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Overview of China Earth Observation Satellite Programs [Space Agencies]
Abstract
China has developed some satellite series such as meteorological satellite series Fengyun (FY), ocean satellite series Haiyang (HY), Earth resources satellite series Ziyuan (ZY), environment and disaster monitoring small satellite constellation (HJ), as well as Shijian satellite series (SJ) for new technological experiments, and has formed a complete Earth observation satellite and ground application system. Until now, 20 Chinese Earth observation satellites have been launched. Chinese Earth observation system has not only largely contributed to the rapid economic development of China, but also become a crucial part of international Earth observation system.
... As an environment and atmosphere observation satellite in the CHEOS (Chinese High-resolution Earth Observation System) program (Gu and Tong, 2015), GF-5 satellite was launched on May 9th 2018. The DPC sensor was developed to meet the requirements of atmospheric pollution study and climate change assessment (Gu and Tong, 2015) and it is the first Chinese multi-angle polarized satellite sensor with 3 polarized wavelengths (490, 670, and 865 nm) and 5 non-polarized wavelengths (443, 565, 763, 765, and 910 nm). ...
... As an environment and atmosphere observation satellite in the CHEOS (Chinese High-resolution Earth Observation System) program (Gu and Tong, 2015), GF-5 satellite was launched on May 9th 2018. The DPC sensor was developed to meet the requirements of atmospheric pollution study and climate change assessment (Gu and Tong, 2015) and it is the first Chinese multi-angle polarized satellite sensor with 3 polarized wavelengths (490, 670, and 865 nm) and 5 non-polarized wavelengths (443, 565, 763, 765, and 910 nm). DPC can provide measurements in up to 9 directions with a spatial resolution of 3.3 × 3.3 km and a global coverage every 2 days. ...
The Directional Polarimetric Camera (DPC) is the first Chinese satellite-borne multi-angle polarimetric sensor onboard the GaoFen-5 (GF-5) satellite, which was launched on May 9th 2018. In this paper, we present the spatiotemporal variability of satellite-measured aerosol components (such as, black carbon, brown carbon, iron oxides contained in mineral dust etc.) over North China Plain (36–41°N, 113–123°E) for a heavily haze-polluted case, to demonstrate the first successful retrievals of aerosol component content and optical property directly from the DPC/GF-5 instrument measurements. We also reveal the relationship between absorbing/scattering component mass ratio and aerosol absorption based on the multi-angle polarimetric satellite measurements, which presents comparable agreement with previous laboratory studies and field studies for an indirect validation of aerosol component retrievals. In addition, the feasibility, stability and uncertainty of the aerosol optical property and component content retrievals from DPC multi-angle polarimetric observations were evaluated and assessed by sensitivity tests. The distributions of DPC/GF-5 aerosol optical depth (AOD) are in line with satellite-observed MODIS (Moderate-resolution Imaging Spectroradiometer) AOD and ground-based CARSNET (China Aerosol Remote Sensing Network) AOD products. Therefore, the multi-angle polarimetric satellite measurements show an encouraging potential for the comprehensive inversion of aerosol optical, microphysical and chemical properties. The results and further applications of this component algorithm in the future are helpful to reduce the uncertainty of climate change assessment by providing the measurements of aerosol component, in particular for black carbon, brown carbon, and iron oxides contained in dust, with large and extensive spatiotemporal scale.
... Therefore, in this paper, we propose to combine the respective advantages of the two types of methods. The specific objectives of this paper are to: 1) introduce a novel method based on the combination of deconvolution and Gaussian decomposition for more accurate peak position extraction, which will result in a more precise description of close object heights and the complex structure within large laser footprints; 2) present a thorough comparison between the deconvolution methods of the Wiener filter, the Richardson-Lucy algorithm, the regularization filter, and blind deconvolution, for applications in large laser footprints, to find the best deconvolution technique; and 3) give an insight into the advantages and limitations of the combination method compared with the benchmark Gaussian decomposition technique, in the expectation of providing a reference for the processing of future waveforms obtained by China's Gaofen-7 (GF-7) satellite and the Terrestrial Ecosystem Carbon Monitoring (TECM) mission, which are mainly for highaccuracy three-dimensional mapping [48] and forest monitoring [49]. GF-7 has a pulse length of 7 ns and a footprint of about 30 m, and was launched on 3 November 2019; TECM will be launched in 2020. ...
... Correspondingly, for a satellite altimetry system, the source signal and the environment's impulse response are both exactly unknown [54]. In our study, the blind deconvolution method based on an accelerated Richardson-Lucy method, as proposed by Fish et al. [48], was used. The transmit pulse and smoothed waveform can be used as the initial input for the system response and object cross-sections in each iteration. ...
In satellite laser altimetry, it is a challenging task to accurately extract peak positions from full waveforms due to the overlapped or weak peaks within the large laser footprints, which substantially affects the subsequent applications. In this paper, to improve the laser ranging resolution and accuracy, we propose a novel approach by combining deconvolution with Gaussian decomposition. The approach is applied in two main phases: 1) the deconvolution is first used to remove the system contribution (the transmit pulse spreading over several nanoseconds, system noise); and 2) Gaussian decomposition is then adopted to extract the peak parameters of each object. Experiments using simulated and ICESat waveforms were conducted to validate and evaluate the proposed approach by comparing it to the benchmark Gaussian decomposition technique. The results indicated that: 1) the combined approach can significantly improve the peak detection rate: the four combined methods found at least 15.8% more echoes in simulated forested areas; and 2) for ICESat waveforms, the quantitative evaluation and visual assessment of the Blind -Gaussian combination obtained more echoes (on average, approximately 2.5 components) than the other combinations (on average, approximately 1.5 and 1.2 components), and the derived relative object heights were very close to the results obtained from airborne LiDAR data. These results confirmed that the Blind -Gaussian combination is more accurate for the range retrieval of vegetated and urbanized landscapes.
... At the 2-to 5-m image resolution scale, the positioning accuracy of Chinese remote sensing satellite images represented by ZiYuan3 (ZY3) has reached a high level, and its geometric accuracy can completely satisfy the accuracy of 1:50,000 mapping (Xin et al., 2017;Xin et al., 2018). As a follow-up development, the GF-7 satellite is included in the Medium and Long Term Development Plan for China's Civil Space Infrastructure (2015-2025Gu & Tong, 2015). It is the first submeter-level stereo mapping satellite in China and is mainly used for 1:10,000 stereo mapping and large-scale updates to geographic information data. ...
... At the 2-to 5-m image resolution scale, the positioning accuracy of Chinese remote sensing satellite images represented by ZiYuan3 (ZY3) has reached a high level, and its geometric accuracy can completely satisfy the accuracy of 1:50,000 mapping (Xin et al., 2017;Xin et al., 2018). As a follow-up development, the GF-7 satellite is included in the Medium and Long Term Development Plan for China's Civil Space Infrastructure (2015-2025Gu & Tong, 2015). It is the first submeter-level stereo mapping satellite in China and is mainly used for 1:10,000 stereo mapping and large-scale updates to geographic information data. ...
Plain Language Summary
Spaceborne laser altimeters are widely used for planet surface measurements due to their high range accuracy. This paper details the GF‐7 laser altimeter system, which is China's first formal spaceborne laser altimeter system for Earth observations. This laser altimeter system will be used in scientific experiments for the first time in China. GF‐7 laser altimeter system is the first to be equipped with laser footprint cameras and a laser optical axis surveillance camera. The GF‐7 satellite will be launched in October 2019, and the scientific experiments will begin in November. All the data it gets will be released globally. In the future, these data will help researchers from different disciplines around the world to conduct scientific experiments and monitor the Earth's resources. The GF‐7 laser altimeter system will play a vital role in global climate change monitoring, environmental protection, disaster warning, etc. The content presented in this article will help students, researchers, and engineers in the fields of geography, oceanography, and glaciology understand and use GF‐7 satellite laser data, which will be of great significance.
... In recent years, with the progress of statistical optimization methods (Dubovik et al., 2011) and polarized surface models (Nadal et al., 1999;Maignan et al., 2009;Waquet et al., 2009), a new generation of multi-parameter POLDER inversion method has basically matured and applied to the reprocessing of POLDER historical data. (Gu et al., 2015), and a series of data has been obtained overworld. Thus, it is necessary to retrieve the global high-resolution map of finemode aerosol optical depth (AODf) over land from DPC/GF-5, and further to monitor the global haze distribution by satellite remote sensing. ...
... As the flagship of the environment and atmosphere observation satellite in the Chinese Highresolution Earth Observation System (CHEOS) program (Gu et al., 2015), GF-5 has been launched in May 5, 2018. There are six payloads onboard the GF-5 satellite, in which one of important senor is Directional Polarization Camera (DPC), just as illustrated in Figure 1 (Li et al., 2018). ...
The Directional Polarimetric Camera (DPC) is the first Chinese multi-angle polarized earth observation satellite sensor, which has been launched onboard the GaoFen-5 Satellite in Chinese High-resolution Earth Observation Program. GaoFen-5 runs in a sun-synchronous orbit with the 2-days revisiting period. DPC employed a charge coupled device detection unit, and can realize spatial resolution of 3.3 km under a swath width of 1850 km. Moreover, DPC has 3 polarized channels together with 5 non-polarized bands, and is able to obtain at least 9 viewing angles by continuously capturing series images over the same target on orbit. Based on the Directional Polarization Camera (DPC) onboard GF-5 satellite, the first global high-resolution (3.3 km) map of fine-mode aerosol optical depth (AODf) over land has been obtained together by Aerospace Information Institute, Chinese Academy of Sciences, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (the manufacturer of DPC sensor) and other institutes. This AODf remote sensing observation dataset has the highest spatial resolution in the world. It can reflect the spatial information of major air pollutants (PM2.5, etc.) and provide critical basic products for "decryption" of global haze distribution.
... In response to the urgent needs of atmospheric pollution issues and climate change assessment, based on the POLDER technology, the Anhui Institute of Optics and Fine Mechanics (AIOFM) Chinese Academy of Sciences, has developed a Directional Polarimetric Camera (DPC) space-borne sensor supported by the National Space Administration of China, with highlights of one time increase in spatial resolution (from about 6 × 7 km to 3.3 × 3.3 km) [45] . The first DPC sensor will be launched at May of 2018 with the Chinese atmospheric environment flagship satellite in the Chinese High-resolution Earth Observation System (CHEOS) program. ...
... From May 2010, China has launched the construction of the Chinese High-resolution Earth Observation System (CHEOS) program. As a major project, CHEOS focuses on building a new Earth observation system with high spatial, temporal and spectral resolution, to achieve all-weather, all-day and global coverage observation capability, and thus further to provide operational applications and services in the various fields, such as agriculture, disaster, resource and environment [45] . The satellite series of CHEOS contain 7 satellites, which are named form GF-1 to GF-7 in sequence. ...
... Numerous countries have established their own Earth observing systems (EOSs). For example, China has been working on the establishment of the meteorological Fengyun (FY) satellite series, oceanic Haiyang (HY) satellite series, Earth resource Ziyuan (ZY) satellite series [1,2], Environment and Disaster Monitoring Huanjing (HJ) satellite series, and China High-resolution Earth Observation System (CHEOS) [3,4]. The United States has developed an EOS plan [5], Earth Science Business Plan (ESE), and Integrated Earth Observation System (IEOS) [6], and it has launched numerous satellites including Landsat, Terra, Earth Observing-1 (EO-1), and other satellites [7]. ...
... As the detector A is a common feature for ground points C and E, the image coordinates are both (x, y). (Lat 1 , Lon 1 ) and (Lat 2 , Lon 2 ) can be calculated by Equation (3). Then, the coordinates of C and E are [X S1 , Y S1 , Z S1 ] and [X S2 , Y S2 , Z S2 ] in geocentric Cartesian coordinate system, which can be derived from Equation (4). ...
Numerous countries have established their own Earth observing systems (EOSs) for global change research. Data acquisition efforts are generally only concerned with the completion of the mission regardless of the potential to expand into other areas, which reduces the application effectiveness of Earth observation data. This paper explores the cartographic possibility of images being not initially intended for surveying and mapping, and a novel method is proposed to improve the geometric performance. First, the rigorous sensor model (RSM) is recovered from the rational function model (RFM), and then the system errors of the non-cartographic satellite's imagery are compensated by using the conventional geometric calibration method based on RSM; finally, a new and improved RFM is generated. The advantage of the method over traditional ones is that it divides the errors into static errors and non-static errors for each image during the improvement process. Experiments using images collected with the Gaofen-1 (GF-1) wide-field view (WFV) camera demonstrate that the orientation accuracy of the proposed method is within 1 pixel for both calibration and validation images, and the obvious high-order system errors are eliminated. Moreover, a block adjustment test shows that the vertical accuracy is improved from 21 m to 11 m with four ground control points (GCPs) after compensation, which can fulfill requirements for 1:100,000 stereo mapping in mountainous areas. Generally, the proposed method can effectively improve the geometric potential for images captured by non-cartographic satellite.
... Since then, outburst records have occurred almost every year. After extensive research by predecessors, the mechanism behind GLOFs is no longer a secret [36]. When the ice dam of a glacial lake lifts due to a continuous increase in water, the lake enters the drainage process. ...
Establishing an effective real-time monitoring and early warning system for glacier lake outburst floods (GLOFs) requires a full understanding of their occurrence mechanism. However, the harsh conditions and hard-to-reach locations of these glacial lakes limit detailed fieldwork, making satellite imagery a critical tool for monitoring. Lake Mercbacher, an ice-dammed lake in the central Tian Shan mountain range, poses a significant threat downstream due to its relatively high frequency of outbursts. In this study, we first monitored the daily changes in the lake area before the 2022 Lake Mercbacher outburst. Additionally, based on historical satellite images from 2014 to 2021, we calculated the maximum lake area (MLA) and its changes before the outburst. Furthermore, we extracted the proportion of floating ice and water area during the period. The results show that the lake area of Lake Mercbacher would first increase at a relatively low speed (0.01 km2/day) for about one month, followed by a relatively high-speed increase (0.04 km2/day) until reaching the maximum, which would last for about twenty days. Then, the lake area would decrease slowly until the outburst, which would last five days and is significant for early warning. Moreover, the floating ice and water proportion provides more information about the outburst signals. In 2022, we found that the floating ice area increased rapidly during the early warning stage, especially one day before the outburst, accounting for about 50% of the total lake area. Historical evidence indicates that the MLA shows a decreasing trend, and combining it with the outburst date and climate data, we found that the outburst date shows an obvious advance trend (6 days per decade) since 1902, caused by climate warming. Earlier melting results in an earlier outburst. This study provides essential references for monitoring Lake Mercbacher GLOFs and building an effective early warning system.
... When the satellite is operating, the electrical power subsystem of the satellite provides electrical energy to each component and also gains recharging electrical power from solar arrays that convert solar energy into electrical power. Some typical EOSs are IKONOS [3], PLEIADES [4], FY, and HY [5]. ...
With the rapid development of agile Earth observation satellites (AEOSs), these satellites are able to conduct more high-quality observation missions. Nevertheless, while completing these missions takes up more data transmission and electrical energy resources, it also increases the coupling within each satellite subsystem. To address this problem, we propose a reasoning-based scheduling method for an AEOS under multiple subsystem constraints. First, we defined the AEOS mission scheduling model with multi-subsystem constraints. Second, we put forward a state variable prediction method that reflects the different coupling states of a satellite after analyzing the coupling relationships between various subsystems and identifying the primary limiting coupling states for each subsystem. Third, we established the reasoning rules corresponding to the planning strategies of different coupling states of the satellite by adding two planning strategies based on the planning strategies of existing planning methods. By comparing the proposed method to three heuristic scheduling methods and a meta-heuristic scheduling method, the results show that our method has better performance in terms of scheduling results and efficiency.
... In recent years, spacecraft attitude control is not only aimed at single body spacecraft but also involves multiple spacecraft coordinated control (Ren & Beard, 2004;Dimarogonas et al., 2009;Sanyal et al., 2009;Hu et al., 2015;Cai & Huang, 2016;Zhang et al., 2018b;Wu et al., 2011;Wei et al., 2018;Zheng & Shen, 2019;Wu & Shan, 2019;Xie et al., 2021). Spacecraft attitude control has a wide range of applications in practical engineering scenarios (Maghami & Hyde, 2003;Gu & Tong, 2015;Wen et al., 2020; ture control (Ren & Beard, 2004) and formation flying control (Hu et al., 2015), usually assume that information interaction is continuously performed between spacecraft. Such communication settings typically require the continuous operation of all sensors and communication modules. ...
This paper is devoted to solving the multi-spacecraft attitude synchronization problem via performance adjustable event-triggered control in the presence of external disturbances and model uncertainties. In the proposed control policy, the inter-spacecraft information interaction is determined by an event-triggered sampling mechanism, and the influences of external disturbances and model uncertainties are counteracted by an adaptive estimation. This sampling method does not only reduce communication resource consumption but also provides a trade-off between resource savings and synchronization accuracy. Moreover, the corresponding performance-adjustable triggering function can achieve Zeno-free triggering. According to the comparison of some numerical simulation results, this paper shows that the provided control policy is effective and superior.
... The continuous advancement of satellite Earth observation projects in various countries (Harris and Olby, 2001;Neeck et al., 2005;Bézy et al., 2007;Shimada, 2014;Gu and Tong, 2015;Guo et al., 2018) has increased the number of remote sensing satellites at orbit, which can provide massive amounts of data for continuous observations of the Earth's surface. However, as an important source of data for satellite Earth observations, optical satellite images are inevitably contaminated by clouds due to the physical limitations of sensor imaging systems. ...
The presence of clouds prevents optical satellite imaging systems from obtaining useful Earth observation information and negatively affects the processing and application of optical satellite images. Therefore, the detection of clouds and their accompanying shadows is an essential step in preprocessing optical satellite images and has emerged as a popular research topic in recent decades due to the interest in image time series analysis and remote sensing data mining. This review first analyzes the trends of the field, summarizes the progress and achievements in the cloud and cloud shadow detection methods in terms of features, algorithms, and validation of results, and then discusses existing problems, and provides our prospects at the end. We aim at identifying the emerging research trends and opportunities, while providing guidance for selecting the most suitable methods for coping with cloud contaminated problems faced by optical satellite images, an extremely important issue for remote sensing of cloudy and rainy areas. In the future, expected improvements in accuracy and generalizability, the combination of physical models and deep learning, as well as artificial intelligence and online big data processing platforms will be able to further promote processing efficiency and facilitate applications of image time series. In addition, this review collects the latest open-source tools and datasets for cloud and cloud shadow detection and launches an online project (Open Satellite Image Cloud Detection Resources, i.e., OpenSICDR) to share the latest research outputs (https://github.com/dr-lizhiwei/OpenSICDR).
... The Fengyun Meteorological Satellites Programs belongs to a series of operational, global Earth-observation satellites [15]. These satellites monitor change of the atmosphere and surface states. ...
A compact polarization Raman lidar has been designed and constructed for using it for atmospheric correction measurements during satellite optical sensor calibration in areas with high altitude and extremely low aerosol loading. The parameters of this lidar, such as laser wavelength, telescope diameter and interference filter bandwidth, were simulated and optimized for the best observation performance. The instrument has low weight, is small in size, and requires air cooling instead of commonly used water-cooling of the laser. Thus, the instrument is suitable for autonomous operation in remote sites. The lidar prototype was installed in Lijiang (26°43' N, 100°01' E), China, a potential observation site for calibrations of optical sensors of satellites. This observation site has been shown to be an appropriate place for remote sensing and satellite calibration activities with low aerosol loading, thin air and a comparably high proportion of cloud-free days. A field campaign carried out between November 2019 and April 2020 allowed for thoroughly testing the instruments. The results of test observations show that complete overlap between emitted laser beam and field-of-view of the receiver unit is achieved at relatively low heights above ground. The measurement accuracy is comparably high. Thus, this instrument is suitable for operating in areas with relatively clean atmospheric conditions.
... A new type of spacecraft with a rapidly rotating payload for imaging represents a breakthrough from conventional large-satellites models. In this system, instead of increasing the payload, the payload continuously rotates to increase the observation width, effectively meeting the requirements of the spacecraft [11]. New rotating satellites are connected to the spacecraft platform and the rotating payload via frictionless magnetic bearings. ...
This study focuses on the attitude control of a flexible spacecraft comprising rotating appendages, magnetic bearings, and a satellite platform capable of carrying flexible solar panels. The kinematic and dynamic models of the spacecraft were established using Lagrange methods to describe the translation and rotation of the spacecraft system and its connected components. A simplified model of the dynamics of a five-degrees-of-freedom (DOF) active magnetic bearing was developed using the equivalent stiffness and damping methods based on the magnetic gap variations in the magnetic bearing. Next, a fixed-time sliding mode control method was proposed for each component of the spacecraft to adjust the magnetic gap of the active magnetic bearing, realize a stable rotation of the flexible solar panels, obtain a high inertia for the appendage of the spacecraft, and accurately control the attitude. Finally, the numerical simulation results of the proposed fixed-time control method were compared with those of the proportional-derivative control method to demonstrate the superiority and effectiveness of the proposed control law.
... In China, an increasing number of studies have been conducted on advanced RS systems, platforms, and sensors (Fan, 2017;Gu and Tong, 2015), including high-resolution earth observation platforms Tong et al., 2016), lidar scanning devices (Chen et al, 2020), oblique photogrammetry technology (Wang et al., 2020), multi-sensor observation (Chen et al., 2020), and video satellites (Yang et al., 2016). However, less consideration has been given to how stakeholders apply these technologies. ...
Remote sensing (RS) is the major technology used to obtain spatial information at disaster scenes. Fast, accurate, and flexible remote sensing emergency service (RSES) capabilities are critical for timely and appropriate emergency response. However, the realization of these capabilities is limited not only by the development of RS science, but also by user’s applications. At present, there is still a gap existing between RS science and its emergency practice. Some non-technical factors, such as the RSES organizational framework, its response procedures, the way it is carried out, and the tasks it involves, have been found to influence the improvement of this gap. In this research, we used a combination of case studies and social surveys to explore these factors and summarize specifics about them. Identifying these elements can help local governments better understand and effectively manage RSES. We also developed a framework and software to help authorities build an RSES contingency plan, convert our research results into policy document, and directly guide RSES practices in emergencies.
... HE dense time series of satellite images with high spatial resolutions are critical for monitoring land surface dynamics in heterogeneous landscapes. In recent years, satellites with advanced sensors, such as the microsatellites by Planet Labs, WorldView-4, and GF-2 [1], can acquire images with high spatial and temporal resolutions to compose dense time series. However, the high cost of data acquisition from these sensors limits their applications for large-scale land surface dynamics. ...
Spatiotemporal data fusion is a cost-effective way to produce remote sensing images with high spatial and temporal resolutions using multisource images. Using spectral unmixing analysis and spatial interpolation, the flexible spatiotemporal data fusion (FSDAF) algorithm is suitable for heterogeneous landscapes and capable of capturing abrupt land-cover changes. However, the extensive computational complexity of FSDAF prevents its use in large-scale applications and mass production. Besides, the domain decomposition strategy of FSDAF causes accuracy loss at the edges of subdomains due to the insufficient consideration of edge effects. In this study, an enhanced FSDAF (cuFSDAF) is proposed to address these problems, and includes three main improvements. First, the TPS interpolator is replaced by an accelerated inverse distance weighted (IDW) interpolator to reduce computational complexity. Second, the algorithm is parallelized based on the compute unified device architecture (CUDA), a widely used parallel computing framework for graphics processing units (GPUs). Third, an adaptive domain decomposition (ADD) method is proposed to improve the fusion accuracy at the edges of subdomains and to enable GPUs with varying computing capacities to deal with datasets of any size. Experiments showed while obtaining similar accuracies to FSDAF and an up-to-date deep-learning-based method, cuFSDAF reduced the computing time significantly and achieved speed-ups of 140.3–182.2 over the original FSDAF program. cuFSDAF is capable of efficiently producing fused images with both high spatial and temporal resolutions to support applications for large-scale and long-term land surface dynamics. Source code and test data available at
https://github.com/HPSCIL/cuFSDAF
.
... SAR land cover classification is an important step in a variety of SAR image interpretations and applications, such as agricultural monitoring, urban planning, and damage assessment [2], [3]. With the development of new generation SAR sensors, e.g., TerraSAR-X [4], Gaofen-3 [5], and airborne SAR, large amounts of high-resolution (HR) SAR images have become available. Although the HR SAR image can provide sufficient detailed information of ground objects, it also presents more complex backscattering and spatial layout hard to deal with. ...
Convolutional neural networks (CNNs) have been applied to learn spatial features for high-resolution (HR) synthetic aperture radar (SAR) image classification. However, there has been little work on integrating the unique statistical distributions of SAR images which can reveal physical properties of terrain objects, into CNNs in a supervised feature learning framework. To address this problem, a novel end-to-end supervised classification method is proposed for HR SAR images by considering both spatial context and statistical features. First, to extract more effective spatial features from SAR images, a new deep spatial context encoder network (DSCEN) is proposed, which is a lightweight structure and can be effectively trained with a small number of samples. Meanwhile, to enhance the diversity of statistics, the nonstationary joint statistical model (NS-JSM) is adopted to form the global statistical features. Specifically, SAR images are transformed into the Gabor wavelet domain and the produced multi-subbands magnitudes and phases are modeled by the log-normal and uniform distribution. The covariance matrix is further utilized to capture the inter-scale and intra-scale nonstationary correlation between the statistical subbands and make the joint statistical features more compact and distinguishable. Considering complementary advantages, a feature fusion network (Fusion-Net) base on group compression and smooth normalization is constructed to embed the statistical features into the spatial features and optimize the fusion feature representation. As a result, our model can learn the discriminative features and improve the final classification performance. Experiments on four HR SAR images validate the superiority of the proposed method over other related algorithms.
... Additional measures taken by the government include: (1) Strengthening the capacity of monitoring, forecasting, and early warning by building more tidal observation stations (tide gauges) and using satellite monitoring systems (Gu and Tong 2015). ...
Marine disasters pose a serious threat to economic and social development; therefore, understanding their occurrence rhythms is of great importance to disaster prevention and mitigation. As a major form of marine disaster in China, storm surges and rough seas are particularly worthy of attention. In this study, statistical data regarding storm surges and rough seas over the past 20 years were collected, and a visual approach was utilized to detect their scope, distribution, and temporal-spatial characteristics. Implementation of disaster prevention and mitigation was then discussed. The results revealed the following: (1) storm surges exhibited significant seasonality (occurring in summer and autumn), while rough sea occurrences occurred throughout the year. (2) The losses caused by storm surges showed clear regional differences. Specifically, the economic losses and death tolls in southern provinces were greater than in northern provinces, but they decreased significantly from 2000–2009 to 2010–2019. (3) The loss caused by rough seas also showed regional differences, with greater loss values in the southern provinces than northern provinces. The total loss has dropped significantly in recent years.
... For satellites, due to the rapid development of small satellites and dissatisfaction with the task requirement of scheduling a single satellite, multi-satellite networking [21], [22] and satellite virtual constellations [23], [24] are emerging to collaborate multiple satellites for improving task completion. Furthermore, these developments urge the reasonable and efficient planning of multiple satellite resources due to the increase in user demand. ...
A multiparameter observation task includes a comprehensive theme with multiple indispensable parameters that need to be monitored simultaneously. However, with limited observation sensor resources for a multiparameter observation task, the spatial misalignment of the coverage area of each parameter decreases the observation efficiency, especially in a space-ground sensor network. To solve this problem, we developed a collaborative planning method in the sensor planning phase. With this method, a space-ground maximal coverage model with multiple parameters (SGMC-MP) was introduced. The proposed collaborative planning method cooperatively utilizes the space-ground sensors to make an observation plan. This method aims to maximize the overlay coverage range among the parameters in the task to reduce the spatial misalignment and improve the utilization of the sensors. The proposed method was applied to a multiparameter observation task in the Three Gorges Reservoir Area in Chongqing. The results indicate that the proposed method exhibits better coverage performance for sensor planning in the multiparameter observation task than the traditional separate planning method. In addition, planning strategies, coverage flexibility, model extension, and algorithm comparison are further discussed.
... GaoFen-5 satellite ( Fig. 1 (a)), launched on May 9, 2018, is the atmospheric environment flagship satellite in the Chinese Highresolution Earth Observation System [47,48] . It integrates satellite modes with both atmospheric and terrestrial observations functions. ...
The Directional Polarimetric Camera (DPC) onboard China's Gaofen-5 (GF-5) satellite has provided multi-angle (3-polarization channel), large-scale (1850 km swath width and 3.3 km spatial resolution), and high-frequency (2-day revisit period) Earth observation since May 2018. These features make DPC imagery application scenarios extensively. However, like other optical imagery, the presence of clouds is also a pervasive and unavoidable issue in DPC imagery. To leverage both radiation and polarization properties of DPC, we proposed a multi-information collaborative (MIC) method to identify clouds in the DPC imagery. Instead of a fixed single threshold, the MIC method adopts dynamic thresholds obtained by simulation in different atmosphere models, at different times, and under different underlying surfaces. Specifically, we included surface albedo and ice/snow cover distribution libraries to the MIC method, as they compensate for fewer spectral bands in the DPC imagery, thereby improving the accuracy of cloud detection results, especially in special bright surface scenarios (e.g., desert, bare soil and ice/snow). We also added an ice/snow detection algorithm to further eliminate the issue of misidentifying ice/snow pixels as clouds. Finally, after obtaining the DPC cloud mask results based on the MIC method, we calculated four cloud confidence levels for different application requirements by cloud quality evaluation criteria.
We evaluated the MIC algorithm by comparing it with two other independent satellite cloud observation products, namely Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). We found that the MIC cloud mask is in good agreement with the other two cloud products, with agreement probabilities of 93.06% (CALIPSO) and 85% (MODIS), respectively. Furthermore, the detected high confidence cloud and clear sky results agree with the CALIPSO cloud confidence products by more than 97.35% and 96.13%, respectively. We therefore suggest that the MIC method can provide the basis for subsequent studies of atmospheric parameters, such as accurate retrieval of aerosol optical thickness (AOT), cloud optical thickness (COT), cloud droplet effective radius (CDR) and land surface reflectance.
... Since the 1980s, China has developed extensive Earth observation satellite programs dedicated to meteorology, oceanography, and Earth surface monitoring (Gu and Tong, 2015), catching up in a field long dominated by the U.S. and Europe. The Feng-Yun 3 (FY-3) program is of particular interest to NWP centers. ...
This paper evaluates the microwave instruments onboard the latest Chinese polar-orbiting satellite, Feng-Yun 3D (FY-3D). Comparing three months of observations from the Microwave Temperature Sounder 2 (MWTS-2), the Microwave Humidity Sounder 2 (MWHS-2), and the Microwave Radiation Imager (MWRI) to Met Office short-range forecasts, we characterize the instrumental biases, show how those biases have changed with respect to their predecessors onboard FY-3C, and how they compare to the Advanced Technology Microwave Sounder (ATMS) onboard NOAA-20 and the Global Precipitation Measurement Microwave Imager (GMI). The MWTS-2 global bias is much reduced with respect to its predecessor and compares well to ATMS at equivalent channel frequencies, differing only by 0.36 ± 0.28 K (1σ) on average. A suboptimal averaging of raw digital counts is found to cause an increase in striping noise and an ascending—descending bias. MWHS-2 benefits from a new calibration method improving the 183-GHz humidity channels with respect to its predecessor and biases for these channels are within ± 1.9 K to ATMS. MWRI presents the largest improvements, with reduced global bias and standard deviation with respect to FY-3C; although, spurious, seemingly transient, brightness temperatures have been detected in the observations at 36.5 GHz (vertical polarization). The strong solar-dependent bias that affects the instrument on FY-3C has been reduced to less than 0.2 K on average for FY-3D MWRI. Experiments where radiances from these instruments were assimilated on top of a full global system demonstrated a neutral to positive impact on the forecasts, as well as on the fit to the background of independent instruments.
... Passive microwave radiometers are utilized by meteorological agencies worldwide to remotely sense precipitation from space (Goldberg, 2018;Levizzani et al., 2018;Skofronick-Jackson et al., 2017). More than 30 radiometers onboard satellites from several international agencies have been used for rainfall measurement since the 1980s, with about 15 currently operational and several follow-on missions being planned (Goldberg, 2018;Gu & Tong, 2015;Hou et al., 2014;Skofronick-Jackson et al., 2017). Rainfall estimates from these radiometer observations make it possible to generate the widely used hourly (or half-hour) global precipitation data sets (Huffman et al., 2007(Huffman et al., , 2015Joyce et al., 2004;Kubota et al., 2007;Xie et al., 2017). ...
Rainfall estimates from spaceborne microwave radiometers form the foundation of global precipitation data sets. Since the beginning of the satellite microwave rainfall estimation era in the 1980s, the primary signature leveraged over land for these estimates has been the brightness temperature (TB) depression due to ice particle scattering. Contrary to this practice, time series analyses based on observations from two spaceborne radars and two spaceborne radiometers reveal a TB increase at H19 due to raindrop emission as the primary cloud particle signature over desert terrain. Low surface emissivity supports the use of liquid raindrop emission as the primary signature over desert surfaces. In these regions, the surface rain rate better correlates with the liquid raindrop emission signal than with the scattering induced by ice further aloft, suggesting a new potential for improving rainfall estimation over deserts by exploiting the liquid raindrop emission signature.
... However, this temporal resolution may be insufficient for monitoring the critical period of vegetation growth, especially for faster-growing crops. The application of multisource data integration is a feasible way to further improve the temporal resolution of high spatial resolution data, such as Landsat (16 days), Sentinel-2 (5 days, pair of Sentinel-2 satellites) (Verrelst et al., 2012), GaoFen-1 (4 days) and GaoFen-6 ( Gu and Tong, 2015). ...
Fractional vegetation cover (FVC) is considered one of the most important vegetation parameters and is relevant to characterizing vegetation status and ecosystem function. An FVC with a fine spatial resolution of 30 m is essential for monitoring vegetation change and regional studies, while an FVC with a coarse spatial resolution of hundreds to thousands of metres plays an important role in global change studies. However, high spatial resolution data usually have low temporal resolution and are often affected by cloud cover. The objective of this study is to propose a practical way to generate spatiotemporally consistent FVC products at Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) scales, which are 30 m and 250 m, respectively. The geostatistical neighbourhood similar pixel interpolator (GNSPI) was first used to fill in the missing values caused by unscanned gaps and clouds/shadows on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data and to generate spatially continuous Landsat reflectance. Then, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was used to generate time series Landsat reflectance data with the same temporal resolution as that of Global LAnd Surface Satellite (GLASS) FVC generated from MODIS data. The high temporal resolution Landsat reflectance was preliminarily used to estimate FVC at the Landsat scale. Finally, MultiResolution Tree (MRT) was employed to fuse the Landsat FVC and GLASS FVC to generate spatiotemporally consistent FVC products at different scales. The results show that the missing Landsat-7 ETM+ data were filled well and spatial texture features were well preserved. The temporal resolutions of the Landsat and GLASS FVC products became consistent with an interval of one day at most. After MRT fusion, most of the root mean square error (RMSE) between the GLASS FVC and aggregated Landsat FVC dramatically decreased. The accuracy of the Landsat FVC validated by the ground-measured FVC improved after MRT fusion (before MRT: RMSE = 0.1031, R² = 0.9172, bias = −0.0697; after MRT: RMSE = 0.0958, R² = 0.9173, bias = −0.054). In addition, in the GNSPI-filled unscanned gaps and the ESTARFM-generated images, the Landsat FVC accuracy also improved slightly (before MRT: RMSE = 0.1065, R² = 0.9011, bias = −0.0644; after MRT: RMSE = 0.1022, R² = 0.9023, bias = −0.051). The accuracy of the GLASS FVC also improved (before MRT: RMSE = 0.0913, R² = 0.884, bias = −0.0504; after MRT: RMSE = 0.0673, R² = 0.9483, bias = −0.0444). Therefore, MRT could decrease the inconsistencies of different scales and reduce uncertainties in the FVC. In addition, MRT could fill in the missing data of the Landsat FVC directly, but there were a certain number of outliers in the fusion results, and the spatial transition was poor.
... Currently, there are multiple satellite series operating in orbit, including the Fengyun (FY) meteorological satellites since 1988, the Ziyuan (ZY) Earth resources satellites since 1999, the Haiyang (HY) ocean satellites since 2002, and the Huanjing (HJ) environment and disaster monitoring satellites since 2008. More recently, a new Earth observation system with high spatial, temporal, and spectral resolution, named 'Gaofeng' (GF), was developed and has operated since 2013, achieving all-weather, all-day, and global coverage observation capability and providing operational applications and services in the fields of agriculture, disaster, resource, and environmental studies [15]. ...
Climate observations and their applications require measurements with high stability and low uncertainty in order to detect and assess climate variability and trends. The difficulty with space-based observations is that it is generally not possible to trace them to standard calibration references when in orbit. In order to overcome this problem, it has been proposed to deploy space-based radiometric reference systems which intercalibrate measurements from multiple satellite platforms. Such reference systems have been strongly recommended by international expert teams. This paper describes the Chinese Space-based Radiometric Benchmark (CSRB) project which has been under development since 2014. The goal of CSRB is to launch a reference-type satellite named LIBRA in around 2025. We present the roadmap for CSRB as well as requirements and specifications for LIBRA. Key technologies of the system include miniature phase-change cells providing fixed-temperature points, a cryogenic absolute radiometer, and a spontaneous parametric down-conversion detector. LIBRA will offer measurements with SI traceability for the outgoing radiation from the Earth and the incoming radiation from the Sun with high spectral resolution. The system will be realized with four payloads, i.e., the Infrared Spectrometer (IRS), the Earth-Moon Imaging Spectrometer (EMIS), the Total Solar Irradiance (TSI), and the Solar spectral Irradiance Traceable to Quantum benchmark (SITQ). An on-orbit mode for radiometric calibration traceability and a balloon-based demonstration system for LIBRA are introduced as well in the last part of this paper. As a complementary project to the Climate Absolute Radiance and Refractivity Observatory (CLARREO) and the Traceable Radiometry Underpinning Terrestrial-and Helio-Studies (TRUTHS), LIBRA is expected to join the Earth observation satellite constellation and intends to contribute to space-based climate studies via publicly available data.
... The success of our integration also suggests that the same approach might be extendable to Sentinel-2 (with 5 day interval, 10 m resolution) (Drusch et al., 2012) and other satellites with both frequent revisit and high spatial resolution (e.g. GeoEye-1, GaoFen-2, VENuS and Pleiades) (Dedieu et al., 2006;Dribault et al., 2012;Gu and Tong, 2015;Pu et al., 2018). We recommend BRDF-adjusted MODIS be used as a calibration reference for such multi-sensor integration. ...
In tropical forests, leaf phenology-particularly the pronounced dry-season green-up-strongly regulates bio-geochemical cycles of carbon and water fluxes. However, uncertainties remain in the understanding of tropical forest leaf phenology at different spatial scales. Phenocams accurately characterize leaf phenology at the crown and ecosystem scales but are limited to a few sites and time spans of a few years. Time-series satellite observations might fill this gap, but the commonly used satellites (e.g. MODIS, Landsat and Sentinel-2) have resolutions too coarse to characterize single crowns. To resolve this observational challenge, we used the PlanetScope constellation with a 3 m resolution and near daily nadir-view coverage. We first developed a rigorous method to cross-calibrate PlanetScope surface reflectance using daily BRDF-adjusted MODIS as the reference. We then used linear spectral unmixing of calibrated PlanetScope to obtain dry-season change in the fractional cover of green vegetation (GV) and non-photosynthetic vegetation (NPV) at the PlanetScope pixel level. We used the Central Amazon Tapajos National Forest k67 site, as all necessary data (from field to phe-nocam and satellite observations) was available. For this proof of concept, we chose a set of 22 dates of PlanetScope measurements in 2018 and 16 in 2019, all from the six drier months of the year to provide the highest possible cloud-free temporal resolution. Our results show that MODIS-calibrated dry-season PlanetScope data (1) accurately assessed seasonal changes in ecosystem-scale and crown-scale spectral reflectance; (2) detected an increase in ecosystem-scale GV fraction (and a decrease in NPV fraction) from June to November of both years, consistent with local phenocam observations with R 2 around 0.8; and (3) monitored large seasonal trend variability in crown-scale NPV fraction. Our results highlight the potential of integrating multi-scale satellite observations to extend fine-scale leaf phenology monitoring beyond the spatial limits of phenocams.
... In the past 30 years, China has developed several satellite series, such as the meteorological series Fengyun ("Wind and Cloud"); ocean series Haiyang ("Ocean"); Earth resources series Ziyuan (ZY) ("Resource"); high-resolution Earth observation series Gaofen (GF) ("High Resolution"); environment-and disaster-monitoring small satellite constellation ("Environment and Disaster"); and experimental series Shijian ("Experiment") for new technological exploration [1]. China's national Earth Observation System contributes not only to rapid economic and social development but also to the international Earth Observation System. ...
The ZiYuan3 (ZY3) is a civilian stereo surveying and mapping satellite from China operating under the framework of the Earth resources satellite series, and its objective is to fulfill 1:50,000 mapping and update largerscale fundamental geographic information products. This article introduces the ZY3-03 satellite's mission and payload specifications as well as its data utilization and distribution policy. In the future, the ZY3-03 will realize the demands of China's national economic and social development and play an important role in the fields of Earth observation and environmental protection.
... We obtained the calibration coefficient (gain) and solar exoatmospheric irradiance (E 0 ) of the GF-1 satellite WFV4 camera. (Table 1) (Bai 2014;Gu and Tong 2015;Gao et al. 2016). ...
As a basis for the quantitative application of satellite remote sensing, surface reflectance can be retrieved through atmospheric correction methods. Currently, most studies have focused on developing or comparing atmospheric correction methods. However, few studies have quantitatively analyzed the effects of input parameters in an atmospheric correction method on retrieved surface reflectance. In this study, we evaluated the effects of the calibration coefficient, aerosol optical depth (AOD), aerosol type, and satellite zenith angle over four typical surfaces using wide field-of-view sensor four data of the Gao Fen-1 satellite. The results showed that (1) the relative errors of shrub, corn, grass, and soil reflectance increased as the calibration coefficient error increased; (2) the calibration coefficient, AOD, aerosol type, and satellite zenith angle affected corn reflectance retrieval the most, whereas they had the smallest effect on soil reflectance retrieval; and (3) the accuracy of the satellite zenith angle on the retrieved surface reflectance was the least pronounced, whereas the accuracy of aerosol type was the most pronounced.
... he GF-7 satellite is the first submeter level resolution mapping satellite in the Medium and Long Term Development Plan for China's Civil Space Infrastructure (2015-2025) [1]. The satellite is equipped with two main payload systems. ...
The GaoFen-7 (GF-7) satellite, which was launched on November 3, 2019, is China's first civilian submeter stereo mapping satellite. The satellite is equipped with the first laser altimeter officially in China for earth observation. Except for the laser altimeter, the GF-7 spaceborne laser altimeter system also includes two laser footprint cameras and a laser optical axis surveillance camera. The laser altimeter system is designed and used to assist improving the elevation accuracy without Ground Control Points (GCPs) of the two line-array stereo mapping cameras. This paper details the design of the GF-7 spaceborne laser altimeter system, its ranging performance in the laboratory and its data processing method. The type of data products is also released. These data will play a vital role in the application of geography, glaciology, forestry and other industries.
... These surface-and geographical-dependent performances are inherited from the level-2 (swath) rainfall retrieval results of passive microwave radiometers, which serves the basis for generating the widely used level-3 (gridded) precipitation datasets (except PERSIANN estimates deriving from IR only). (Goldberg 2018;Gu and Tong 2015). ...
This study assesses the level-2 precipitation estimates from 10 radiometers relative to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR) in two parts. First, nine sensors—four imagers [Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounder (SSMIS)] and five sounders [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounder (MHS)]—are evaluated over the 65°S-65°N region. Over ocean, imagers outperform sounders, primarily due to the usage of low frequency channels. Furthermore, AMSR2 is clearly superior to SSMISs likely due to the finer footprint size. Over land all sensors perform similarly except the noticeably worse performance from ATMS and SSMIS-F17. Second, we include the Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie (SAPHIR) into the evaluation process, contrasting it against other sensors in the SAPHIR latitudes (30°S-30°N). SAPHIR has a slightly worse detection capability than other sounders over ocean but comparable detection performance to MHSs over land. The intensity estimates from SAPHIR show a larger normalized root-mean-square-error over both land and ocean, likely because only 183.3 GHz channels are available. Currently, imagers are preferred to sounders when level-2 estimates are incorporated into level-3 products. Our results suggest a sensor-specific priority order. Over ocean, this study indicates a priority order of AMSR2, SSMISs, MHSs and ATMS, and SAPHIR. Over land, SSMIS-F17, ATMS and SAPHIR should be given a lower priority than the other sensors.
... A recent study by Feng et al. (2017) showed that use of the red-edge slope instead of the NDVI in the linear VI and semi-empirical VI models could improve the precision of the retrieved f c values. In addition, China's high-resolution earth observation system (CHEOS) satellite series also play an important role in longterm remote sensing services (Gu and Tong, 2015). According to Jia et al. (2016), GF-1 wide field view (WFV) surface reflectance data can produce satisfactory f c products. ...
Green fractional vegetation cover () is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a comprehensive and systematic review of RA algorithms considering these factors based on the scientific papers published from January 1990 to November 2019. This review revealed that the key issues related to RA algorithms is the determination of the appropriate normalized difference vegetation index (NDVI) values of the full vegetation cover and bare soil (denoted hereafter by NDVI∞ and NDVIs, respectively). The existing methods used to correct for these issues were investigated, and their advantages and disadvantages are discussed in depth. In literature trends, we found that the number of reported studies in which RA algorithms were used has increased consistently over time, and that most authors tend to utilize the linear NDVI model, rather than other models in the RA algorithm family. We also found that RA algorithms have been utilized to analyze the images with spatial resolutions ranging from the sub-meter to kilometer, most commonly, using images of 30-m spatial resolution. Finally, current challenges and forward-looking insights in remote estimation of using RA algorithms are discussed to guide future research and directions.
... The GaoFeng (GF-7) satellite to be launched soon in China will carry a laser altimeter that will record full-waveforms data, and its main objective is to acquire the height control points with higher accuracy to further facilitate high resolution mapping (Gu and Tong 2015). The principle of it is combining the range information retrieving from the full-waveforms, with the precise orbit and attitude determination for the spacecraft to obtain three-dimensional information of the target surface and other characteristics (e.g. ...
Full-waveform decomposition is crucial for obtaining accurate satellite-ground distance, the accuracy of which is severely affected by noises. However, the traditional filters all depend on filtering parameters. This paper presents a new and adaptive method for denoising based on empirical mode decomposition (EMD) and Hurst analysis (EMD-Hurst). The noisy full-waveforms are first decomposed into their intrinsic mode functions (IMFs), and the Hurst exponent of each IMF is established by the detrended fluctuation analysis. The IMF is regarded as the high-frequency noise and is deleted if its Hurst exponent is ≤0.5. Both simulated and real full-waveforms were conducted to validate and evaluate the method by comparing with six other IMF selection methods via metrics like waveform decomposition consistency ratio (CR), average error of decomposition parameters, and ICESat/GLAS waveform-parameter product GLAH05. The comparisons show that: (1) under different SNR conditions, EMD-Hurst performs robustly and obtains a higher CR than other EMD based methods; (2) obtains the highest average CR and a relatively lower average error for the echo parameters; and (3) peak numbers and fitting accuracy for GLAH01 are more reasonable and precise than those of GLAH05, which could offer a good reference for the processing on future space-borne full-waveform data.
... The workflow combined RCF [32] and U-Net [33] models respectively to detect soft edges (rivers and roads) and to detect regions such as hard edges and types of farmland. To evaluate the methodology they used a pansharpened image from the GF-2 satellite [34] with a coverage area of 1808 km 2 , which is equivalent to a square image of 1680 by 1680 pixels (spatial resolution of 0.8 m). While the work is difficult to replicate due to the lack of information provided in the methodology, the results showed a promising future for DL-based techniques. ...
Accurate and up-to-date information on the spatial and geographical characteristics of agricultural areas is an indispensable value for the various activities related to agriculture and research. Most agricultural studies and policies are carried out at the field level, for which precise boundaries are required. Today, high-resolution remote sensing images provide useful spatial information for plot delineation; however, manual processing is time-consuming and prone to human error. The objective of this paper is to explore the potential of deep learning (DL) approach, in particular a convolutional neural network (CNN) model, for the automatic outlining of agricultural plot boundaries from orthophotos over large areas with a heterogeneous landscape. Since DL approaches require a large amount of labeled data to learn, we have exploited the open data from the Land Parcel Identification System (LPIS) from the Chartered Community of Navarre, Spain. The boundaries of the agricultural plots obtained from our methodology were compared with those obtained using a state-of-the-art methodology known as gPb-UCM (global probability of boundary followed by ultrametric contour map) through an error measurement called the boundary displacement error index (BDE). In BDE terms, the results obtained by our method outperform those obtained from the gPb-UCM method. In this regard, CNN models trained with LPIS data are a useful and powerful tool that would reduce intensive manual labor in outlining agricultural plots.
... As the flagship of the environment and atmosphere observation satellite in the Chinese High-resolution Earth Observation System (CHEOS) program (Gu and Tong, 2015), Gaofen-5 (GF-5) has been launched in May 2018, and the Directional Polarization Camera (DPC) is one of payloads (Li et al., 2018;Zheng et al., 2019). By inheritance the technology of Polarization and Directionality of the Earth's Reflectances (POLDER) (Deuzé et al., 2001), The DPC employed a charge coupled device (CCD) detection unit and realized spatial resolution of 3.3 km under a swath width of 1850 km, which has integrated 3 polarized channels (490, 670, 865nm) together with 5 non-polarized bands (443, 565, 763, 765, 910 nm). ...
Many previous studies have shown that multiangle, multispectral polarimetric remote sensing can provide valuable information on aerosol microphysical and optical properties, in which the aerosol layer height (ALH) is an important parameter but with less studies, especially in the near-ultraviolet (near-UV) and visible (VIS) wavelength bands. Based on the optimal estimation (OE) theory and information content analysis method, we focus on the sensitivity study of ALH with the synthetic data in the near-UV and VIS wavelength in the range of 410–865 nm, and further to assess the capability of multiangle intensity and polarization measurements for the retrieval of ALH. Unified Linearized Vector Radiative Transfer Model (UNL-VRTM) has been used as the forward model to simulate the intensity and polarized radiance at the top of atmosphere (TOA), as well as the Jacobians of TOA results with respective to corresponding parameters. The degree of freedom for signal (DFS) and a posteriori error are introduced to quantity the information content of ALH from the intensity and polarization measurements, respectively. By assuming the surface type, aerosol model, aerosol loads, prior errors and observation geometries, the sensitivity of ALH has been preliminarily investigated. The sensitivity study results show that the near-UV and polarization measurements are the important source of information content for the aerosol height retrieval in satellite remote sensing.
... Synthetic aperture radar (SAR) has been used in a wide range of remote sensing applications for many years because it provides many unique advantages, such as day-and-night acquisition, certain penetrability, and polarimetric capability [1,2]. With the development of SAR sensors, e.g., TerraSAR-X [3], RADARSAT-2 [4], Sentinel-1 [5], and Gaofen-3 [6], large amounts of SAR images have become available and the automatic interpretation of such massive data has been an active research topic. This paper deals with the classification of single-polarized SAR image, which is one of the fundamental problems in the automatic interpretation task [7][8][9][10]. ...
The classification of synthetic aperture radar (SAR) images is of great importance for rapid scene understanding. Recently, convolutional neural networks (CNNs) have been applied to the classification of single-polarized SAR images. However, it is still difficult due to the random and complex spatial patterns lying in SAR images, especially in the case of finite training data. In this paper, a pattern statistics network (PSNet) is proposed to address this problem. PSNet borrows the idea from the statistics and probability theory and explicitly embeds the random nature of SAR images in the representation learning. In the PSNet, both fluctuation and pattern representations are extracted for SAR images. More specifically, the fluctuation representation does not consider the rigorous relationships between local pixels and only describes the average fluctuation of local pixels. By contrast, the pattern representation is devoted to hierarchically capturing the interactions between local pixels, namely, the spatial patterns of SAR images. The proposed PSNet is evaluated on three real SAR data, including spaceborne and airborne data. The experimental results indicate that the fluctuation representation is useful and PSNet achieves superior performance in comparison with related CNN-based and texture-based methods.
... Brightness temperature (TB) observations from these passive microwave radiometers are combined with infrared sensor observations to produce global high-quality precipitation estimates every 30 min. Several planned satellite missions also will house radiometers suitable for precipitation estimation (Goldberg, 2018;Gu & Tong, 2015). ...
This study investigates the correlation between the upwelling microwave brightness temperature measured by satellite radiometer and surface precipitation rate from ground radar observations at different time lags. Results show that brightness temperatures correlate more strongly with the lagged surface precipitation rate than the simultaneous surface precipitation rate. The lag time for snowfall ranges from 30 to 60 min. This time lag effect has an important influence when evaluating the satellite retrieval results relative to ground observations. For example, the falsely identified pixels can decrease by as much as 23.88% when considering a 30‐min lag time. Furthermore, the satellite‐retrieved snowfall rate shows much stronger correlation with the time‐lagged surface snowfall rate than the simultaneous snowfall rate in cold environments and for tall storms. This work implies that the time of the level‐2 swath‐retrieved snowfall rate needs to shift forward when incorporated into the level‐3 gridded products.
... After the NASA EO-1 satellite (mounted onboard the Hyperion HS sensor) was deactivated in March 2017 and the HS Imager for the Coastal Ocean failed because of a solar storm in September 2014, few spaceborne sensors are still operating. Among them, there are the European Space Agency's compact high-resolution imaging spectrometer sensors mounted on the PROBA-1 satellite [2], the HS imager sensor mounted onboard the Chinese satellite HJ-1B [3], the HS imager mounted onboard the Indian IMS-1 satellite [4], and the visual and infrared (IR) HS sensor mounted on the Chinese Gaofeng-5 satellite launched on 9 May 2018. Therefore, there is limited availability of multitemporal HS data, which has led to limited development of ad hoc CD methods. ...
The expected increasing availability of remote sensing satellite hyperspectral (HS) images provides an important and unique data source for Earth observation (EO). HS images are characterized by a detailed spectral sampling (i.e., very high spectral resolution) over a wide spectral wavelength range, which makes it possible to monitor land-cover dynamics at a fine spectral scale. This is due to its capability of detecting subtle spectral variations in multitemporal images associated with land-cover changes that are not detectable in traditional multispectral (MS) images because of their limited spectral resolution (i.e., sufficient for representing only abrupt, strong changes in the spectral signature, as a rule). To fully exploit the available multitemporal HS images and their rich information content in change detection (CD), it is necessary to develop advanced automatic techniques that can address the complexity of the extraction of change information in an HS space. This article provides a comprehensive overview of the CD problem in HS images, as well as a survey on the main CD techniques available for multitemporal HS images. We review both widely used methods and new techniques proposed in the recent literature. The basic concepts, categories, open issues, and challenges related to CD in HS images are discussed and analyzed in detail. Experimental results obtained using state-of-the-art approaches are shown, to illustrate relevant concepts and problems.
... Synthetic aperture radar (SAR) has significant research value and very broad application prospects because it can obtain ground information all day and during all weather [1], etc. SAR is widely used in land resources, disaster management, agroforestry, oceans and other fields [2][3][4]. Germany, Canada, Russia, China, and other countries have successfully developed SAR systems, such as TerraSAR-X [5], RADARSAT-2 [6], Sentinel-1 [7], and Gaofen-3 [8], etc. With the increasing abundance of SAR image data and the gradually increasing resolution of SAR images [9], the problem of automatically interpreting SAR images has received extensive attention, especially for classification purposes [10]. ...
The convolutional neural network (CNN) has shown great potential in many fields; however, transferring this potential to synthetic aperture radar (SAR) image interpretation is still a challenging task. The coherent imaging mechanism causes the SAR signal to present strong fluctuations, and this randomness property calls for many degrees of freedom (DoFs) for the SAR image description. In this paper, a statistics learning network (SLN) based on the quadratic form is presented. The statistical features are expected to be fitted in the SLN for SAR image representation. (i) Relying on the quadratic form in linear algebra theory, a quadratic primitive is developed to comprehensively learn the elementary statistical features. This primitive is an extension to the convolutional primitive that involves both nonlinear and linear transformations and provides more flexibility in feature extraction. (ii) With the aid of this quadratic primitive, the SLN is proposed for the classification task. In the SLN, different types of statistics of SAR images are automatically extracted for representation. Experimental results on three datasets show that the SLN outperforms a standard CNN and traditional texture-based methods and has potential for SAR image classification.
... The GF-1 is equipped with two 2 m resolution panchromatic/8 m resolution multispectral cameras and four 16 m WFV (wide field of view) multispectral sensors. Because of their high spatial resolution and short revisit period, GF images have been widely used in environmental protection, land resources survey, and other fields (Xingfa and Xudong, 2015). ...
... The GF-3 satellite went through a four-month payload performance commissioning phase and a two-month application performance commissioning phase, and from January 2017 began to provide customers with advanced, commercially-available spaceborne SAR imagery which had fully polarization mode and resolution as fine as 1 m in spotlight mode [1]. The GF-3 system is able to generate a greater diversity of data products than any other civilian satellite SAR in China [2]. Besides single polarization stripmap and scanSAR mode as in HuanJing-1C (HJ-1C) SAR [3], GF-3 SAR can also operate at high resolution spotlight mode, dual-receive stripmap mode, dual polarization stripmap or scanSAR mode, and quad polarization stripmap mode, which can be separated into 12 observing modes by different resolution and swath [1]. ...
The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are referred to as data ingesting subsystem (DIS) and product generation subsystem (PGS). The primary purpose of DIS is to record and catalogue GF-3 raw data with a transferring format, and PGS is to produce slant range or geocoded imagery from the signal data. This paper presents a brief introduction of the GF-3 data processor, including descriptions of the system architecture, the processing algorithms and its output format.
... The recently emerged geostationary satellite GaoFen-4 (GF-4) also has a high application value in rapid assessment and emergency response of floods [25]. Due to its optical geostationary orbit, GF-4 shows a better performance in time resolution over other satellites. ...
Due to its capacity for temporal and spatial coverage, remote sensing has emerged as a powerful tool for mapping inundation. Many methods have been applied effectively in remote sensing flood analysis. Generally, supervised methods can achieve better precision than unsupervised. However, human intervention makes its results subjective and difficult to obtain automatically, which is important for disaster response. In this work, we propose a novel procedure combining spatiotemporal context learning method and Modest AdaBoost classifier, which aims to extract inundation in an automatic and accurate way. First, the context model was built with images to calculate the confidence value of each pixel, which represents the probability of the pixel remaining unchanged. Then, the pixels with the highest probabilities, which we define as 'permanent pixels', were used as samples to train the Modest AdaBoost classifier. By applying the strong classifier to the target scene, an inundation map can be obtained. The proposed procedure is validated using two flood cases with different sensors, HJ-1A CCD and GF-4 PMS. Qualitative and quantitative evaluation results showed that the proposed procedure can achieve accurate and robust mapping results.
Typhoons are one of the most serious natural disasters, which are extremely destructive and pose a great threat to the safe operation of power grids. To improve the risk warning and pre-control capability of power grid operation under typhoon weather, this paper proposes a typhoon cloud system identification method based on a two-dimensional convolutional neural network (CNN) and long short-term memory (LSTM) network. First, the spectral features are selected according to the physical characteristics of clouds, combined with the square field of point clouds as the spatial information of point clouds to construct a sample library of typhoon cloud blocks; then, the spatial features are automatically extracted by the convolutional neural network; Finally, the LSTM network extracts the spatial local difference features and the time series features of continuous changes of a typhoon cloud system to provide multi-angle features for satellite cloud map to identify typhoon cloud system. Combined with the multi-channel scanning imaging radiometer AGRI (Advanced Geostationary Radiation Imager) data in the geostationary Fengyun-4 meteorological satellite (FY-4A), the monitoring and research of typhoon weather in Guangdong Province, China, is taken as an example. The experimental results show that compared with the Faster-RCNN method of abstracting typhoon features for identification, the CNN-LSTM model-based typhoon identification method can achieve a more detailed division between typhoon and non-typhoon regions based on the multidimensional features of the cloud system in typhoon regions, and achieve better identification results.KeywordsTyphoon Cloud System IdentificationCNN-LSTM Hybrid ModelFeature ExtractionFY-4A Satellite
Carrying onboard remote sensing systems, Earth observation (EO) satellites provide unique global, systematic, and consistent space-based measurements of natural and man-made phenomena. Measurements can be produced on atmospheric, surface, and subsurface characteristics, properties, and constituents as well as other indicators and related data, enabling comparisons in time and across different parts of the globe, including remote and otherwise inaccessible areas.
The Chinese High-resolution Earth Observation System (CHEOS) program has successfully launched 7 civilian satellites since 2010. These satellites are named by Gaofen (meaning high resolution in Chinese, hereafter noted as GF). To combine the advantages of high temporal and comparably high spatial resolution, diverse sensors are deployed to each satellite. GF-1 and GF-6 carry both high-resolution cameras (2 m resolution panchromatic and 8 m resolution multispectral camera), providing high spatial imaging for land use monitoring; GF-3 is equipped with a C-band multipolarization synthetic aperture radar with a spatial resolution of up to 1 meter, mostly monitoring marine targets; GF-5 carried 6 sensors including hyperspectral camera and directional polarization camera, dedicated to environmental remote sensing and climate research, such as aerosol, clouds, and greenhouse gas monitoring; and GF-7 laser altimeter system payload enables a three-dimensional surveying and mapping of natural resource and land surveying, facilitating the accumulation of basic geographic information. This study provides an overview of GF civilian series satellites, especially their missions, sensors, and applications.
Abstract: Aerosol fine-mode fraction (FMF) is a key parameter in the near-surface PM2.5 physical estimation model of particle pollution. However, there still have great challenges for the remote sensing of FMF. Polarimetric measurements shows unique capabilities in aerosol remote sensing. In order to investigate the contribution of polarization to the inversion of FMF, an algorithm for FMF retrieval from multispectral intensity and degree of linear polarization (DOLP) measurements is proposed in this study.
The algorithm is based on the optimal estimation (OE) inversion theory. The UNified Linearized Vector Radiative Transfer Model (UNL-VRTM) is adopted as the forward model, and the quasi-Newton approach implemented by the L-BFGS-B code is used to find the minimum of the cost function. In order to test the performance of the algorithm, synthetic data for ground-based measurements of sky light are simulated. After several iterations, the aerosol optical depth (AOD) and FMF can be retrieved simultaneously.
Sensitivity analysis show that the DOLP is more sensitive to FMF in the near-infrared band than in the visible band. Numerical inversion test show that the algorithm has well self-consistency. The average fitting residual, difference between the measurements and the simulations with best inversion results, is 5.2%. By introducing DOLP measurements into the retrieval, the inversion accuracy improved significantly than only using the intensity measurements. The retrieval error of AOD has decreased from 1% to 0.3%, and the error of FMF has decreased from 1.4% to 0.8%.
The results strongly validate the feasibility and potentiality of the proposed OE inversion method in atmospheric aerosol remote sensing. It is expected to be a new way to improve the remote sensing capabilities of PM2.5 monitoring.
Chinese GF-1 and GF-6 satellites are currently operational for routine Earth observation, which can acquire multi-spectral data with moderate spatial resolution, high temporal resolution and wide coverage. There are slight differences between the wide field view (WFV) sensors onboard both satellites. Therefore, consistency analysis is very necessary for the networking of GF-1 and GF-6 WFV data to enhance the observation frequency. In this study, spectral band reflectance and vegetation indices in three regions dominated by urban, cropland and grassland, and forest were used to analyze the consistency of GF-1 and GF-6 WFV data. Consistency analysis results showed that the average coefficients of determination (R²) between GF-1 and GF-6 WFV data were 0.86, 0.88, 0.88 and 0.93 for blue, green, red and NIR bands, as well as 0.86, 0.82 and 0.6 for normalized difference vegetation index (NDVI), ratio vegetation index (RVI) and soil adjusted vegetation index (SAVI), respectively. Therefore, the subtle difference and high correlation of spectral band reflectance and vegetation indices indicated that the GF-1 and GF-6 WFV data had a degree of good consistency and had the potential of networking to monitor the rapid change of the Earth surface.
The development of spaceborne remote sensing has greatly facilitated the land cover mapping at various spatial scales. Classification accuracy, however, is usually affected by the heterogeneous spectra of different land cover types for medium–low-spatial-resolution images. The study is aimed at improving the classification accuracy at a city scale by proposing a hierarchical classification method. Time-series Landsat-5 and Landsat-8 Operational Land Imager remote sensing images of 4 years were used as the classified images. A total of six first-class land cover types were determined, namely woodland, grassland, cropland, wetland, artificial surface and others. The object-based image analysis was chosen over pixel-based approaches. More specifically, the nearest-neighbor (NN) classification and SEparability and THresholds (SEaTH) algorithm were combined to produce a hierarchical classification method (NN-SEaTH). SEaTH algorithm was first used to extract the wetland after performing image segmentation in eCognition Developer. Then, the non-wetland was further classified to vegetation and non-vegetation by using a normalized difference vegetation index image. Finally, the other types were then obtained using the NN classification. To validate the proposed method, the NN classifier and NN-SEaTH method were compared. The proposed technique is shown to increase the overall accuracy (OA) and kappa coefficient (k) for the 4 years. The OA and k are, respectively, 96.46% and 0.9231, 96.63% and 0.9269, 96.88% and 0.9394, 95.22% and 0.9239 that are much larger than 88.13% and 0.7503, 88.83% and 0.7660, 88.64% and 0.7630, 87.33% and 0.7371 derived from the NN approach. The study provides a reference for medium-resolution-based land cover mapping by a hierarchical classification.
This article addresses the attitude reorientation problems of rigid bodies under multiple state constraints. A novel reinforcement learning (RL)-based approximate optimal control method is proposed to make the tradeoff between control cost and performance. The novelty lies in that it guarantees constraint handling abilities on attitude forbidden zones and angular velocity limits. To achieve this, barrier functions are employed to encode the constraint information into the cost function. Then, an RL-based learning strategy is developed to approximate the optimal cost function and control policy. A simplified critic-only neural network (NN) is employed to replace the conventional actor-critic structure once adequate data are collected online. This design guarantees the uniform boundedness of reorientation errors and NN weight estimation errors subject to the satisfaction of a finite excitation condition, which is a relaxation compared with the persistent excitation condition that is typically required for this class of problems. More importantly, all underlying state constraints are strictly obeyed during the online learning process. The effectiveness and advantages of the proposed controller are verified by both numerical simulations and experimental tests based on a comprehensive hardware-in-loop testbed.
The one belt and one road initiative, an international tourism island construction and the sustainable development of the Hainan province in China, requires the global Earth observation information obtained via satellite remote sensing. However, the unique geographical location of the Hainan province and its cloudy and rainy climate causes several challenges for the acquisition of high-quality satellite remote sensing images. To address this issue, the proposed Hainan satellite constellation is aimed at building a remote sensing satellite system equipped with optical, hyperspectral, and radar payloads as well as a payload for ship automatic identification system, considering specific business requirements. The application will focus on ship detection and islands and marine environment observations. Once the complete operation of the Hainan satellite constellation is established, it will supply high-frequency and coverage observations in low-latitude regions, covering major portions of the maritime silk road areas. This will facilitate marine environment protection and services, and marine resources exploration. This paper introduces the design of the Hainan satellite constellation, including its architecture, characteristics and innovations, and potential applications for ocean observations.
To improve the automaticity for identifying geomorphological hazards in underground coal mining areas. An improved region merging watershed segmentation (Lab-RMWS) algorithm was proposed based on the Lab color space. It is generated by combining the classical watershed segmentation algorithm with the region merging algorithm. Additionally, the program was also developed in the VC++.net environment. To demonstrate its universality, a total of three experiments were performed using the Chinese GF-2 (Experiment I), Pleiades image (Experiment II), and QuickBird image (Experiment III). The results were comparatively analyzed with the interactive Self-Organization Data analysis (ISODATA), the maximum-likelihood classification (MLC), and the Luv color space based RMWS (Luv-RMWS). The results showed that the running time for the Lab-RMWS algorithm were, respectively, 112.83 s, 218.41 s, and 93.17 s for the target extraction, which could also achieve an integration of image segmentation and region merging. In comparison with the ISODATA and MLC methods, the time was reduced by more than 1 h, and the time efficiency was significantly improved. In the three experiments, the Lab-RMWS had the highest overall accuracy and kappa coefficient for the three experiments, with the values of 98.21% and 92.21%, 98.53% and 95.47%, and 97.59% and 89.18%, respectively. In general, Lab-RMWS had the best segmentation effect by considering the visual observation and evaluation indices. It is obvious that the proposed Lab-RMWS can be effectively used to identify the geomorphological hazards in underground coal mining areas.
Data from the directional polarimetric camera (DPC) instrument onboard Chinese Gaofen-5 satellite dedicated to aerosol monitoring have been available recently. By measuring the spectral, angular and polarization properties of the radiance at the top of atmosphere (TOA), a DPC provides the aerosol optical depths (AODs) as well as partial microphysical aerosol properties. In order to evaluate the capability and the retrieval uncertainty of DPC sensor systematically, the information content and a posteriori error analysis are applied to the synthetic data of DPC multi-angle observation in this paper, which inherits from the optimal estimate theoretical framework. The forward simulation is conducted by the unified linearized vector radiative transfer model (UNL-VRTM), and the Jacobians of four Stokes elements with respect to aerosol and surface model parameters can be obtained simultaneously. Firstly, the error influences of surface parameter on the TOA measurements are simulated. The results indicate that a 10% relative error of parameter k1 in the improved BRDF model results in about 4.65% error of the TOA reflectance, while the error of TOA polarized reflectance caused by the same error of parameter C in BPDF model is negligibly small. Secondly, the multi-angle dependence of total information content in DPC measurements is investigated. It is shown that the information content increases significantly with the number of viewing angles, especially for the measurements of the first 9 angles. The DPC multi-angle observation can provide extra 5 degrees of freedom for signal (DFS) for the retrieval of aerosol and surface parameters, in which the retrieval of aerosol parameters is more sensitive to observation geometries than the retrieval of surface parameters in most cases. In addition, the total aerosol DFS increases with the range extension of scattering angle under the same number of viewing angles. After that, the DFS of each retrieved aerosol and surface parameter are given. For the aerosols, the volume concentration, real-part refractive index and effective radius show a high DFS (greater than 0.8). For the surfaces, the mean DFS of each parameter is greater than 0.5, which indicates the well capability of DPC in the surface retrieval. Finally, the a posteriori error of each aerosol, surface parameter and corresponding vary with the number of viewing angles, and the observation error and aerosol model error are discussed. The a posteriori error decrease significantly with the number of viewing angles, and the influence of the aerosol model error on the a posteriori error is not remarkable. In general, the observation error is the main influence factor on the uncertainty of the inversion results.
GaoFen-3 (GF-3) is the first fully polarimetric C-band synthetic aperture radar (SAR) satellite in China. Interferometric observation is an important capability of GF-3. However, the long temporal baseline is a great challenge for repeat-pass interferometric synthetic aperture radar (InSAR) processing, especially over mountainous areas. Phase noise and complex topography make phase unwrapping difficult. This paper proposes a step-wise approach for high-resolution InSAR data processing focusing on facilitating phase unwrapping. First, the coarse GF-3 digital elevation model (DEM) is reconstructed by InSAR processing with multilooking of large looks, which is used to suppress phase noise. Second, the coarse GF-3 DEM is used to remove the terrain phase from the InSAR phase with multilooking of small looks, which can reduce phase gradient and improve the accuracy of phase unwrapping. After several iterations, the GF-3 DEM is finally inversed with multilooking of 2 looks, which is of high resolution and high accuracy. The performance of GF-3 C-band InSAR data on the extraction of DEM is examined over a deciduous forested area with the proposed method. The root-mean-square error of the extracted GF-3 DEM is 3.78 m in comparison with the LiDAR DEM. © 2018 Society of Photo- Optical Instrumentation Engineers (SPIE).
Status, issues and trend of remote sensing application of civilian satellites in China [J]
- Qingyan Meng
- Xingfa Gu
- Tao Yu
- Feng Jin
Potential applications of HJ-1 optical satellites [J]
- Chunrong Li
- Lingli Tang
- Jian Hu
- Ziyang Li
- Yuanyuan Jia
Potential applications of HJ-1 optical satellites [J]
- li
Status, issues and trend of remote sensing application of civilian satellites in China [J]
- meng