed strips in each satellite transit are obtained based on the idea of greedy algorithm， which can improve the single data acquisition efficiency and the overall coverage efficiency. This method has been applied to the project of "Data Cube for large coverage datasets of Chinese high resolution and broad band and multispectral satellite constellation"， which provides support for Jilin-1 GP01/GP02 satellite to quickly acquire data in the 65 countries and regions along the Belt and Road. By comparing the acquisition of regional coverage data before and after using the method， the data coverage efficiency was improved by about 44%.

Remote sensing images have low detection accuracy due to the characteristics of different object angles， generally arranged densely， high proportion of small objects and complex background. In view of the inapplicability of the horizontal detection algorithm for remote sensing rotating object detection， and the periodicity and edge interchangeability of angle in the mainstream five-parameter method， a VR-CenterNet is proposed， which used the vector representation to detect the rotating box and design the loss function to avoid the problem of angle regression， and to optimize the high displacement sensitive problem of slender objects. For the high redundancy problem of shallow feature fusion， self-adaptive channel activation is introduced to automatically filter impurity information. In order to strengthen the key point information， an improved global contextual self-adaptive layer activation attention block is introduced in the output of backbone. First， the performance of different algorithms is compared on HRSC2016 and UCAS-AOD data sets. Then， the module ablation experiment is conducted on the two data sets to verify the effectiveness of each improved method. Experimental results show that： 88.48% and 90.35% accuracy are obtained on HRSC2016 and UCAS-AOD data sets respectively. The improved algorithm can improve the detection accuracy of remote sensing rotating objects， and provide another problem-solving idea for the accurate detection of remote sensing rotating objects.

UAV remote sensing technology can quickly obtain the Canopy Height Model（CHM） of the survey area. How to identify tree vertices more accurately from CHM is key to tree height extraction. This paper discusses the influence of different window types， window sizes， and stand canopy density on the extraction of tree vertices. Using the university campus as the study area， two local areas of dense and sparse forest land were selected based on canopy density. GIS rectangular neighborhood analysis， GIS circular neighborhood analysis， and local maximum algorithm are used to extract tree vertices. The results show that the accuracy of tree vertex extraction is not only affected by the window size and canopy density， but also closely related to the window type， and the result of GIS rectangular neighborhood analysis to extract tree vertices is more stable and accurate， and the highest F-Measure value is 78.13% in dense forest， and 96.94% in sparse forest. Comparing the extracted tree heights corresponding to the tree vertices obtained based on this result with the tree height values measured in the field， the RMSE is 37cm for dense forest and 39cm for sparse forest. The results proved the feasibility of extracting tree heights of broad-leaved forests with higher canopy density based on the visible light remote sensing technology of small UAVs， which provided a reference for the subsequent identification of tree vertices based on the canopy height model and improved the accuracy of tree height extraction.

Some of the data of the second Chinese glacier inventory（CGI2.0） are replaced by the first Chinese glacier inventory， and these data are concentrated in southeastern Tibetan Plateau. Where the terrain is steep， the climate is harsh， and it is covered by clouds all the year round. There is no systematic glacier survey due to the inability to obtain effective optical images. Aiming at the problems that the traditional threshold segmentation method is influenced by noise， and the standard Unet has a large amount of computation， which leads to slow operation， compressedUnet model is designed to improve model training efficiency and glacier extraction accuracy by modifying model parameters such as sample size， number of convolution kernel and optimizer. Using the polarization characteristics and topographic features of glaciers， 45-scene ENVISAT ASAR images and NASA DEM are selected to carry out deep learning based on Unet and compressed Unet. By referring to optical images and other auxiliary data， the misclassified and missed glaciers are visually interpreted one by one. Finally， the extraction and correction of the glacier boundaries without update are completed， and their attributes are updated. The results show that deep learning based on SAR images and topographic features can effectively identify glaciers in cloud-covered areas. In the areas where the CGI2.0 is not completed， there are 8 374 glaciers with a total area of 5 622.65±303.58 km²， and the error accounts for 5.4% of the total glacier area， most glaciers are retreating and fragmenting. The dataset updates the alternative data in CGI2.0， and provides reliable data support for related studies of glacier changes and mass balance in southeastern Tibetan Plateau.

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.

Based on the night lighting data of Luojia 01 and the energy statistics of Xi'an， combined with the ArcGIS spatial analysis method， this paper uses the high oligomeric model to spatially simulate the carbon emission of Xi'an in 2018， calculate and classify the carbon emission intensity of all districts and counties in the city， and study the distribution characteristics of carbon emission of all districts and counties in Xi'an. The results show that there is a good correlation between Luojia-01 light data and carbon emissions， the linear correlation coefficient is 0.720 3， and the correlation coefficient of quartic function polynomial is the highest， which is 0.843 5； In terms of annual carbon emissions， Xi'an's carbon emissions show the spatial distribution characteristics of high in the central main urban area and low in the surrounding counties， which is a cluster distribution， and the clustering results are clustered in the high value area； There are many low-carbon emission intensity districts and counties in the city， and there are a few high-carbon emission intensity districts and counties. The industrial structure needs to be further adjusted to realize the green development model.

The launch of the Jilin-1GP satellite has enhanced China’s Earth observation capabilities， and has great potential in agricultural quantitative inversion. To invert the key crop parameters accurately and effectively， it is of great significance to analyze the inversion capability of Jilin-1GP satellite images. The farmland of Urad Front Banner， Zhenglan Banner and Horqin Right Front Banner in Inner Mongolia were taken as the study area in this study， and based on the Jilin-1GP images， the optimized PROSAIL model and curve matching algorithm were used to invert the Leaf Area Index（LAI） of maize and rice in different phenological periods， and the accuracy was verified by combining the measured LAI data. Results showed that the parameter range and step size of the optimized PROSAIL model were more suitable for crop LAI inversion， and the capacity of the look-up table was reduced on the premise of ensuring the accuracy； The curve matching algorithm based on eigenvalues improved the computational efficiency by an average of 41.43% when the spatial distribution was highly consistent and the mean absolute value of the error was 0.41； The LAI inversion accuracies R² of maize and rice in different phenological periods of the study area ranged from 0.72 to 0.9， and the RMSE ranged from 0.32 to 0.49. Among them， the precision of maize in the flowering stage was the highest （R²=0.9， RMSE=0.4）， and the precision of maize in the maturity stage was the lowest （R²=0.72， RMSE=0.47）. This study showed that the crop LAI inversion based on Jilin-1GP images had the characteristics of high precision and small error. The research results can provide scientific methods and basis for the accurate inversion of crop LAI with Jilin-1GP images.

To reveal the long-term variation trend of vegetation cover and further determine the main climatic driving factors affecting vegetation variation in Hainan Island. Also， to provide scientific evidence related to the impact of climate change on vegetation and scientific basis for achieving vegetation optimum development in island regions. The spatiotemporal variation trend of vegetation in Hainan Island from 1982 to 2015 was explored by applying trend analysis method to the GIMMS NDVI data. The effects of temperature， precipitation， and solar radiation on vegetation variability in Hainan Island were investigated by partial correlation analysis and principal component regression analysis over the 34 years. Results show that： ①Spatially， vegetation exhibited a significant increasing trend in the northern and coastal regions of Hainan Island while displayed a degeneration trend in Sanya city and its surrounding areas. ②Temporally， we found the vegetation in Hainan Island showed a slowly increased trend in most areas with a speed of 0.019/10 a and its intra-annual variability was obvious. ③In general， temperature and solar radiation jointly dominate the vegetation growth in 88% areas of Hainan Island in a significant way. Solar radiation was the most important climate driving factor to control vegetation variability in Hainan Island， followed by temperature， and precipitation had a small impact on the vegetation variability. ④Temperature dominated the vegetation variability in the northern and western areas of the Hainan Island. By contrast， solar radiation dominated the vegetation variability in the southern areas of the Hainan Island. Precipitation was the dominant climate driving factor to explain the variability of forests in the middle of the Hainan Island. Overall， this study found that temperature and solar radiation were two major climate driving factors which affected vegetation growth in the Hainan Island.

Sea ice classification using Synthetic Aperture Radar （SAR） images is a crucial aspect of sea ice monitoring. Existing methods have mainly relied on spatial features of SAR images， but rarely consider temporal features， which can potentially provide additional information. A novel approach called SE-ConvLSTM has been developed to combine both spatial and temporal features for sea ice SAR image classification. Firstly， ConvLSTM is used to extract the spatial-temporal features of HH and HV polarization SAR images respectively. Then， the spatial-temporal features of different layers and channels are concatenated， and the channel feature response is adaptive recalibrated by using SE channel attention. Finally， SoftMax function is used for image classification. To evaluate the effectiveness of the SE-ConvLSTM method， six time-step image blocks of SI-STSAR-7 dataset were used for comparison with other classification methods. The results indicate that SE-ConvLSTM achieved an overall accuracy of 97.06% and 90.01% for the thick one-year ice which is difficult to classify. This suggests that adding temporal information can significantly improve classification accuracy. Additionally， the proposed network has better recognition ability for regions with low density of main ice types and for boundary positions of SAR images， making it an effective tool for generating sea ice distribution maps.

As black and odorous water bodies in rural areas have negative influence to environment， it is important to monitor the rural black and odorous water bodies by high resolution remote sensing. While， the spectral curve from remote sensing of rural black and odorous is similar to some vegetation， green roofs and greenhouses， which bring difficulties to identify the rural black and odorous in remote sensing images with satisfactory repeatability and accuracy， and automation， by using the color purity on a Commission Internationale de L’Eclairage （CIE） model and spectroscopic method. Thus， we collected and interpreted 325 rural black and odorous water bodies by GF1/2/6， covering several counties in Xi’an and including various type of polluted object， to train the model using DeeplabV3+ with ResNet101 as the backbone to identify the rural black and odorous water bodies， in which we imported the Efficient Channel Attention （ECA） and pre-processed the samples by increasing the brightness and correcting the color difference. The F1-score， MIoU （Mean Intersection over Union）， IoU （Intersection over Union） and FOR （False Omission Rate） of the model were 0.931， 0.935， 0.935 and 0.085 respectively， which indicated that the model could efficiently， accurately， and repeatedly identify rural black and odorous water bodies from high-resolution remote sensing images and offer assistance for government departments to regulate rural black and odorous water bodies.

Sichuan is one of the regions with frequent extreme precipitation in China. Based on the grid precipitation dataset （CMPA） provided by China Meteorological Administration as the reference of surface precipitation. We systematically evaluated the accuracy of extreme precipitation monitoring in Sichuan province by using a variety of extreme precipitation index including GSMaP （NRT， MVK， Gauge） and IMERG （Early， Late， Final） satellite precipitation products. The results are as follows： （1） The frequency and intensity of extreme precipitation in the basin and its surrounding areas are significantly higher than those in other areas. There is an obvious boundary line of extreme precipitation along the basin and the plateau. （2） IMERG products can detect rainstorm， continuous precipitation events and drought events more accurately， of which Final product have the highest precision， and the precision of extreme precipitation index of IMERG products is significantly better than GSMaP. （3） The results of probability density distribution function （PDF） show that the PDF characteristics of IMERG-Final products and CMPA are the most similar， followed by GSMaP-Guage， However， the Gauge product corrected by the site completely changed the PDF characteristics of NRT and MVK， ignoring many heavy rain and rainstorm events， which will be extremely unfavorable to the monitoring of extreme precipitation. In general， the detection accuracy of GPM products for extreme precipitation has great regional differences. IMERG products show higher extreme precipitation detection accuracy than GSMaP. We also found that there are large precipitation errors in IMERG and GSMaP products in the Western Sichuan Plateau with complex terrain.

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.

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.

The precise absolute radiometric calibration of satellite on orbit is the basis for quantitative application of its observation data. Compared with the traditional site calibration method， the on orbit calibration method based on stable targets has the advantages of low cost， high frequency and historical data calibration. The MCD19A1 product， MCD43A1 product and MOD03 （MYD03） product of the Moderate-resolution Imaging Spectroradiometer （MODIS） are applied to simulate the directional reflectance of MODIS band8 under the observation conditions of GP02 satellite. Then combined with the directional reflectance of MODIS band1~band5 in MCD43A1 products， aerosol parameters and water vapor parameters in MCD19A2 products and ozone parameters in MOD07 （MYD07） products， the radiation transfer process of the incoming pupil radiance of multiple stable targets imaged by Multi-Spectral Imager of GP02 satellite in October 2021 was simulated to achieve the absolute radiometric calibration in orbit. The validation results of these calibration coefficients show that a smaller difference between GP02’s atmospheric correction results and Sentinel-2 reflectance products is performed when the re-calibration coefficients rather than pre-launch coefficients are applied； the average relative difference between GP02’s measured radiance of all bands after re-calibration and the automatic radiometric calibration data of Baotou is 3.18%， indicating the high accuracy of the calibration results. The research results can provide a methodological support for the on-orbit absolute radiometric calibration of medium or high spatial resolution optical remote sensing satellites using stable targets.

It is an important basis of analyzing long-term phenological changes that extracting phenological parameters based on different vegetation indices. Takes the cloudy and foggy area， Chongqing， as an example. Three long-term vegetation index data of NDVI， EVI， and EVI2 are extracted based on MODIS remote sensing images from 2010 to 2019， and the characteristics of different vegetation indexes are analyzed through D-L filtering. The results， which is of phenological parameters extracted based on three vegetation indices， were studied using dynamic threshold method and trend analysis method， and their response relationships and differences to topographic factors are compared. The results are as follows： ①The time series fitting curve of EVI and EVI2 is smoother than the fitting curve of NDVI. The differences between the original values of the three vegetation indices and the fitted values are mainly distributed in NDVI （0.05~0.18）， EVI （0.03~0.11）， EVI2 （ 0.03~0.1）. ②The spatial distribution and change trend of the phenological parameters extracted from the three plantations were consistent. The vegetation index parameters extracted from EVI and EVI2 were similar， accounting for more than 79% within 5 days， and the significant change area of SOS_EVI2 was the highest （16.36%）， while the lowest SOS_NDVI was 12.37%.③SOS was delayed with the increase of altitude， EOS was delayed and then advanced with the increase of altitude， LOS was extended and then shortened with the increase of altitude，and EOS_NDVI and LOS_NDVI were significantly different from EO_SEVI/EOS_EVI2 and LOS_EVI/LOS_EVI2 with the increase of altitude， respectively. The phenological parameters extracted by EVI were similar to those of EVI2， and the variation trend was consistent. The phenological parameters can be better extracted based on EVI/EVI2 in cloud and fog areas， and the results are similar and can be used interchangeably. The phenological parameters extracted based on EVI and EVI2 have more obvious differences in altitude， slope， and slope direction.

Forest is a valuable non-renewable resource， but the ecological environment of forest is seriously threatened by many natural or man-made factors such as fire， flood， and deforestation interference. Accurate grasp of forest resource changes can provide effective information for forest resource management and protection. In the task of forest change detection， traditional machine learning change detection methods have difficulty in capturing deep semantic information due to large differences in forest categories and tree species， and suffer from poor adaptability of extracted features， weak recognition ability， and pseudo-change due to seasonal phases. We propose to build a deep learning model with Siamese neural networks for forest change detection experiments. Three different feature extraction methods， ResNet50 （Residual neural network）， CBAM （Convolutional Block Attention Module） and SE （Squeeze and Excitation） with different lightweight attention mechanisms are used as backbone feature extraction modules， respectively. All three backbone feature extraction networks are trained based on pre-trained weights， which improve change detection by fusing the extracted multi-scale feature maps so that the coarse and fine details of information in different feature maps complement each other. It also has the advantage of sharing weights with the same number of parameters. Taking Jiande Forest Farm in Zhejiang province as the experimental area， two phases of GF-2 images in 2015 and 2020 are acquired to construct a forest change detection dataset with a resolution of 1m. The results of Siamese neural network change detection are compared with the true change labels （Ground truth）， where the backbone feature extraction network SE-ResNet50 has the best combined results with Precision （0.91）， Recall （0.78） and F1-score （0.83）， which is better than mainstream change detection models FC-Siam-conc， FC-Siam-diff. It is proved that Siamese neural networks can accurately capture forest changes in the task of forest lad change detection from high-resolution remote sensing images， and provide a new forest change detection method for forest resource management departments.

Rapid acquisition of large area data is an important research topic in the field of remote sensing satellite task planning. Relying on the project of "Data Cube for large coverage datasets of Chinese high resolution and broadband and multispectral satellite constellation"，Jilin-1GP01/02 satellite was used to carry out effective coverage of 65 countries and regions along the "The Belt and Road Initiative" twice within three years.This paper summarizes the strategy， methods and experience of data acquisition in large areas of the project， and focuses on various influencing factors such as the resource of satellites and ground stations related to data acquisition，the strategy of dividing large areas in time and phase， and the dynamic planning process of large areas based on effective imaging strips of cloud forecast， that is，within the single transit range of the satellite，，select the imaging strips with the maximum probability of obtaining effective data in combination with the cloud map. The research has provided normalization support for the project，and the relevant methods and project experience can provide reference for the general satellite remote sensing large-scale and wide-area data acquisition tasks.

Baihetan Hydropower station is the second largest hydropower station in China after the Three Gorges Hydropower Station. With the rapid change of water level during the reservoir impoundment period， the geoenvironment of the reservoir area is changed， which is easy to cause sudden geological disasters. In addition， the maximum reservoir level of the transformed hydropower station is 825m， which has a large storage capacity and higher sudden-onset. In order to ensure the normal impoundment of the hydropower station and the safety of residents' life and property， it is necessary to quickly and dynamically identify geological hazards in the reservoir area. Therefore， based on the short baseline DInSAR method， this paper carried out the identification of geological hazards in the key reservoir bank section of Baihetan Reservoir area （Hulukou-Xiangbi Ling section） during the water storage period of the Jinsha River Basin. The sentinel-1A /B data were used for joint monitoring， and the re-entry period was increased to one scene every 6 days. Finally， a total of 66 scenes of data were obtained during the study area's water storage period from April 2021 to November 2021. DInSAR processing based on the same main image was performed combined with SRTM DEM data. The fast and dynamic deformation monitoring can be achieved by combination analysis of interference pair. Don't out of the 92 consensus exists significant deformation of strong deformation zone， are distributed in Jinsha River two sides， including eight strong deformation area have been inundated by the highest water level 800 m， 38 strong deformation area near the water， part of the small bank collapse， at the same time has chosen three continuous deformation area in combination with the actual topography analysis， found signs of deformation is more obvious， The location of strong deformation area is consistent with the monitoring results of DInSAR method， which proves the feasibility of using DInSAR method to quickly and dynamically discover new hidden points. The disaster analysis and monitoring and early warning during water storage period is of great significance to ensure the normal water storage and power generation of Baihetan Hydropower Station. The method provided in this paper can be used for large-scale， efficient， rapid and dynamic geological disaster monitoring， and the hidden danger points can be found in the first time and qualitative analysis can be carried out， which provides a new idea for the identification of potential geological disaster caused by the change of water level during the reservoir bank impoundment period.

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.

Remote sensing Visual Question Answering （VQA） is to answer natural language questions related to image content based on a given remote sensing image， which is essential for fast investigating and monitoring global resources. With the complexity and diversity in remotely sensed imagery， the scale variation is unequivocally challenged in the observation of images from understanding global scenes to identifying local objects. To address the problem of scale variations in the remote sensing visual question answering system， in this paper， a new model Multi-scale Remote Sensing Visual Question Answering（MRS-VQA model） and a dataset （MRS-VQA dataset）， which include multi-scale scenes of question-answer pairs of remote sensing images， are created. In addition， the attention mechanism is employed in the fusion module of the MRS-VQA model to show the visualization results of the combination of two modalities， which effectively improves the accuracy and interpretability of the model. Experimental results illustrate that the proposed MRS-VQA model with two attention layers （96.82% accuracy） outperforms other remote sensing visual question answering models （81.36% accuracy on RSVQA）， which means that multi-scale feature fusion is of great significance in remote sensing VQA.

A brief review has been conducted on the progress of typical spaceborne and airborne polarimetric Synthetic Aperture Radar（SAR） systems at home and abroad， for which the implementation of radiometric and polarimetric calibration accuracies has been focused and surveyed. First the general requirements for the polarimetric SAR data calibration accuracy have been drawing from literature research， and then the status quo of representative polarimetric SAR systems in the word and the system data calibration accuracy achievements have been systematically presented， including the relative radiometric calibration accuracy， the absolute radiometric calibration accuracy， the polarization channel crosstalk accuracy， the polarization channel amplitude imbalance accuracy， and the polarization channel phase imbalance accuracy， etc. Finally， the key factors affecting the calibration accuracy of polarimetric SAR data have been analyzed， and the future calibration tasks meeting the new polarimetric SAR system design has been briefly discussed. This paper comprehensively describes the calibration accuracy information index of polarimetric SAR systems and their development status， and provides relevant researchers with timely， comprehensive and systematic information on the development requirements of polarimetric SAR systems and the research progress of calibration accuracy achievements.

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.

In order to explore the causes and distribution characteristics of waterlogging disasters in urban areas， taking the central city of Lanzhou as an example， based on the monthly average precipitation data and historical water accumulation data from 2010 to 2020， population density， road network density， water supply and drainage facilities distance， elevation， topographic relief， slope， normalized difference vegetation index， and percentage of impervious surface were selected as indicators to explore the driving factors of waterlogging， and the time-space distribution pattern， spatial correlation analysis and geographic detector of waterlogging disasters were studied. Drivers and interactions. The results show that：（1） the flood disasters in Lanzhou City during 11 years highly coincide with the precipitation concentration period， both in June-September， and the waterlogging disaster is the most serious in the main flood season in July-August， indicating that the frequency distribution of the flood disasters is closely related to the precipitation pattern； （2） In space， Lanzhou water accumulation points are mainly distributed in Chengguan District， followed by Anning and Qilihe District， and Xigu District is the least. （3） NDVI and the percentage of impervious surface are the main drivers of flooding disasters in Lanzhou， followed by population density， but the interaction of any two drivers is much greater than a single factor. Therefore， under the premise of comprehensive consideration of multi-factors， Lanzhou waterlogging disaster needs to be comprehensively managed by multi-measures.

Snow Water Equivalent （SWE） is defined as the height of melting snow， regarded as the most important variable describing the amount of snow. Space-borne passive microwave （PMW） remote sensing is currently viewed as a attractive option for SWE estimation at global scale. One challenge for SWE estimation is that most inverse algorithms were built based on a non-comprehensive RTM in which it neglects other components， e.g.， forest. Forest canopy not only attenuates the microwave radiation from soil， but also emits some radiation into the sensor. Therefore， forest canopy increases the uncertainness of SWE retrievals. This paper aims to study the sensitivity of forest parameters （transmissivity， cover fraction， and single albedo） to brightness temperature based on a proposed systematic RTM， and further analysis the robust of SWE algorithms to brightness temperature noise.To better describe the transmission of electromagnetic wave in a real complex contracture， a systematic RTM was built firstly， considering the soil， snow， forest and atmosphere. The Advanced Integral Equation Model （AIEM） model was applied to simulate soil-snow boundary reflectivity. A semi-empirical radiative transfer theory （HUT） model was applied to simulate snow microwave emission. A zero-order tau-omega model was used to describe the interactions between snow and forest canopy. Then a database based the built RTM was generated， and applied to conduct sensitivity analysis of forest and snow parameters to brightness temperature. Meanwhile， noise tests of brightness temperature to SWE retrieving algorithms were done based modeling data and satellite observations.The results indicate that canopy transmissivity is the most sensitive factor among three forest parameters， secondly forest fraction， and lastly single albedo. Meanwhile， the brightness temperatures raise with increasing forest fraction， but decline with increasing canopy transmissivity and snow grain size. Namely， there is ‘neutralize effect’ among these three parameters （forest fraction， canopy transmissivity， and snow grain size）. This is because forest canopy attenuates the radiation from snow and the brightness temperature （Tb） of snow is typically lower than canopy radiation. Thus， the Tbs in forested areas are higher than those in open areas.The noise analysis based on modelling data and satellite observation shows that the influence of brightness temperature uncertainness on AMSR2 SWE retrieving algorithm is more serious than FY-3B method， especially in forest areas. This is maybe because a polariton index charactering grain size evolution is empirical. The influence of brightness temperature uncertainness on FY-3B algorithm is very small， even can be negligible.The paper first quantitatively investigated the sensitivity of forest （transmissivity， fraction， single albedo） and snow （grain size） parameters to microwave brightness temperature based on the built systematic ourselves. What's more， this study determined the key parameters affecting SWE retrievals， including canopy transmissivity， forest fraction and snow grain size. Additionally， the noise analysis reminds us the robust of algorithms to brightness temperature uncertainness must be considered， not just their accuracy. This study provides the theoretical basis and direction for improving SWE in the near future.

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.