Deep learning has significantly advanced remote sensing image processing technology， demonstrating notable improvements in both accuracy and speed. However， deep learning models typically require large amounts of manually labeled training samples in practical applications， and their generalization performance is relatively weak. In recent years， the development of visual foundation models and large language models has introduced a new paradigm for research on large models in remote sensing image processing. Remote sensing large models， also known as remote sensing foundation models， have garnered attention for their outstanding transfer performance in downstream tasks. These models are first pretrained on large datasets unrelated to specific tasks and are then fine-tuned to adapt to various downstream applications. Foundation models have already been widely applied in language， vision， and other fields， and their potential in the field of remote sensing is increasingly gaining attention from the academic community. However， there is still a lack of comprehensive surveys and performance comparisons of these models in remote sensing tasks. Due to the inherent differences between natural images and remote sensing images， these differences limit the direct application of foundation models. Against this backdrop， this paper provides a comprehensive review of common foundation models and large models specifically designed for the field of remote sensing from multiple perspectives. It outlines the latest advancements， highlights the challenges faced， and explores potential future directions for development.

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 PM_2.5 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.

The semantic segmentation of remote sensing images by using machine learning and deep learning is an important means in the field of intelligent interpretation of remote sensing. Optical and SAR images can reflect different land surface characteristics and provide additional information for land use/cover. Effectively integrating and improving the recognition of land use/cover types is a major difficulty in remote sensing semantic segmentation. To solve these problems， a land use/cover segmentation method based on multi-source data fusion and channel correlation perception was proposed. Firstly， using Gaofen-6 optical image and Gaofen-3 radar image to produce high-resolution image sample data； Then， channel attention mechanism and spatial multi-scale correlation are introduced into deeplabv3p framework using coding-decoding architecture， and CCAMNet（Cross-channel sensing Module network） model based on full convolutional network structure is established. Finally， a comparative experiment is conducted with UNet， Deeplabv3p， SA-Gate， v-fusenet， MCANet and other semantic segmentation models on the research region data set. Moreover， it is compared with Deeplabv3p， PSCNN， MRSDC， v-fusenet， MBFNet， MCANet and other models on WHU-OPT-SAR public data set. The results show that compared with other models in the study area， the overall accuracy of the proposed model is 79.68%， which is better than other models. On the open data set， the overall accuracy is 81.8%， which is better than other models in classification accuracy and can significantly improve the accuracy of semantic segmentation， and verify the feasibility of the model in the application of semantic segmentation in remote sensing land use classification. The research results filled the gap of the lack of high-resolution land use classification dataset in the study area， and verified the feasibility of optical and SAR fusion in significantly improving classification accuracy.

Pine wilt disease is a devastating pine disease， which seriously threatens forest ecological security. Timely and reliable acquisition of the extent and severity of pine wilt disease is very important for forest management and disease prevention and control. However， pine wilt disease spreads rapidly and is difficult to control， and the traditional manual survey methods can hardly meet the demand. Unmanned Aerial Vehicle （UAV） remote sensing can quickly and accurately obtain the extent and severity of forest diseases， and provide reliable information support for forest pest control and management. In this study， UAV was used to acquire high-resolution Red-Green-Blue （RGB） visible light images. Firstly， object-oriented multi-scale segmentation algorithm was used to extract the crown of a single tree， and Vegetation Index （VIs） and texture （GLCM） features were calculated. Then the feature selection algorithm was used to optimize the feature set， and Random Forest （RF） classification and Support Vector Machine （SVM） classification algorithm were used to construct the pine wilt disease classification model based on different feature sets. Through the ablation experiment， the optimal classification model was selected and the object-oriented method was used to monitor the disease degree and spatial distribution of pine wilt disease. The results show that the vegetation index and texture features of pine canopy with different disease degrees are different on the object-oriented single tree crown scale， and the accuracy of classification results using vegetation index was better than texture characteristics （VIs RF：OA=76.52%，Marco-F1=0.77；SVM：OA=79.68%，Marco-F1=0.79）. Compared with a single feature set， the combination of vegetation index and texture features can significantly improve the classification accuracy （VIs&GLCM RF：OA=79.47%，Marco-F1=0.80；SVM：OA=85.45%，Marco-F1=0.85）， indicating that multi-feature combination can effectively improve the pine wilt disease classification. The SVM model outperforms the RF model for classification， both for single feature set modeling and combined feature set modeling. This study provides timely and reliable information to support a comprehensive grasp of the extent and severity of pine wilt disease， and helps to promote the construction of a major forestry pest control system and maintain ecological security.

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.

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”

Large-scale Earthquake disasters often result in significant losses and even casualties. Making prompt assessments of the disaster situation is crucial in the aftermath. Synthetic Aperture Radar （SAR） possesses advantages such as all-weather and all-day capabilities， as well as resilience to lighting and weather conditions. Therefore， the use of SAR imagery for change detection has garnered significant attention in various fields， including post-earthquake rescue and damage estimation， flood extent detection， urbanization studies， and coastline extraction. In this context， this paper proposes a deep learning-based earthquake damage extraction method that integrates spatial and frequency domain texture features. The method demonstrates a robust capability to identify collapsed and intact structures. Using the 2023 earthquake in Kahramanmaras， Turkey， as a case study， the region severely affected by the earthquake， this research incorporates both spatial and frequency domain features into the deep learning network for classification. Experimental results show that the proposed method achieves a classification accuracy of 80.98%， significantly surpassing the original image's accuracy of 47.84%. Moreover， this accuracy is higher than using only spatial domain features （73.30%） or only frequency domain features （73.42%）. The proposed method in this study provides fundamental support for post-earthquake disaster assessment and situational awareness.

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.

Over the past two decades， studies about the spectral invariant theory have been developed rapidly in vegetation remote sensing. The theory has been widely used in vegetation parameter measurement， canopy reflectance modeling， and biophysical parameters retrieval. A review paper titled "Photon recollision probability and the spectral invariant theory： Principles， methods， and applications" was published by the author in “Remote Sensing of Environment” in 2023 （DOI： 10.1016/j.rse.2023.113859）. The current paper provides a comprehensive overview of the background of spectral invariant theory， general principles of the theory， determination and applications of spectral invariants. Recent progresses of the theory are summarized and potential future developments are discussed. A special section is dedicated to the researches made by Chinese scholars. The goal is to provide a synthetic overview of the theory. Some new thoughts about the theory are also given in the paper.（1） The spectral invariant theory evolves from the successive orders of scattering approximation method of the multiple scattering process of photons in vegetation canopy. The theory faciliates the conversion of spectral parameters between different spectral bands and different scales （mainly between leaves and canopy） and provides new means for calculating the directional reflectance， albedo， and fluorescence escape probability.（2） Spectral invariants can be obtained through empirical methods， spectral methods， and structural methods. The spectral method is divided into single scale spectral method and spectral scaling method， and the structural method is divided into the Silhouette to Total Area Ratio （STAR） method， clumping index method， Stenberg method， and approximation method. Different methods can be cross-validated.（3） The spectral invariant theory has been applied in a number of canopy reflectance models. Based on these models， researchers have carried out a large number of inversion studies for vegetation structural parameters and physiological parameters. The principles， methods and applications of the theory can be further explored in the future.（4） New approximation methods for the spectral invariants are proposed and new formulae for the visible sunlit leaf area index and the hemispherical directional area scattering factor are summarized. Chinese researchers have made significant contribution to the development of the theory， especially in calculating the escape probability of the solar induced fluorescence using the theory.

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.

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.

Based on six indexes of greenness， humidity， heat， dryness， salinity and soil and water conservation factors， a remote sensing ecological index URSEI suitable for arid areas was constructed by using PCA， and the tem-spatial characteristics of ecological quality changes in Shiyang River Basin from 2000 to 2018 were explored by using trend analysis and Hurst persistence analysis. The findings are as follows： （1）URSEI considers salinity and soil and water conservation indicators， and the contribution rate of the first principal component is higher than 80%， integrating the main information of each indicator， which is conducive to improving the comprehensiveness of ecological quality assessment. （2） From 2000 to 2018， the URSEI in Shiyang River Basin increased from 0.288 to 0.316， which tended to improve on the whole， mainly due to the strengthening of ecological management and control policies and the implementation of water transfer projects such as Jingdian Phase II. Among them， the area of ecological quality improvement is about 10 495.75 km²， which is distributed in a belt along Jinchang City， Liangzhou District and Gulang County； The degraded area covers 2 388.50 km²， which is concentrated in the main urban area of Liangzhou District in the middle reaches， the Longshoushan mining and the middle oasis area of Minqin in the lower reaches， and there is a risk of continuous degradation in the future. （3） In the future， the ecological quality of the basin will be mainly unchanged and continuously improved， accounting for about 72.55%， but at the same time， about 2 231.25 km² of the area will develop from improvement to degradation， distributed in Minquan Township of Gulang County， Huangyang Township of Liangzhou District， Yongfengtan Township， Jiaojiazhuang Township of Yongchang County， etc.， basically corresponding to the surrounding areas of the main urban areas of each district and county. It is the main expansion area of urbanization in the future.

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.

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 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.

Accurately and rapidly assessing soil salinity is crucial for land quality evaluation and agricultural development. Hyperspectral remote sensing， as an effective monitoring technology， offers new avenues. Optimizing hyperspectral data processing to enhance features is critical for accurately estimating soil parameters. However， the impact mechanisms of different spectral combination treatments on soil salinity estimation need further study. This study aims to investigate the potential of hyperspectral data for estimating soil salinity under nonlinear transformation and fractional derivative combination treatments. Based on 60 typical soil samples from the Yulin area， the spectral characteristics of saline soils under different spectral transformation combinations were analyzed. The Competitive Adaptive Reweighted Sampling method （CARS） and Successive Projections Algorithm （SPA） were used to select characteristic bands as input variables. Partial Least Squares Regression （PLSR） and Random Forest （RF） models were established to compare and analyze the estimation capabilities of different spectral processing and modeling strategies for soil salinity. The results indicate that fractional derivatives， compared to integer derivatives， better reflect the absorption characteristics of saline soil spectra. Reciprocal and logarithmic transformations effectively enhance the correlation between spectral data and soil salinity information， especially in the 1905–2078 nm range. The PLSR model generally outperforms the RF model， with the CARS-PLSR achieving optimal modeling accuracy （R²=0.966）. These findings can provide a theoretical basis for the implementation of saline soil prediction and precision agriculture.

Fire threatens the safety of human life and property and causes great damage to ecosystems. The study of remote sensing response characteristics of burned area is important for the accurate extraction of area， quantitative assessment of fire damage and vegetation restoration. Based on Sentinel-1 SAR remote sensing images， the characteristics of unburned forest， burned area， buildings， water bodies were analyzed in six fire cases. The time series of burned area from one year before the fire to two years after it was analyzed. The results shows that the cross-polarization ratio and the backward scattering intensity of VH polarization are lower in the burned area compared to the unfired area， the backward scattering intensity of VH and VV polarization of buildings are much higher than those of other features， and the backward scattering of water bodies in both polarizations is very low， while the cross-polarization ratio is higher. From time series perspective， the backward scattering intensity for VV polarization shows obvious seasonal variations. In most cases， the backward scattering intensity for VV polarization is significantly higher within one month after the fire， and the cross-polarization ratio rapidly decreases. The time series variation of Normalized Burned Ratio index （NBR） calculated from Sentinel-2 MSI follows a consistent pattern with SAR images， showing obvious seasonal changes. It rapidly decreases within half a month after the fire and gradually recovers.

Rapid and accurate acquisition of cultivated land change information is of great significance to food security management. This paper aims at the problem that remote sensing semantic segmentation method has many errors and omissions due to insufficient model applicability in large-scale and high-resolution image cultivated land non-agricultural detection. Multiscale Scene Classification-Xception （MSC-Xception）， a multi-scale scene classification method for high-resolution cultivated land images based on Xception， is proposed. The convolutional attention module CBAM is embedded into the output layer of the lightweight scene classification network Xception， which has outstanding performance in cultivated land scene classification， to enhance the model's ability to extract channel and spatial features. At the same time， the problem of low separation degree and rough details of mixed scenes existing in the single-scale scene-level classification in large-scale cultivated land extraction is also overcome. Firstly， a feature fusion method of multi-scale cultivated land scene is introduced to improve the separation degree of mixed scene， and then the boundary constraint of multi-scale segmentation vector is used to achieve the boundary refinement of scene-level classification. Compared with the typical Unet， PSPNet and DeeplabV3+ semantic segmentation methods， this method can better reduce the missed detection of large map spots， and the recall rate and F1 score in the cultivated land extraction experiment of GF-2 images in Qixia District in April 2018 increased by at least 15.1 percentage points and 8.8 percentage points respectively. In the non-agricultural detection of cultivated land in Qixia District from 2018 to 2022， the recall rate of suspicious spots increased by at least 7.16 percentage points.

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 terrain of the “Asian Water Tower” area centered on the Qinghai-Tibet Plateau is complex and the weather is changeable. Especially in the alpine areas， ground observations are scarce. The existing remote sensing observation energy and water cycle elements lack a comprehensive systematic framework， and the accuracy is not high， making it difficult to obtain the understanding of the temporal and spatial distribution and variation characteristics of water balance in the Asian Water Tower area. Relying on the special project of “The Second Comprehensive Scientific Expedition to the Qinghai-Tibet Plateau”， systematic satellite remote sensing observation research on energy and water cycle in the Qinghai-Tibet Plateau was carried out. The development and sharing of satellite remote sensing datasets for 15 key elements of energy and water cycle were completed. The retrieval methods and technologies for each element were improved and developed. A satellite networking observation system for key elements of regional energy balance （6 types） and water cycle （9 types） in the Asian Water tower has been formed （including cloud， surface temperature， emissivity， radiation， albedo， Precipitation- Evapotranspiration， atmospheric water vapor， soil moisture， soil freeze-thaw， snow cover and snow depth， surface water change and terrestrial water storage， etc.）， and a systematic remote sensing dataset of energy and water cycle elements with high precision， spatiotemporal continuity and high spatiotemporal resolution has been produced. The systematic analysis based on this dataset indicates that the multi-year average spatial distribution pattern of surface evapotranspiration on the Qinghai-Tibet Plateau is controlled by the precipitation distribution and shows a decreasing trend from southeast to northwest. Evapotranspiration is mainly controlled by moisture conditions in most areas （arid and semi-arid regions）， and is dominated by radiation factors only in a few humid areas. The energy parameters that affect the evapotranspiration of the plateau and other factors of the water cycle， such as the downward long-wave radiation on the surface， show an increasing trend， while the surface reflectivity shows a decreasing trend， which has a significant correlation with the decreasing trend of snow coverage. In addition， key parameters of the water cycle such as atmospheric water vapor， soil moisture， lake area and land water storage all show a significant increasing trend， while snow coverage and the annual freezing days of the surface show a decreasing trend. Overall， since 2000， the Qinghai-Tibet Plateau has generally shown a trend of warming and humidification. The dataset provided in this paper is conducive to supporting the combination of remote sensing and models， and promoting the verification and improvement of models for regional climate-land surface and multi-sphere hydrology.

Hongze Lake serves as the primary water supply source in northern Jiangsu and functions as the water storage reservoir for the eastern route of the South-to-North Water Diversion Project. The quality of its water is crucial to ensuring safe water supply and the sustainable utilization of water resources. Secchi Disc Depth （SDD） is an important index to measure water environment quality and plays an important role in water ecosystem. In this study， Hongze Lake was taken as the research area， and a remote sensing estimation model suitable for Hongze Lake SDD was constructed based on Landsat 8 OLI remote sensing data by using field measured SDD data and spectral data. The verified results were the Mean Absolute Percentage Error （MAPE） and Root Mean Square Error （RMSE） as 19.8% and 0.07m respectively. The constructed model was applied to Landsat 8 OLI images of Hongze Lake from 2013 to 2022， and the following conclusions were obtained： （1） The average inter-annual variation range of SDD was 0.18~0.25 m， with the highest value and the lowest value appearing in 2019~2020 and 2013~2014， respectively. The overall variation trend of the three lakes was consistent， with the highest SDD in Chengzi Lake Bay and the lowest in Huaihe Lake Bay. The main factors affecting the SDD of the lake area are wind speed， among which the sediment discharge is the main influencing factor of the Huaihe River Bay. （2） The monthly variation of SDD increased from January to August， and decreased month by month after August， with the highest value of 0.36 m in August and the lowest value of 0.20 m in May. The SDD of the lake area is mainly affected by wind speed. Chengzi Lake Bay and Lihe Lake Bay are obviously affected by meteorological factors， and Huaihe Lake Bay is affected by sediment discharge and shipping， and the factors are complicated.

Henan is a major maize growing province in China， while the southeastern region of Henan is an important maize producing area within the province. Evaluating land suitability can optimize land use structure and strengthen the effectiveness of land management systems. This article took maize cultivation in the Henan Southeast as the research object， comprehensively considering environmental and regional characteristics， and based on the Analytic Hierarchy Process， constructed an evaluation index system for the suitability of maize cultivation and designed a corresponding Comprehensive Evaluation Index （CEI） to complete the regional suitability evaluation. This study calculated the distribution of the CEI for maize planting suitability in the Henan Southeast， and ultimately extracted data within the dryland range for analysis. It is believed that the overall suitability for maize planting in the Henan Southeastern is relatively high， with 98.21% of the dry land being very suitable or more suitable for planting maize. After analysis， this is mainly due to the complementary terrain and hydrothermal conditions in the study area， which have certain environmental advantages and are conducive to the growth of maize.

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.

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.

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.

Change detection refers to the technology of extracting land cover changes by comparing and analyzing multi-temporal remote sensing images acquired at different periods covering the same area. With the advancements in satellite and sensor technologies， there has been a significant increase in Earth observation data. Change detection plays an important role in various fields， including geoinformation survey and ecological environment protection. In recent years， deep learning technology has become an advanced method for change detection due to its powerful feature mining ability. This paper provides a comprehensive overview of deep learning-based change detection methods from three aspects： pixel-level， object-level， and scene-level. Furthermore， this paper discusses the practical implementation of deep learning in change detection through three research examples. Finally， the study concludes by outlining future development trends in deep learning-based change detection.

The super-resolution reconstruction of remote sensing images is a type of method that uses image analysis methods to reconstruct high-resolution images from one or more low resolution images， in order to restore high-frequency details lost during sensor imaging， storage， and transmission， and improve the quality of remote sensing image data. The core lies in constructing a mapping relationship between high- and low- resolution images. This paper reviews the mainstream methods and representative research works in image super-resolution reconstruction and focuses on analyzing the recent advances in traditional methods and deep learning methods in the field of remote sensing image super-resolution reconstruction. The results indicates that： （1） Methods based on deep learning frameworks are the main focus and frontier of research in remote sensing image super-resolution reconstruction methods； （2） Model lightweighting and real-time performance are the main challenges faced by super-resolution reconstruction methods for multispectral remote sensing images in complex scenes； （3） There is an urgent need to construct public datasets for research on remote sensing image super-resolution reconstruction methods and to improve the evaluation index system. In addition， this paper also discusses the effects of methods based on bicubic interpolation， CNNs， GANs， and DPMs frameworks on remote sensing image super-resolution reconstruction in complex scenes through experiments.

Emergencies such as natural disasters and armed conflicts have become significant research hotspots in the global academic community due to their sudden onset and substantial impact. An important research topic is how to efficiently and accurately assess the effects of these events and monitor the subsequent recovery processes. Nighttime light remote sensing， a dynamic subfield within remote sensing， has gained widespread attention in recent years across socio-economic and environmental studies because of its ability to reflect variations in human activities on the Earth’s surface. This study systematically reviews the application of nighttime light remote sensing data in Chinese and international research from 2012 to 2022， focusing on three key areas： armed conflicts， public health emergencies， and natural disasters. The analysis highlights research themes， methodological approaches， and interpretative frameworks. Findings indicate that nighttime light data are used to capture the impacts of emergencies through changes in human activities， the status of public infrastructure， and unexpected shifts in regional economic development. These data have been extensively applied to assess the spatial extent， severity， and recovery progress of emergencies， providing robust evidence for governments to formulate emergency response and post-disaster recovery strategies. With advantages such as accessibility， relative objectivity， and strong correlations with socio-economic factors， nighttime light remote sensing supports rapid and extensive assessment of emergency impacts. Future research should aim to develop nighttime light datasets with higher spatial and temporal resolution， enhance integration with ground monitoring， socio-economic data， and other Earth observation data， and establish a more comprehensive system for emergency monitoring and assessment. This would provide more scientific and effective support for emergency management.

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.

The monitoring of river runoff is of great significance to the management and utilization of water resources， but how to obtain river runoff flexibly and accurately is still a difficult problem. Due to the limited resolution of satellite remote sensing， it is difficult to accurately invert the runoff of small and medium-sized rivers. The traditional river flow monitoring technology is complex and expensive， and its application is limited in the areas without data and in the emergency monitoring of sudden disasters. Therefore， this study takes advantage of the fast and flexible characteristics of UAVs and the advantages of LiDAR to obtain terrain information with high accuracy. Based on the 3D model of UAVs LiDAR point cloud data， combined with the Particle Tracking Velocity （PTV） method， This paper presents a method of runoff monitoring for small and medium-sized rivers. In this method， the boundary line between water body and land is extracted by using the strong absorption characteristics of the near infrared band of LiDAR， and the cross section is obtained by matching and merging with the original profile of the river. Based on low-altitude UAV optical remote sensing images， the particle tracking velocity measurement method is used to calculate the river velocity， and then the river runoff is estimated by the velocity area method. After 24 UAV runoff monitoring experiments in the reach of Liancheng Hydrology Station， the following conclusions are reached： The average relative Error between the flow monitored by LiDAR and the measured flow is 8.67%， the minimum relative error is 0.46%， and the Root Mean Squared Error （RMSE） is 0.09 m³/s. MPE （Mean Percentage Error） is 0.02， Pbias （Percent bias） is 1.95%， the Nash-Sutcliffe efficiency coefficient （NSE） was 0.94， which could meet the monitoring accuracy requirements of small and medium-sized rivers in areas without data. By comparison， the monitoring accuracy of runoff using this method is significantly higher than that of Manning formula runoff estimation （RMSE， NSE）. This study demonstrates the feasibility and reliability of the unmanned aerial vehicle Lidar point cloud data runoff monitoring， and provides a new idea for the emergency monitoring of sudden disasters in areas without data and the runoff monitoring of small and medium-sized rivers.

Land use/cover change is an essential part of the frontier of global climate change and sustainable development. In this paper， based on Google Earth Engine （GEE） platform， we used Landsat remote sensing images of 1990， 2000， 2010， and 2020， combined with Random Forest （RF） algorithm and field survey to sequentially decode the four phases of land use/cover data in the order of build-up area， water， vegetation， farmland， and unused land and tracked the spatial and temporal patterns of land use/cover changes in the Triangle of Central China. The results show that ① In 2020， the Triangle of Central China will mainly be composed of farmland and vegetation， accounting for more than 86% of the area， with farmland and vegetation mainly distributed around the urban agglomeration， build-up area in a spatial pattern of scattered clusters mosaic； ②Over the past 30 years， the quantitative structure of land use and land-use transfer in the study area have varied， with the land use structure showing an increased continuously in build-up area and farmland， a fluctuating increase trend of vegetation and unused land， and a weak increase trend of water； and ③ The results of the analysis of the evolution of spatial clustering of land use focusing on build-up area and farmland show that the hotter and hot spots of land use degree are increasing. In general， the extreme hot spot area is gradually decreasing， while the hotter sites are on the contrary， and the hot spots are growing in trend， and all of them located in and around Wuhan city circle. The study results lay the foundation for the research on the conservation and sustainable use of land resources in ecological urban agglomeration.

It is important to analyze the change of the quantity and spatial pattern of cultivated land for evaluating the current situation of regional food security and sustainable utilization of cultivated land resources. Based on the second national land survey and change data， this paper analyzes the changes in the quantity and spatial distribution of cultivated land in Northeast China from 2009 to 2018 by means of spatial analysis and trend analysis， and reveals the pattern of newly added cultivated land and its physical geographical characteristics. The results showed that： （1） the cultivated land in Northeast China increased slightly from 2009 to 2018， and the growth rate was slower than before； （2） The newly added cultivated land was mainly distributed in the west Liaohe Plain， the west Songliao Plain and the Sanjiang Plain， mainly from the reclamation of forest grassland and unused land； （3） In Northeast China， the water and heat conditions of newly added cultivated land were slightly better than those of traditional cultivated land， but the situation was opposite in Sanjiang Plain and Lesser Hinggan Ling-Changbai Mountains. Although the newly added cultivated land is mainly distributed in the plain area， the phenomenon of cultivated land going up the mountain still appears in some areas. At the same time， it was found that the soil fertility of newly added cultivated land in eastern Inner Mongolia and Songliao Plain was slightly weaker than that of traditional cultivated land. The study suggests that the natural conditions of newly added cultivated land are different from those of traditional cultivated land. Considering the ecological risks brought by cultivated land reclamation， quality assessment and monitoring of newly added cultivated land should be strengthened in the future， especially the risks of economic value-added and ecological degradation of cultivated land reclamation should be weighed， so as to promote the orderly utilization and sustainable development of cultivated land resources.

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.

The basic carrier of grain production is farmland. The rapid and accurate acquisition of non-agricultural information of farmland is of great significance to the management of farmland resources and the implementation of related policies. In order to explore the non-agricultural changes of farmland in Shangnan County in recent 10 years， Google Earth Engine （GEE） was used as the platform， random forest method was used for classification， and temporal and spatial distribution information of farmland in Shangnan County in 2010， 2015 and 2020 was obtained based on multi-temporal Landsat remote sensing images. By means of land transfer matrix， geographic detector technique and grid element method， the important characteristics and driving factors of non-agricultural farmland in Shangnan County were analyzed. The results showed that： （1）The random forest method based on GEE platform could effectively obtain farmland information in Shangnan County， the overall accuracy of land use classification was higher than 88%， and the Kappa coefficient was greater than 0.85. （2） The spatial distribution of farmland in Shangnan County is uneven， mostly distributed in the central and southeastern regions， the farmland area is decreasing continuously， and the non-agricultural development is showing a trend of increasing. The non-agricultural type of farmland is mainly garden land， and the conversion land is mainly concentrated in the central and southeastern areas of Shangnan County. （3） Natural and social factors have a common driving effect on the non-agricultural conversion of farmland， and natural factors are the prerequisite for the non-agricultural conversion of farmland， and the influence of human activities on the non-agricultural conversion of farmland under the limitation of natural factors can be reflected by social factors. The main natural driving factor of farmland non-agricultural transformation in Shangnan County is soil type， and the social factor from road distance has the greatest influence on the transformation of farmland non-agricultural transformation， indicating that the suitable soil type and rapid economic development have a greater impact on the farmland non-agricultural transformation in Shangnan County.

The continuous changes in land use patterns have profound impacts on the ecological environment and socio-economic development of the Yellow River Basin. Understanding the dynamic changes in land use in the middle reaches of the Yellow River Basin can provide a scientific basis for achieving sustainable development in the Yellow River Basin. This study is based on four periods of Landsat remote sensing data from 1995， 2005， 2015， and 2023 in the middle reaches of the Yellow River Basin. Obtain the spatial distribution of land use in the demonstration area through Support Vector Machine （SVM） and maximum likelihood method classification， and analyze the characteristics of land use change in the study area from 1995 to 2023 using quantitative indicators such as land use change and transition matrix. The Markov model was applied to predict the land use changes in 2025 and 2030. By establishing the FLUS-Markov model， the land use changes in the study area in 2025 and 2030 were predicted. For the six types of land use changes （forest， grassland， wetland， cultivated land， construction land， and unused land）， the results show that： （1） Over the past nearly 30 years， cultivated land and forest land have decreased by 8 600 km² and 6 400 km² respectively， while the area of construction land has significantly increased by 7 500 km²； （2） The transfer directions of land use types are diverse， mainly from cultivated land to construction land and vegetation， and the landscape of each land type tends to be balanced in spatial distribution， with coordinated urban development； （3） Between 1995 and 2023， the number of forests， grasslands， and construction land showed an upward trend， while the number of wetlands， cultivated land， and unused land showed a downward trend； （4） In the next 10 years， there will be significant changes in land use in the middle reaches of the Yellow River， as humans require more land for construction.

Rapid urban expansion leads to drastic changes in the urban and surrounding ecological environment， which further intensifies the urban heat island （Urban Heat Island， UHI） effect. However， the effect of urban expansion on urban heat island effect in arid areas is still unclear. Gansu province extends from southeast to northwest for more than 1 600 kilometers， and the climate type has gradually changed from humid and semi-humid climate in southeast to extreme arid climate in northwest. Furthermore， Gansu province has experienced rapid urban expansion since 2000， so Gansu province is an ideal experimental area to study the effects of urban expansion on UHI effect under different dry and wet climate backgrounds. This study used the MODIS land surface temperature data set， estimates the surface urban heat island intensity （Surface urban heat island intensity， I_s ） and its inter-annual features in 14 cities of Gansu province from 2003 to 2021， to explore the change characteristics of UHI effect and its response to urban expansion in Gansu province under different climatic background. The results show that I_{sand δISP （Urban-rural contrast in impervious surface percentage， δISP） have obvious spatiotemporal differences among cities in Gansu province. The change trend of Is was mainly affected by vegetation coverage （R² =0.406， P<0.05）， followed by precipitation （R² =0.377， P<0.05）， and the effect of urban population （R² =0.069， P>0.05） was negligible. In addition， due to the difference in land surface temperature （Land surface temperature， LST） among different land cover types， the type of land cover occupied in the process of urban expansion also has an impact on the Is trend. If the type of bare land occupied in the process of urban expansion， it will have a cooling effect. The influence of urban expansion intensity on Is trend has obvious thresholds， and the thresholds of cities in different climate zones are different. The threshold of city in humid zone （Tianshui， δISP=32%） is smaller than that of city in arid zone （Jiuquan， δISP=41%）. The change rate of Is before and after the threshold Is different， and the change rate of Is before and after the threshold Is in arid region Is more significant. The results of this study provide a scientific basis for the impact assessment and management decision-making of rapid urban expansion in arid areas.}