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.

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.

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.

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.

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.

The spatial distribution and winter wheat harvesting area is of great significance to accurately estimate production and ensure food security. However， the vast majority of study and statistical data is based on planting area of winter wheat， and few studies have been done on the winter wheat harvesting areas. In this study， Puyang County was selected as the study area， and the harvesting area of winter wheat was estimated by combining Sentinel-2 remote sensing imagery at the maturing period in 2019 and random forest model. Firstly， best feature subsets were obtained through feature selection. And then， the separability between winter wheat and other land types was analyzed by the J-M distance of these best feature subsets， the harvesting area and planting area of winter wheat were identified and extracted， and the harvesting area of winter wheat was mapped. Finally， the differences in harvesting area and planting area of winter wheat and the influencing factors of harvested area were further analyzed. The results found that the overall accuracy and Kappa coefficient of winter wheat harvested area estimated by the best feature subset of Sentinel-2 images were 94.62% and 0.93， respectively. The planting area of winter wheat in Puyang County in 2019 was 79.47 thousand hectares， and the extracted harvesting area was 76.74 thousand hectares， their difference （2.73 thousand hectares） was largely attributed to human activities， and timely monitoring of the harvesting area of winter wheat can provide a certain scientific reference value for related research and decision-making such as winter wheat yield prediction.

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.

In recent years， global warming has led to an increase in strong convective weather， and hail disaster has become one of the main disasters in agricultural production. Carrying out remote sensing assessment of cotton hail disaster is of great significance for disaster prevention and mitigation， insurance claims and planting structure adjustment. Taking the cotton hail disaster in the Kuitun River Basin in the southwest of Junggar Basin， Xinjiang， on 23 August， 2019 as the research object， with the support of field measured samples， the multi-temporal Sentinel-2 remote sensing images before and after the hail disaster were obtained. We analyzed the dynamic changes of various vegetation indexes before and after the hail disaster， and screened the sensitive vegetation index difference feature combinations which can effectively characterize the hail disaster. The range and grade of cotton hail disaster were automatically extracted by using machine learning algorithms such as logical regression， decision tree， gradient lifting decision tree and random forest， and the accuracy was compared and analyzed via field measured samples. The results showed that NDVI was the best indicator of hail disaster among single vegetation index， with an overall accuracy of 84.39% and a Kappa coefficient of 0.75. The combination of multi-temporal vegetation index differences was significantly more indicative for hail disaster than that of single vegetation index. Compared with the time series characteristics of vegetation index differences before and after hail disaster， the indicative of hail disaster between August 30 and August 20 was obviously stronger than that between August 25 and August 20， which indicated that it was necessary to consider the self-recovery ability of cotton plants after hail disaster grade for remote sensing monitoring. The combination of the pre- and post-disaster vegetation indices and the random forest classification algorithm was the most effective methods in monitoring the level of cotton hail disaster level， with an overall accuracy of 89.51% and a Kappa coefficient of 0.83. In conclusion， the extent and degree of cotton hail disaster can be effectively evaluated based on multi-temporal Sentinel-2 image.

.CO₂ is one of the important greenhouse gases in the atmosphere. Since the Industrial Revolution，the concentration of CO₂ in the atmosphere has been increasing continuously， which has an important impact on global climate change. High precision，high coverage and high temporal and spatial resolution CO₂data tends to be more significant in the study of carbon neutral and global CO₂ change. Thus， in this study， we compared the XCO₂ products between the satellites OCO-2 and OCO-3， and formed a joint data set from the two satellites. Because there are still some regions without observation data in the joint dataset， this study uses Kriging interpolation algorithm to fill the regions without data. Considering the temporal and spatial variation characteristics of CO₂ concentration in different latitudes， the algorithm divides theworld into six regions and selects the appropriate variogram.The results show that the XCO₂ data coverage increases by 52.32%， 46.77%， 44.04%， and 33.81% on the 3-day， 8-day， 15-day， and 30-day timescales，respectively. By comparing the monthly interpolation data set with the TCCON site data to verify the accuracy， the mean absolute error is 1.049 ppm， the root mean square error is 1.024 ppm， and the coefficient of determination is 0.82. It can be seen that this method can accurately fill in the blank area of the j-oint dataset， and improve the accuracy， coverage and spatiotemporal resolution of the data.

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.

Atmospheric haze pollution is one of the major environmental problems facing China in recent years. Haze monitoring is an important part of haze pollution governance system， and remote sensing can realize large-scale and long-term dynamic monitoring， which to a certain makes up for the shortcomings of traditional site monitoring methods. Based on MODIS/Terra data， this study takes seven provinces and municipalities in North China Plain as the study area， built a 10-dimensional feature space by using the spectral and spatial characteristics of atmospheric haze， and built a regional identification model of haze using random forest algorithm. This model not only realizes the function of haze region identification and extraction， but also realizes the function of light and heavy haze region classification. Through verification of PM_2.5 monitoring data from ground stations， the overall accuracy of haze region identification is 87.82%， and the Kappa coefficient is 0.75. The overall accuracy of light and heavy haze region identification is 86.81%， and the Kappa coefficient is 0.73. The study results show that the model has a good identification effect on haze region in satellite images， which can provide data support for air pollution monitoring， and has certain reference significance for monitoring other atmospheric pollutants.

The change of coastline affects the production and life of coastal residents and the national military strategic deployment. It is of great significance to detect the change of coastline quickly and accurately. Navigation X-band radar can continuously and real-time detect the nearshore marine environment. This paper proposes a method to detect coastline using navigation X-band radar images. Firstly， the navigation X-band radar image is preprocessed by averaging， contrast enhancement and Gaussian filtering to reduce the impact of sea clutter on the radar image； Then， the preprocessed image and the edge detection operator are convolved to obtain the gradient image， and an adaptive threshold estimation method based on the histogram bimodal method is established to automatically extract the boundary of the target island from the gradient image； Finally， the edge expansion， cavity filling and denoising are used to extract the complete island， and the area and perimeter of the island are estimated. The proposed method is verified by using the navigation X-band radar images observed in the experiment. Compared with the coastline in the electronic chart and Google Earth map， the edge accuracy of the target island extracted by Sobel operator and Prewitt operator is higher， and the area and perimeter of the extracted island vary with the tide height， sea conditions， etc. The results show that the navigation X-band radar can effectively monitor the real-time changes of the island coastline.

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.

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%.

The launch of the Gaofen-1 satellite has further enhanced China's Earth observation capabilities. Compared with Sentinel-2， the GF-1 WFV image has a high spatial resolution of 16 m， but lacks the Red-Edge （RE） band and the Short-Wave Infrared （SWIR） band. It is important to analyze the differences in the accuracy of estimating vegetation physiological parameters between the two satellites for further application. In this study， we used linear regression models and a Look-Up Table （LUT） based on the PROSAIL model to assess the performance of GF-1 and Sentinel-2 in estimating LAI of soybean and maize. The results showed that： （1） The EVI simple linear regression of GF-1 outperformed other vegetation indices with a R² value of 0.81 and the MNLIre model was the best Sentinel-2 model with a R^{2value of 0.86. （2） GF-1 obtained a comparable accuracy to Sentinel-2 with multiple linear regression models based on spectral bands. The best LAI estimation model of GF-1 produced a Root-Mean-Square Error （RMSE） of 0.54 and a coefficient of determination （R2 ） of 0.90， and the best Sentinel-2 model achieved a RMSE of 0.54 and R2 of 0.89. （3） In terms of LUT based on the PROSAIL model， the optimal band combination for GF-1 were B2 and B4 with a R2 of 0.76 and a RMSE of 0.81， and the optimal band combinations for Sentinel-2 were B3， B6， B7， B8， B8a， and B12 with a R2 of 0.87 and a RMSE of 0.62. This study showed that GF-1 satellite has the ability to accurately monitor crop LAI， which can provide a theoretical basis for the application of GF-1 in agriculture monitoring.}

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.

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.

Obtaining high-resolution image features of crops and exploring the influence of multi-feature learning on actual crop classification are of great significance for agricultural departments to grasp the information of crop planting fine structure and efficiently implement production management. For the high-resolution RGB image acquired by UAV， this paper proposes a new classification method of crops based on object-oriented and multi-feature learning. Firstly， the HSI model is used to transform the colour space of RGB images to mine the potential information of images further. Secondly， the ESP algorithm and CART are used to determine the optimal image segmentation scale and construct the optimal feature learning data set of classification. Finally， object-oriented Random Forest classification algorithm was used to learn and train the multi-feature space， so as to achieve fine crop classification， and accuracy evaluation was carried out in combination with validation data set. The experimental results show that the overall accuracy of the classification in the study area reached 90.18%， and the Kappa coefficient reached 0.877， both of which were greater than the accuracy based on pixel-level and single-feature learning. The optimal feature learning space constructed in this paper have a good classification effect on cotton， corn， cocozelle， grape and other major crops in the study area. The producer's accuracy of each crop type is greater than 89%. This research can provide a reference for agricultural precision management and planting structure optimization.

Mycotoxin is major threat to food security and safety in China， and their accurate prediction can support effective loss control and reduction. Based on the summary of the impact of crop mycotoxins on food， food and agriculture， this paper analyzes the current research progress of mycotoxin prediction from three aspects： meteorological statistical model， mechanistic model and machine learning model.The feasibility of using satellite remote sensing monitoring technology to carry out a wide range of crops mycotoxin prediction is discussed by analyzing the current influencing factors of crop contamination mycotoxins and combining with the research progress of remote sensing technology in monitoring crops and their growing environment.It is also pointed out that analyzing the main influencing factors of crop mycotoxins from remote sensing and their change patterns， developing remote sensing estimation methods of mycotoxins by combining relevant factors within the reproductive period， and coupling remote sensing technology oriented to farm-scale dynamic prediction with multiple models will become the research focus in this field.

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.

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.

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

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.

Satellite precipitation products with a high spatio-temporal resolution are essential for hydrometeorological research on the regional or watershed scale. By comparing the accuracy of IMERG and GSMaP satellite precipitation products in the Yangtze River basin during 2015~2020， the outperformed product is then used for spatial downscaling. Considering the relationship between precipitation and geographic features （DEM）， vegetation index （NDVI）， and Land Surface Temperature （LST）， various statistical downscaling models based on random forest regression are developed， such as DEM model， DEM+NDVI model and DEM+NDVI+LST model. Then， the performance of the three models is further evaluated against observations from 133 stations in the study area. The results indicate GSMaP outperforms IMERG in the Yangtze River basin. Among the three downscaling models， DEM+NDVI+LST model performs best on the multi-year mean scale and annual scale， and the performance of the model remains stable on the monthly scale. This study can provide a new way for spatial downscaling of satellite precipitation products.

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.

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.

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.

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.

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.

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

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

Fractional Vegetation Cover （FVC） is an important index to evaluate the quality of ecological environment and characterize the ground cover and growth status of vegetation. There are many FVC products at home and abroad. However， different FVC products have certain spatio-temporal differences. In order to accurately understand the differences of FVC products and their causes， two global FVC products， GEOV3 and GLASS， were selected to assess their spatial and temporal differences in southwest China by resampling and difference analysis， and to analyze the influence of topography and land use type on FVC products by combining topographic and land use data. The results show that： （1） there were obvious spatial and temporal differences between GLASS and GEOV3 products with seasonal characteristics， with GLASS FVC values slightly lower than GEOV3 FVC values in spring and summer， and the smallest difference between the two values in autumn， while GLASS FVC values were significantly higher than GEOV3 FVC values in winter； （2） the values of the two products differed significantly in different land use types， and the differences were： shrub > forest > cropland > grassland > other， and the differences were the largest in winter； （3） the values of the two products also differed significantly in different slopes and altitudes， and the changes in slope had more obvious effects on the products. This study revealed the influencing factors that lead to inconsistency among FVC products， which can provide a reference for the improvement of FVC product generation algorithms in mountainous areas.

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.

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.

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.

The integration of marine， land and air map data is a commonly used remote sensing data in research. However， existing marine， land and air data are based on vector management， which has problems in terms of intuitiveness， convenience， uniformity and efficiency. In this study， a hexagonal global discrete grid-based marine， land and air map integration algorithm is proposed to solve these problems. Firstly， according to the hexagonal subdivision method， the icosahedron is projected onto the earth’s surface to generate 20 basic triangles， which are then combined into 30 basic hexagons. Secondly， the corresponding relationship between scale and grid level is determined， and the corresponding scale level grid data is recursively subdivided. Then， the marine， land and air map is parsed into a nested structure of multi-line segment sets， multi-line segments and straight line segments， with straight line segments being the minimum unit. The grid adjacency search algorithm is used to grid the straight line segments， and finally the marine， land and air map data is expressed in the form of a hexagonal global discrete grid. The experiment used 1∶50 000 and 1∶25 000 marine， land and air map vector data in China and hexagonal global discrete grid data. The experimental results show that the marine， land and air map integration algorithm based on hexagonal global discrete grid can perfectly convert map vector data into map grid data. In summary， the marine， land and air map integration algorithm based on hexagonal global discrete grid can make marine， land and air map management and display more convenient and efficient， with clearer topological relationships， and provide reference for marine and land integration collaborative applications and data integration and management using global discrete grids.

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.