最新刊期
卷 28 ， 期 8 ， 2024
- “在遥感信息提取领域，极化SAR技术的研究取得显著进展。本文综述了极化SAR地物分类方法及其在海面溢油探测、舰船检测等任务上的应用，为相关研究提供了新方向。”
摘要：Remote sensing technology enables us to monitor the Earth from space and sense the rhythm of rivers, lakes, and seas and the pulse of social and economic development in real time. It also facilitates effective early warning, prevention, and evaluation of natural disasters, in which SAR technology plays an increasingly important role. Remote sensing image classification is an important step of remote sensing image analysis, and it has always been one of the hot spots in related research fields. Owing to the complexity of ground target characteristics and the diversity of remote sensing imaging techniques, the accurate interpretation of remote sensing images requires a deep understanding of the characteristics of the image and fully utilizing the prior knowledge of ground objects. In recent years, the development of Synthetic Aperture Radar (SAR), especially polarimetric SAR technology, has facilitated the rapid growth in the research on remote sensing object classification. In this study, the research progress of polarimetric SAR remote sensing image classification is reviewed. This study firstly introduces the basic theory of SAR remote sensing and the main data sources of spaceborne SAR. Then, it introduces the decomposition of polarimetric SAR data, the classical machine learning algorithms for polarimetric SAR, the deep learning-based algorithms, the methods of fusing optical and SAR images, and the classification algorithms based on compact polarimetric SAR. Next, this study introduces the research progress of polarimetric SAR image classification for marine oil spill detection, ship detection, coastline extraction, land use classification, and sea ice/ice cap classification. Finally, the development trend of polarimetric SAR image classification is prospected. From the perspective of the authors, the development of polarimetric SAR classification has the following trends: (1) from single polarimetric to multi- and compact polarimetric SAR modes; (2) from medium/low resolution, small range to high resolution, large range remote sensing applications; (3) from single temporal to multiple temporal sequence image analysis applications; (4) from manual design of feature extraction methods to automatic feature extraction using deep learning models; (5) from single-source SAR image classification to SAR, optical, LiDAR, and other multi-source image fusion classification. The key technologies of radar signal processing, image analysis, pattern recognition, multi-source information fusion, big data analysis, and other aspects need to be understood to fully utilize the information provided by polarimetric SAR data sources. The rapid development of technology requires talents with interdisciplinary backgrounds such as electronic engineering, remote sensing, and artificial intelligence in this field. The authors hope that through the introduction of this article, readers can improve their understanding of the field of SAR remote sensing classification to a certain extent for better grasping the development trends of this technology.关键词：polarimetric SAR;remote sensing;classification;multi-source information fusion;feature extraction;machine learning;object detection;scattering characteristics- 83
- |
- 50
- |
- 0
发布时间：2024-08-28 - “多源遥感技术在森林火情监测领域取得显著进展，为火灾防控提供低成本、高效率的解决方案。”
摘要：In the past decades, remote sensing methods of forest fire monitoring were mainly ground patrol, visual interpretation of aerial images, and remote sensing satellite observation with low spatial and temporal resolution. Nowadays, mobile measurement backpack system, light and small UAV, image fusion technology with high spatial and temporal resolution, and near real-time data sharing platform are driving remote sensing into broader forest fire application scenarios. The spatiotemporal-spectral resolution of a single data source is difficult to improve simultaneously as restricted by satellite orbit, observation mode, and sensor performance. The monitoring results may also be constrained by environmental factors such as cloud and rain. This condition leads to reduced monitoring accuracy and inability to collect reliable and detailed fire data to meet the emergency needs of fire location and continuous monitoring. Determining the spatial and temporal characteristics of forest fires is important for disaster prevention and control. A large number of new-generation sub-meter satellite platforms and sensors are currently used, and intelligent remote sensing inversion methods are constantly optimized. With the support of these technologies, the current fire monitoring capability based on multi-source remote sensing methods has the advantages of low cost, near real-time performance, multi-scale, wide coverage, and high precision. The monitoring, analysis, and continuous tracking of forest fires with multi-source remote sensing data can provide effective prediction and evaluation for forest fires.In general, in pre-fire, based on traditional fire risk factors such as meteorological, topographical, and human factors, multi-source remote sensing data and inversion optimization algorithm of fuel parameters are used to provide more accurate three-dimensional characteristic information of vegetation, including fuel moisture content, canopy height, and forest biomass. In during-fire, the accuracy and timeliness of a single remote sensing data source need to be improved due to spatial and temporal heterogeneity of ground objects. Matching the spatial and temporal domains between polar-orbiting meteorological satellite fire detection results and the geostationary satellite fire intensity monitoring results can make up for the shortcomings of a single remote sensing data source and realize dynamic monitoring of forest fires with high spatial and temporal resolution. Satellite monitoring is limited by revisit cycles and dense cloud cover in some cases. This problem can be effectively solved by data complementation, fusion, or using airborne or ground platform monitoring. Fire intensity monitoring results can also be used as dynamic input data for biomass burning in atmospheric dispersion models, which provides the basis for fire emission dispersion simulation. In post-fire, optical, radar, and LiDAR data can be combined to improve the ability to gauge environmental changes caused by fire.For the rapid development of multi-source remote sensing technology, this study summarizes current fire risk assessment, fuel parameter inversion, fire detection, fire behavior analysis, burned area identification, fire intensity evaluation, and vegetation recovery monitoring. In general, future research is expected to be based on the synergy of multi-source remote sensing technologies. This synergy can be made through the optimization and integration of new remote sensing analysis methods to further understand fire patterns and improve fire monitoring ability.关键词：multi-source remote sensing;forest fire;image fusion;fire risk assessment;fire detection;fire emissions;fire environment;fire damage- 31
- |
- 12
- |
- 0
发布时间：2024-08-28 - “临边散射卫星遥感技术为平流层臭氧监测提供数据支持。20余年来，OSIRIS、SCIAMACHY、OMPS和OMS等载荷搭载于多颗卫星，通过正向模型和反演算法，可获取O3、NO2、BrO廓线及平流层气溶胶和云信息。本文综述了该技术进展，为我国临边探测技术发展提供参考。”
摘要：The satellite-based limb scattering measurement technique has provided valuable datasets for long-term dynamic monitoring of stratospheric ozone and ozone-related atmospheric components, such as NO2 and BrO. Since 2001, the development of this field has spanned more than 20 years. We summarize the principles and applications of this technique while analyzing the associated problems. This information provides a reference for the development of domestic limb scattering detection technology.Since the OSIRIS onboard the Odin satellite platform, followed by SCIAMACHY onboard the ENVISAT platform, OMPS onboard the Suomi NPP, and NOAA-21 platform, as well as OMS-L onboard the FY-3F platform, all include limb scatter detection capability. Based on the design specifications of the payloads, such as wavelength coverage, spectral resolution, signal-to-noise ratio, and instrument response function, with a Radiative Transfer Model (RTM) capable of simulating the observed limb radiance at a series of tangent heights, O3, NO2, and BrO profile, stratospheric aerosol, and cloud information can be retrieved from limb scattering spectra. In this study, we reviewed the development of satellite-based limb scattering technique, including instruments characteristics, RTM, inversion algorithms, products, and applications.In terms of forward models, the simulation of limb scattering needs to consider atmospheric scattering (single and multiple scattering), refraction, aerosol parameterization schemes, and instrument characteristics under full spherical atmospheric conditions. In the aspect of retrieval algorithms, the wavelength shift correction, pointing information correction, and stray light correction are needed to construct observation vectors for atmospheric parameters. The retrieved parameters have played an important role in analyzing stratospheric ozone dynamics and its related nitrogen oxides (e.g., NO2) and halogen (e.g., BrO), as well as monitoring stratospheric clouds and aerosols.Overall, limb scattering satellite remote sensing technology can provide 2—3 km vertical resolution and nearly global coverage detection capability due to its advantages in sampling frequency and observation geometry. However, limb scattering technology still has some unresolved problems. For the forward model, a fast RTM specific for limb scattering sensors is critical to meet the needs of operational application. In addition, limb scattering sensors all have the problem of pointing information error and the subjectivity of the field of view to the pollution of stray light. Accurate laboratory calibration and further analysis of the source of pointing error are effective ways to correct the influence of stray light and the registration error of tangent height. Meanwhile, accurately characterizing the aerosol characteristics and cloud top height on the limb path are also key steps to reduce inversion uncertainty. This study can facilitate the development and application of domestic limb scattering detection technology.关键词：limb scattering;stratospheric ozone;RTM;Inversion algorithm;application progress- 56
- |
- 49
- |
- 0
发布时间：2024-08-28
Reviews
- “最新研究揭示了中国27个省会城市地表与冠层热岛日内逐时变化特征差异，为深入理解城市热环境垂向空间多层次演变规律提供了重要依据。”
摘要：Investigations into the diurnal evolution differences between surface urban heat islands and canopy urban heat islands (termed Is and Ic, respectively) hold great values in enhancing our comprehension of the vertical structure of urban climates at a fine time-scale. However, the hourly surface air temperature (Ta) from densely distributed weather stations within cities and the hourly land surface temperature (Ts) that possesses a relatively high spatial resolution and that can be employed for monitoring thermal conditions of urban surfaces are largely lacking. Previous studies comparing hourly Is and Ic have mostly focused on individual cities. In this work, we utilize hourly Ta measurements from high-density meteorological stations (1544 stations) and Ts observations derived from a Diurnal Temperature Cycle (DTC) model to examine the hourly Is and Ic and the associated hourly differences (quantified as ΔUHI, calculated by subtracting Is from Ic) over 27 Chinese megalopolises. Furthermore, we analyze the hourly patterns of ΔUHI (e.g., maximum ΔUHI, minimum ΔUHI, and duration of ΔUHI > 0) across cities with different climate backgrounds and city sizes. We obtain the following findings: (1) at the national scale, the annual mean ΔUHI remains positive throughout the diurnal cycle. The hourly ΔUHI pattern generally exhibits a peak shape, with the ΔUHI increasing from morning and reaching its maximum (1.7 ℃) at around 4:00 PM. Subsequently, it gradually decreases and reaches its daily minimum (0.1 ℃) at around 2:00 AM, with the most rapid decline occurring around sunset. (2) Across different climate zones, from subtropical to temperate cities, the maximum and minimum ΔUHIs follow a decreasing trend, the times at which they occur are gradually delayed, and the duration of ΔUHI greater than 0 ℃ gradually decreases. (3) For cities with different sizes, the variation magnitude of ΔUHI curve generally decreases and the time of minimum ΔUHI advances as city size increases. The duration of ΔUHI greater than 0 ℃ also increases with city size. This study can promote the understanding of the contrasting patterns between hourly differences in surface urban heat islands and canopy urban heat islands across cities with diverse background climates. The research results contribute to a deeper understanding of the vertical spatial characteristics of urban heat islands at a fine time scale.关键词：Surface urban heat island;Canopy urban heat island;Diurnal variation;Climate zone;City size;thermal infrared remote sensing- 202
- |
- 150
- |
- 0
发布时间：2024-08-28 - “南京市江宁区居民区植被降温效率研究取得新进展，专家利用无人机数据建立区域绿地容积率指标，提出改进的计算方法，为城市微热环境改善提供科学依据。”
摘要：Most previous studies on the cooling efficiency of urban vegetation are based on large-scale satellite remote sensing data, which leaves a gap in fine-scale investigations at the micro-level. Therefore, this study took the vegetation of typical residential areas in Jiangning District of Nanjing City as the research object. Visible light data from Unmanned Aerial Vehicles (UAVs) were used to obtain the fine classification of residential green space, and the Regional Green Plot Ratio (RGPR) index was constructed.We proposed an improved calculating method of vegetation cooling efficiency, that is, Regional Cooling Efficiency (RCE). RCE is based on hour-by-hour UAV thermal infrared data. We applied this method to calculate the cooling efficiency of vegetation in residential areas. This way enabled us to investigate the response relationship between the surface temperature of different local climate zones in residential areas and the RGPR indicator. The optimal thresholds of cooling efficiency for different RGPRs in residential areas could also be determined.Results showed that (1) the daily variation curve of the cooling efficiency of vegetation in the residential area exhibited a “peak” pattern, and the RCE increased with the improvement in solar radiation. During the observation period, the minimum value of RCE appeared at 8:00 a.m. (all lower than 1.0 ℃), and the maximum value appeared at 14:00 p.m. (all higher than 1.4 ℃). At the level of local climate zone, the RCE of compact residential areas was higher than that of open residential areas. In the case of open residential areas, the RCE decreased with the increase in the average height of the buildings in the area. (2) Under compact local climate zone, the higher RGPR meant a stronger cooling effect brought by vegetation. Meanwhile, in open residential areas, regardless of the height of the building, a certain RGPR threshold was observed for the cooling efficiency of vegetation. When the RGPR reached a certain threshold, the cooling efficiency of the RGPR on surface temperatures reached its maximum intensity.Overall, this study proposes a method for calculating the cooling efficiency of vegetation in residential areas at the micro-scale for high-resolution UAV data. The cooling efficiency of vegetation and the optimal cooling efficiency thresholds for different types of residential areas at the level of localized climate zones are investigated. This study can help improve the urban thermal environment and provide more specific and scientific theoretical guidance and support for enhancing urban sustainability.关键词：Urban residential areas;Microthermal environment;Regional Cooling Efficiency (RCE);Local Climate Zone;regional green plot ratio (RGPR);UAV thermal infrared remote sensing;Cooling efficiency threshold;Green space planning in urban residential areas- 99
- |
- 147
- |
- 0
发布时间：2024-08-28 - “最新研究揭示，荧光光谱技术可有效识别城市黑臭水体中溶解有机质的污染源，为治理提供科学依据。”
摘要：The research presented in this paper explores a method for identifying pollution sources in urban black-odor rivers using UAV hyperspectral imaging technology. Excessive Dissolved Organic Matter (DOM) is a primary cause of black-odor water bodies, and fluorescence spectroscopy can identify the fluorescence characteristics and composition of DOM, providing insights into pollution sources. The study focuses on developing a model based on the fluorescence characteristics and optical properties of DOM to distinguish between different types of pollution sources, such as domestic sewage, industrial wastewater, and mixed wastewater.Key findings of the research include the identification of the fluorescence peak integral ratio (IA∶IT) as a reliable indicator for tracking dynamic changes in DOM components in urban rivers. This ratio proved to be more effective than other fluorescence indices. By determining the threshold values for IA∶IT, the study categorizes pollution sources in heavily polluted water bodies into three types. The developed model, which incorporates remote sensing reflectance and the absorption coefficient of CDOM at 275 nm (aCDOM(275)), is validated using UAV hyperspectral images from the Tunliang River in Nanjing. The results show that the identified pollution sources in the river section are consistent with actual field investigations.The methodology involves extensive data collection from several industrialized cities in Jiangsu Province, China, including Nanjing, Wuxi, Changzhou, and Yangzhou. Parameters such as dissolved oxygen, oxidation-reduction potential, and remote sensing reflectance were measured. UAV hyperspectral data was collected using a hyperspectral imager with a spectral range of 400—1000 nm and 270 spectral channels. The study employed three-dimensional fluorescence spectroscopy to analyze the fluorescence characteristics of DOM and used Parallel Factor Analysis (PARAFAC) to decompose the fluorescence data into individual components.Statistical methods were utilized to establish relationships between fluorescence indices and water optical properties. Linear models were developed to predict IA∶IT based on the absorption coefficient aCDOM(275) and remote sensing reflectance ratios. The models were validated and applied to UAV hyperspectral imagery to classify sections of the river based on their primary pollution sources.In conclusion, the study demonstrates the effectiveness of combining UAV hyperspectral imagery with fluorescence spectroscopy to identify pollution sources in urban black-odor rivers. The developed models provide a robust method for monitoring and managing water quality in urban environments, offering a promising approach for pollution source identification and water body management. The findings emphasize the potential of this technology to aid in the effective management and remediation of polluted urban waterways, highlighting the importance of accurate pollution source identification for sustainable urban development and water quality improvement.关键词：urban black-odor water bodies;fluorescence index;optical features;pollution source identification;UAV hyperspectral imagery;industrial sewage;absorption coefficient of CDOM- 16
- |
- 2
- |
- 0
发布时间：2024-08-28 -
Object units and Transformer networks combined with urban functional zone classification method AI导读
“在城市规划领域，一项新研究提出了一种结合对象单元和Transformer网络的城市功能区分类方法，有效提升了城市功能区分类精度，为城市合理规划和科学管理提供了解决方案。”
摘要：Urban Functional Zones (UFZs) refer to specific areas within a city that have distinct functionalities and land uses. These zones are designated based on their primary activities and the roles they play in the urban environment. Accurate extraction of UFZs and a comprehensive understanding of their spatial distribution play an important role in urban planning and management. Traditional Convolutional Neural Networks (CNNs) focus on local features through convolutions, but they often miss the broader spatial relationships. Vision Transformer (ViT), while advanced, still has limitations; its tokenization method and learnable position encoding do not effectively represent geographical entities and their spatial relationships, which is a crucial feature in geospatial analysis.This study proposes a UFZ classification method combining object units and ViT to address this issue. First, this method utilizes over-segmented objects generated from a multi-scale segmentation approach as analysis units to avoid the presence of multiple kinds of UFZs within a single object. Over-segmentation helps in creating smaller, more homogeneous units, thereby increasing the precision of the classification process. Then, considering that current methods often focus on the inherent analysis of objects while ignoring their spatial relationships, ViT is employed for spatial relationship modeling between objects, with the geographic attributes of objects serving as position embeddings. In this way, both the inherent features of a single analysis unit and the inter-spatial features among objects are considered for UFZ classification. Position embeddings using geographic coordinates allow the model to understand spatial proximity and relationships, which are crucial for accurate classification. We chose Beijing as the study area and downloaded imagery of the area within the Sixth Ring Road from Bing Maps. We also collected labels from OpenStreetMap and reclassified them into 10 typical urban functional zones according to the “Code for classification of urban land use and planning standards of development land (GB 50137-2011)”. This dataset provided a comprehensive and diverse set of examples that are representative of different urban functionalities.Experimental results show that, firstly, compared with the results of existing methods, over-segmented objects can improve boundary accuracy. This enhancement avoids the jagged boundaries resulting from grid units and the presence of multiple UFZs within a single unit due to road-block units. The improved boundary accuracy ensures that the functional zones are delineated more precisely, reflecting true urban layouts and reducing classification errors. Secondly, the accuracy of UFZ classification increases by 13.9% compared to the method that employs objects as analysis units while ignoring their spatial relationships. This significant improvement highlights the importance of considering spatial relationships in UFZ classification. Additionally, the traditional position encoding method achieved similar accuracy to the method without position encoding, indicating that traditional position encoding does not effectively provide positional information. The kappa coefficient of the proposed method, which uses geographic coordinates for encoding, shows an average improvement of 0.042 compared to the traditional Transformer position encoding method. This demonstrates that the introduction of geographic coordinates can effectively provide spatial relationship information, leading to better classification results. The kappa coefficient is a measure of classification accuracy adjusted for chance agreement, and an improvement in this metric underscores the robustness of the proposed method.关键词：urban functional zone;remote sensing;deep learning;spatial relationship modeling;transformer networks- 160
- |
- 231
- |
- 0
发布时间：2024-08-28
Urban Remote Sensing
- “最新研究利用甲烷卫星观测和大气传输模型，对全球甲烷排放清单进行校核，发现中国、印尼排放量低于先验清单，俄罗斯、印度、孟加拉国排放量高于先验清单。该技术为温室气体排放校正提供了新方向。”
摘要：Methane is a strong greenhouse gas and is responsible for roughly a quarter of radiative forcing since industrialization. Reducing anthropogenic methane emissions is considered a vital measure to slow down climate change. Conventional “bottom-up” inventories, which are compiled based on emission factors and activity data, are subject to large uncertainties. Atmospheric observations such as those made by satellite instruments provide a useful “top-down” approach for verifying and improving emission inventories. This study aims to present the application of satellite observations to the verification of methane emission inventories through forward simulations and inversion analyses.We first presented varied types of satellite observations (short-wave infrared vs. thermal infrared observations, area-flux mappers vs. point-source imagers) and retrieval methods (CO2-proxy method vs. full-physics method) and their suitability for inventory verification. We then extensively discussed the role of chemical transport models in interpreting satellite observations, the method of properly comparing model methane simulations to satellite column observations, and the effects of simulation errors on emission inventory verification. Finally, we demonstrated the two-stage verification with forward simulations and then an inverse analysis for global methane emissions. They were conducted using the GEOS-Chem chemical transport model, GOSAT methane column observations for 2020, and two sets of bottom-up emission inventories.In the forward simulation, we developed a bias correction procedure to reduce the season- and latitude-dependent systematic biases. The bias-corrected simulation results were compared with satellite column observations to qualitatively assess the quality of emission inventories. In the inverse analyses, we presented the quantification of emissions for top methane-emitting countries. Results showed that the prior inventory significantly overestimated methane emissions in China and Indonesia, underestimated those in Russia, India, and Bangladesh, and statistically agreed with the rest. Finally, an outlook for the application of satellite observations in the field of methane monitoring was provided, and the direction for future technical developments was discussed.This study demonstrated the potential of using satellite observations to verify methane emission inventories. While the forward simulation is a fast but qualitative method, the inverse analyses provide a more quantitative verification of emission inventories.关键词：remote sensing;Greenhouse gas;methane;satellite;carbon emissions;Inventory verification;inversion- 272
- |
- 248
- |
- 0
发布时间：2024-08-28 - “最新研究进展显示，地基遥感技术在监测大气温室气体方面取得显著成果。COCCON项目利用便携式傅里叶光谱仪，通过高精度反演算法PROFFAST，有效补充全球碳柱总量观测网。项目在卫星遥感反演验证、城市排放、煤矿区排放、畜牧业排放等领域取得突破，为中国城市及煤矿区甲烷监测提供新思路。”
摘要：Ground-based remote sensing technology is an effective tool for monitoring atmospheric greenhouse gas column concentrations to calibrate satellites and study carbon sinks. The COllaborative Carbon Column Observing Network (COCCON) is a global network of portable Fourier transform infrared (FTIR) spectrometers EM27/SUN and is compact and mobile. The COCCON instrument is appropriate for field campaigns and for long-term deployment at a site which effectively complements the Total Carbon Column Observing Network (TCCON). In addition, its excellent robust and reliable characteristics have been demonstrated in several successful field campaigns. Karlsruhe Institute of Technology (KIT) conducts optimization by subjecting each unit to an expert performance assessment and fine-tuning. KIT also calibrates it in reference to the EM27/SUN spectrometer utilized at KIT and the TCCON site in Karlsruhe. KIT, as part of European Space Agency initiatives, has developed open-source and freely accessible codes for processing and analyzing COCCON measurement spectra.In this study, we introduce the Instrumental Line Shape (ILS), which is a critical factor for obtaining precise information from measurements. The ILS parameters can be derived by conducting an open-path observation of a few meters of lab air. In addition, the high-precision inversion algorithm called PROFFAST is proposed. This algorithm is designed for the retrieval of column-averaged dry-air mole fractions of gas (Xgas). This work also presents various studies on satellite validation and methane emission from urban areas, coal mines, and livestock using COCCON methane observations. Furthermore, we provide preliminary applications of COCCON instruments in monitoring methane levels within urban region (Xuzhou) and coal mining region (Shanxi) in China. Initial analyses have revealed that methane concentrations in the coal mines under study in China surpass those observed in European contexts. These elevated concentrations are closely related to variations in mining activities in different coal mines. Subsequent findings are currently being prepared for further publication.Considering that the COCCON network is mainly applied in western countries, developing region-specific ground-based remote sensing inversion techniques is necessary for assessing EM27/SUN methane emissions in China. This urgency stems from the complex nature of emission sources and high-concentration pollution scenarios unique to China. Two different aspects should be considered: dual influences of diffusion and transport, such as mutual diffusion effects among multiple coal mine emissions; the effects of complex atmospheric boundary layer changes under aerosol–atmospheric boundary layer interactions.关键词：greenhouse gases;ground-based remote sensing;fourier transform infrared spectrometer;EM27/SUN;COCCON;methane;coal mine emissions- 284
- |
- 206
- |
- 0
发布时间：2024-08-28 -
摘要：Methane (CH4) is the second most important greenhouse gas in the Earth’s atmosphere, after carbon dioxide (CO2). Understanding the change in CH4 concentration is a challenging task in atmospheric research given that it has various sources. Remote sensing has now become an effective technique to monitor CH4 concentrations globally. In this study, we presented an overview of CH4 column retrievals based on ground-based Fourier Transform Infrared spectrometer (FTIR) and space-based infrared measurements. Satellite validations were also discussed.Currently, three ground-based remote sensing international observation networks provide CH4 columns: the Total Carbon Column Observing Network (TCCON), the NDACC-IRWG (Network for the Detection of Atmospheric Composition Change - the Infrared Working Group), and the COCCON (COllaborative Carbon Column Observing Network). The main characteristics of the three networks were presented and discussed in our study, such as the measurement instrument, the observed spectra, the retrieval algorithm, and the post-correction. TCCON and COCCON provide dry-air column-averaged mole fraction of CH4 (XCH4) measurements, with a systematic/random uncertainty of 0.1/0.5%. NDACC provides a total column of CH4, with a slightly large systematic/random uncertainty of 0.2/1.0%. However, it also provides a vertical profile of CH4, which allows us to observe the CH4 variations in the troposphere and stratosphere separately.Regarding the satellite CH4 retrievals, we compared several popular sensors with a nadir-view geometry and their retrieval algorithms, such as the TANSO-FTS/GOSAT, TROPOMI/S5P, IASI/MetOp, and AIRS/Aqua. Basically, the short-wave infrared measurements (GOSAT and TROPOMI) have more sensitivity to the low troposphere, while the thermal infrared measurements (IASI and AIRS) are mainly sensitive to the mid- and upper troposphere. The difference in their vertical sensitivity comes from the CH4-specific absorption lines in the infrared region. All satellite retrievals are affected by the cloud, aerosol, and surface parameters. They also need to be validated and calibrated against ground-based measurements. Here, key steps during the satellite CH4 validation were discussed, including the statistical parameters, the a priori substitution, the smoothing correction, and the surface altitude correction.Finally, we showed the CH4 retrievals observed by the ground-based FTIR system at Xianghe, North China. We operated TCCON-type and NDACC-type measurements for the Bruker 125HR instrument and COCCON-type measurements for the Bruker EM27/SUN instrument. The entire FTIR measurement system at Xianghe was well described. Then, we used the TCCON- 377
- |
- 162
- |
- 0
发布时间：2024-08-28 - “最新研究利用国产高分五号02星和欧空局哨兵五号星遥感数据，成功检测并量化了中美两国煤矿和油气设施的甲烷排放点源，为全球能源行业甲烷泄漏检测提供重要数据支撑。”
摘要：Rapid identification of anomalous methane sources in the fossil fuel industry would enable action to reduce greenhouse gas emissions. Spaceborne hyperspectral imaging spectrometers have recently been shown to be instrumental for this mission. In this study, we utilize the rapid development of spaceborne imaging spectroscopy technology and data processing methods to perform a satellite-based large-scale and high-resolution survey of methane super-emitters in China and the United States. Our dataset is acquired by the Advanced Hyperspectral Imager (AHSI) onboard domestic GF-5 02 satellite (i.e., GF-5 02AHSI) and TROPOspheric Monitoring Instrument (TROPOMI) onboard European Space Agency's Sentinel-5 Precursor satellite (Sentinel-5P). Our core objective is to identify, quantify, and assess uncertainty of methane point emissions from coal mines and oil/gas facilities in China and the United States, with the overarching motivation of assisting future emission reduction efforts.Findings demonstrate the potential of GF-5 02AHSI in remote sensing identification and estimation of global methane point emissions, which can provide important data support for future methane leak detection in the global energy industry.Major steps include the following(1) We retrieve methane concentration enhancements (i.e., increments above background levels in the amount of methane present in the atmospheric column, ∆- 836
- |
- 1055
- |
- 0
发布时间：2024-08-28
Remote Sensing of Atmospheric Methane
- “最新研究揭示了中尺度涡对海气界面调制机制的新见解，利用遥感数据和分析方法，系统探究了海表特征参数的相关性分布，为海洋数值模拟提供了理论支持。”
摘要：Mesoscale eddies are broadly distributed in the global ocean and have significant effects on the Sea Surface Temperature (SST), Sea Surface Height (SSH), Chlorophyll (Chl), Wind Speed (WS), and other ocean parameters. Accordingly, the coupling analysis of mesoscale eddies and ocean key parameters is an important aspect of ocean research. The Anti-cyclonic Eddy (AE), Cyclonic Eddy (CE), and Outside Eddy (OE) can be better distinguished with the realization of the individual eddy identification technology. Accordingly, we can comprehensively study the distribution and difference of correlation of sea surface features by comparing OE with AE and CE, providing a theoretical basis for further clarifying the modulation mechanism of the mesoscale eddy on the air‒sea interface and improving ocean numerical simulation. This study calculated the Pearson correlation coefficient using Sea Surface Temperature Anomaly (SSTA), Sea Level Anomaly (SLA), Chlorophyll Anomaly (CHLA), and Wind Speed Abnormal (WSA) data from 2010 to 2019 through distinguishing AE, CE, and OE and compared the smoothness of the correlation coefficient. Results show that the correlation distribution among the parameters has significant regional characteristics. In CE and AE, the correlation is ±0.5 in most areas of the ocean and ±0.7 in the Northern Indian Ocean and the Equatorial Pacific. In OE, the correlation is ±0.2 in most regions and ±0.4 in the Northern Indian Ocean and the Equatorial Pacific. In addition, when a positive (negative) correlation occurs in OE, it generally shows a large range of positive (negative) correlation, and a noticeable transition region exists between the two. However, the extreme correlation regions are smaller and mainly present as scattered points under the influence of eddy, and the transition regions between the positive and the negative regions are narrow. The smoothness coefficient of the correlation coefficient of each parameter in OE is about 15, while those of AE and CE is about 450, which is much lower than that in the OE area. We conclude that the influence of eddy on the correlation of each parameter is mainly reflected in the value, distribution, and smoothness of the correlation coefficient. The correlation coefficient of each parameter in OE is about 0.2 lower than that in CE and AE, and the smoothness of the correlation coefficient in OE is about 30 times that in AE and CE. The correlation distribution also has a strong point feature in the original distribution mode due to the modulation of the eddy.关键词：Remote sensing observation;mesoscale eddy;coupling analysis;sea surface temperature;sea surface height;Chlorophyll concentration;wind speed- 10
- |
- 3
- |
- 0
发布时间：2024-08-28 - “最新研究揭示了一种利用遥感数据和深度学习技术实现农业种植结构精准制图的方法，为精细化农业应用提供了新的解决方案。”
摘要：Agriculture is an important component of the national economy. Obtaining the spatial distribution information of crops accurately is the basis of precision agricultural application. In this paperstudy , we explore the complementary advantages of remote sensing data from different sources in terms of spatial and temporal characteristics, and. We also design a remote sensing mapping method of agricultural planting structure based on spatial and temporal collaboration. The cropland-parcel extracted from high spatial resolution remote sensing images of high spatial resolution was is taken as the basic unit, and the spectral time sequence information of multi-temporal remote sensing images was is combined. With the support of deep learning, crop classification and identification at cropland-parcel scale and precise mapping of planting structure can be realized, and the spatial distribution characteristics of main crops can be analyzed. The eExperimental results in Yellow River Irrigation Area of Ningxia (YRIA-NX ) show that: (1) A a total of 1.49 million cropland-parcels were are obtained in the study area, with a total area of about 540,000 hm2, and the overall classification accuracy was is 0.80; (2) Compared with the traditional mapping unit and machine learning method, the crop planting structure information obtained by the Bi-LSTM network based on the cropland-parcel scale is more consistent with the actual agricultural tillage management unit, and the classification accuracy can be guaranteed higher; (3) The maize, rice, wheat, and vegetables are the main crops of in the study area. Maize is the most dominant crop with the largest planting area and the most extensive spatial distribution. The vegetable fields are mainly concentrated and distributedion in Yongning Town and Qingtongxia Town. Rice is concentrated in areas with convenient irrigation, while wheat planting area on a large scale is less. The planting of other crops after wheat harvest in summer is mainly concentrated in the Qingtongxia, and the multiple cropping index decreasesd gradually from south to north.关键词：remote sensing;crop planting structure;spatial-temporal collaboration;deep learning;cropland-parcel;NDVI time series;Yellow River Irrigation Area of Ningxia (YRIA-NX)- 149
- |
- 156
- |
- 0
发布时间：2024-08-28 - “在南黄海浒苔灾害监测领域，中国专家提出了一种基于Sentinel-1影像的自动提取方案，实现了2021年5—7月区域的动态监测，为灾害评估和防控策略制定提供了重要信息。”
摘要：The recurring Ulva prolifera disasters in the South Yellow Sea region of China during summers significantly impact the environment, ecology, and economy. To address this issue, accurate spatial and temporal distribution information of Ulva prolifera needs to be obtained for quantitative assessment of the disaster and development of effective prevention and control strategies. Optical remote sensing images provide spatial information on Ulva prolifera at a regional scale. However, they are limited by low spatial resolution and the influence of clouds and rain. Thus, regular and stable monitoring of Ulva prolifera is challenging. By contrast, synthetic aperture radar images provide all-weather, all-day observation, which enables more possibilities for dynamic monitoring and research on the spatial and temporal distribution of Ulva prolifera. To this end, this study proposes an automatic extraction process for Ulva prolifera using the backscattering coefficient and standard deviation features, supported by Sentinel-1 images. The method involves an adaptive thresholding approach to segment the image. The seawater with higher backscattering coefficient is excluded to obtain the initial extraction result of U. prolifera, which is based on the difference between edge Ulva prolifera and seawater in the standard deviation of backscattering coefficient. The post-processing schemes are designed based on temporal information and backscattering coefficient thresholds for targets with similar characteristics to Ulva prolifera in the sea according to different types. The proposed method is employed to monitor the dynamic distribution of Ulva prolifera in the South Yellow Sea region from May to July 2021 by using the Google Earth Engine platform. Results show that the accuracy of the extraction method reaches 93%, and the maximum coverage of Ulva prolifera observed in the South Yellow Sea region in 2021 was over 1700 km2. The analysis reveals an overall trend of “scattered development, aggregation outbreak, and diffusion extinction” in the process of Ulva prolifera development.关键词：remote sensing;Ulva prolifera;Sentinel-1 images;spatiotemporal variation;Google Earth Engine;South Yellow Sea Region- 10
- |
- 3
- |
- 0
发布时间：2024-08-28 - “在卫星遥感领域，科学家们提出了一种新的太阳反射波段传递定标方法，通过精确匹配条件，将总传递定标不确定性控制在1.5%以内，为卫星数据定量化应用提供重要保障。”
摘要：The stability of Pseudo-Invariant Calibration Sites (PICS) contributes significantly to the improvement in calibration accuracy. The number of PICS is increasing as the work continues to advance. Therefore, the frequency of cross calibrations based on dessert sites has been significantly increased. Establishing a generic site-based cross calibration and uncertainty analysis method is necessary to confirm calibration uncertainties for different sites.Our study aims to improve the overall accuracy of satellite remote sensor observations by developing a cross calibration method over desert sites. In this study, a cross calibration and uncertainty assessment scheme aiming at solar bands is described, and the optimal matching scheme of the cross calibration is given by sensitivity analysis of the uncertainty.With image data of Libya sites from MODIS and MERSI-II, the main uncertainty contributors are found in the geometric, temporal, spatial, and spectral domains. For the four aspects, the uncertainty analysis model is independently constructed using the atmospheric radiative transfer model and the bi-directional reflectance distribution function. The sensitivity of each matching condition to the effect of uncertainty is multiplied and simulated by Monte Carlo method.The geometric and atmospheric distribution patterns of satellite matching data are summarized, which is conducted through statistically analyzing the matching data of MODIS and MERSI-II over Libya sites in 2020. The probability distribution density of the matching condition is used as the input condition, and the discrete distribution of the relative deviation of Top-Of-Atmosphere (TOA) reflectance is obtained by the uncertainty analysis model. The standard deviation of the distribution of relative deviations of TOA reflectance is statistically considered the standard uncertainty. After independent analysis of each factor of uncertainty, the total uncertainty is obtained by the Root-Sum-Squared method.The total uncertainty of each channel could be controlled under 1.5% (at ) when the difference in sensor zenith angles between the two remote sensors is than ±7°, the difference between the solar zenith angles is less than ±6°, the aerosol thickness is less than 0.39, and the uniformity of the observation site is less than 0.02. The results between the MODIS reflectance and the digital number recorded by MERSI reveal a good linear relationship. This cross calibration result also has an accuracy in the range of 0.5%—1.5% for each band compared with operational calibrations. Although we only applied the algorithm to MERSI-II as a demonstration, our algorithm is applicable to other sensors with few modifications.关键词：remote sensing;uncertainty;cross calibration;Monte Carlo method;reflection band;medium resolution imaging spectroradiometer- 651
- |
- 157
- |
- 0
发布时间：2024-08-28 - “在北极海冰漂移研究领域，本文通过比较SIFT、SURF、ORB、A-AKZE四种特征匹配算子，提出了一套高效准确的错误矢量滤除算法，并通过MOSAiC浮标定位数据验证了算法的精度。实验结果表明，基于A-KAZE算子的算法在提取结果的数量与分布上优于其他算子，且具有更高的空间分辨率及更大的覆盖范围。”
摘要：Sea ice drift is an important natural phenomenon in the Arctic, and it is important for climate research and human activities such as shipping security in the Arctic area. At present, sea ice drift products are often derived with space-borne radiometers and scatterometers with the template matching algorithm and suffer from low resolution and low accuracy. Sentinel-1 synthetic aperture radar imagery has high spatial resolution and holds great potential for deriving sea ice drift fields with high resolution and high accuracy by applying feature matching algorithms.This research compared sea ice drift results derived from four popular features including SIFT, SURF, ORB, and A-KAZE by using two pairs of Sentinel-1 Arctic sea ice SAR images. The similarities and differences between the performances of HH and HV imagery were also analyzed in terms of spatial distribution and coverage of the derived sea ice drift vectors. We proposed a filtering method combined with two published methods to identify incorrect vectors after the NNDR test with high calculation efficiency and accuracy. Finally, we evaluated the accuracy of sea ice drift vectors by comparing our derived results and DTU sea ice products with GPS data of MOSAiC buoys.Employing A-KAZE features to Sentinel-1 EW imagery can effectively derive sea ice drift fields with high spatial resolution and coverage rates. A-KAZE feature performs better than SIFT, SURF, and ORB in terms of spatial distribution and the number of vectors. Combining the vectors obtained from HH and HV polarization imagery can effectively extend the coverage of sea ice motion fields. The incorrect vector filter checks the similarity of a vector to its neighbors only if its speed or direction exceeds two times the standard deviation. It improves computational efficiency and retains more correct vectors than the two traditional methods. Validation with data of MOSAiC buoys found that the average speed error of sea ice drift vectors extracted using the proposed A-KAZE-based method was less than 0.2 km/d, and the average direction error was less than 1°. These products share a high consistency with DTU sea ice drift products obtained through employing Sentinel-1 SAR imagery but applying the template matching algorithm. However, our proposed methods presented a higher spatial coverage.This study demonstrates the potential of deriving sea ice drift vectors by applying dual-polarized Sentinel-1 SAR imagery and A-KAZE features. This approach can effectively and quickly generate sea ice drift vector fields of high spatial resolution with high spatial covering rates and high accuracy, which can serve as an accurate data source for climate research and maritime security in the Arctic.关键词：remote sensing;Arctic;Sea ice drift;Image feature tracking;A-KAZE;Sentinel-1- 749
- |
- 216
- |
- 0
发布时间：2024-08-28 - “在光谱解混领域，研究者提出了KGESU算法，通过知识图谱嵌入和先验知识引入，有效提升了端元选择的可靠性和解混精度。实验结果表明，该算法在真实图像解混中具有更好的效果，为光谱解混研究开辟了新方向。”
摘要：Selecting effective endmembers from a set of endmembers is important in the process of spectral unmixing. However, the selection of endmembers will be affected by the spectral variability of endmembers, which results in a certain uncertainty in the results of selection and the accuracy of unmixing. This study combines geoscience prior knowledge with sparse unmixing to solve this problem, and a Knowledge Graph Embedding Spectral Unmixing (KGESU) algorithm is proposed. While utilizing spectral features, certain prior knowledge is introduced to further improve the reliability of endmember selection.The implementation steps of the KGESU algorithm involve two issues the embedding training of geoscience knowledge graph and spectral unmixing with priori knowledge. The embedding training of geoscience knowledge graph transforms geoscience knowledge into a structured expression form through knowledge graph. Then, the TransE model is used for graph embedding. We perform knowledge reasoning according to the knowledge graph embedding to address the second issue. Then, a reasoning–weighting sparse unmixing algorithm is developed to integrate the process of reasoning and unmixing.Experiments are conducted to validate the effectiveness of the proposed method. The prior knowledge is instantiated with the aid of auxiliary data such as Landsat 8 and GDEMV2. The spectral unmixing data are GF-5 satellite data. The GF-2 data with a resolution of 1 m after graphic fusion are used for verification. Compared with the traditional pixel-by-pixel evaluation, this study expands the evaluation window. The sensitivity of different resolution images to registration errors is reduced by increasing the overlap area between pixels and allocating the residuals. The root mean square error of each endmember, the mean of the root mean square error of each endmember, and the overall root mean square error of the image are used as evaluation indexes to evaluate the unmixing results. Results demonstrate that the KGESU algorithm outperforms the state-of-the-art algorithms.By the guidance of geo-prior knowledge in the unmixing process, the uncertainty caused by factors such as data itself and external noise can be reduced. The ability to discriminate endmembers can be improved to a certain extent. At the same time, the method proposed in this study combines the advantages of knowledge reasoning and numerical computation. Furthermore, we use geoscience knowledge and spectral characteristics to select endmembers. The unmixing result can be more reliable. In the future, the research has the following issues that need further consideration. (1) In this study, a knowledge graph is constructed only from the perspective of land use classification, and prior knowledge is introduced. In the follow-up work, secondary and even more precise classification can be considered to highlight the advantages of hyperspectral data. (2) In the future work, we will consider more complex relationships between ground objects, introduce more abundant geoscience knowledge, and further build a more perfect geoscience knowledge graph. (3) Knowledge reasoning based on graph embedding is a relatively good method to integrate reasoning results into spectral unmixing at present. With the continuous development of technology, we will further attempt to introduce knowledge through other knowledge reasoning mechanisms.关键词：remote sensing;Knowledge Graph;knowledge graph embedding;endmember selection;spectral unmixing- 549
- |
- 128
- |
- 0
发布时间：2024-08-28 - “在光学遥感图像处理领域，专家提出了一种基于多时相图像的两阶段云去除算法，有效恢复被云遮挡的地面区域，为遥感图像的进一步利用提供解决方案。”
摘要：Cloud occlusion often occurs in optical remote sensing images. Cloud occlusion may reduce or even completely occlude some ground cover information in the images, which limits ground observation, change detection, or land cover classification. Cloud removal is an important task that urgently needs to be solved. Thin and thick clouds usually coexist in optical remote sensing images, and the cloud removal algorithm for single-frame remote sensing images is only suitable for solving the problem of thin cloud occlusion. Therefore, using multi-temporal remote sensing images of the same area at different times to remove clouds has become a major issue. This study aims to fully utilize images in the same location without cloud time period to replace cloud-occluded images for restoring the ground area occluded by clouds. For this purpose, a two-stage cloud removal algorithm for multi-temporal remote sensing images based on U-Net and spatiotemporal generative network (STGAN) is proposed. The first stage is cloud segmentation, which directly uses the U-Net model to extract clouds and remove thin clouds. The second stage is image restoration, which directly uses STGAN to remove thick clouds. It inputs the seven frames of ground images after removing thin clouds into the STGAN model to obtain a single, detail-rich cloud-free ground image. The generative model of STGAN adopts an improved multi-input U-Net to recover the corresponding irregularities in the thick cloud cover area by extracting key features from seven frames of images at the same location at a time. The thin cloud processing in the first stage is beneficial to the subsequent STGAN to capture more ground information. The proposed algorithm can solve the inability of U-Net to handle cloud occlusion in thick cloud areas. It can also capture more ground information than directly using STGAN for cloud removal. It has a better cloud removal effect. The experimental results on our dataset show that only using the first-stage U-Net model and only using the second-stage STGAN model for cloud removal are inferior to the proposed two-stage cloud removal algorithm in terms of subjective visual effects and objective quantitative evaluation indicators such as peak signal-to-noise ratio and structural similarity. This performance fully verifies the effectiveness of the cloud removal algorithm in this study. Compared with traditional cloud removal methods such as RPCA, TRPCA and deep learning algorithms such as Pix2Pix, the proposed algorithm is superior to the comparison algorithm and has a significant improvement, which fully verifies the advancement of the cloud removal algorithm in this study. The proposed algorithm fully utilizes the spatiotemporal information of multi-temporal cloudy satellite images of the same area at different times. It also has good cloud removal performance, which is conducive to the further utilization of optical remote sensing images. Although the proposed algorithm has achieved a relatively good cloud removal effect, it also has certain limitations. The cloud removal effect of the algorithm is not ideal for cloud image sequences with a large area covered by thick clouds. In the follow-up research, the spatiotemporal features of image sequence frames will be explored to better reconstruct large areas covered by thick clouds.关键词：remote sensing images;multi-temporal;Cloud removal;Image restoration;U-Net;STGAN- 141
- |
- 101
- |
- 0
发布时间：2024-08-28 - “在精细化工业热源监测领域，研究者基于Landsat 8卫星影像，提出了一种多层级识别方法，有效监测工业企业精细尺度发热单元，为环境保护与管理提供技术支撑。”
摘要：This study focuses on the internal thermal field distribution of a single large-scale factory to address the problems of insufficient refinement and large errors in the existing remote sensing monitoring of industrial heat sources. A device-level industrial heat source identification method based on medium and high-resolution satellite images is also proposed.The surface temperature is first obtained based on the dual-channel nonlinear split-window algorithm. Then, several spatial statistical analysis methods are used to identify candidate high-temperature areas, and the location of high-temperature devices in the plant is determined by multi-temporal superposition analysis of the identification results. By using high-resolution satellite images, the range of the high-temperature device is determined, the difference in the accuracy of the recognition results of several methods is compared and analyzed, and the method with the best recognition effect is ascertained.(1) Each method has the ability to identify heat source devices. The boundary of the standard deviation grading of temperature, that is, 1.5×, and the focal statistical analysis method is clearer. The accuracy rate and fall rate of the cell point are more than 85%. However, the omission rate of the general heat source device is also high at more than 50%. Thus, it is suitable for capturing the most important heat source device in the factory area. The identification results of the cold and hot spot analysis method are relatively stable, but the range is larger than that of the actual heat source device, and the non-falling rate is 23%. The result is more misidentified pixels, but more heat source devices can be captured. The identification results of the temperature 1 standard deviation grading method and the clustering and outlier analysis method are closely aligned. The accuracy rate is about 82%, and the omission rate is within 10%, which is more suitable for identifying heat source devices in the plant area. (2) Comprehensive analysis of the identification effect of the five methods shows that cluster and outlier analysis methods have good identification ability, the correct detection rate of identification results is more than 80%, the overall omission rate is less than 10%, and the identification results of each period can be obtained. This method is less affected by seasonal changes and is more suitable for identifying general heat source devices in the factory. (3) Judging by the characteristics of high-resolution satellite images, the heat source device mainly includes production device, circulating water, boiler device, storage warehouse, flare device, and tank farm. Among them, the production device accounts for about 55%, which is the main heat source device in the factory. The difference between the average temperature of each type of device and the factory area shows that the production equipment is also the main high-temperature area.Medium and high-resolution satellite remote sensing image data sources can effectively monitor the fine-scale heating units of industrial enterprises. They can provide technical support for environmental protection and management and industrial overcapacity reduction monitoring.关键词：remote sensing;Industrial heat source device;High temperature unit identification;Surface temperature;geostatistical analysis- 667
- |
- 393
- |
- 0
发布时间：2024-08-28 -
Comparison of lake surface water temperature retrieval algorithms： A case study of Lake Qiandaohu AI导读
“最新研究揭示了千岛湖水体表层温度的时空分布特征，为清洁型深水湖库温度监测提供了新方法。”
摘要：Lake surface water temperature is an important indicator of water quality, lake physical environment, and climate change. Monitoring lake surface water temperature and understanding its spatiotemporal variations are critical for local governments to protect lake ecosystems. Remote sensing is an effective method to monitor lake surface water temperature, and many algorithms have been developed and applied to retrieve lake surface water temperature. However, the suitability of these algorithms varies in different lakes. Especially, the suitability of these algorithms in deep, oligotrophic-to-mesotrophic lakes still needs to be discussed. Thus, taking Lake Qiandaohu, China as the study area, we attempt to validate the performance of various land surface temperature retrieval algorithms, analyze the sensitivity of the parameters in each algorithm, and map the spatiotemporal distribution of lake surface water temperature.In this study, six land surface temperature retrieval algorithms (i.e., radiative transfer equation algorithm, monowindow algorithm, generalized single-channel algorithm, practical single-channel algorithm, and two split-window algorithms) were selected to retrieve lake surface water temperature using Landsat 8 data in Lake Qiandaohu. The performance of these algorithms and the Landsat 8 Collection 2 Level-2 (C2L2) temperature product were validated with in-situ buoy data. By applying the best performing algorithm to 37 cloud-free Landsat 8 data collected from 2013 to 2021, the spatial and temporal distribution of lake surface water temperature in Lake Qiandaohu were mapped. Furthermore, the sensitivity of the relevant parameters (i.e., water surface emissivity, effective mean atmospheric temperature, atmospheric water vapor content, upwelling radiance, downwelling radiance, and atmospheric transmittance) in each algorithm were explored.In conclusion, in Lake Qiandaohu, the Split Window algorithm has the best performance and the least dependency with atmospheric parameters. By contrast, the single-channel algorithm is suitable for retrieving long-term lake surface water temperature utilizing Landsat series data. Our study validated the performance of various land surface temperature retrieval algorithms in a deep, oligotrophic-to-mesotrophic lake and provided a reference for the remote estimation of lake surface water temperature in other similar lakes.The results showed the following(1) For bands 10 and 11 of Landsat 8 data, the most suitable water surface emissivity in Lake Qiandaohu is 0.9926 and 0.9877, respectively. (2) The accuracy of the split-window algorithm is better than that of the single-channel algorithm, and the estimation accuracy of Landsat temperature product is moderate. The split-window algorithm, SWA_G, showed the optimal performance, with a mean absolute percentage error (MAPE) of 7.61% and a root mean square error (RMSE) of 2.0 ℃. The MAPE and RMSE values of the Landsat 8 C2L2 temperature product were 9.33% and 2.08 ℃, respectively. (3) Lake surface water temperature in Lake Qiandaohu has considerable spatial and temporal variation. Seasonally, the lake surface water temperature of Lake Qiandaohu has the lowest value (14.2 ± 0.6 ℃) and highest value (31.0 ± 0.5 ℃) in winter and summer, respectively. Spatially, the lake surface water temperature was higher in the northwest segment (23.0 ± 0.3 ℃) and southwest segment (22.8 ± 0.2 ℃) and lower in the northeast segment (22.2 ± 0.3 ℃). (4) The radiative transfer equation algorithm is sensitive to the upwelling radiance and atmospheric transmittance. The monowindow algorithm shows less sensitivity to the effective mean atmospheric temperature and atmospheric transmittance.关键词：remote sensing;lake surface water temperature;Landsat;Lake Qiandaohu;water surface emissivity;sensitivity analysis- 667
- |
- 453
- |
- 0
发布时间：2024-08-28
Models and Methods
- Postal code：100190
- Tel：010-58887052 Email：nrsb@aircas.ac.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备20021838号-9
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0