Exploring snow's response to surface temperature is crucial for understanding snow cover dynamics. In the central Tianshan Mountains， using daily cloud-free snow depth and surface temperature data from 2010~2019， we analyzed coupling， coordination， and lag times via coupled models. Key findings： ①Annual coupling and coordination vary spatially with altitude （rising-declining-rising）， and seasonally （decreasing from winter to summer）， displaying distinct vertical patterns. ② Over the decade， coupling and coordination fluctuated， with increases in the east， decreases in the north， and significant declines below 1 600 m，contrasting with slight gains above the snowline. ③Lag times of snow depth response to temperature increased from spring to winter， peaked during ablation seasons at higher altitudes， and exhibited yearly trends of rising in spring and slight declines in autumn， winter， and summer.
To effectively address the issues of particle degradation and depletion in particle filtering， this paper integrates genetic algorithms into particle filtering for particle resampling， and develops a snow data assimilation scheme based on genetic particle filtering. Using the default combination of the Noah-MP model as the model operator， a snow data assimilation system is constructed. The assimilation performance of ensemble Kalman filtering and particle filtering is compared in both real and synthetic assimilation scenarios， examining the impact of different resampling methods on particle filtering assimilation performance. The feasibility of genetic particle filtering as a snow data assimilation method is explored. In ideal experimental scenarios， genetic particle filtering exhibits overall assimilation performance inferior to systematic resampling particle filtering but significantly better than polynomial resampling particle filtering. Furthermore， genetic particle filtering proves to be more effective in utilizing observational information to calibrate the model during the rapid snowmelt phases， demonstrating superior assimilation performance during the snowmelt period. In real experimental scenarios， genetic particle filtering outperforms particle filtering using other resampling algorithms. Additionally， the experimental results in both assimilation scenarios consistently show that particle filtering outperforms ensemble Kalman filtering in terms of assimilation performance. Using genetic particle filtering as a snow data assimilation scheme is both feasible and effective.
Obtaining the boundaries of glacial lakes quickly and accurately from massive remote sensing data is crucial for their inventory. To achieve this， an automatic extraction method based on remote sensing data is needed. This paper presents an improved instance segmentation model based on the YOLOv5-Seg network， which was applied to the automatic extraction of mountain glacial lake boundaries. The results demonstrate that the use of Coordinate-Attention （CA） enhances the network's attention to the glacial lake area. Additionally， a small target detection layer was added to the original three detection layers to improve the network's ability to detect small-area glacial lakes. By modifying the nearest neighbor upsampling method to the deconvolution upsampling method， the upsampling loss feature is solved. Combined with the transfer learning method， this approach reduces the cost of manual labeling. On average， the improved YOLOv5-Seg network achieves an accuracy that is 2.7% higher than that of the original network， reaching 75.1%， and 10% higher than that of other mainstream algorithms. Using the improved instance segmentation model of the YOLOv5-Seg network and Sentinel-2 satellite images， 10 668 glacial lakes were identified in the Hindu Kush-Karakoram-Himalayan region （HKH） in 2022， with a total area of 768.3 km2. The study provides the technical basis for the automated mapping of glacial lakes for large geographical regions through the integrated capabilities of deep convolutional neural networks and multi-source remote sensing data.
Black carbon and mineral dust are the main light-absorbing impurities in snow， which can reduce the albedo of snow in different degrees， accelerate the melting of snow， and change the regional energy balance and the spatiotemporal distribution of water resources. To distinguish the difference between black carbon and mineral dust on the reflectance of snow is the basis of studying their energy and hydrological effects and the quantitative inversion of their concentration in snow by remote sensing. In order to avoid the interference caused by the change of physical properties of snow and observation conditions， the spectral reflectance characteristics of polluted snow were obtained by artificial sedimentation of different types of pollutants， and the difference of effects of black carbon and mineral dust on the reflectance of snow was analyzed. The results showed that black carbon pollutants almost caused the decrease of snow reflectance in the whole visible and near infrared bands， and their concentration was negatively correlated with the snow reflectance. In visible band， the concentration of mineral dust pollutants in snow was negatively correlated with the reflectance of snow. However， different from black carbon pollutants， when the concentration of mineral dust pollutants is high， the reduction of the reflectance of polluted snow will rapidly decrease in the band of 0.35~0.6 μm. In the near infrared band， about 1.2 μm， the concentration of mineral dust pollutants was almost irrelevant to the reflectance of snow. When the wavelength is greater than 1.2 μm， the mineral dust pollutants increased the snow reflectance， and their concentration was positively correlated with the snow reflectance. The reduction of the reflectance of polluted snow was not linearly related to the pollution concentration， and the sensitivity of snow reflectance to the pollutant concentration decreased with the increase of the pollutant concentration. These differences are helpful to the study of albedo variation of polluted snow and the inversion of black carbon and sand in snow by remote sensing.
Polar sea ice， with its important impact on global climate change， makes accurate acquisition of multi-element information of sea ice the core task of polar observation. Satellite is the main technical means of polar sea ice monitoring， which has been widely used to observe polar sea ice at the domestic and foreign. To clarify the current status of satellite remote sensing of polar sea ice at home and abroad， which is an important guideline for the development of new remote sensing sensors for sea ice in polar regions in the future. In this paper， the domestic and foreign satellites with polar sea ice information acquisition capability that are currently in orbit are reviewed in detail. On this basis， the main application progress in polar sea ice observation based on satellite data is summarized. Finally， it points out the shortcomings of the existing global earth observation system of polar sea ice， and puts forward suggestions for the development of China's subsequent polar sea ice observation.
The remote sensing identification of river ice provides important support for ice condition monitoring. River ice index identification methods are core tools in river ice remote sensing. However， there is currently a lack of comprehensive comparative studies on common index identification models across different river types. To address this issue， this study applies five remote sensing index models （RDRI， NDSI， MNDSI， NDWI， and reflectance threshold method） to analyze the threshold stability， accuracy， and applicability of these models across six study areas with different river characteristics in the upper reaches of the Yellow River， covering three river types. The results show that the construction methods of the five remote sensing index models consistently indicate that the spectral characteristics of river ice in visible， near-infrared， and shortwave infrared bands are the most critical foundation for river ice identification. The RDRI index performs best in multiple aspects， with an average kappa coefficient of 0.914 4， and is recommended as the optimal choice for river ice index identification. The NDSI and MNDSI indices can effectively eliminate shallow snow interference by adjusting thresholds. The NDSI， MNDSI， and NDWI indices perform well in the headwater study areas， while the reflectance threshold method， though slightly inferior to the RDRI index in performance， still has certain application value due to its simplicity. Among different river types， the five remote sensing index models exhibit the highest accuracy in straight rivers， followed by meandering rivers， and the lowest in braided rivers.
Glacier is one of the most important freshwater reservoirs. Accurate identification of glaciers and monitoring of glacier changes are of great significance for understanding climate change and water resources management. Based on Landsat 8 images， this paper takes the Karakoram region as the research object， and uses single-band threshold method， snow cover index method， unsupervised classification， supervised classification and U-Net convolutional neural network to extract glacier boundaries. The accuracy of glacier boundary extraction results is evaluated by intersection ratio and confusion matrix. The results show that unsupervised classification and single-band threshold method have serious omissions for surface moraine-covered glaciers and glaciers in shadows， and it is easy to misclassify snow-covered mountains into glaciers. The extraction effect of K-means is the worst， with an intersection ratio of 57.69 % and a Kappa coefficient of 0.57. The supervised classification method has significantly improved the extraction effect of moraine-covered glaciers， but the extraction effect of glaciers in the shadow is not good， and the Kappa coefficient of the extraction results is above 0.70. The snow cover index method can effectively extract the glaciers in the shadow， but it is easy to misclassify the non-glacial areas in the large-scale glaciers into glaciers. The intersection ratio is 74.49 %， and the Kappa coefficient is 0.76. The U-Net convolutional neural network can extract the glacier boundary more completely， and the accuracy is significantly higher than other classification methods. The overlapping area is closest to the ground true value area， and the intersection ratio is 88.57 %， and the Kappa coefficient is 0.90. Although the U-Net convolutional neural network performs well， there are still missing points for very small area glaciers. Subsequent research can improve the accuracy by improving the network structure.
The Karlik Mountains glacier located in the eastern part of Xinjiang Tianshan is a typical continental glacier， which is extremely sensitive to the response of climate change. Based on Landsat TM， ETM+ and OLI remote sensing images， DEM data and other information， the glacier boundary information was extracted for four periods of 1990， 2000， 2010 and 2020 using a combination of band ratio method and visual interpretation， and the distribution and variation of glacier area in Karlik Mountain in the eastern Tien Shan Mountains and its response to climate change during the past 30 years were studied.The results show that： （1） the glacier area showed a continuous retreat trend from 1990 to 2020， and the glacier area shrank by 28.34 km2 with an average annual retreat rate of 0.73%·a-1， among which， the retreat rate of the glacier end was the fastest after 2010. （2） As the altitude rises， the distribution of glaciers in the study area shows a trend of increasing and then decreasing， with the most glaciers distributed at altitudes of 3 800~4 600 m； the number and area of small-scale glaciers （≤0.5 km2） are increasing， while the area and number of larger-scale glaciers （≥1 km2） are decreasing； glaciers on different slopes also show different degrees of retreat， with the fastest rate of retreat on the east slope. The distribution of glaciers is characterized by more in the west and less in the east， and more in the north and less in the south； glaciers of different slopes also have obvious retreat trends， among which the retreat is the fastest in the range of 30°~35°. （3） A comprehensive analysis of the climate data in the study area shows that the change of glacier area in the study area from 1990 to 2020 is mainly related to the increase of temperature and decrease of precipitation in the period， and the increase of temperature is the main reason for the acceleration of glacier area retreat.
Snow depth is an important physical variable in global energy balance and climate change， and accurate snow depth parameters are crucial for global and regional climate and hydrological studies. Active microwave remote sensing has high spatial resolution and is suitable for basin-scale snow depth inversion. As one of the key technologies of active microwave remote sensing， Synthetic Aperture Radar （SAR） can image regardless of weather conditions. However， early SAR systems， while offering high spatial resolution， had low temporal resolution， which made it impossible to perform time-series inversion of snow depth.With the development and launch of new generation SAR satellites，there has been a significant improvement in temporal resolution，providing support for time-series analysis of snow depth. In this study， we selected high-resolution Sentinel-1 data， extracted the phase discretization index threshold， combined with the optical image and high coherence coefficient area， and explored a time series snow depth inversion method based on D-InSAR technology， which successfully inverted the distribution of snow depth in the Wusu area of the northern slope of Tianshan Mountain in the snow accumulation period of 11 days.Sources of snow depth estimation errors are explored based on daily measured snow depth data from three meteorological stations.The study demonstrates that relatively accurate snow depth inversion results can be achieved by employing a phase discretization index threshold extraction method， in conjunction with optical imagery and high-coherence areas for correcting the unwrapped phase.is 0.93， the Root Mean Square Error （RMSE） is 3.98 cm， and the Mean Absolute Percentage Error （MAPE） is 25.49%. Due to differences in interferogram pair coherence and internal properties of the snow， the accuracy of the inversion results was higher when the snow was shallow， with most inverted snow depths being lower than the measured values. Large errors began to appear when the station-observed snow depth exceeded 17 cm， with the maximum error being approximately 7.3 cm.An analysis of the differences reveals that the snow depth inversion accuracy is significantly affected by the differences in image-pair coherence and the actual snow depth. In addition， the inconsistency of the temporal resolution between the optical image and the SAR image may also be one of the factors contributing to the error in snow depth inversion.This method can provide a good estimation of time-series snow depth using SAR data， and reference for “D-InSAR based time-series snow depth inversion”
Terrestrial ecosystem models are important tools for investigating the complex feedback mechanisms between the global carbon cycle and climate change. However， terrestrial ecosystem models are subject to great uncertainties. Constraining model parameters based on observational data is an effective technical approach to realize accurate modelling of the terrestrial ecosystem models. In order to investigate the ability of different observations and their combinations to constrain the parameters of terrestrial ecosystem models and to improve the understanding of terrestrial ecosystem processes， the assimilation of Carbonyl Sulfide（COS）， Sun Induced chlorophyll Fluorescence（SIF）， and Soil Moisture （SM） data were conducted based on the Nanjing University Carbon Assimilation System （NUCAS）. Results showed that the assimilation of COS， SIF and SM could optimize the parameters related to plant photosynthesis and soil hydrology， and improve the modelling of photosynthesis， transpiration and soil hydrological processes in the model. The joint assimilation of COS， SIF and SM can effectively improve the performance of the model in modelling total primary productivity， latent heat flux， sensible heat flux and soil moisture.
In order to monitor the large-scale and long time-series soil moisture changes in the Tibetan Plateau region， a refined grid-by-grid machine learning model is established based on the microwave bright temperatures of the polar-orbiting satellite passive microwave sensor AMSR2 （Advanced Microwave Scanning Radiometer 2） and the inclined-orbiting satellite passive microwave sensor GMI （Global Precipitation Measurement Microwave Imager）， aiming at the polar-orbiting satellite SMAP （Soil Moisture Active Passive） high-precision soil moisture obtained by the Multi-Channel Collaborative inversion Algorithm （MCCA）. The advantage of MCCA SMAP data was transferred from AMSR2 to GMI to reflect the intra-day soil moisture changes on the Tibetan Plateau. The average Pearson correlation coefficient R for the training period reached 0.82， and the root mean square error RMSE was 0.050 m³/m³. The average R for the test period was 0.81 and the RMSE was 0.055 m³/m³. The remodeled GMI soil moisture significantly increased the number of valid inversions and was highly consistent with the ground observations， with an average R of 0.81 and an unbiased root mean square error ubRMSE of 0.039 m³/m³. Meanwhile， the combination of GMI and SMAP may provide an option for monitoring short-term extreme climate change and long-term trends on the Tibetan Plateau.
The accurate and rapid acquisition of soil moisture plays an important role in monitoring， forecasting and warning of regional drought and flood disasters. The high-frequency observation feature of geostationary meteorological satellites provides an effective method for real-time acquisition of large-scale soil moisture information. The reflectance and brightness temperature data of Himawari-8/9， vegetation indices and brightness temperature indices conducted by Himawari-8/9， geographical data， soil data， vegetation status and spatio-temporal information were taken as input features， and the measured soil moisture was taken as expected output feature. A random forest model of soil moisture over Qinghai Plateau was established， and its accuracy was evaluated through independent site testing and spatio-temporal variation analysis of drought processes. The results showed that， the correlation coefficients of Henan soil moisture remote sensing test field and Huzhu remote sensing drought field in 2022 were 0.899 and 0.740， the root mean square errors were 0.062 and 0.044 m3•m-3， and the mean absolute errors were 0.048 and 0.035 m3•m-3. In the Huzhu drought process of July 2021， and the Nangqian drought process of August 2022， the variation trend of estimated soil moisture was consistent with the reality. So， the random forest model of soil moisture can meet the real-time monitoring requirement of soil moisture over Qinghai Plateau.
Alpha approximation method is based on the time-invariant of vegetation and surface roughness that receive a great accuracy in soil moisture retrieval. However， the errors may transfer and accumulate as the extension of time scale， and the selection of different prior-information， the retrieval accuracy of this method need to be under reconsideration. This study is based on Sentinel-1 images and carried out in the Tianjun soil moisture observation network that is aimed at the conditional constraints of soil moisture retrieval through Alpha approximation method. The results indicate that： （1） As the ground measurement is used as prior information， the Root Mean Square Error （RMSE） are 0.061 m3/m3， 0.077m3/m3， and 0.090m3/m3 for the monthly， quarterly， and yearly retrieval of soil moisture through Dobson dielectric model. The error is increase with the extension of time scale. （2） As the SMAP product is used as prior information， the RMSE are 0.088 m3/m3， 0.088m3/m3， and 0.101m3/m3 for retrieved soil moisture from the same retrieval strategy， the error increase compared to the results from the ground measurement using. Therefore， the retrieval accuracy is influenced by the quality of prior information. （3） The accuracy of soil moisture retrievals based on Dobson dielectric model and Topp dielectric model is similar in this paper， the difference of RMSE between the retrievals of soil moisture is lower than 0.005 m3/m3. However， the combination of Alpha approximation method and Topp dielectric model can easily extend to the soil moisture retrieval of surface scale.
The generation of look-up table based on atmospheric radiative transfer model， such as MODTRAN， 6SV， etc.， is a key step for the operational remote sensing atmospheric correction and atmospheric parameter inversion. For the general lookup table with fine resolution， it is often necessary to set more input parameters and a broader spectral range， resulting into great computing time and huge storage demands. Therefore， the high processing flow and lookup tables generation method in the multiple node high performance cluster computing environment， was proposed in this work to solve the problems of great computing time consuming and huge storage space when running the radiometric radiative transfer model. It was implemented by reasonably assigning computing tasks， task scheduling of multiple computing node， and writing results in binary mode， etc. The results showed that： ① the computing time could be greatly reduced to less than 1 000 h from more than 10 000 h by using the cluster—single node memory of 6 G and dominant frequency of 2.1 GHz， versus using the single computer， when building a fine airborne atmospheric correction lookup table with the characteristics of 24 flight altitudes between 50 m and 7 500 m， 10 altitude ranges between 0 and 6 000 m and covering visible–SWIR range with the spectral resolution of 1cm-1； ②storing the lookup table in binary mode can effectively reduce the size of LUTs and increase I/O speed； and ③the relative error of the LUTs is less than 1% by comparing 100 random groups of interpolating results from LUTs versus radiative transfer model running results.
Graph geometrical deep learning has the advantages of modeling topological relationships of long-range ground objects， and describing the boundary of multiple land classes. Existing studies use Principal Component Analysis （PCA） to achieve effective dimensionality reduction of hyperspectral images， but most of them have poor feature separability， which makes the classification performance unable to be further improved. Therefore， the novel hyperspectral remote sensing image classification algorithm based on Graphics Processing Unit （GPU） accelerated t-distributed Stochastic Neighbor Embedding （t-SNE） manifold learning and localized spectral graph filtering was proposed in this study. On the other hand， considering Graph Attention Network （GAT） solves the known shortcomings of previous Graph Convolution Network （GCN） or its approximations by using the hidden self-attention layer， especially since it is good at efficiently processing graph-structured hyperspectral data. Then， the second novel method combining localized spectral graph convolution filtering and GAT network is presented to classify hyperspectral images. Experiments with real hyperspectral datasets on the Microsoft Planet platform show that the proposed methods not only provide new insights into promising hyperspectral image classification performance， but also demonstrate the importance of combining spatial and spectral information for hyperspectral remote sensing image classification.
Impervious surface is an important component of urban surface elements. Knowledge about its spatial distribution can provide a scientific reference for urban development and disaster protection. However， due to the similarity of spectra， it is challenging to accurately obtain the impermeable surface material. Object-based and machine learning methods are applied to extract materials of urban impervious. Based on the aerial visible waveband remote sensing imagery with a spatial resolution of sub-meter， the variables including spectrum， vegetation index， texture and shape properties are constructed. Combining Fisher Discriminant Ratio（FDR） and Recursive Feature Elimination （RFE） algorithms， the final variables for training machine learning model were determined. Machine learning algorithms such as Random Forest （RF）， XGBoost， GBDT， CatBoost and LightGBM were developed to construct impervious material classification models （FDR-RFE-RF， FDR-RFE-XGBoost， FDR-RFE-GBDT， FDR-RFE-CatBoost， FDR-RFE-LightGBM）. The best model was selected and to extract the spatial distribution of impervious materials in the study area by comparing the accuracy and the local spatial pattern of impervious materials of different models. The results showed that， compared with the impervious surface material extraction model constructed using all variables， except for GBDT and LightGBM， the overall accuracy and Kappa coefficient values of the models constructed using the variables optimized by FDR and RFE algorithms on the point scale are improved by 0.933%~1.171% and 1.229%~1.542% respectively. Moreover， the phenomenon of spatial fragmentation of classification results is improved. Combining the verification accuracy at the point scale and the local spatial classification results， it was found that the FDR-RFE-RF model showed the most robust performance （OA=0.926， Kappa Coefficient=0.906）， and the spatial distribution of impervious materials extracted for the whole study area was basically accurately represented the ground truth. From our results， we can conclude that variable selection can improve the robustness of impervious surface material extraction based on machine learning to a certain extent. We can also draw the following conclusion that although the aerial visible waveband remote sensing imagery only contains three bands （R， G， B）， it got a reasonable spatial distribution of impervious materials which verifies the potential of visible waveband imagery in urban impervious material extraction.
It is of great theoretical and practical significance to study the temporal and spatial changes of ecological quality and its driving factors in the riverbank of the Yangtze River Basin for the coordinated development of ecological environment protection and economic strategy. Based on three Landsat series remote sensing images in 2000， 2010 and 2020， this paper comprehensively evaluated and analyzed the spatial-temporal changes of ecological quality and land use in the Main Stream of the Yangtze River over the past 20 years through the RSEI and Geo Detector. The results showed that： ① The regional ecological quality in the study area showed a trend of decline first and then increase， and the overall trend of regional ecological environment quality decreased obviously from 2000 to 2010， while the ecological environment quality of some regions was significantly improved from 2010 to 2020.②The spatio-temporal variation of land surface type in the study area was obvious， especially the construction land in each county during 2000~2010.③The factor detection found that the influencing intensity of construction land， forest and grass land and cultivated land on eco-environmental quality was different in each year， but they all had strong explanatory power in the study period.④The relevant interactive detection showed that the interactions among the factors of all the years exhibit dual-factor or nonlinear enhancement， while the q values were all greater than 0.48， which indicates that the change of ecological quality further promoted by the interactions among all the factors. ⑤From 2010 to 2020， the ecological protection and restoration in the Main Stream of the Yangtze River achieved initial results. This study provides monitoring and analysis methods and scientific basis for coordinating regional land resource development and ecological environmental protection under the background of rapid urbanization.
The optical properties of inland water bodies are complex and variable. To improve the accuracy and reliability of remote sensing technology in monitoring chlorophyll-a concentrations in inland waters， this study analyzes the impact of water on the optical path transmission process based on the radiative transfer theory. By integrating the Deng model modeling method， a remote sensing inversion model for chlorophyll-a concentration is constructed. Sentinel-2A satellite images are used to achieve remote sensing for chlorophyll-a concentration in rivers， lakes， and reservoirs in the middle and lower reaches of the Dongjiang River. The accuracy of the remote sensing results is verified through synchronous water quality sampling and laboratory analysis. The constructed radiative transfer model has good applicability， with an average absolute error of 2.67μg/L， an average relative error of 13.67%， and a correlation coefficient of up to 82.7%. The inversion results show that water bodies with high chlorophyll concentration are mainly concentrated in the middle reaches of the main stream of the river， in the bays of lakes and reservoirs where agriculture and industry are concentrated， as well as in the inflows and outflows of reservoirs. Through investigating and analyzing the polluted river sections， it is found that the pollution sources in the middle reaches of the river are mainly point sources， while the pollution sources in the tributaries are mainly diffuse sources. The polluted water bodies in the reservoirs are affected by both endogenous ecological factors and exogenous artificial factors.
To investigate whether urban expansion exacerbates the Urban Heat Island effect （UHI） in Guangzhou， the thermal infrared imagery of Landsat satellite was used as the data source， the single-channel algorithm was used to invert the land surface temperature， and the Urban-Heat-Island Ratio Index （URI） was calculated to measure UHI effect. Coupled with the meteorological data， the heat wave index was calculated to analyze the UHI distribution and its variation during 2011~2022 in Guangzhou and to explore the possibility of its synergistic use to study urban heat island effect with satellite thermal infrared data. The results show that： （1） From 2011 to 2022， the spatial range of urban heat island of Guangzhou was basically the same as the spatial range of expanded urban built-up area， which has expanded to north and south from the old city center. （2） From 2011 to 2022， URI in Guangzhou showed a slightly increasing trend though the city expanded dramatically. This suggests that the UHI effect was effectively controlled due to the "Cool City Action" carried out in Guangzhou to cool down the city. （3） Comparing the domestic and international heat wave indexes， it was found that the domestic heat wave index was more suitable for the situation of Guangzhou， which can explain the changes of UHI in Guangzhou more reasonably and achieve a better result when synergistic use with the remote sensing based-URI index.
With the acceleration of urbanization and industrialization in China， the urban thermal environment has undergone tremendous changes， and the urban heat island effect is gradually strengthening， which has adverse effects on the urban ecological environment and climate. This paper calculated Surface Urban Heat Island Intensity （SUHII） by grids using MODIS Land Surface Temperature （LST） of Taiyuan main urban area from 2003 to 2021， analyzed the spatiotemporal distribution changes of Surface Urban Heat Island （SUHI） and their relationship with urban expansion using spatial statistical methods， and then explored the influencing factors on SUHI based on random forest model. The results showed that： （1） In the past 20 years， the heat island effect in Taiyuan City has shown a growing trend with urban development， with significant seasonal differences， with the strongest in summer and the weakest in winter. （2） The urban heat island effect in the main urban area of Taiyuan City has significant positive spatial autocorrelation， and the highly agglomeration area is significantly expanded. （3） The spatial expansion direction of urban heat islands is basically the same as the direction of urban expansion.（4） Human factors are the main factors affecting the urban heat island effect， with GDP and PM2.5 having the greatest impact. This study can provide a methodological reference for for the quantitative evaluation of urban effects in areas with significant terrain fluctuations， and provide a understanding of the thermal environment in Taiyuan City and a scientific reference for formulating urban planning strategies.
Oilfield injection and production operations can easily cause changes in underground reservoir pressure， leading to local deformation of the oilfield surface and inducing shear casing damage.， Conducting surface deformation monitoring in oil fields can identify the main deformation areas and potential casing damage areas， which is of great significance for adjusting oil field operation planning， rational exploitation of underground resources， and guiding high-quality development of oil fields. Synthetic Aperture Interferometry （InSAR） technology can achieve large-area and high-precision surface deformation monitoring， but traditional time-series InSAR technology faces the problem of insufficient measurement points and uneven spatial distribution in oil fields with complex surface coverage. Therefore， taking Daqing Oilfield as an example， based on the seasonal frozen soil characteristics in the region， the periodic model is integrated into DS-InSAR technology to carry out surface deformation monitoring， and the correlation between surface deformation and oilfield injection and production volume is analyzed. The results show that： ① The surface deformation distribution in the oilfield area is uneven and exhibits significant nonlinear characteristics due to differences in injection and production， with a maximum subsidence rate of about 47 mm/a and a maximum uplift rate of about 45 mm/a. ② The surface deformation of the oilfield area is highly correlated with the injection and production operations in the oilfield. Injection operations increase the pressure of the underground reservoir， causing surface uplift， while oil production operations reduce the pressure of the reservoir， resulting in surface subsidence. This study can provide scientific data basis for optimizing the injection production strategy of oil fields， further expanding the application of InSAR technology in oil field areas.
Based on the system characteristics of the one-dimensional scanning phased array weather radar， this paper proposes a method of using a high-performance UAV to mount a metal sphere equipped with a GPS module to do the radar test and calibration. Three different calibration procedures performed at different periods of the Shanghai X-band array weather radar sites were selected， combined with flying modes such as drone hovering and constant speed cruise， and two different types and scattering cross-sectional sections of metal spheres were used. Combining the information such as the coordinates of the metal sphere， the scattering cross-sectional sections， and the GPS movement trajectory， it can simultaneously calibrate the radar's antenna spatial pointing precision， beam width， radial velocity and reflectivity factor. The test results show that： combined with the GPS information of the metal sphere， the antenna space pointing compensation value of the radar can be evaluated and given； the radial velocity observed by the radar can be calibrated； the reflectivity factor corresponding to the metal sphere with the change of azimuth and elevation angle， the radar azimuth and elevation beamwidth information can be calculated and verified； according to the multiple calibration results of the metal sphere， the calibration equation of the reflectivity factor value in the actual operation of the radar is given. The test provides a reference for the external calibration of phased array radar system.
An efficient and accurate method for extracting photovoltaic strings was proposed in this research， enabling the assessment of power generation potential and emission reduction effects of photovoltaic systems under different scales and layouts. It provides technical support for monitoring and managing existing photovoltaic power stations， as well as for the planning and construction of future ones. Three agrophotovoltaic power stations， namely Songyang， Yueqing， and Jiangshan stations， were selected as the study area in Zhejiang Province， covering ten types of “PV +” modes. The photovoltaic strings were extracted based on UAV images and object-oriented classification methods， and their power generation potential and emission reduction effects were estimated by module area method. The results revealed that： （1） highly precise extraction of photovoltaic strings within complex land cover types was achieved using unmanned aerial vehicle imagery and object-oriented methods， with an overall accuracy exceeding 96% and a Kappa coefficient higher than 0.89； （2） among different “photovoltaic +” modes， the extraction accuracy was higher for grassland and nursery photovoltaic areas， while lower for vegetable photovoltaic areas； and （3） the annual power generation capacities of Jiangshan， Songyang， and Yueqing stations could reach up to 225 million， 37 million， and 165 million kilowatt-hours， respectively， resulting in emission reductions of 119 100， 19 600， and 87 300 metric tons of carbon dioxide.
Vegetation cover （FVC）， as an indispensable climate parameter， and the spatial and temporal evolution characteristics of long time series FVC can provide data reference for assessing the surface vegetation condition. MODIS-NDVI data were used to estimate FVC using the image element dichotomous model， and the spatial and temporal evolution characteristics of vegetation cover in Shenyang from 2000 to 2020 were explored by using trend analysis and deviation analysis， while multi-scenario simulation prediction of vegetation cover in Shenyang in 2030 was carried out based on land use data in 2010， 2015 and 2020 combined with PLUS model. The results show that （1） in time， the annual average FVC in Shenyang City increases at a rate of 3.14%/10 a，the high and medium-high vegetation cover shows an increasing trend， and the proportion of vegetation improvement area is higher than that of deterioration. （2） Spatially， the high value areas of FVC in Shenyang are mainly distributed in Shenyei New District， Hunnan District and Sujiatun District， while the low value areas are distributed in the five districts and the central part of districts and counties in the city. （3） The simulation results found that： in the historical trend scenario， the area of arable land， forest land， grassland and water area decreased； in the arable land protection scenario， the area of arable land increased and forest land decreased； in the low-carbon development scenario， forest land increased significantly. The results of the study provide a theoretical basis for the future formulation of environmental management policies in Shenyang.
