最新刊期
卷 25 ， 期 1 ， 2021
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摘要：It has been 25 years since the journal of Remote Sensing of Environment was renamed as Journal of Remote Sensing（Chinese）. In the past 25 years, China's economy and society as well as science and technology have changed significantly. The remote sensing in China has also developed rapidly. The Journal of Remote Sensing （Chinese）have participated, witnessed and been a peer of this time of change.Firstly, the paper discusses the historical background of the development of remote sensing in China and abroad. In the world the strategy for development of remote sensing was changed from the resources exploration to the environmental study and the global change has become a major topic. In the paper the major progress of remote sensing in China is discussed. In the past 25 years since the journal renamed the state-level satellite system of the earth observation has been established in areas of meteorology, ocean, environmental∕disaster reduction and land resources, etc. At the same time, as an important supplement for the national system the private remote sensing satellite companies have also been built and developed.With the progress of remote sensing technology and system, it has provided an indispensable technical and information support for important governmental department business. The application of remote sensing in land resources, agriculture, forestry, hydrology, meteorology, ocean, city, surveying and mapping, environment and disaster monitoring has obtained a great achievement. The remote sensing applications have been in the forefront of the world with regards of breadth, depth and scale.For 25 years, the Journal of Remote Sensing（Chinese） has always been the process of improving. It has taken the important historical responsibilities of promoting the technical exchange, propagating the science and technical progress, and disseminating the achievement of remote sensing applications. The Journal of Remote Sensing（Chinese）, as an epitome and the fruit of remote sensing development and achievement, always progresses together with the development of remote sensing in China.关键词：25 years;China;remote of environment;Journal of Remote Sensing (Chinese);scientific and technological innovation;resources exploration;global change;remote sensing technology and applications;space-sky-ground system integration- 751
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发布时间：2021-03-03
Academician Forum
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摘要：The aerospace industry in China has developed rapidly over the past 40 years. Remote sensing satellites have grown and gradually developed, and this situation has formed a variety of various satellite series. The communication satellite series, navigation satellite series, Earth observation satellite series, and science and technology test satellite series constitute the current application satellite system in China, which is used in national land and resources survey, meteorological services, environmental monitoring, ocean remote sensing, and other fields. This system provides the foundation for the development of satellite applications.At present, the satellite systems of communication, navigation, and remote sensing are separate and independent. Thus, they cannot meet the real-time, intelligent, and diversified requirements in the era of big data. In addition to the application of land, surveying and mapping, planning, geology and mining, agriculture, transportation, marine, and other industries, the government, enterprises, and the public have shown a large and urgent need for satellite remote sensing navigation data and services, especially high-resolution satellite remote sensing data and services. The demand of users in different industries and fields for remote sensing data products has gradually changed from singularity and standardization to diversification and specialization; from static investigation to dynamic monitoring, forecasting, and forecasting; from qualitative analysis to quantitative research; and from general application to batch business. Therefore, with the application requirements as the traction, focusing on improving the service capabilities of satellite remote sensing, the communication-navigation-remote sensing integrated space-based information service system with integrated communication, navigation, and remote sensing can be built to provide all-day, all-weather, all-region application-oriented services. This system fully utilizes the benefits of satellite applications. Thus, it breaks the barriers of satellites of communication, navigation, and remote sensing.Building a space-based information real-time service system (positioning, navigation, timing, remote sensing, communication, PNTRC) based on “one-satellite multitasking, multi-satellite networking, multi-network integration, and intelligent services” that integrates communication, navigation, and remote sensing, has become an important direction for the development of contemporary aerospace information technology. In the era of 5G, Internet of Things, big data and artificial intelligence, it is inevitable to study remote sensing service technologies for real-time applications in the era of 5G, Internet of Things, big data, and artificial intelligence is important to meet people’s strong demand for “fast, accurate, and flexible” remote sensing information services with the way of B2B, B2G, or B2C.This paper first studied the real-time service capabilities of space-air-ground-sea integrated Earth observation network. Then, the real-time application service requirements of remote sensing technology (requirements for high-efficiency remote sensing services for earthquake and disaster relief, remote sensing monitoring requirements for land surface deformation, and demand for public real-world services) were elaborated. Finally, the on-orbit processing technology based on artificial intelligence was analyzed, and the trend of remote sensing technology transformed from remote sensing information to real-time services was discussed.关键词：remote sensing;space-air-ground-sea integrated earth observation network;real-time application services;artificial intelligence on-orbit processing- 1615
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发布时间：2021-03-03 -
摘要：The urgent need of environmental pollution prevention and ecological civilization construction in the new period has promoted the rapid development of environmental remote sensing monitoring technology. Over the past 20 years and more, China’s environmental remote sensing monitoring has gradually entered the main battlefield of national ecological and environmental protection from scratch. It has become an indispensable and important technical means for national environmental management and decision making, playing a key supporting role. Faced with the new situation and new requirements of implementing the strictest ecological and environmental protection system, scientific, precise, and law-based pollution control, China’s environmental remote sensing monitoring is faced with unprecedented opportunities and challenges. This paper first reviews the work history of environmental remote sensing monitoring in China with the construction and application of environmental monitoring satellites as the main line. Starting with the research overview of typical key technologies, this paper then summarizes the progress of key technologies of water environment remote sensing monitoring, atmospheric environment monitoring, and ecological environment monitoring. Combined with the development of advanced technologies, such as high-performance Earth observation and big data, the frontier issues of subsequent environmental satellites, active remote sensing discovery of ecological environmental problems, big data of remote sensing monitoring, and environmental remote sensing inversion based on deep learning, are discussed. The problems and development direction of environmental remote sensing in China are pointed out. The development and application prospects of relevant technologies are analyzed and prospected. The national environmental remote sensing monitoring technology system has been successfully established, and the operational capability of environmental remote sensing has been basically formed. These achievements are due to the great attention of relevant national departments and the joint efforts of the author’s team and relevant researchers. A new generation of environmental monitoring satellites featuring high-resolution detection is rapidly developing, The technical performance of the environmental satellite payloads will be greatly improved. The study of environmental remote sensing mechanism is attracting much attention, and the accuracy and efficiency of environmental remote sensing monitoring will be improved. China’s environmental remote sensing monitoring is integrated with artificial intelligence, big data, and other new technologies, and accelerates from the model-driven era centered on mathematical modeling to the data-driven era characterized by intelligent perception. These conditions will give birth to new environmental remote sensing application scenarios and big data products and make environmental remote sensing monitoring develop toward intelligent perception, intelligent warning, intelligent decision, and intelligent service.关键词：remote sensing monitoring of ecological environment;high performance environment satellite;big data of environmental monitoring;deep learning- 2215
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发布时间：2021-03-03
Academicians’ Forum
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摘要：Inland water, including rivers, lakes and reservoirs on the earth’s surface, is the main component of water resources. It is related to human life, ecological environment construction and protection, and social and economic sustainable development. The temporal and spatial distribution and variation of inland water body and water quality triggered by climate change and human activities have caused attention of scientists and governments all around the world. Compared with conventional field sampling monitoring methods, remote sensing monitoring has the advantages of long time series and large-scale coverage. There are series of optical remote sensing satellites which mainly based on visible/near-infrared bands and have a long history. In addition to monitor water surface information, they can also obtain water substances information within water. Therefore, optical remote sensing plays a particularly important role in inland water environment monitoring. Compared with the ocean water with simple optical properties, the optical properties of inland water body are more complex and vary greatly with region and season. Moreover, there is a lack of satellite specially designed for inland water, so the water color remote sensing of inland water is more difficult. However, due to the promotion of ocean color remote sensing theory and methods, as well as the continuous accumulation of inland water optical property data, the research of optical remote sensing of inland water has made great progress in recent years. It has developed from the experimental research of typical algorithms in typical study areas to the production of long time series and large-scale water products, and from the scientific research of water color remote sensing algorithm to the geological discovery of spatio-temporal variation of inland water body parameters and finally to provide enhanced decision support to water environment supervision sectors. In particular, important progress has been made in water distribution extraction, atmospheric correction for water body, chlorophyll-a concentration inversion, water color monitoring, water clarity inversion, trophic state evaluation, black and odorous water monitoring, lake ice monitoring, etc., and some water color remote sensing products for long time series and large-scale inland water bodies have been produced. In the future, in order to further improve the application of optical remote sensing of inland water, it is necessary to further strengthen the acquisition and analysis of optical property data from different types of inland water, and also improve the water color remote sensing algorithms for long time series and large-scale inland water. In addition, to solve the problem of the lack of useful satellite data source, it is necessary to launch satellite constellation specially designed for inland water monitoring, or to consider the needs for inland water monitoring in the sensor design in general land satellite constellation.关键词：inland water;water color remote sensing;long time series;large-scale;optical remote sensing- 1713
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发布时间：2021-03-03 -
摘要：Reduction of greenhouse gas (GHG) (carbon dioxide (CO2) and methane (CH4)) emissions is a crucial way to mitigate global warming. Traditional estimation of anthropogenic carbon emissions mainly relies on inventory method and lacks independent validation data. The 49th IPCC plenary session (2019) proposed the use of “top-down” inversion with atmospheric observations to support and verify GHG emission inventories. The “top-down” method depends on atmospheric concentration observations, chemical transport models, and data assimilation algorithms. Global covered atmospheric concentration measurement with high accuracy and precision is a key element in better using the “top-down” method in global carbon flux investigation. Measurements from space provide global and regional datasets that improve the spatial coverage of existing in-situ networks. Understanding the development of spaceborned GHG monitoring techniques and “top-down” method has become an important issue in China’s response to international climate change affairs.We divided the carbon monitoring remote sensing technology into three phases (1999—2008, 2009—2019, 2019—) based on the development process of satellite remote sensing technology and monitoring requirements. The corresponding satellites in the first two phases were called the first generation, and the corresponding satellites in the third phases were called the second generation. The first generation of GHG satellites was tested in many aspects, such as measurement principle, calibration, and validation. These processes were performed to improve the observation accuracy and the spatial and temporal resolutions of measurements. These efforts made continuous improvement on measurement accuracy and obtained approximately 10 years of scientific data and research results. The first generation of GHG monitoring satellites mainly focused on technical verification and scientific target exploration flying a polar-orbit and onboarded passive remote sensing instrument with narrow swath, mainly aiming to obtain high-precision remote sensing data. The first generation laid the foundation, and the second generation entered the decade of rapid development and application from 2019 to 2028. The second generation of GHG monitoring satellites mainly aimed to improve the spatial and temporal resolutions of observations, such as increasing the swath and observation data in the cross-orbit direction (≥200 km) or using geostationary orbit to increase the observation frequency and data coverage, thereby greatly improving the observation efficiency. Active laser detectors can be used to obtain profile data with high accuracy (0.5 PPM), which are unaffected by sunlight.Optimizing the retrieval algorithm to improve the accuracy and scientifically planning the operational constellations of satellites to improve the monitoring efficiency are necessary. These processes are required to meet the major demand of global and regional monitoring of anthropogenic carbon emissions. Furthermore, the verification of the inventory algorithm is introduced by using the “top-down” data assimilation method with high precision, high spatial and temporal resolution measurements of the satellite constellations. The future development trend of hyperspectral remote sensing and new generation of carbon monitoring satellites and the potential of estimating anthropogenic carbon emissions are provided.关键词：carbon monitoring satellites;greenhouse gases;carbon source and sink;MRV;satellites virtual constellation- 1888
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发布时间：2021-03-03 -
摘要：Land surface Evapotranspiration (ET) is an important component of surface water cycle and energy balance, and its accurate estimation is essential for agricultural irrigation and drought monitoring, water resources management and climate change prediction. Retrieval of land surface ET based remotely sensed surface temperature versus vegetation index triangular/trapezoidal characteristic space is one of the international hot spots and frontier topics in the quantitative remote sensing of surface evapotranspiration.Due to the unclear understanding of the soil evaporation/vegetation transpiration processes and mechanisms, the interpretation schemes of retrieval models differ significantly for the scientific question of how soil evaporation/vegetation transpiration in characteristic space changes with surface soil moisture and vegetation coverage (including cover type and coverage), resulting in significant differences in ET retrieval results between different models. This paper comprehensively, systematically and deeply reviews the research on surface ET retrieval and soil evaporation/vegetation transpiration separation based on surface temperature versus vegetation index triangular/trapezoidal characteristic space. The basic theories, advantages and disadvantages of each method for the determination of dry/wet edges and ET retrieval modeling are described in detail, the applicable conditions of each method are clarified, and the problems to be solved are sorted out. Based on these, this paper finally points out the future development direction of the remote sensing retrieval of ET in the triangular/trapezoidal characteristic space.To correctly reveal the variation law of land surface ET in the triangular/trapezoidal characteristic space of surface temperature-vegetation index and improve the accuracy and operational capability of surface ET estimation based on characteristic space, several suggestions for future research are proposed as follows: First, using model simulation data, ground observation data, and combining model comparison, theoretical analysis, mathematical derivation to comprehensively and thoroughly study the agreement, differences and compatibility between the triangular and trapezoidal characteristic space in terms of the physical concepts, causes, and retrieval results. Then clarifying the applicability, the connection and difference in the separation of soil evaporation/vegetation transpiration between the “simultaneous separation method” and the “two-stage separation method”, and investigating the variation of ET and soil evaporation/vegetation transpiration in characteristic space with the change of soil moisture and vegetation index. Finally, with the use of long-term remote sensing data and the successful application of the negative correlation between land surface temperature and vegetation index (i.e., spatial information) in the retrieval of soil moisture, developing a new model for remote sensing retrieval of ET of a characteristic space without the determination of dry and wet edges or end-member land surface temperature. Meanwhile, based on the recently proposed shortwave infrared reflectance-vegetation index trapezoidal characteristic space, which has the advantages of high soil moisture retrieval accuracy and less impact by atmospheric forcing changes, the establishment of surface ET retrieval models can be based on the shortwave infrared reflectance-vegetation index characteristic space.Through this paper, it is helpful to further understand the mechanism of land surface ET retrieval based on the triangle/trapezoid characteristic space, provide inspiration for the establishment of new methods for remote sensing retrieval of ET and soil evaporation/vegetation transpiration separation, and promote the research level of quantitative remote sensing of evapotranspiration in China.关键词：land surface evapotranspiration;remote sensing;surface temperature versus vegetation index triangular/trapezoidal characteristic space;determination of dry and wet edges;evaporation and transpiration- 813
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发布时间：2021-03-03 -
摘要：With the development of remote sensing technology, two or more viewing directions become available for the same target, and thus a new research field – multi-angle remote sensing appears. Compared with the traditional remote sensing which only views the ground surface in one direction, multi-angle remote sensing provides angle-dimensional information and improves the capability of obtaining vegetation structure parameters. It helps to improve the retrieval accuracy of key biophysical parameters and provides better data support for the research of ecological environment and climate change. After a detailed analysis of the publications in multi-angle remote sensing, we summarize the basic concepts, characteristics, advantages and developments of multi-angle remote sensing. Multi-angle remote sensing platforms vary from ground-based, airborne to spaceborne observation equipment. The first ground-based observation equipment appeared in 1952. All the ground-based equipment is classified as the fixed field of view mode or the changeable field of view mode. For the airborne or spaceborne platforms, only the fixed field of view mode is acceptable due to the heterogeneity of the land surfaces. With the development of UAV technique, the airborne multi-angle remote sensing is becoming more and more popular due to its flexibility and high spatial resolution. The multi-angle models play important roles in parameters inversion. Classic multi-angle remote sensing models include radiative transfer models, geometric optical models, hybrid models, and computer simulation models. They are all physical models which are developed based on some assumptions and theoretical analysis. Semi-empirical models combine the advantages of the empirical model and the physical model, as a result, they are simple and stable in inversion. The most widely used semi-empirical model is the linear kernel driven model used by the operational MODIS BRDF/albedo products algorithm. With the development of observing equipment and models, multi-angle remote sensing is widely used in many applications. Due to the anisotropic reflection characteristic, land surface albedo can only be retrieved by multi-angle remote sensing with high accuracy. Multi-angle remote sensing shows great potentials in vegetation structural parameters inversion which include the clumping index, LAI, FVC profile and canopy height. It has been found to be superior in vegetation type identification than the traditional vertical observation. Multi-angle remote sensing is also very useful in the cloud and aerosol parameters retrieval, such as the cloud albedo, height and types, as well as the aerosol optical depth and shapes. Large difference of optical scattering between the cloud and ice/snow in different viewing directions makes the identification of these covers easier with multi-angle remote sensing. The sea ice roughness can also be retrieved by multi-angle observations. In the last of this paper, we put forward the prospects of multi-angle optical quantitative remote sensing. As the multi-angle remote sensing observation data based on spaceborne, airborne, and ground platforms become more and more abundant, the main research direction of multi-angle remote sensing in the future should focus on the following aspects: developing multi-angle reflection/radiation models for complex surfaces, enhancing the preprocessing capabilities of multi-angle remote sensing data, and promoting the comprehensive abilities of multi-source data integration in application, etc.关键词：multi-angle remote sensing;Bidirectional Reflectance Distribution Function (BRDF);sensor;radiative transfer model;geometric optical model- 1206
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发布时间：2021-03-03 -
摘要：Since the beginning of this century, more and more attention has been given to using geostationary meteorological satellite data in the retrieval of land surface parameters. This paper gives an overview of the recent developments on the retrieval of land surface parameters from geostationary satellite data. Geostationary meteorological satellites have been developed in Europe (Meteosat), United Sates (GOES-R), Japan (Himawari), and China (FY series). The geostationary satellite data volume is usually high because of the high temporal revisit frequency, which poses great challenges to data storage, parameter retrieval and product distribution. Nonetheless, each satellite program has developed a series of land surface products to support near real-time applications.Various methods to estimate land surface parameters are described. The Meteosat SEVIRI is leading the development of land surface products, especially the unique long-term Thematic Climate Data Record (TCDR) products. The standard products include Land Surface Temperature (LST), longwave and shortwave radiances, albedo, fraction vegetation cover, leaf area index, the fraction of absorbed photosynthetic active radiation, and gross primary production, evapotranspiration, latent and sensible heat flux, wild fire, and snow cover. Among the standard products, the LST, radiance, and albedo are also distributed in the TCDR products. GOES-R and FY-4A are also releasing similar preliminary products, but their quality still need to be fully validated. The Himawai-8 products are still in the research stage. Further physical retrieval methods and various machine learning inversion methods can be explored to improve the accuracy of parameter inversion. It is also necessary to improve the quality of auxiliary atmospheric field data and model simulations to facilitate the land surface parameter retrieval.Multiple geostationary satellites can be integrated to provide long-term observations and global coverage. EUMETSAT has integrated all TCDR products generated from the first-and second-generation Meteosat data, and the National Oceanic and Atmospheric Administration (NOAA) has released all GOES level-1B data since 1979. These two dataset can be used to generate long-term continuous land surface products. GOES-R and Himawari-8 data have also been combined to generate high quality top-of-atmosphere reflectance and bright temperature products, which will greatly facilitate the generation of other land surface parameters. Geostationary satellite data can also be combined with the polar orbiting satellite data in land surface parameter retrieval; however, current studies are mostly carried out on small regional scales, whereas national and global studies are relatively deficient.Land surface parameters retrieved from geostationary satellites can be validated through comparison with concurrent ground measurements, polar orbiting satellite products and model simulations. However, simultaneous real-time surface data are lacking. On the other hand, current geostationary meteorological satellite data are in the kilometric spatial resolution and are limited for high resolution applications. With its high temporal and spatial resolutions (50 m), the GF-4 satellite launched by China provides a great potential for high resolution land surface monitoring and is worthy of further exploration.Future researches using geostationary satellite data to estimate land surface parameters include: (1) exploration of new techniques to improve the efficiency and accuracy of geostationary satellite data acquisition and processing; (2) integration of multiple geostationary and polar-orbiting satellite data to produce long term global land surface parameters; (3) exploration of automatic field measurement methods to enhance the validation of land surface parameters derived from geostationary satellite data.关键词：geostationary meteorological satellite;land surface parameters;Climate Data Record (CDR);MSG SEVIRI;GOES-R ABI;Himawai-8 AHI;FY-4A AGRI- 709
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发布时间：2021-03-03
Quantitative Remote Sensing
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摘要：Sustainable development goals such as food security, high-quality habitat construction, biodiversity conservation, planetary health, and the understanding, modeling, and management of the Earth system urgently require multi-scale, long time series, high-accuracy, and consistent remote sensing observation datasets and mapping products with flexible classification systems to meet user needs. However, due to technical and cost constraints, it is difficult for conventional remote sensing satellites to provide observations with high spatial resolution, high temporal frequency, and high quality at the same time. The existing mapping and inversion schemes are mostly for a single sensor, making it difficult to fully exploit and jointly utilize the information potential of multi-source heterogeneous remote sensing big data, resulting in limited observation periods and resolutions, low spatial and temporal consistency and comparability. Therefore, new technical paradigms are urgently needed in the field of remote sensing. In this paper, based on advanced technologies, including cloud computing, artificial intelligence, virtual constellation, spatio-temporal fusion reconstruction, an intelligent mapping framework is proposed for remote sensing big data. The framework is user-driven and problem-driven, which can significantly improve the current situation that remote sensing data products can hardly meet users’ diversified and high-precision surface monitoring needs in agriculture and forestry management, national monitoring, ecological environment protection, disaster prevention and mitigation, urban planning, etc. Under this framework’s guidance, we built an online real-time, automated, serverless, end-to-end remote sensing big data production chain and parallel mapping system based on Amazon Web Services (AWS) high-performance, elastic, and scalable distributed computing resources. We produced the first set of 21st century daily Seamless Data Cube (SDC) and seasonal to annual land cover and land use mapping products of China. Integrating Landsat and MODIS satellite as a virtual constellation, through multi-source spatio-temporal data fusion and reconstruction technology, the daily SDC, cloud-free, high-precision reflectance product, is developed. As Analysis Ready Data (ARD), it lays the foundation for high-precision quantitative remote sensing inversion and mapping. Based on SDC, we developed the seasonal to annual mapping product with multiple multi-level land cover and land use classification systems, whose mean annual accuracy exceeds 80%. The main mapping pipeline includes migrating the all-season sample set based on stable classification theory with limited samples, optimizing and ensembling multiple classifiers by Automatic Machine Learning (AutoML) strategies, and using change detection and post-processing techniques to achieve consistency. The two sets of products demonstrate the feasibility and effectiveness of the intelligent remote sensing mapping framework proposed in this paper. We will continue to improve and develop the framework with an open and flexible concept to provide new ideas to promote remote sensing development in China.关键词：seamless data cube;daily;seasonal mapping;cloud computing;intelligent remote sensing mapping;long time series;change monitoring;artificial intelligence;spatio-temporal big data- 1040
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发布时间：2021-03-03 -
摘要：The development of multispectral, hyperspectral, infrared, radar, and other sensing technologies in recent years has facilitated the use of remote sensing methods in precision agriculture, resource investigation, environmental monitoring, military defense, and other fields. Multi-source remote sensing images in the same scene can capture the same ground objects, while the dimensions of the observations are independent of each other. Therefore, the imaging scale, spatial resolution, time resolution, and target characteristics may be quite different in different observations. The information provided by massive multi-source remote sensing data is redundant, complementary, and cooperative. Multi-source remote sensing image fusion can utilize the complementary information obtained from different sources to achieve accurate and comprehensive Earth observations. Thus, it is one of the key technologies in remote sensing.From the perspective of data sources, this review summarizes the research status and future development trends of multi-source remote sensing image fusion. In the introduction, the importance of multi-source image fusion and the motivation of this review are illustrated briefly. The second section outlines the main sources and image characteristics of nine typical remote sensing data: panchromatic images, multispectral images, hyperspectral images, infrared images, nighttime light images, stereo images, video images, Synthetic Aperture Radar (SAR) images, and light detection and ranging (LiDAR) images. The typical applications of these multi-source data are also briefly concluded while introducing the characteristics of these multi-source remote sensing images separately. Moreover, the development trend of multi-source remote sensing image fusion is evaluated according to the number of publications. In the third section, latest studies on multi-source remote sensing image fusion are introduced in detail in the order of optical image fusion, optical and SAR image fusion, optical and LiDAR image fusion, and other types of remote sensing image fusion. The third section also puts forward some challenging problems in remote sensing image fusion. For example, the registration problem of multi-source images, the application problem of fusion in specific domain, and the representation of features during cross-modal fusion are all important problems that need to be solved urgently. In the conclusion section, this review summarizes the research status of the multi-source remote sensing image fusion field. This also section prospects the future development trend of multi-source remote sensing image fusion.First, the study of related fusion technologies for new types of remote sensing images will be a major future research. Second, the integration of data acquisition and image fusion techniques can reduce the difficulty and improve the performance of image fusion with the help of novel hardware designs. Therefore, multi-modal fusion-based computational imaging systems should be designed. Third, fusing multi-source images with other types of data, such as geographical, ground station, and web data, is an interesting research topic in addition to the fusion of remote sensing images. Finally, evaluating the performance of image fusion is an important problem. Image fusion aims to help better understand the land covers from different dimensions of Earth observations. Whether the fusion can help the understanding of the Earth is unclear. Therefore, the improvement in application performance, such as detection or classification accuracy, may be an important index compared with the enhancement in the quality of the fused image.关键词：remote sensing image;image fusion;multi-modal;ground observation- 4835
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发布时间：2021-03-03 -
摘要：Digital elevation products are the digital expression of terrain and elevation information, which has been widely utilized in the fields of climate, meteorology, topography, geological disasters, soil, and hydrology. Meanwhile, the development of information and digitalization all over the world and the research of major global issues have emphasized the increasingly becoming important role of high-precision and -resolution global digital elevation products. Therefore, the public free digital elevation products are comprehensively described and analyzed in this study to facilitate different users to select appropriate data products depending on their personal requirements.This study first discusses the different measurement indexes of digital elevation products’ accuracy. Moreover, the equivalence relationship between the common measurement indexes is derived to compare and analyze different digital elevation products. The global elevation data acquisition modes are explored first. The main properties and characteristics of ETOPO, GTOPO30, GMTED2010, ASTER GDEM, AW3D30, SRTM, and TanDEM-X DEM global data products are introduced in detail through the initial data fusion and the subsequent global mapping based on optical stereo photogrammetry and interferometric synthetic aperture radar techniques. The development history of different products is also briefly described. The parameters and elevation accuracy among the aforementioned digital elevation products are then summarized and comparatively analyzed. The results are shown in Table 7 and 8, respectively.On this basis, the different data products under 1″ and 3″ resolution for a mountain located in Wuzhong City, Ningxia Hui Autonomous Region are analyzed in detail by means of qualitative and quantitative comparison. The visual analysis shows that the AW3D30 and ASTER DEM products exhibit the relatively rich landform detail features, and they are both superior to the SRTM and TanDEM-X DEM elevation products. However, the particle effects obviously appear in the ASTER GDEM products, and the precision of these products is low. Among these products, the TanDEM-X DEM products are relatively smooth because they are derived by resampling the high- resolution products. In terms of elevation accuracy, TanDEM-X DEM products have the highest accuracy and are followed by AW3D30 and SRTM products. These products are greatly superior to the global digital elevation products obtained by multi-source data fusion. The quantitative difference analysis results of different products are consistent with the conclusion mentioned above.In general, adopting the advanced satellite remote sensing technology to obtain elevation data with uniform data source, quality, and precision will be the development trend of global digital elevation products. Furthermore, the application field of digital elevation products with high precision and resolution obtained by advanced techniques will be expanded from the initial change research of global large area to the application research related to elevation of urban and even local small area. Therefore, domestic digital elevation products, which can be controlled independently, are urgently demanded. Meanwhile, the popular domestic LuTan-1 SAR satellite with the interference as main task will soon be launched, and it will provide a good technical foundation for the production and acquisition of domestic global digital elevation products.关键词：global digital elevation product;optical stereo photogrammetry;InSAR;digital elevation product’s accuracy- 981
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发布时间：2021-03-03 -
摘要：High-resolution remote sensing image interpretation is a major topic in remote sensing information processing. It plays a vital role in the knowledge mining and intelligent analysis of remote sensing big data and has important application values in civil and military fields. The traditional methods of high-resolution remote sensing image interpretation generally use manual visual interpretation, which is time consuming and laborious and has low accuracy. Therefore, interpreting high-resolution remote sensing images automatically and efficiently is an urgent problem to be solved. The rapid development of artificial intelligence technology in recent years has made machine learning the mainstream research direction of high-resolution remote sensing image interpretation. In this study, we systematically review five kinds of representative machine learning paradigms on the basis of the typical tasks of high-resolution remote sensing image interpretation, such as object detection, scene classification, semantic segmentation, and hyperspectral image classification. Specifically, we introduce their definitions, typical methods, and applications. The representative machine learning paradigms include supervised learning (e.g., support vector machine, k-nearest neighbor, decision tree, random tree, and probabilistic graph model), semi-supervised learning (e.g., pure semi-supervised learning, transductive learning, and active learning), weakly supervised learning (e.g., multiple instance learning), unsupervised learning (e.g., clustering, principal component analysis, and sparse coding), and deep learning (e.g., stacked auto-encoder, deep belief network, convolutional neural network, and generative adversarial network). Then, we comprehensively analyze the strengths and limitations of the five kinds of machine learning paradigms and summarize their typical applications in remote sensing image interpretation. Finally, we summarize the development direction of high-resolution remote sensing image interpretation, such as few-shot learning, unsupervised deep learning, and reinforcement learning.关键词：remote sensing image interpretation;machine learning paradigm;deep learning;weakly supervised learning;few-shot learning;reinforcement learning- 1475
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发布时间：2021-03-03 -
摘要：The increasingly developing information age puts forward urgent need for stable and precise quantitative remote sensing information. The accuracy of ground object information retrieved by remote sensing technology is essentially decided by the sensor performance and timely evaluation of its variation occurred during long-term operational running. Since the “11th Five-Year Plan”, the Key Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy of Sciences, has organized relevant domestic advantageous institutions to design and realize the comprehensive calibration technical system for high resolution remote sensors which is dedicated to quantitative remote sensing, and built up the “Baotou Comprehensive Calibration Site for High Resolution Remote Sensors” (the Baotou site, for short). From the top-level viewing, the Baotou site consists of five main systems: (1) the standard test targets system, which contains a variety of permanent artificial optical targets, portable artificial optical targets, optical geometric control point targets, permanent bases for SAR corner reflector, SAR corner reflectors and natural scene targets. Within these miscellaneous targets, the knife-edge/greyscale dual functional target is a natural-material-paved permanent target for simultaneous evaluation of sensor radiometric/spatial/spectral characteristics, which is the first one in China and the largest one in the world; the microwave/optical dual functional artificial target is dedicated to directly detecting SAR/optical image resolution, which is the first one in the world. (2) the ground/atmospheric truth measurement system, which contains the automatic observation system for target characteristics, the automatic measurement system for atmospheric environment parameters, the surface flux automatic observation system, other ground feature measurement devices and the real-time monitoring system of automatic observation data. (3) the aerial flight test technical system, which contains the flight test standard sensors, the flight control & management system, the sensor ground calibration & testing system and the aerial flight data in-situ fast processing system. (4) the observation data processing and analysis system, which contains the RadCalNet automated radiometric calibration data processing system, the sensor performance analysis & evaluation system and the target characteristics knowledge base. (5) the basic guarantee facilities, which contains the test flight guarantee facilities, the ground test guarantee facilities and the logistical guarantee facilities. The rich types of optical/microwave targets and diverse environment measurement devices assure the polyfunctionality and flexibility of the site shown in various RS calibration/validation tasks. In general, the Baotou site can afford field comprehensive calibration for airborne/spaceborne high resolution remote sensors, which involves rigorous test flight for sensor performance, sensor on-orbit calibration and performance evaluation, and remote sensing product validation. Its powerful capability in comprehensive calibration and test has been widely acknowledged in the international earth observation community, and it was entitled the “National Calibration and Validation Site for High Resolution Remote Sensors” by the Ministry of Science and Technology of China. This paper will firstly point out the calibration requirements conforming to the trend of high resolution remote sensing, then describe in detail the system structure of the Baotou site and its miscellaneous functions, and finally show some successful applications based on the Baotou site in high resolution sensor calibration and performance evaluation, which can provide references for researchers in remote sensing field.关键词：calibration site;high resolution;remote sensor;the Baotou site;test target- 854
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- 7
发布时间：2021-03-03 -
摘要：Global scale historical remote sensing data has been accumulated for more than half a century. The remote sensing big data formed by these continuously emerging massive remote sensing data provides abundant data support for Earth science research. Furthermore, it is a new challenge for the rapid processing, analysis and mining of remote sensing big data. The emergence of Remote Sensing Cloud Computing Platform (RS-CCP) provides unprecedented opportunities for remote sensing big data mining. Meanwhile, it completely changes the traditional remote sensing data processing and analysis mode, making it possible to quickly analyze and apply long-term sequences on a global scale.This study systematically combed the state-of-the-art development of Google Earth Engine (GEE), including the origin, current progress, petabyte scale catalog of public and free-to-use geospatial datasets, computing capability for planetary-scale analysis of Earth science data, Application Programming Interface (API), and GEE Apps. Combined with GEE, the RS-CCPs at home and abroad, including NASA Earth Exchange, Descartes Labs, Amazon Web Services (AWS), Data Cube, Copernicus Data and Exploitation Platform-DE (CODE-DE), CASEarth EarthDataMiner, Pixel Information Expert (PIE)-Engine, were analyzed from the aspects of public data achieve, platform type, and APIs. Meanwhile, the RS-CCP developed by Chinese Business Company were also taken into account, such as SenseEarth, Analytical Insight of Earth (AI EARTH), WeEath. Furthermore, this study summarized the main applications of RS-CCPs in the field of Earth sciences according to Amani et al. (2020) and Tamiminia et al. (2020). Specifically, the RS-CCPs based applications published on Nature (and its series), Science (and its series) and Proceedings of the National Academy of Sciences of the United States of America (PNAS) were summarized as applications related to land cover/land use, vegetation changes, animal, climate change, Human social and economic activities.On this basis, the limitations of current RS-CCPs were discussed, such as (1) Limited storage and computing resources, (2) Some geospatial data types are not compatible, (3) Insufficient support for different projection formats, (4) Difficult to achieve calculation between pixels, (5) Not support mobile applications, (6) The typesetting and drawing module is not perfect. The key technologies and core issues that need to be resolved in the future were prospected. Subsequently, some recommendations were provide for the development of China’s RS-CCP: (1) Integration of multi-source data resources, especially domestic remote sensing data, (2) Guarantee the quality and reliability of domestic remote sensing data, (3) Promote a new data-driven geoscience research paradigm. With the increasing demand of human understanding of the Earth, RS-CCPs will play a greater role in Earth science, serving the deepening of Earth science knowledge and the sustainable development of human society.关键词：remote sensing;big data;remote sensing cloud computing platform;earth science application- 2276
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- 24
发布时间：2021-03-03 -
摘要：This paper aims to elaborate two large-scale point cloud benchmark datasets, namely, WHU-TLS and WHU-MLS, for deep learning purposes. The benchmark of the Whu-TLS data set comprises 115 scans and over 1740 million 3D points collected from 11 different environments (i.e., subway station, high-speed railway platform, mountain, forest, park, campus, residence, riverbank, heritage building, underground excavation, and tunnel environments) with variations in the point density, clutter, and occlusion. The aims of the proposed benchmark are to facilitate better comparisons and provide insights into the strengths and weaknesses of different registration approaches based on a common standard.The ground-truth transformations and registration graphs are also provided to allow researchers to evaluate their registration solutions and for environmental modeling. In addition, the Whu-TLS data set provides suitable data for applications in safe railway operation, river surveys and regulation, forest structure assessment, cultural heritage conservation, landslide monitoring, and underground asset management. WHU-MLS benchmark dataset includes more than 30 kinds of objects and 5000 typical instances in urban scene. We manually labeled MLS point cloud, each point with spatial coordinates and normal. We totally labeled 40 scenes with average number of points 8 million, of which 30 scenes are split for training and 10 scenes for testing.The coarse and fine categories are defined as follows. The Construction: building (including the building façade and other clutters in the building), fence (including isolation structure on the road and wall); Natural: trees, low vegetation, including grass, shrub and other low tree; Ground: driveway (not including road mark), non-drive way, the ground that does not belong to the driveway, road markings; Dynamic: person (including person and bikes), car; Pole: light, electric pole, municipal pole, signal light, detector, board (usually attached to the light). The semantic labeling and instance labeling in WHU-MLS provide important references for point cloud deep learning. On the one hand, these datasets can be used for point cloud deep learning networks the training, testing, and evaluation of point cloud deep learning networks. On the other hand, the benchmark datasets would can promote the benchmarking of state-of-the-art algorithms in this field, and ensure better comparisons on a common base. WHU-TLS and WHU-MLS are freely available can be used freely for scientific research. We hope that the Whu-TLS and Whu-MLS benchmark data sets meet the needs of the research community and becomes important data sets for the development of cutting-edge TLS point cloud registration and point cloud segmentation methods.关键词：remote sensing;deep learning;registration;semantic segmentation;instance segmentation;benchmark- 1278
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- 312
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- 3
发布时间：2021-03-03 -
摘要：Remote sensing images with high spatial and temporal resolutions are vital for the real-time and fine monitoring of land surface and atmospheric environment. However, a single satellite sensor has to tradeoff between the spatial and temporal resolutions due to technical and budget limitations. In recent years, numerous spatial and temporal image fusion models have been proposed to produce high-resolution images with low cost and remarkable effectiveness. Despite the varying levels of success in the accuracy of fused images and the efficiency of algorithms, challenges always remain on the recovery of spatial details along with the complex land cover changes. This study presented an enhanced unmixing model for spatial and temporal image fusion (EUSTFM) that accounts for phenological changes (e.g., vegetation growth) and shape (e.g., urban expansion) and non-shape land cover changes (e.g., crop rotation) on the land surface simultaneously. First, a change detection method was devised to identify the pixels with land cover change. The similar pixels of the detected pixels were then searched in the neighborhood to recompose the spectral reflectance on the prediction date. Thus, the real land cover class on the prediction date can be defined using the recomposed high-resolution image rather than directly using the classification result from a prior date. Subsequently, the spatial unmixing of pixels can be conducted on the prior and prediction dates to produce a medium-resolution image pair with accurate spatial details. Finally, the calculation of the similar pixels in the neighborhood was implemented for the final prediction of the fused images using all the original high and low-resolution image pair in the prior time, low-resolution image in the prediction time, and the produced medium-resolution image pair in the prior and prediction times. This study tested the algorithms with two actual Landsat-MODIS datasets: one dataset focusing on typical phenological changes in a complex landscape in Australia and the other dataset focusing on shape land cover changes in Shenzhen, China, to demonstrate the performance of the proposed EUSTFM for complex temporal changes on various landscapes. Comparisons with the popular spatiotemporal fusion models, including Spatial and Temporal Adaptive Reference Fusion Model (STARFM) and Flexible Spatiotemporal DAta Fusion (FSDAF), showed that EUSTFM can robustly achieve a better fusion accuracy for all the phenological, non-shape, and shape land cover changes. The fused results using STARFM and FSDAF showed significant differences between the green band and the two other bands for typical phenological changes on a complex landscape in Australia. By contrast, the fused images using EUSTFM showed consistently high accuracy in all the three bands. This finding revealed a better performance for the fusion of images with various spatial resolution gaps, including a factor of 8 in near-infrared and red bands and a factor of 16 in the green bands. The proposed EUSTFM shows great potential in facilitating the monitoring of complex and diverse land surface dynamics.关键词：spatial and temporal fusion;remote sensing images;spatial unmixing of pixels;change detection;temporal resolution;spatial resolution- 737
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- 189
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- 4
发布时间：2021-03-03 -
摘要：Established in 1986, China Remote Sensing Satellite Ground Station (RSGS) is one of China’s major scientific infrastructures and an important member of the International Ground Station (IGS) Network. After more than 30 years of construction, development and operation, RSGS has developed a system that is centered around the Beijing headquarters with five ground stations located in Miyun (operation since 1986), Kashi (since 2008), Sanya (since 2010), Kunming (since 2016), and the Arctic (since 2016). Its real-time data acquisition covers all territory of China and 70% of Asia’s land areas. It is also equipped with the initial ability to acquire global earth observation data efficiently.By the continuous system development and technical improving, RSGS is currently the most compatible and expandable ground receiving system for satellite data in China. Its overall performance has achieved the international advanced standard as some indicators approach the international leading level. For example, S, X and Ka band downlink reception capability with bit rate up to 2×1200Mbps (in X band) and 4×1.5Gbps (in Ka band), satellite signal tracking efficiently for high dynamics and low signal-to-noise ratio case, multiple-satellite data recording and quicklook in real time, high speed data transferring fiber link with bandwidth 200Mbps, 622Mbps or 10Gbps between domestic stations and RSGS headquarter, the worldwide standard LANDSAT, RADARSAT, SPOT and PLEIADES data processing and production system, the on-line archiving data querying/ordering and product delivery system, and the integrated ground station operation management system to monitor and manage the daily data acquisition, recording, transferring and so on.In recent years, the number of domestic and international satellite missions, the data reception passes and successful rate, and the data processing amount are all increasing continuously. From January to September of 2020, RSGS automated the operation of 32 domestic and overseas earth observation satellites as well as China’s Space Science satellites, and the total number of data reception is 42,183 passes with the successful rate 99.8%.RSGS also made a series of technical breakthroughs, including Ka band data reception, the VCM (Variable Coding and Modulation) mode data receiving technology, satellite high speed data recording and quicklook platforms for rapid application, ultra-distance data transmission network, automatic planning for data reception operations, centralized digital 3D virtual simulation monitoring of remote sensing satellite ground station, and etc. Meanwhile, the new data and application products are provided to public, such as Earth Observation Data Sharing Plan, virtual ground station, RTU (Ready-to-Use) data service, InSAR monitoring of land subsidence nationwide, and etc. On the other hand, remote sensing applications were accomplished by the national requirements, such as flood and earthquake monitoring, forest fire investigation, and sea supervision.According to the guidance of national programme, RSGS is carrying out the research and construction of the national civil space infrastructure data receiving system project. In the future, with the enhancement of system capabilities, RSGS will continue to provide China’s earth observation with powerful quantitative support and contribute greatly to national economic development, social progress, and scientific research.关键词：RSGS;national major science and technology infrastructure;satellite data receiving station network;ground system for satellite data- 1033
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- 4
发布时间：2021-03-03
Data Processing
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摘要：With the support of the national science and technology programs,China has been gradually accumulateda series of key technologies and core achievements in the field of earth observation since 2000. Taking this as an important lead, it has formed a series of operational remote-sensing satellites for meteorology, marine and land resources, and a series of multi-category remote-sensing scientific experiment satellites. Relatively comprehensive data acquisition systems for satellite andflexible and diverseaerial remote-sensing datawere created. At the same time, the systemsfor data acquisition, processing, product services and applications oriented to different needs weredeveloped to meet daily and emergency needs of major industries. In particular, the establishment of the earth observation and navigation field in the National High Technology Research and Development Program (namely 863 Program) and the implementation of the National Major Science and Technology Project “China High-Resolution Earth Observation System(CHEOS)” have systematically strengthened China’s overall earth observation technology and capability. Through multi-lateral and bilateral internationalcooperation, China started to play a leading role in the global earth observation process, and has developed and consolidated important international cooperation channels such as Sino-EU, Sino-US. With the continuous development of talent teams, the cultivation environment for industry-university-research cooperation promoting the commercialization of remote-sensing scientific and technological achievements has being constantly improved, and a number of leading enterprises have emerged. Diversified commercial remote-sensing satellites have begun to take shape, and various types of remote-sensing products were booming, and a number of typical enterprises have emerged. In the future, the space-air-ground integrated observation capability, quantitative information acquisition technology, and intelligent observation will be further developed. The remote-sensing big data management technology and the sharing service mechanism will also have new breakthroughs.关键词：earth observation;science and technology program;satellite;UAV;remote sensing application;international cooperation;industry-university-research team- 1294
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- 426
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- 10
发布时间：2021-03-03 -
摘要：Tropical areas are located between the Tropic of Cancer where the sun can directly shine, while subtropical areas are roughly between 40° North and South and the Tropic of Cancer (23° 26 min North and South). The tropical and subtropical regions have rich natural resources while undergoing rapid urbanization. Thus, the ecology and environment in these regions are facing unprecedented challenges. Meanwhile, a large number of natural disasters (e.g., typhoons, droughts, and earthquakes) occur in tropical and subtropical regions, and they threaten the sustainable development of human society and the economy in these areas. The application of remote sensing technologies to comprehensively monitor the tropical and subtropical regions is important to the sustainable development of these areas and even the world. However, remote sensing monitoring needs to overcome special challenges due to the complex geographic conditions of tropical and subtropical regions (e.g., cloudy and rainy throughout the year).This review analyzed 7594 research papers from the Web of Science Core Database and summarizes the state of the art of tropical and subtropical remote sensing, including the demand, status, challenges, and opportunities of tropical and subtropical remote sensing. The techniques of co-citation analysis and term frequency analysis were applied by investigating the clustering characteristics and patterns of the 7594 papers through their lists of references and the frequent terms in the title, abstracts, and keywords. A co-citation relationship network and a term frequency network were established to identify the clusters of research by unsupervised machine learning methods.A total of 22 co-citation clusters and 6 subject clusters were identified. Through an in-depth analysis of these categories, this study summarized (1) the major application topics of tropical and subtropical remote sensing, including urban land surface, tropical rain forest, mangrove, coral, tropical grassland, biodiversity, and natural disasters; and (2) the main remote sensing technologies used in tropical and subtropical regions, including selection of remote sensing data, remote sensing data analysis techniques, solutions to overcome cloud contaminations, and multi-source remote sensing technologies.From the rapid development of modern remote sensing technologies, this study discusses the challenges and future opportunities of tropical and subtropical remote sensing from eight different aspects. (1) Cloud detection, cloud restoration, and sub-pixel unmixing technologies promote the better applications of optical remote sensing in tropical and subtropical areas. (2) Multi-source and -modal fusion technology will bring new opportunities to overcome the problem of cloud contaminations in tropical and subtropical regions with the increasing availability of SAR remote sensing data. (3) Long time-series fusion technology can be used to monitor the tropical and subtropical regions with high temporal resolution and medium-to-high spatial resolution images. (4) New-generation observation satellites from China, Europe, Japan, and United States, as well as aerial remote sensing and UAV platforms, have provided great opportunities. Cloud computing platforms facilitate a long-term comprehensive analysis of full-coverage datasets in tropical and subtropical regions. (5) The in-depth application of tropical and subtropical remote sensing promotes the formation of a more comprehensive interdisciplinary method in Earth system science and sustainable development.关键词：tropical;subtropical;rainforest;mangroves;corals;savanna;natural disasters- 783
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- 284
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- 5
发布时间：2021-03-03 -
摘要：According to the progressing characteristics of research project achievements and industry applications of forestry remote sensing, the development course of China's forestry remote sensing in the past 70 years (1951—2020) is divided into three periods and reviewed. 1951—1980 was the phase of remote sensing application by visual interpretation based on aerial photo. During this phase, China has established forest inventory technology system combining aerial photography and comprehensive ground survey. 1981—2000 was the pioneering and innovative developing phase of satellite remote sensing. For the first time, the satellite remote sensing digital image processing system for forest resource inventory was developed, and some major breakthroughs were made in key technical fields such as renewable resource inventory, series thematic map production, and ecological benefit evaluation using remote sensing. Meanwhile, the application fields have been expanded to the remote sensing inventory and monitoring fields of wetland resources, desertification and desertification land, forestry disasters, etc. 2001—2020 is the phase of rapid development of quantitative remote sensing and initiative construction of comprehensive application service platform. Through in-depth research on the basic theory of forestry remote sensing application and quantitative remote sensing technology, China has promoted the rapid development of quantitative remote sensing technology and designed a comprehensive forestry monitoring technology system, and established a comprehensive forestry remote sensing application service platform. In the end, we put forwards some suggestions for the future development of scientific research and application of forestry remote sensing, in order to meet the new requirements and tasks faced by the forestry and grassland sectors in a new era.关键词：China;forestry remote sensing;development course;three periods;high resolution remote sensing- 1276
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- 597
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- 6
发布时间：2021-03-03 -
摘要：Oceanic eddies are known for their massive quantity, broad distribution, high energy, and strong entrainment, and are therefore an ideal proxy for studying substance cycling, energy cascade, and multi-sphere coupling in the ocean. Tracking of mesoscale eddies for their entire lifetimes is one of the most significant advances in ocean remote sensing during the first two decades of the 21st century, leading to a new wave of active eddy research. The principles and methodologies for remote sensing of oceanic eddies by infrared radiometer, optical scanner, microwave altimeter, and synthetic aperture radar based on their temperature anomaly, substance tracer, swirling flow, and enclosed topology are briefly described. In particular, the algorithms for eddy identification and tracking, as well as their applications to eddy morphology, kinematics, and dynamics are highlighted. Firstly, the eddy identification methods based on infrared remote sensing technology are described multistage. From the early stage of visual decipherment relying on human eye recognition to automatic interpretation stage represented by edge detection algorithms, feature extraction algorithms and isotherm algorithms, then to the intelligent analysis stage based on artificial intelligence technology. It is pointed out the important leading role infrared remote sensing plays, as the first remote sensing technology applied to ocean eddy detection. Secondly, based on the development stage of ocean color satellite, this paper divides it into early exploration stage and extensive application stage, and carries out a enumeration from the perspective of time, space and ecology to illustrate the irreplaceable advantages of ocean color remote sensing in the study of ocean eddies. Thirdly, the eddy identification algorithms of satellite altimeter, such as the OW(Okubo-Weiss) based method, the winding angle methods, the flow direction based methods, sea surface height based methods and the Lagrange-coherent-structures methods, and the tracking algorithm represented by the nearest neighbor methods, the similarity methods and the pixel connectivity methods are described; and the application of satellite altimeter in eddy morphology, kinematics and dynamics is supplemented. By comparing the results of different identification and tracking algorithms, their respective characteristics and diversities are described. It is pointed out that the satellite altimeter technology is widely used in eddy research, and the applications of satellite altimeter in eddy morphology, kinematics and dynamics are described systematically. Meanwhile, the role of Synthetic Aperture Radar in the study of ocean eddy is no negligible, its common tracer observation, flow field retrieval and intelligent mining methods are also mentioned in this paper. Theapplication in recent years show that it has more advantages in small scale detectionand expose the structure detail of eddies. In addition, eddy-related research frontiers and corresponding latest advances involving multiple disciplines of the oceanic, atmospheric, and ecological sciences are outlined from a virtual satellite constellation perspective, especially the important influence of eddies on primary and secondary productivity. Finally, three major challenges in eddy remote sensing, i.e., submesoscale resolving, vertical profiling, and interdisciplinary investigation, are addressed with an outlook of applying next generation remote sensing technology to future marine science and eddy oceanography.关键词：eddy remote sensing;altimeter;radiometer;ocean color scanner;synthetic aperture radar;eddy oceanography;interdisciplinary research;new generation ocean satellite- 1207
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- 293
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- 2
发布时间：2021-03-03 -
摘要：Synthetic Aperture Radar (SAR) has gained more and more attention in the field of target identification and disaster monitoring because of its all-weather observation capability. Many more advance SAR satellite developed in the last decade, e.g. high resolution SAR and full polarized SAR. However, the mechanism of interaction between electromagnetic wave and target in microwave band is still limited in the current research. Measurement of microwave characteristics in a controllable and non-interference environment can recur the interaction between electromagnetic wave and target on the ground, and can greatly help improving the cognition of SAR imaging as well. In this paper, the Laboratory of Target Microwave Properties (LAMP) was introduced and a full-parameters microwave properties measurement experiment was demonstrated. The internal size of LAMP is: 24 m (length) ×24 m (width) ×17 m (height). The positioning accuracy of the straight orbit system is 0.1 mm, while 0.01 °for the arc orbit. This guarantees that LAMP could implement the quantitative control of the relative motion between the antenna and the target under measurement, with high-precision. The dynamic range of LAMP is better than 100 dB, and the sensitivity is greater than -60 dBsm. The platform could conduct either imaging in conventional SAR imaging modes such as spotlight, stripmap and ISAR or in complex SAR imaging modes such as POLSAR, InSAR, polInSAR, with the highest spatial resolution is as high as 1cm. In this experiment, two kinds of typical man-made targets (medal ball and four-wind UAV) and natural targets (rice), were measured in LAMP, in conditions of multi-frequencies, multi-polarization, multi-incidence angles and multi-azimuth angles. The test results showed that the measured value of Radar Cross Section (RCS) of the metal ball was acceptable (2.5 —17 GHz): the RMSE is 1.09 dBsm and 1.00 dBsm for HH and VV polarization respectively, relative to the Mie scattering simulation value. For the rest of frequency band (lower than 2.5 GHz or higher than 17 GHz), however, the deviation between the measured and the theoretical RCS value of the medal ball was observed. The reason why it happened is that the surface of the medal ball is not smooth enough, on the other hand, the frequency band, lower than 2.5 GHz, is located at resonance area because of the diameter is of the same order of the length of incidence electromagnetic wave. At the same time, the scattering characteristics of multi-incident Angle and multi-azimuth Angle can be well presented in the experiment. On the other hand, the natural targets show varied microwave spectral graph (0.8—18 GHz), resulting from their irregular structures and discontinuous dialectical properties. This is the reason why it is tough to interpret SAR imageries in terms of objects of the nature.关键词：microwave anechoic chamber;scattering properties;RCS;synthetic aperture radar;simulation imaging- 712
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- 554
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- 4
发布时间：2021-03-03 -
摘要：Landslides are one of the most frequent natural disasters around the world. The surface deformation measurement is important for early identification, monitoring and early warning of landslides. Radar remote sensing has the advantages of large-scale non-contact high-precision deformation measurement, which has been widely used in the field of landslide geological disasters. This paper summarizes the recent research results of the InSAR group in Wuhan University in landslide deformation monitoring using radar remote sensing. The researches include the feasibility and applicability of radar remote sensing in landslide deformation monitoring, large-scale identification of potential landslides, measurement of landslide deformation in complex mountainous areas, measurement of landslides with large deformation gradients, 3D deformation extraction of landslide, etc.The landslides have varying movement velocities. The phase-based InSAR method is only suitable to monitor very slow-moving landslides, while the amplitude-based offset tracking mothed can measure relatively large landslide movements. The potential active landslides across wide areas can be identified through inspecting the InSAR deformation rates. We took the Three Gorges Reservoir Region and Danba County as examples to demonstrate the effectiveness of InSAR landslide identification. Once the landslides are found out, we apply satellite InSAR to conduct fine monitoring of some important landslides. The Coherent Scatterers InSAR (CSInSAR) combines persistent scatterers and distributed scatterers to efficiently increase measurements points to ensure robust InSAR deformation results in complex mountainous regions. Meanwhile, we proposed two methods to correct the tropospheric atmospheric delays for time series InSAR analysis when studying single landslide. One is the Iterative Linear Model (ILM) as an improved version of the traditional Linear Model. The other is to fuse tropospheric delays predicted by several global weather models (FDWM) with different temporal intervals and spatial resolutions.The amplitude-based offset tracking method is applied to measure fast landslide movements. Particularly, a new Time-Series Point-like Target Offset Tracking (TS-PTOT) method is proposed to retrieve time-series surface displacements at point-like targets from SAR image pairs properly combined with large temporal baselines and small spatial baselines. We took the Shuping landslide, Guobu landslide, and Huangnibazi landslide as examples to prove the ability of offset tracking method for monitoring fast moving landslides. In addition, three-Dimensional (3D) displacement field, which can render the real movement of the slope surface, is of great significance to the analysis of deformation characteristics and deformation mechanism of a landslide. We took the Guobu landslide and the Jiaju landslide as examples to present the 3D displacements extraction from multiple observations.关键词：remote sensing;landslide monitoring;time series InSAR;pixel offset tracking;3D deformation- 1884
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发布时间：2021-03-03 -
摘要：Nighttime light remote sensing is a unique optical remote sensing technology that can record ground object radiation information at night that cannot be obtained by daytime remote sensing. Given that artificial light in urban areas is the main source of stable nighttime light, nighttime light remote sensing images have been proven to reflect the variation in human activities at night. At the same time, they have extensive coverage, are time intensive and readily available, and have widely been a proxy for urban studies on the multi-scale or long-term analysis. The application related to the nighttime light data is growing at present. However, most reviews have focused on the preprocessing and potential application of nighttime light data, and the summary of nighttime light data in urban studies is still limited. In this study, we reviewed nighttime light-related research in three aspects: multi-scale analysis of the urban spatial structure, multi-scale estimation of urban socio-economic indicators, and research in urban public security. Three challenges, namely, the application of nighttime light data with a short time interval, the generation of longer nighttime light time series, and the quantitative validation, are also discussed to explore the potential applications in the future.关键词：nighttime light;remote sensing;urban study;multi-scale;review- 3292
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发布时间：2021-03-03 -
摘要：Planetary remote sensing images are an important data source for planetary observations and are the basis for qualitative and quantitative analysis of the planet’s surface. Analyzing the features of the planet’s surface based on remote sensing images and recognizing and classifying topographic features from massive planetary remote sensing data are significant fundamental tasks in planetary science research. In this new era for deep space exploration and development, multiple missions from different countries and agencies are being implemented. Accordingly, enormous amount of data will be obtained, and this situation requires using automatic target recognition and terrain classification technologies. This study systematically reviews and summarizes the research progress and advances of topography and landform recognition and classification technologies using planetary image data since the start of lunar and deep space exploration missions. First, the moon, Mars, and other planetary exploration missions and the acquired image data are briefly described. After a short introduction to the research progress of general target recognition and classification techniques, the applications of these techniques using the image data of the moon, Mars, and other planets are then elaborated as follows. (1) For lunar images, review of target recognition and classification progress is detailed in three aspects: recognition of the circular structure (i. e., crater), recognition of linear structure (e.g., wrinkle ridge), and terrain classification of the lunar surface. (2) For Mars images, the detailed advances including recognition of tectonic (e. g., crater and volcano), aeolian (e.g., slope streak, sand dune, and dust devil track), fluvial landforms (e.g., channel and gully), and other features (e.g., rock), as well as terrain classification of the Martian surface, are elaborated. (3) For target recognition and classification from other planetary images, the study introduces the research advances on other terrestrial planets (e.g., Mercury and Venus) in the solar system and asteroids that have been explored. Specifically, the asteroid parts are elaborated according to different exploration approaches: close flyby, orbiting, anchoring, and sample acquisition. Finally, future research directions of target recognition and classification using planetary image data are discussed. The future research directions include (1) target recognition and classification using multi-source data: data from different types of sensors, data of different resolutions, and data from different platforms and time; (2) automatic recognition and classification using unsupervised approach; and (3) multi-task image intelligence applications. Achieving high-precision automatic recognition and classification of the planetary surface is still challenging because of the complex environment and featureless texture of the planetary surface. In the future, automatic recognition and classification will surely play increasingly important roles in supporting planetary exploration engineering missions and scientific research through the continuous improvement in data quality and development of related field technologies.关键词：planetary exploration;remote sensing data;target recognition;terrain classification;machine learning- 804
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- 305
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发布时间：2021-03-03 -
摘要：Natural resources, as the necessary conditions for human survival and development, play an important role of driving force and main support for achieving high-quality and sustainable economic development, and are also the fundamental carrier for building a beautiful China and deepening the system reform of ecological civilization. Thus it is of great significance for human survival and development to achieve the high-precision and high-efficiency investigation, evaluation and monitoring of various natural resources. As an active three-dimensional remote sensing observation technology, Light Detection and Ranging (LiDAR) is playing an increasingly important role in the three-dimensional dynamic monitoring of multi-scale natural resources, such as land, mineral, forest, grassland, wetland, water, and ocean resources. To better understand the development and application situation of LiDAR in the three-dimensional dynamic monitoring of multi-scale natural resources, in this paper we first briefly introduced the current development status of LiDAR technology, including the review of technological development history of LiDAR and the brief elaboration of different LiDAR platforms (e.g. spaceborne LiDAR systems, airborne LiDAR systems, terrestrial LiDAR systems, etc) and their components. Then we reviewed respectively the application of LiDAR technology in three-dimensional dynamic monitoring of land, mineral, forest, grassland, wetland, water as well as ocean resources, and preliminarily analyzed the potentials and limitations of different LiDAR platforms in the three-dimensional dynamic monitoring of multi-scale natural resources. Based on the above review, we then comprehensively analyzed the potentials and limitations of applying LiDAR in natural resource surveys. The analysis showed that it is no doubt that the LiDAR technology will show the enormous advantages and potential in the three-dimensional dynamic monitoring of multi-scale natural resources in the future, as the fast development of the single photon LiDAR, multispectral LiDAR, hyperspectral LiDAR as well as Unmanned Aerial Vehicle (UAV) LiDAR platform. Certainly, LiDAR technology also demonstrated some limitations in natural resource surveys, which were mainly embodied in the following four aspects: (1) it was difficult for LiDAR technology to provide rich spectral information of natural resource; (2) A wide range of the all-weather and full-coverage LiDAR data normally was inaccessible; (3) The full three-dimensional information of natural resource was hard to be generated from a single LiDAR platform; (4) The data processing and information extraction algorithms of LiDAR were not yet systematic and prefect. Finally, we discussed the future development trend and direction of the three-dimensional dynamic monitoring of natural resources based on LiDAR technology. It is believed that the continuous development in LiDAR hardware and software platforms will continue to promote the in-depth mining of LiDAR data in the applications of three-dimensional dynamic monitoring of natural resources. However, current LiDAR technology still cannot meet the requirements of full-element, full-processes, full-coverage, high-precision and high-efficiency monitoring of natural resources, owing to its shortcoming of lack of spectral information, full three-dimensional information as well as all-weather and full-coverage data. Therefore, how to fuse multi-source, multi-scale, and multi-platform remote sensing data by taking advantages of artificial intelligence to build an integrated natural resource monitoring system is the future direction of three-dimensional dynamic monitoring of natural resources.关键词：remote sensing;lidar;natural resources;three-dimensional information;dynamic monitoring;data fusion- 1415
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发布时间：2021-03-03 -
摘要：The changes in global climate and the accelerated development of trade have continuously expanded the distributions, host ranges, and impacts of crop diseases and pests. They have become one of the most important threatening factors of crop quality, yield, and food safety in the whole process of agricultural production. The monitoring and identification of crop diseases and pests are always based on visual inspection. However, the artificial-based method is time and labor consuming, and the survey results cannot satisfy the requirements of large area and exact analysis. Biological and chemical-related professional bacteria detection method is also costly and unsuitable for promotion in farmers. Remote sensing, which is a typical non-invasive method, provides reliable and precise technical support for real-time and large-scale monitoring of crop diseases and pests in recent decades. Each remote sensing system, such as visible and near-infrared spectral sensors, fluorescence and thermal sensors, and synthetic aperture radar and light detection and ranging system, has its own characteristics and maturity in detecting and monitoring plant diseases and pests. Hyperspectral remote sensing technology can easily, quickly, non-destructively, and accurately assess information of diseases and pests, including type identification, detection, mapping, and severity and loss assessment, because of its continuous narrow waveband characteristics.The occurrence of crop diseases and pests is a dynamic and complex process. On the one hand, crop diseases and pests are often caused by more than one causal agent, and each has different symptoms. On the other hand, host plant pathogen and pest interaction is a complex dynamic process with changes in various physiological and biochemical parameters. The two main aspects make the application of hyperspectral technology in the monitoring of diseases and pests particularly prominent because it can cover a spectral range of up to 350—2500 nm and can yield a narrow spectral resolution of less than 10 nm. These characteristics are suitable not only for disease differentiation based on slight differences but also for monitoring and analysis of dynamic disease processes. This extra information will provide additional benefits for plant disease detection, especially for detection during the latency period when symptoms are invisible to the human eye.This review first describes the basic principles of hyperspectral remote sensing and introduces the investigating mechanism of crop diseases and pests. On the basis of bibliometric analysis on the hyperspectral remote sensing-based monitoring of crop diseases and pests and detection literature from WOS and CNKI, four main research directions are summarized: identification of diseases and pest and healthy crops, classification of different diseases and pests, quantitative analysis of severity, and early asymptomatic detection. Then, we review the main development of related technologies and research status in detail. Finally, three major challenges are put forward on the basis of the abovementioned summary on technologies, developments, advantages, and disadvantages of monitoring of crop diseases and pests. This review proves that the establishment of standard spectral library of crop diseases and pests on different scales, the improvement of satellite hyperspectral sensors, and the construction of the integrated monitoring platform will be the key points to applying hyperspectral remote sensing technology.关键词：remote sensing;crop diseases and pests;hyperspectral remote sensing;monitoring and identification;future prospects- 1823
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发布时间：2021-03-03 -
摘要：With the rapid development of the society and economy as well as the growth of population, the contradiction between our country's timber supply and demand is still prominent, and its dependence on foreign countries is high. Faced with limited land resources, there is an urgent need to cultivate forest resources more efficiently and with high quality, and to apply precision silviculture technologies in various links such as directive breeding and intensive management. The multi-platform, multi-angle, multi-mode three-dimensional observation system and quantitative analysis method constructed by modern remote sensing technology are the key technologies for precision silviculture. The integrated and accurate new precision silviculture system, built with remote sensing technology as the core, from soil type analysis, land adaptability evaluation, ecological environment simulation to tree breeding, irrigation and fertilization, forest growth monitoring, pest control, etc., will fully support the overall quality and efficiency improvement of modern forestry as well as the precise improvement of forest quality. This review article first introduces the application status of RGB cameras, multispectral, hyperspectral, LiDAR, thermal infrared and fluorescence sensors in precision silviculture, and makes a comprehensive comparison of their application characteristics and measurement indicators; then, focuses on the use of remote sensing in the three key application directions i.e., high-quality species selection, monitoring and diagnosis of nutrient stress, accurate water and fertilizer sprinkler irrigation, as well as the analysis of the common needs of each application direction; finally, from three aspects, i.e., multi-source remote sensing information fusion, artificial intelligence, Internet of Things and 3S technology integration, and the integrated application of remote sensing data with physiological and ecological models and radiation transmission models, the development trend and application prospects of future remote sensing technology in precision silviculture are analyzed.关键词：precision silviculture;remote sensing;phenotyping;forest genetics;tree breeding;forest health- 827
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发布时间：2021-03-03
Remote Sensing Applications
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摘要：Hyperspectral remote sensing technology can acquire an object’s geometric, radiation, and spectral information. This technology is an important technique in Earth observations and is increasingly becoming important in applications of natural resource survey, environment and disaster monitoring, precision agriculture, oceans and costal monitoring, and urban planning. In the past decades, several advanced hyperspectral imaging systems from airborne (e.g., AVIRIS, Hymap, OMIS, and PHI) to spaceborne (e.g., EO-1/Hyperion and PROBA/CHRIS) platforms have been designed, built, and operated globally. On the one hand, airborne hyperspectral imager has been developed into commercial operation stage. Examples of international companies that develop airborne systems are Spectra Vista Corporation of America, Specim of Finland, and ITRES Research of Canada. On the other hand, GF-5/AHSI, which is a pioneer in Chinese spaceborne hyperspectral imager, has first realized wide spectrum, wide swath width, and high detection sensitivity. It marks a new era ever since the appearance of EO-1/Hyperion in 2000.In the future, the outlook for hyperspectral remote sensing technique is as follows:(1) The development of large-scale plane array detector, optical machining detection, and signal processing has improved not only the spectral resolution but also the spatial resolution and swath width of hyperspectral imaging. Hyperspectral imager’s spectrum range will cover from UV to LWIR to obtain more abundant spectral information of ground objects, all-day reflectance, and emission spectral characteristics. In addition, the integrated calibration methods of laboratory, in-orbit, and the Earth, the Sun, the Moon, the cold air, and the stars are becoming increasingly abundant and refined to ensure the application efficiency of hyperspectral imager at higher performance. The hyperspectral imaging technology with super wide width and higher resolution also puts forward higher requirements for the further development of large-scale detectors and large-aperture optics with wide working band range.(2) The development of information, imaging, and optical processing technology has introduced new beam splitting technologies and developed the core beam splitting elements from the mature dispersion and interference type to the diversified direction. Many novel optical splitting schemes, such as Acousto-optic Tunable Filter (AOTF), Liquid Crystal Tunable Filter (LCTF), Linear Variable Filter (LVF), Integrated Stepwise Filter (ISF), Tunable Fabry-Perot Filter (TFPF) and computational spectral imaging system based on compressed sensing, are available at present. These spectroscopic image methods are still in the stage of laboratory experiments. An increasing attention has also been paid to the chip-level hyperspectral spectroscopy, which combines light splitting with photoelectric conversion.(3) With the advances in the “artificial intelligence,” machine learning data process, such as neural network and deep learning, has become a trend with hyperspectral imaging to construct an ‘intelligent’ hyperspectral remote sensing satellite system. This technology will integrate the ability of automatic optimization of onboard load parameters and automatic real-time processing of onboard data and product generation. Meanwhile, the amount of remote sensing data obtained is explosively growing with the increase in resolution and information dimensions of hyperspectral imaging instruments. “Big data” feature is significant. Data transmission is an important issue in successfully using the effective data mining and information extraction and improving the efficiency of data compression in the future.(4) The development of small UAV and micro-nano satellite technology has developed hyperspectral imaging toward a low-cost, flexible, integrated, and real-time technology. At present, the light and small hyperspectral imaging technology based on small UAV is greatly demanded and valuable in the fields of agricultural, forestry diseases and insect pests’ detection, target search, and rescue and relief. Micro-nano satellites have low cost and short development cycle and can conduct complex space remote sensing tasks. The combination of hyperspectral imaging and micro-nano satellite technologies will promote the integration of multi-functional structure and space exploration payload. Lightweight, integrated, and systematized hyperspectral remote sensing with space networking and all-time detection will play an important role in the future. It will provide the possibility for hyperspectral remote sensing satellites to enter the commercial field.Many new principles, schemes, and technologies are being implemented and applied in hyperspectral imaging. The integrated acquisition and processing ability of multiple information is also greatly enhanced. The hyperspectral load is gradually developing in the direction of large field of view, large relative aperture, high resolution, and high quantification. The cost of hyperspectral remote sensing technology will be greatly reduced with its continuous development and maturity. The commercial application of this technology will also be an important direction of future development.关键词：remote sensing;hyperspectral imaging;remote sensing payload;wide spectrum;wide swath width;high resolution- 1570
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发布时间：2021-03-03 -
摘要：Ocean observation is one of the major parts of the global integrated observation system, where ocean remote sensing (or satellite oceanography) takes a key position. Nowadays, there are stronger requirements than ever that ocean remote sensing technology should make direct detection of three-dimensional (3D) stratification structure of the upper ocean. Traditional two-dimensional (2D) remote sensing, based on ocean color (OC), thermal infrared, and microwave sensors (radiometer, scatterometer, altimeter, and SAR, etc.), can only detect sea-surface or sea-skin properties, and then retrieve or deduce the profile-structures of the water body. Global Climate Observing System (GCOS) has defined 31 ocean variables as ECVs (Essential Climate Variables), which is identical to the EOVs (Essential Ocean Variables) defined by the Global Ocean Observation System (GOOS). But, only 11 in those 31 variables can be measured by traditional 2D remote sensing technologies, and yet with some accuracy or uncertainty problems. If 3D remote sensing technology could be developed, half a dozen more variables (namely the subsurface ones) would be acquired from space, which could bring forth great benefits to the ocean and earth observation system. Besides the observation subsurface variables, other critical defects of traditional 2D sensors are the inability of measuring the ecosystem activities and changes under low-light conditions, as that in the arctic ocean, and the incapability of monitoring the vast diel-vertical-migration of zooplankton at dawn/dusk and night. It seems that the active optical sensing system, i.e., the ocean profiling lidar or oceanographic lidar (not the ones for shallow water bathymetry or mapping), is currently the only feasible technology that can make direct 3D detection for the upper ocean profiles and work in a whole diel cycle to monitor the plankton activities to facilitate the studies of the life system in ocean. This paper aims to give an overall but concise review of the progress of ocean profiling lidar technology for the past 50 years, especially those of recent 15 years, including the theory, models, techniques, and preliminary experiments and applications practiced in-lab, in-situ and by airborne or spaceborne systems. The airborne oceanic lidar systems mainly refer to elastic polarimetric lidar or HSRL ones from NOAA or NASA, and the spaceborne lidars and their ocean profiling applications, refer mainly to the CALIOP onboard CALIPSO and the ATLAS onboard ICESat-II, though with limited sensing capability and coarse resolution. Some of the key issues of oceanographic lidar sensing are discussed, including the Mueller matrix, volume scattering function (VSF) of complex water constituents, blue-green dual-bands elastic polarimetric, the maximum detecting depth, inelastic scatterings (Brillouin, Raman), and the stringent engineering restraints, etc. The methods and mechanisms of oceanic lidar to probe the stratified bio-optic properties, NPP and carbon stocks of the euphotic layer, thermal structures of the upper ocean, plankton migration, fish flocks, air-sea interface properties, internal waves, etc., are given in the view of applications other than in that of instrumentation. The specific and effective applications, based on LiDAR’s unique profiling and night-time sensing ability, include the sensing of changes in the arctic ocean ecosystem during the polar nights, and the vertical-diel-migration of zooplanktons, these are largely missing in traditional ocean color. Monte Carlo (MC) models are powerful and versatile tools for the researches and system designs of oceanic lidars. Lidar MCs are capable of dealing with ray-tracing and polarimetric radiative transfer in a real-3D time and space frame. The MC models from distinguished research groups in ocean optics and lidar sensing are briefly reviewed. From the early 1990s, though left behind by international counterparts in some degree, Chinese experts on oceanographic lidar technology have also made many achievements in almost every aspect concerned, which are reviewed also in this paper. One of the outstanding achievements is the ~90m world record of the deepest detection depth in May 2019, obtained in the Southern China Sea by an airborne lidar system—the blue-green dual-bands oceanic lidar, developed jointly by SIOM/CAS and other institutes. The successful launch of CALIPSO-CALIOP in 2006 was the dawn of spaceborne oceanographic lidar technology. With CALIOP’s residual subsurface signals of backscattering, some tremendous oceanic applications have tried out and well demonstrated the necessity and revolutionary contributions of dedicated future spaceborne missions of oceanic lidar sensing. The technology of lasers and receivers for spaceborne system is much more matured and feasible than 15 years before, at least for the elastic polarimetric profiling lidar. As simulated by various Monte Carlo models, the detection depth of an affordable and engineeringly reliable oceanic lidar, no matter elastic, HSRL, or inelastic, is quite limited within 100m to 150 m with a vertical resolution of 1m or less. This depth may not be satisfactory to those serious oceanographers, but the ability of upper ocean profiling is definitely a break-through of the three dimensional ocean sensing technology. This depth may penetrate over 80% of the global euphotic zone or photosynthetic depth in which most of the ocean-NPP is originated. Along with the introduction of the Guanlan (means watching the waters) satellites project, an ocean science mission focused on three dimensional sensing of the upper ocean and sub-mesoscale phenomena, proposed and initiated by the National Laboratory of Marine Science & Technology (Qingdao), a road-map of oceanographic profiling lidar series is suggested in 4 stages, from elastic, HSRL, Brillouin and multi-beam push-broom. As the primary and promising candidate sensor of 3D ocean sensing, spaceborne oceanic profiling lidar can be realized in near future. Technically, China has the ability and chance to be the leading runner.关键词：ocean remote sensing;ocean three-dimensional remote sensing;lidar;ocean profile detection;marine science satellite- 1405
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发布时间：2021-03-03 -
摘要：In the development of earth observation technology, the integrated acquisition and application for high-resolution 3D-spectral information of targets is one of the frontier scientific issues. In order to achieve the integrated acquisition of geometric characteristics and spectral information for targets, a lot of exploration studies have been carried out at home and abroad based on the current active and passive remote sensing technology. Combining the technical advantages of hyperspectral imaging and lidar ranging, multispectral/hyperspectral lidar for earth observation came into being and has become an important direction for the future development of remote sensing.This article reviews the development of the hyperspectral lidar system for earth observation in three stages. In the initial exploration stage, the research mainly focused on dual-wavelength lidar, mainly using specific wavelength lasers for specific applications. Then, in the progressive development stage, multispectral lidar was proposed to achieve spectral information acquisition of multi-wavelengths, including prototype systems with multiple single-wavelength lasers and supercontinuum lasers. Finally, in the gradual development stage, hyperspectral lidar was developed to obtain spectral information by more wavelengths. Which can achieve wider spectrum coverage and higher spectral resolution in visible-near infrared bands.Subsequently, the exploratory research for data processing of hyperspectral lidar was expounded. It mainly involves two aspects: processing of full-waveform data in multi-bands; geometric correction and radiometric correction. In terms of full-waveform data processing, hyperspectral lidar has better capability of full-waveform decomposition for weak echo bands and low signal-to-noise bands. In terms of geometric correction, the asynchronization caused by full-waveform pulse echo in different bands needs to be solved. And radiometric correction should be mainly focused on the influence of distance, angle of incidence and roughness of the target.In addition, this paper analyzes the potential application of hyperspectral lidar in the field of surveying and mapping, agriculture and forestry. In the field of surveying and mapping, hyperspectral lidar can be widely used for classification of targets and land cover. In the field of agriculture and forestry, hyperspectral lidar can simultaneously obtain the spatial structure and spectral information of vegetation. Thus, it provides a new method for detecting the three-dimensional distribution of vegetation physiological and biochemical characteristics. Finally, we look forward to the challenges for the future development of hyperspectral lidar for earth observation. And the direction for future development is also putted forward: miniaturization, practicality and demonstration applications.关键词：lidar;hyperspectral;spatial-spectral integration;all day and night;vegetation remote sensing- 1116
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发布时间：2021-03-03
Platform & Load
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摘要：Spaceborne cameras are often called as detector resolution limited system. This is because the detector array generally suffer under sampling. Therefore, high frequency information beyond the detector sampling frequency will leak into the detector array, i.e. every remote sensing image will include some high frequency components. Since the platform keep drifting and vibrating even in the geostationary orbit, every remote sensing image contains unique high frequency information. By collecting those high frequency components from a sequence of images, a high resolution image can be reconstructed. This is the theoretical basis of super resolution technique. Moreover, with the widespread use of spaceborne CMOS array detectors, it is possible to obtain hyper-temporal data, which brings opportunities for spaceborne CMOS Cameras. A new hyper-temporal imaging mode for spaceborne CMOS cameras was proposed in the paper. By using a CMOS camera to continuously and quickly capture sequence of data, many frames of images within the same area can be extracted. By solving an ill-conditioned equation, high resolution images with improved quality can be achieved, that is, digital time delay integration TDI (Time Delay Integration), Modulation Transfer Function (MTF) and Super Resolution can be implemented at the same time. In general, engineers would like to set long expose time for spaceborne cameras to ensure SNR for remote sensing images. However, long expose time inevitable bring blur which severely decrease the quality of remote sensing images. The advantage of this new imaging method is that it can freeze the images to avoid blur as speckle imaging technique widely used in astronomy community. To reconstruct an improved quality and high resolution image, we need a good understanding of the whole process of capturing LR images. Since spaceborne cameras can only capture the reflected light from the surface of the earth and the reflected light suffers from the air turbulence and diffusion from the optical lens system. Therefore, mathematically modeling the image degenerating procedure is very important. As we all know that image restoration is an ill-conditioned problem. In terms of solving the ill-conditioned problem, a mixed sparse representations is used. In general, it is very difficult to find a common sparse representation for remote sensing images because of complicated ground features. In the paper, a remote sensing image is regarded as a combination of sub-image of smooth, edges and point components, respectively. Since each domain transformation method is only capable of representing a particular kind of ground objects or textures, a group of domain transformations are used to sparsely represent each sub-images. By using the generalized sparse representation, image restoration can be solved through the traditional L1 norm based optimal algorithm method the iterative thresholding algorithm. Experimental results based on the low-orbit optical remote sensing satellite OVS-1A, Jilin-1 video 03 satellite and the geostationary optical satellite GF-4 show that both the signal-to-noise ratio, image clarity and spatial resolution have been significantly improved. The proposed method holds promise to bring new remote sensing imagery products with high resolution of improved quality for satellites in orbit. Moreover, the method can also save the cost for future planned satellites by reducing the volume and weight of the optical camera payload.关键词：hyper-temporal data;small satellite;Time Delay Integration(TDI);Modulation Transfer Function(MFT)- 685
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发布时间：2021-03-03
Platforms & Loads
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