Long time series LAI remote sensing inversion algorithms use only a few leaves spectra to represent the global leaf spectral characteristics throughout the year.while due to the variation of leaf spectra，it may introduce uncertainties to LAI remote sensing products.An amount of spectrum databases containing leaf spectrum of different vegetation species，geographical locations and time phase and corresponding biochemical parameters have been constructed to provide support for the analysis of spectral characteristics of leaves.This paper mainly uses the leaf spectral database LOPEX’93，ANGERS’03，Spectral library of typical ground objects in China and field experimental data to analyze the effects of spectral characteristics of different plant species and different climate zones on MODIS reflectance of specific channels and further to provide prior information for the development of LAI inversion algorithms with consideration of leaf spetra differences.The result suggests that：There exists diversity in vegetation leaf spectra.The spectral differences mainly affect the reflectance in red and green band (green band is most sensitive to leaf spectra variation).Only considering vegetation types without taking leaf spectral variation into account may induce error over 3 in remote sensing LAI inversion algorithms.

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.

Using the observation snow cover data from Landsat TM and ETM+ from January 2000 to May 2001, the inter annual temporal and spatial characteristics of snow cover over middle Tianshan mountains are analyzed. Combining with digital elevation model (DEM) data, the distribution of snow cover in different terrain conditions and different altitude per-month are acquired. After analyzing the spatial distribution and temporal variation regulation of snow cover, it comes to a conclusion that the snow cover within year is correlated with altitude, aspect and slope. On the whole, the ratio of snow cover within year increases when the altitude increases and it decreases when the slope increases. The average height of snow cover boundary is high in summer and autumn but low in spring and winter. The difference of snow cover in aspect west and east is obvious in certain times, but the difference is less than that of the aspect north and south. This study provides a scientific support for utilization of water resource and the research of climate and environment in Tianshan Mountains.

Visible and Near-Infrared Reflectance Spectroscopy (VNIRS) has extensively been used to estimate soil total nitrogen (TN) concentration,and can provide a rapid,convenient method for quantitatively obtaining soil TN content in a wide range of areas.In this study,we evaluated the prediction ability of Visible and Near-Infrared Reflectance Spectroscopy (VNIRS) for estimating soil TN in the Sanjiang Yuan regions of Qinghai province.Firstly,we collected about 146 surface soil samples (0~30 cm),including four soil types during the period from August 7 to 17 of 2012 in Yushu and Maduo counties;secondly,we respectively measured soil reflectance spectrum by ASD FieldSpec 4 portable spectrometer (Analytical Spectral Devices,Inc.,Boulder Colorado,2012) with the spectral range of 350～2 500 nm,and soil TN by using Vario EL Ⅲ element analyzer of ELEMENTAR Inc.in the laboratory;and then we respectively adopted PLSR and BPNN models to relate soil TN to raw spectral reflectance and its four pre-processing transformations for the overall soil samples and each soil types samples.The results showed that the average coefficients of determination(R2) of calibration and validation for BPNN are respectively 0.87 and 0.81 with the mean RPDval of 2.28,whereas those of PLSR model are 0.75,0.72 and 1.95 respectively,which suggest that BPNN has a better prediction ability than PLSR as a whole;The combination of BPNN and the raw reflectance spectrum (REF) and its all pre-processing transformations performed a good or closer good prediction ability for different and overall soil types;whereas the combination of PLSR model and REF,Log(1/R),BD produced a rough or good prediction ability for estimating TN,however,FDR and SDR with poor prediction ability,especially SDR (R2 cal<0.5,R2val<0.5,RPDval=1.10~1.27) hasnt the ability to predict soil TN;As a whole,TN estimating from the overall soil samples can produce more stability prediction accuracies than single soil types,whereas that from single soil type samples can reflect the difference among soil types;BPNN model accuracies are superior to those of PLSR model,but PLSR has stronger operability,and can show the difference among soil types,and different among transformation indicators as well as.

Based on the water quality parameters and spectrum data measured in Taihu Lake from November to December 2007.Firstly,using the Gaussian equation to filter and disassemble remote sensing reflectivity to identify the fluorescence reflection peak after the chlorophyll a (chl\|a) 675 nm absorption peak.Then using the 662 nm reflectivity as a benchmark,adopting the normalized fluorescence height method to inverse Chlorophyll a concentration (C chl-a) and got the best inverse model.Based on Gaussian decomposition reflectivity R acquired chl\|a fluorescence reflection peak R(FP) and 662 nm at (662) ratio［R(FP)/R(662)］between C chl-a has a significant correlation .The model is the best inversion model  of the fall Taihu Lake water.In high suspended sediment conditions,the model can be better expressed the relationship of chlorophyll fluorescence height and chlorophyll a concentration.This paper provides a new method and basis for C chl-a  inversion,and provides a reference for the sensor sensitive band selection and settings.

In this paper, the hyperspectral data of cotton canopies grown in north Xinjiang at the main growing stage under water stress are derived by using an ASD spectrocoparator made in USA, the rededge integral areas are used to estimate the total N content in leaves of cotton canopies, and the analyzing method based on the spectral position variables is derived from the first differential spectral data. An analysis on the correlation between the red-edge integral areas (used as the independent variables) and the total N contents in leaves of cotton canopies (used as functions) is carried out so as to develop a mathematical model about the correlation between the red-edge integral areas and the total N contents in canopy leaves of cotton variety of Xin Luzao No.6. The correlations between the chlorophyll contents and the total N contents in separate leaves of cotton canopies under the different irrigation water volumes are researched. The results show that there is a significant positive correlation between the chlorophyll content and the total N content in leaves of cotton canopies (R=0.8723, n=39), and the data of chlorophyll contents can be used to effectively estimate the total N content in separate cotton leaves; there is also a significant correlation between the red-edge integral areas and the total N contents in leaves of cotton canopies,and the correlation coefficient is 0.7394 (n=40). The total N contents in canopy leaves of cotton varieties of Xin Luzao No. 6 and No.8 can be accurately estimated by using the developed model, and the values of RMSE are 0.3859 and 0.4272 respectively. It is considered that there is a potentiality to use the variables of red-edge integral areas for predicting the total N contents in leaves of cotton canopies, and it is also feasible that the data of displacement and change of red-edge extent can be used to recognize the moisture stress of cotton plants if a rational recognition system is develop. The conclusions of the study are as follows: (1) The physiological and biochemical properties of both cotton leaves and canopies are changed with cotton growth; (2) There is a significant correlation between the chlorophyll content and total N contents in cotton leaves (R=0.8723, n=38), and the total N contents in cotton leaves can be estimated by a mathematical model; (3) The analyzing method based on the variables of spectral position of "red edge" of cotton leaves, derived from the first differential spectral data, reveals that the change extent, shape and area of the "red edge" contain the information of various wavebands, and the capability of predicting Nnutrient in cotton leaves and canopies can be provided by using the developed model based on the variables from these wavebands; (4) It is reveals that the moisture supply is sufficient, the N metabolizing in cotton plants is hearty, the cotton plants grow luxuriantly, and the red edge of cotton leaves shifts towards blue light if the chlorophyll content in cotton leaves is high. It is feasible that the data of displacement and change of red-edge extent can be used to recognize the moisture stress of cotton plants if a rational recog-
nition system is develop.

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

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.

Snow cover， snow depth/snow water equivalent， surface soil frozen/thaw state and soil moisture are the key variables in the three cycles including energy， water and carbon cycles. In order to better understand the remote sensing techniques of above parameters， this paper presents a comprehensive review of the progress in remote sensing of snow， soil frozen/thaw state and soil moisture， including the methods and theories of snow cover， snow depth / snow water equivalent， surface soil frozen/thaw and soil moisture remote sensing monitoring from visible， microwave techniques and the integration of multi-sources of remote sensing. The research progress of these parameters is summarized， and the prospects of these parameters are also discussed. The capability of snow， surface soil frozen/thaw state and soil moisture with remote sensing has been demonstrated to be improved greatly due to the retrieval algorithms development based on from single-sensor to multi-sensor combination， single-band to multi-band integration， especially on the virtual satellites constellation. Long time series data set of these surface parameters about 40~50 years were generated， then these products provide our better understanding on surface response to global climate change， and accelerating the application into the research of hydrology， climate and carbon cycles. This review will be helpful for the application of key parameters retrieval in water cycle with remote sensing.

Mineral resources are important production materials for human survival and development， and the monitoring of mine environment is crucial for mineral resources exploitation and protection. Due to the advantages including large-scale， multi temporal and comprehensive， remote sensing technology has become the main means of mine monitoring. Aiming to the requirements of mine development and utilization， geological disasters， ecological environment monitoring and quality evaluation， we systematically summarized data sources， methods and models used in remote sensing monitoring of mine environment. Especially， data sources adopted in remote sensing monitoring of mine have tended to diversify and involve in all aspects of mine monitoring. Along with the rapid development of cloud computing platform and artificial intelligence technology， methods such as big data analysis and deep learning have gradually played an important role in remote sensing monitoring of mine environment， while multi-source data fusion， intelligent extraction of features， three-dimensional deformation monitoring and quantitative inversion are the main problems and challenges.

Taking Shanghai region as the study areas,by using thermal infrared data of multi-sources polar orbit meteorological satellite,the research selected 264 preprocessed images to couple with the synchronous air temperature data gathered from 52 weather stations and then built two kinds of air temperature retrieval models changed with seasons and times.Results showed that the stable models for retrieving the air temperature could be constructed based on large numbers of samples from multi-sources remote sensed data,and furthermore the retrieval precision could be improved by using a field-measured data to modified the model.

As an important complementarity of aerial remote sensing,the UAVRSS(Unmanned Aviation Vehicle Remote Sensing System) has many advantages in specific field.Firstly,the paper  described 4 times aviation experimentations,then based on the experiment images and aviation data of UAV,all images were  mosaicked to a panorama image.And the mosaicked image was analyzed from many factors for evaluating the image quality.Finally,some improvements to the UAV image quality were discussed.

With the increasingly maturing of the consumer multi-rotors drone techniques， its application for photogrammetry has been a reality. The paper employs consumer four-rotors drone equipped with ordinary camera for the 1∶500 large scale mapping task. Based on the image collecting characteristic of consumer drone and non-measure camera， the implementation plan is set out and the data is produced. The practical result indicates that： it is feasible to make large scale mapping with consumer drone， and its mapping precision can be qualified with the standard requirement； three-dimensional model of real-scenery generated through collecting image can be an auxiliary data for mapping inside， which can largely reduce the work of investigation and survey outside. Research shows that the program improves the efficiency of large-scale mapping， reduces the overall cost of the project， and has good economic benefits and application prospects.

Airborne LIDAR is a new technology that can obtain high\|precision three-dimensional geographical data quickly.Filtering process for LIDAR points cloud data is to separate the non\|ground points from the ground points.Several important useful filtering algorithms for LIDAR points cloud data\|\|mathematical morphology based filtering algorithm,slope based filtering algorithm,TIN based filtering algorithm,pseudo scanning lines based filtering algorithm,etc.have been introduced,discussed and contrasted.their advantages and disadvantages,the improving advice to each filtering algorithms also have been presented in the paper.

The surface Evapotranspiration （ET） is an important controlling factor to water cycle and energy transmission in the biosphere， atmosphere and hydrosphere. Satellite provides an unprecedented spatial distribution of ET in the past decades. In this paper，the estimation methods of evapotranspiration using remotely sensed data were summarized，and the existing issues that should be further studied were discussed. In the future research，we should strengthen the improvement of the evapotranspiration regarding scale effect， nighttime ET， the general validation method of different ET products， remotely sensed data in China， the ET products with higher spatial-temporal resolution， and the new ET model using the machine learning methods.

Air aerosol pollution has become increasingly serious and become the focus of atmospheric research with the development of urban industrialization.Remote sensing technology is widely used in atmospheric research as a means of scientific，rapid and large-scale monitoring.The main content of remote sensing for atmospheric aerosol retrieval including Aerosol Optical Depth (AOD) of aerosol，aerosol concentration aerosol particle size distribution and air pollution are analyzed based on the atmospheric radiation transmission theory.Main progress around of the world in areas of atmospheric aerosol research by remote sensing techniques are introduced，especially the advantages and disadvantages of the different aerosol retrieval algorithms.At last，some existing problems and the trend of remote sensing for atmospheric aerosol retrieval are discussed.

Vegetation phenology information is a key indicator for evaluating climate-vegetation interaction， land coverage， and interannual productivity changes in ecosystems. Traditional phenological monitoring methods are based on visual observation， the monitoring range is limited and requires a lot of manpower and resources. As a new monitoring method in recent years， remote sensing technology has the characteristics of large monitoring range， convenient information acquisition and saving manpower and material resources. Its application has promoted the development of vegetation phenology dynamic monitoring research. Firstly， this paper combs the process of vegetation phenology remote sensing monitoring in recent years， and clarifies the existing remote sensing phenology monitoring system； The remote sensing data sources that can be used to establish vegetation growth curve are summarized， and the application scenarios of different data sources are discussed； The existing curve noise reduction algorithms and application processes are summarized， and the causes of errors in different methods are analyzed； The main vegetation phenology extraction methods are summarized； Finally， the remaining uncertainties in remote sensing monitoring of vegetation phenology， such as data resolution， vegetation phenology stage definition， and monitoring timeliness， were discussed， and the main directions for future research on remote sensing monitoring of vegetation phenology were prospected.

There are kinds of methods for ortho-rectification in application of remote sensing images，including Collinearity Equation Model，Strict Geometric Model based on Affine Transformation，Improved Polynomial Model，Rational Function Model，Method based on Neural Network，and so on.But there is lack of system comparison among these methods.On the basis of introducing the principle of the methods above，advatanges and drawbacks about these algorithms are summarized in this paper.Specific emphasis is the mathematical derivation and algorithm design of FM.Tikhonov method is taken to the progress of computation  of RFM.Two kinds of algorithm based on neural network was taken in application of ortho-rectification.To compare accuracy and effectiveness between the above methods，we make some experiments.The result shows that: on the condition of the same GCPs distribution，Rational Function Model that can reach sub pixel accuracy is the best of all from the viewpoint of precision and can be used in practice in spite of its relatively slower speed.

Three-dimensional （3D） radiative transfer model can accurately describe the interactions between solar radiation and heterogeneous land surfaces. Recently， it has become an important tool for quantitative remote sensing studies. LESS is a ray-tracing based 3D radiative transfer model， which take full advantage of the forward ray-tracing techniques for simulating radiative budget and backward ray-tracing for simulating large-scale images， which makes it possible to simulate various remote sensing data in a single model. Currently， LESS can simulate multi-angle Bidirectional Reflectance Factor （BRF）， multi-spectral/high-spectral images， fish-eye cameras， upwelling/downwelling shortwave radiation in rugged terrains and layered FPAR， etc. This simulated dataset can be used for validating physical modes， developing parameterized models， as well as training neural networks. This paper presents the fundamentals of LESS and its applications. LESS can be downloaded from www.lessrt.org.

The Remote Sensing based Ecological Index （RSEI） has been widely used since its publication and was modified recently. In this paper， the differences between RSEI and the Modified Remote Sensing Ecological Index （MRSEI） are analyzed and compared based on the principle of the principal component analysis and an application case. The results show that the MRSEI index unreasonably adds the second principal component （PC2） and the third principal component （PC3） into the first principal component （PC1）， as PC2 and PC3 have no clear ecological meanings. The addition also reduces the weight of PC1. Therefore， the MRSEI does not improve the original RSEI， but reduces the value of RSEI as the added principal component components can cancel each other. Therefore， the modification made in MRSEI lacks rationality. This paper also analyzes and discusses some issues that users encountered in calculating and applying the RSEI index. The RSEI should be calculated using surface reflectance data rather than the top of atmospheric reflectance data or Digital Numbers （DNs）. Also， the imagery should be acquired in plant growing seasons. When there is large-area open water in study images， the water must be masked in advance. The "1–PC1" procedure can only be performed when the loadings of the greenness and wetness indicators in PC1 have negative signs.

In recent years,airborne laser bathymetry technology has got a rapid development.It plays an important role in monitoring of offshore area,measuring water depth and surveying underwater topographic and geomorphic features.This paper analyzed the development history of airborne laser bathymetry in the world and discussed the research progress of data processing and applications.This paper studied the problems of airborne laser bathymetry technology in the development and application,and pointed out future development trend and direction in the end.This research will provide detailed reference information for researchers and users of airborne laser bathymetry technology.

Object detection has always been a hot topic in the field of remote sensing images information extraction， and has a wide range of application prospect in many fields. The development of deep learning in the field of computer vision provides a strong technical support for the extraction of massive remote sensing images， and greatly improves the accuracy and efficiency of object detection in remote sensing images. However， objects in remote sensing images have the characteristics of multiple scales， multiple rotation angles and complex scenes， deep learning technique still faces great difficulties in the application of remote sensing images object detection with limited high-quality labeled samples. According to five aspects of scale invariance， rotation invariance， complex background interference， limited training samples and detection of multi-band data， the existing algorithms of object detection based on deep learning in the field of remote sensing images in recent years are introduced and summarized. In addition， the difficulties and methods of detecting typical objects in remote sensing images are analyzed and summarized， and the common datasets of remote sensing images object detection including optical images and SAR images are also given general introduction. Finally， the future trends of object detection in remote sensing images are analyzed.

It is of great significance to automatically detect aircrafts from remote sensing imagery to get their locations.However，due to aircraft posture variance，complicated background and incomplete outlines，it is challenging to achieve a high aircraft detection accuracy.Traditional aircraft detection methods are usually based on hand\|crafted features and machine learning based classifiers，which is not robust enough for the translation and rotation variations.To tackle the above issues，this paper introduces deep convolutional neural network and the strategy of transfer learning to detect aircrafts from Chinses domestic satellite remote sensing images.Specifically，this paper first constructs an aircraft sample database，which consists aircrafts of different sizes and poses.Afterwards，YOLO V2 trained with natural images is utilized as the detection model and is further fine\|tuned with aircraft samples to increase the robustness and performance.Experiments were done on the Shanghai Pudong airport from Chinese GF\|2 remote sensing data.Experimental results showed a good performance with a recall of 92.25% and a precision of 94.93%.It is indicated that deep learning together with model transfer can get a high aircraft detection accuracy with limited training samples.The method in this paper can be generalized to other land object detection problems which shows a good promotional value.

Google Earth Engine （GEE） is a comprehensive application platform that integrates remote sensing image storage and analysis. It can conveniently and quickly call remote sensing images and information extraction. Therefore， GEE has attracted more and more scientific researchers' attention. With the continuous expansion and upgrade of GEE， the system platform has become more and more complex. For ordinary users， it is becoming more and more difficult to quickly understand its architecture and functional algorithms. In response to this problem， this article systematically introduces the technical architecture， data resources， model algorithms and computing resources of GEE， and summarizes the application results of GEE in various fields， hoping to provide GEE users with a quick understanding of the platform Window to help them make better use of the GEE platform to carry out their own application research.

Chlorophyll-a concentration is an important proxy for defining the tropic status of various bodies of water. Using remote sensing technology to retrieve chlorophyll-a concentration is an effective method for water eutrophication monitoring and a great number of algorithms for chlorophyll-a concentration retrieval are developed. These algorithms have different advantages and ranges of application. Because the optical characteristics vary in different bodies of water， it is hard to achieve desired results if blindly applying algorithms. In order to promote the further development of water quality remote sensing， the theory and data sources of remote sensing inversion are introduced.Then，domestic and foreign algorithms of retrieving chlorophyll-a concentration in water by remote sensing are summarized.The algorithms studied are categorized into six types by their architectural designs， namely： fluorescence peak and maximum peak algorithms， band algorithms， chlorophyll-a index algorithms，artificial intelligence algorithms，algorithm systems based on optical water types and analytical algori-thms.Each algorithm is presented systematically and its characteristics are analyzed.Then，all the aforementioned algorithms are compared regarding their applicable range of chlorophyll-a concentrations as well as water types.The applicability， merits and demerits of each category of algorithms are analyzed and concluded in order to provide reference for environmental and remote sensing researchers.The main conclusions are as follows：①the algorithm applicability for Case II waters is limited. More in-situ observations should be conducted to establish and supplement the database. Similarities and difference of various optical water types should be further studied to establish global algorithm systems based on optical water typologies； ②The combination of UAVs and hyperspectral sensors could provide new thoughts in monitoring inland water quality； ③Machine learning algorithms and mechanism models should be integrated to develop physical constrained models with high accuracy.

Methane （CH₄） is a chemically and radiologically active gas in the atmosphere. With the development of human civilization， the total amount of CH₄ has been increasing. Before the industrial revolution， the global CH₄ concentration was 700 ppbv， and it reached 1714 ppbv by the 1990s. This paper describes the development of CH₄ ground-based detection， space-based detection， satellite-based detection and inversion algorithms. During 1979~1983， the SAMS on Nimbus-7 satellite observated CH₄ concentration in the stratosphere for the first time. After that， many sensors have been launched to observe CH₄. The satellite-based instruments mainly observe CH₄ in the infrared band by nadir viewing， limb sounding or occulation observating. In recent years， the spatial resolution and spectral resolution of the sensors are greatly improved. The TROPOMI has a spatial resolution of 7 km×7 km； the spectral resolution of ACE-FTS、AIUS reach 0.02 cm^-1. In the near infrared band， WFM-DOAS is the main algorithm for inversion of CH₄ concentration， and OEM is mainly used in the mid and far-infrared band. With the development of satellite observation technology and the improvement of retrieval algorithms， the accuracy of CH₄ retrieved has gradually improved. The deviation between CH₄ concentration retrieved by TROPOMI in the nadir viewing and the CH₄ concentration provided by ground-based stations is 14 ppbv （0.8%）； The accuracy of CH₄ profile retrieved by ACE-FTS in the limb sounding/occultation observing is within 10% from troposphere to lower stratosphere.Then， the trends and distribution characteristics of global CH₄ concentration at 300 hPa and 150 hPa are described in this paper. The global CH₄ concentration increased by ~50 ppbv from 2010 to 2020， with an average annual growth rate of ~0.29%； At 300 hPa， the global CH₄ concentration was 1 767 ppbv in 2010 and increased by 55 ppbv in 2020； At 150 hPa， the global CH₄ concentration was less than 1 700 ppbv in 2010 and reached 1 745.6 ppbv in 2020； The global CH₄ concentration is higher in the north and lower in the south， the reason lies in that there are numerous methane emission sources （freshwater wetlands， rice cultivation， fossil fuel combustion and biomass combustion， etc） in the northern hemisphere； The high concentrations of CH₄ are distributed in the mid-Atlantic， northern Africa， the Middle East and western China.

Chlorophyll content, as a key factor influencing photosynthetic efficiency, serves as a vital indicator for vegetation health assessment. Traditional ground-based measurement methods are limited by time-consuming procedures and destructive sampling, whereas remote sensing technology enables non-destructive and efficient monitoring of leaf chlorophyll content. The establishment of a high-accuracy, strongly generalizable remote sensing estimation model for chlorophyll content is crucial for vegetation physiological monitoring and ecosystem health evaluation. Eleven field-measured datasets and one PROSPECT simulated dataset, covering multiple vegetation types and ecosystems, were employed to systematically compare the accuracy, universality, and cross-scenario transferability of chlorophyll estimation models developed using nine spectral indices and seven machine learning algorithms. Among spectral indices, the red-edge chlorophyll index (CIred-edge) demonstrated superior performance in both measured (=0.7567, nRMSE=13.73 μg/cm2, MAE=11.83 μg/cm2) and simulated datasets (=0.9578, nRMSE=5.31 μg/cm2, MAE=4.93 μg/cm2), while showing the strongest generalization capability in simulated-to-measured transfer (=0.7567, nRMSE=15.27 μg/cm2, MAE=14.36 μg/cm2). For machine learning approaches, GBRT achieved optimal accuracy and universality (=0.8172, nRMSE=11.58 μg/cm2, MAE=3.93 μg/cm2), with SVM exhibiting the best transfer learning performance between simulated and measured data (=0.8113, nRMSE=11.81 μg/cm2, MAE=4.14 μg/cm2). These results provide methodological guidance for selecting appropriate remote sensing inversion models, confirm the application potential of simulated data, and offer a practical transfer learning solution for chlorophyll monitoring in data-scarce scenarios, thereby supporting precision agriculture applications and global carbon cycle studies with cost-effective, high-precision estimation techniques.

The signal-to-noise ratio (SNR) is one of the most important indices which can be used to evaluate the data quality obtained by a remote sensor.To a great extent,the SNR of an image reflects the SNR of the remote sensor.Several typical methods to estimate the SNR of optical remote sensing imagery are summarized in this paper,and their merit and restrictions are presented.And this paper also performs the comparison and analysis between these methods based on their own principles,from six aspects including the automatic computation,the computing time,the stability,the applicability,the suitable sensor category,and the uniformity of estimating areas.In addition,the paper points out that the comparison and analysis between methods in various specific applications should be done in the future.The study will help to choose a reasonable SNR estimating method aiming at different remote sensors and different types of remote sensing images.

To take Wuhu in Anhui province for example.Firstly,the author analysed the water body in Wuhu and the spectral characters of the earth's surface,then,summarized every class separability of the water information and the earth's surface in every band of ASTER Remote Sensing Image,after repeated experiments and analysis constructed the extracting model of water body which based on threshold of bands and relation of spectrum:B2>B3,B1+B6<127,B3+B4<54 and B3<24.In the end,the author compared and evaluated the conclusion of extracting model of water body with the conclusion of unsupervised classification,supervised classification and NDVI.The result of experiment indicated that this method is feasible and more simple,but higher demands were needed in the selected samples of the analysing process of spectral characters.

Wetlands are usually featured by evident seasonality， and thus high temporal-resolution remote sensing monitoring of their consecutive changes would greatly benefit to more objectively and accurately detecting the characteristics of spatial-temporal changes. The Poyang Lake wetland， as the largest freshwater lake in China， which shows significant intra-annual variability， was selected as the demonstrative case in this study. By collecting all available remote sensing images of Sentinel-1 & 2 and Landsat-8 from 2017 to 2019 based on the Google Earth Engine platform， we adopted the Random Forest （RF） method to map various types of wetlands of the Poyang Lake. It aims to demonstrate the capacity of Sentinel-2 optical images integrated with Sentinel-1 SAR and Landsat-8 data applicable to monitor wetland variations at both the inter-annual and intra-annual timescales. Results show that the Sentinel-2 images enable to provide a powerful data base for monitoring the dynamics of Poyang Lake wetland， and the overall classification accuracy was higher than 90%. the areas of the classification results were statistically analyzed in the 3 years， in February of each year， mudflat and vegetation reach the maximum area， while water area is the minimum.In June and July of each year， the water area reaches the largest in the year， while the mudflat and vegetation area is the smallest. All types of wetlands in the Poyang Lake show evidently seasonal changes， and the monthly classification results can more accurately illustrate the intra-annual changes characteristics of various types. Overall， the integration of Seninel-2 data with Sentinel 1 and Landsat-8 images， can effectively monitor the wetland changes at fine timescale， which is crucial for timely and costly management of wetland resources.

Evapotranspiation retrievals in conditions of rugged terrain and arid/semiarid sparse vegetation are always the hotspot in estimation of regional evapotranspiration from remote sensing images.Concerning with the complex characteristics of the loess hilly-gully region,such as undulating topography,un-unique land use/covers and less vegetated land surface,etc,land surface characteristic parameters and flux was retrieved,and the instantaneous vegetation transpiration and soil evaporation was estimated with the TSEB parallel model by using the Landsat TM data for an experimental study site located on the conjuncture area of Shaanxi and Gansu where the terrain surface is very undulating with less vegetation developed,heavy soil and water loss loess plateau.And then the calculated result was merged into daily evapotranspiration,also the spatial pattern of the daily evapotranspiration was analyzed.Actual evapotranspiration of the study area was calculated with the extra resistance method and Penman-Monteith equation to compare with the TSEB parallel model estimated,which suggested that the TSEB parallel model can be used for the accurate estimation of evapotranspiration in loess hilly-gully region.

With the rapid development and large integration of global informatization and industrialization since the 21st century，the Internet of things and cloud\|computing have emerged.The world has entered an era of big data.There are a huge amount geographical and remote sensing data generated every day in the field of geoscience，environmental science and related disciplines.However，the traditional approaches for storing，managing and analyzing massive data on the local platform，which take up lots of resources，time and energy，have been unable to meet the needs of the current researches.Google Earth Engine(GEE) cloud platform is powered by Google’s cloud infrastructure，and it combines a large number of geospatial datasets and satellite imagery，in which the datasets could be processing，analyzing as well as visualizing on a global scale.Meanwhile，it uses Google’s powerful computational capabilities to analyze and process a variety of environmental and social issues including climate change，vegetation degradation，food security and water resource shortages.Firstly，an introduction of GEE cloud platform has been given.Secondly，recent researches that using GEE cloud platform were reviewed.Thirdly，GEE cloud platform and MODIS land cover type data were used to analyze spatio\|temporal changes patterns of major land use and land cover type in Three Gorges Reservoir in the period of 2002~2013.The results indicate the largest changes occurring in forest lands，shrub grasslands and croplands.Finally，after a rough calculation，GEE cloud platform is superior to the traditional approaches in terms of both cost and economic efficiency，improving the overall efficiency by more than 90%.GEE cloud platform could not only provide powerful support to experts in the field of geosciences and remote sensing，but also offer valuable help to researchers in related disciplines.GEE cloud platform is an excellent tool for scientific research in geosciences，environment sciences and related disciplines.

Rapid and accurate acquisition of forest structural parameters has been significant for forest resource investigation. In this study， photogrammetric and field-based tree height measurement of the Picea crassifolia were validated in the east and central of the Qilian Mountains. The individual segmentation algorithm using Canopy Height Model was applied to identify the position and height of the Picea crassifolia within each plot. The extraction accuracy of the average tree height was recognized the highest among the four indexes of maximum value， minimum value， mean value and standard deviation， with Root Mean Square Error （RMSE） values of 1.39 m and R² values of 0.93（P<0.05）. Tree heights extracted from LiDAR data of Picea crassifolia were used to analyze the spatial distribution of tree height in the Qilian Mountains. There was a downward trend of the average forest canopy height from east to west in the Qilian Mountains. As the altitude rises， the forest canopy height showed a “unimodal” change， which peaked at change an altitude of 2 900 m. This study shown that UAV photogrammetric tree height measurements was a viable option for intensive forest monitoring plots. Additionally， it was shown that underestimated evident in field-based and UAV laser scanning tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation.

The global environment change has become one of the three global environmental issues with the greatest global impact.Lake is of great ecological significance in regional ecosystem study.Dynamic change as one of three main lake environmental problems of lakes has became the key point of the research area.Remote sensing techniques,as scientific tools of rapid investigation and monitoring,have been widely applied in the research of dynamic change in lakes.Main progress around of the world in areas of lake dynamic change research by means of remote sensing techniques are introduced in the aspects of basic principles and remote sensing data sources,remote sensing classification and dynamic change monitoring methods of lake information | especially,the advantages and disadvantages of remote sensing data source and dynamic analysis methods are analyzed deeply.In addition,some existing problems and the development trends of the recent lake dynamic change research are discussed.

 

Building extraction is one of the most challenging research topics in remote sensing image understanding.It is of great significance in practice to exploit automatic,intelligent,accurate building extraction approaches.This paper firstly outlines the history and recent development of building extraction from remote sensing imagery,and then provides a comprehensive survey of state-of-the-art approaches,to divide them into the bottom\|up (data-driven) methods and top-down (model-driven) methods.Finally,the remaining problems and future development trends are provided for building extraction from high resolution remote sensing imagery.

Evapotranspiration（ET） is one of the most important term of land surface， which is an indicator of agriculture growth conditions and yield. Remote sensing technology has advantages in monitoring evapotranspiration， however， remote sensing evapotranspiration products develop slower than other remote sensing products. This paper compared seven widely used ET products and two ET systems， including MODIS-MOD16， SSEBop， ET_PT-JPL， ET_ALEXI， LSA-SAF， GLEAM， BESS， ETWatch and ETMonitor. We introduced the details of these ET products. In order to propose a framework of estimating high accuracy and widely used remote sensing ET products， and to better understand the complexity and uncertainty in ET estimation， we focused on comparing the design ideas and processing procedure of these ET products through summarizing algorithms of estimating actual ET， resistances parameterizations， available energy calculating methods， temporal upscaling methods， estimating procedures and input data sources， respectively. In addition， the current problems of ET calculating from remote sensing are proposed， including complex algorithms with multi parameters， inconsistent spatial and temporal resolutions， and difficult to be validated. Finally， we discussed possible directions of the future remote sensing ET products.

Ulan Buh Desert is one of Chinas major deserts.In recent years its rapid expansion has seriously affected the local ecological security.Desert vegetation is the most important ecological protection barrier in this region.Gaining the knowledge accurately of the distribution of vegetation is important.Calculated NDVI,and integrated principal component analysis combined with Gray Level Co\|occurrence Matrix texture analysis to analysis the ALOS image in the reserch area.Using NDVI and mean texture as the classification indices,the article determined the appropriate threshold range,and abstracted the vegetation information by using the decision tree method.The result shows that the decision tree method could use texture and other auxiliary information effectively,and achieve better classification results compared with traditional classification method.