最新刊期
卷 22 ， 期 4 ， 2018
- “在高光谱遥感领域，专家建立了辐射动态范围预测模型，为智能高光谱卫星主相机的辐射动态范围调整提供了重要依据。”
摘要：In this study, the sensor bands of a fore-field sensor and the minimum and maximum radiometric range simulation models are discussed by focusing on a tilt angle. The radiation performance design of a fore-field sensor for an intelligent hyperspectral remote sensing satellite with a spectral range of 0.4—1.0 μm is examined. This study provides an important reference for the dynamic range adjustment of the main camera of the hyperspectral remote sensing satellite. The tilt angle of a fore-field sensor is affirmed by considering the processing time, imaging distance, and spatial resolution. The fore-field sensor bands are set by achieving cloud detection, aerosol optical thickness, and water vapor retrieval. The radiance of numerous objects is simulated using MODTRAN. The minimum and maximum radiometric range simulation models are built on the basis of the radiance of several bands with a spectral range of 0.4—1.0 μm. The tilt angle of a fore-field sensor with a width of 100 km is 11° given the response time of 2 s for the fore-viewing sensor processing and main sensor adjustment. The minimum five spectral bands are 0.49, 0.66, 0.87, 0.94, and 2.1 μm. These bands are carefully selected to identify clouds and estimate aerosol parameters and water vapor. The minimum and maximum radiometric range simulation models can be constructed using the radiance of several bands, with a spectral range of 0.4—1.4 μm. The maximum radiance of vegetation, mineral, soil, and man-made environment can be predicted using a radiance with wavelengths of 0.66 and 0.87; 0.49, 0.66, and 0.87; 0.49 and 0.66; and 0.49 μm, respectively. The minimum radiance of vegetation and mineral can be predicted using a radiance with a wavelength of 0.66 μm. The minimum radiance of soil and man-made environment can be predicted using a radiance with a wavelength of 0.49 μm. The verification correlation of the maximum radiometric range simulation model reaches 0.98, with an accuracy maintained at 0.05. The verification correlation of the minimum radiometric range simulation model reaches 0.75, with an accuracy maintained at 0.25. The fore-viewing angle allows the onboard device to have additional time in updating the radiometric dynamic range for the hyperspectral sensor to achieve the cloud detection and parameter retrieval methods. The fore-viewing sensor must obtain at least five spectral bands, namely, 0.49, 0.66, 0.87, 0.94, and 2.1 μm. In the spectral range of 0.4—1.4 μm, the radiance range of the vegetation, mineral, soil, and manmade environment can be predicted using the radiance with wavelengths of the few designed bands. An appropriate radiance range is important for the main camera to adjust its dynamic range by optimizing the gain values. As expected, additional detailed ground surface information can be recorded. In the future, an increasing number of ground objects and other spectral ranges must be investigated.关键词：intelligent hyperspectral;fore-field sensor;radiation transfer model;apparent radiance;radiometric dynamic range- 617
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发布时间：2021-06-07
Fundamental Research
- “在现代农业技术领域，专家提出了一种新思路，利用作物模型与时间序列遥感数据相结合，实现耕层土壤速效养分反演，为变量施肥提供高精度的土壤养分信息。”
摘要：Soil available nutrients are important for crop growth and yield accumulation. The improvement of yield and protection of the environment can be achieved by maintaining soil available nutrients at an optimal level. Currently, more than half of the available nutrients come from fertilization in a modern farm management. Appropriate fertilization can control these nutrients at an appropriate level. The precondition of fertilization optimization is acquiring the status of soil available nutrients. We proposed a new method for simulating the available nutrients to address the abovementioned issue. On the basis of the advantages of a crop model in simulating crop growth accurately and steadily, WOFOST crop model was selected as the primary model to simulate a nutrient-limited crop growth. The key parameters were calibrated through a documentary method, farm data collection, field observation, and Remote Sensing estimation prior to applying the WOFOST model. Moreover, necessary model optimizations, such as changing the structure of the WOFOST and adding a new algorithm to the soil nutrient module, were implemented. The EnKF method was selected for assimilating time-series HJ-1 A/B data into the WOFOST to realize the simulation at field and pixel scales. On the basis of the model calibration, optimization, and assimilation method construction, the simulation method for soil available nutrients was established. In this study, the theoretical basis of this new method was analyzed, and several necessary analyses were conducted to check the feasibility of the WOFOST in estimating the soil available nutrients. A lookup table method was used to realize the model simulation in a reverse order. The contents of the soil available nutrients were simulated as the output data by taking a Leaf Area Index (LAI) that was estimated from time-series HJ-CCD data as the input. This method was applied in the Shuangshan Farm in 2014. The available nitrogen (N), phosphorus (P), and potassium (K) in the maize fields were simulated. The time of the end of SAN simulation sage (ESS) can influence the simulation results; thus, we varied such time from 173 to 273 with 10 steps and repeatedly operated using the WOFOST model to obtain different simulation results of various method application times. Furthermore, we conducted several field campaigns to obtain the observation data of the soil nutrients. These data were used to analyze the precision. Results showed that the optimal time of the ESS for N, P, and K are different, and the highest R2 values are 0.68, 0.74, and 0.52, correspondingly. The average relative errors are 7.45%, 6.17%, and 9.97% respectively. This new method can reliably simulate the status of the soil available nutrients in terms of prediction accuracy, stability, and application value.关键词：WOFOST model;assimilation method;remote sensing;simulation algorithm;soil available nutrients- 572
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发布时间：2021-06-07 - “在遥感分类领域，研究者提出了一种基于人工蜂群算法改进SVM参数的方法，通过模仿蜜蜂采蜜行为，优化SVM分类器参数，提高分类精度。实验结果表明，该方法在UCI数据集和遥感影像上的分类精度均优于其他人工智能算法优化的SVM算法。”摘要：In the applications of a Support Vector Machine (SVM) classifier in solving classification problems, setting model parameters significantly influence the classification result, which causes a necessity for optimizing the key factors (cost and kernel parameters) in the SVM classifier to obtain the optimal classification results. Artificial Intelligence (AI) methods, which provide solutions for the parameter optimization problem of the SVM classifier, have emerged in recent years with the rapid advancement of the AI technology. However, these methods can easily trap the local optimal solution during the optimization process. An Artificial Bee Colony (ABC) algorithm exerts advantages, such as fast convergence and uneasiness of falling into a local optimum. Therefore, this study is aimed at introducing the ABC algorithm into optimizing the key parameters of the SVM classifier, thereby attempting to achieve a high precision in remote sensing image classification. The ABC algorithm imitates the behavior of honey bees in searching for the optimal parameters (also referred as the nectar) in the SVM classifier. The quality of the parameters is continuously validated using a threefold cross-validation accuracy of the training samples until The quality of parameters reaches the maximum accuracy. Then, the SVM with the optimized parameters are applied to classify the remote sensing images. First, three types of SVM classifier, namely, SVM optimized by an ABC algorithm (ABC-SVM), SVM optimized by a genetic algorithm (GA-SVM), and SVM optimized by a particle swarm algorithm (PSO-SVM), were compared when classifying five standard UCI datasets to verify the classification accuracy and efficiency of the ABC-SVM. The operation efficiency of the three AI algorithms was compared in the classification experiment based on the same UCI dataset, during which the training time and convergence curve of the three algorithms were recorded. Results showed that the ABC-SVM method has the highest classification accuracy, which is 6.55% and 6.07% higher than that of the GA-SVM and PSO-SVM algorithms, respectively. In terms of the efficiency of the three AI algorithms, the GA-SVM and PSO-SVM undergo minimal training time in the classification experiments of the five datasets. However, their global searching capability is inefficient, and the convergence rates are slow. The ABC-SVM undergoes the longest operation time among the three AI algorithms, but its fitness value is the highest and the convergence rate the fastest. Then, classification accuracies were evaluated based on a remote sensing image in the test area (Landsat-8 OLI image in Huairou area, Beijing), in which the SVMs were optimized by the three AI algorithms, and the original SVMs were compared. Results showed that the ABC-SVM method has the highest classification accuracy, which is 10.50% higher than that of the original SVM classification algorithm and is 1.67% and 1.50% higher than those of the other two AI algorithms (GA-SVM and PSO-SVM, correspondingly). The conclusions are listed as follows: (1) The ABC-SVM shows the highest classification accuracy among the results of the three types of AI optimized using the SVM algorithm when classifying the UCI datasets. The average classification accuracy of the five datasets is 6.55% and 6.07% higher than that of the GA-SVM and PSO-SVM, respectively. In addition, ABC-SVM has the fastest convergence among the three AI methods and shows a complex trapping local optimum. (2) The results of using the remote sensing image to verify the accuracy of the four classification algorithms show that the average accuracy of the SVM optimized by the three AI algorithms is 9.45% higher than the original SVM algorithm. Among these AI algorithms, the classification accuracy is 1. 67% and 1.50% higher in the ABC-SVM than in the GA-SVM and PSO-SVM, respectively.关键词：artificial bee colony algorithm;Support Vector Machine (SVM);remote sensing image;classification
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发布时间：2021-06-07 - “在全极化合成孔径雷达影像去噪领域，专家提出了一种结合相似块匹配和线性最小均方误差原理的去噪方法，有效抑制了影像噪声，同时保持了影像细节信息。”摘要：Synthetic Aperture Radar (SAR) systems are capable of capturing images of the earth in day and night and for almost all weather conditions. Polarimetric SAR (PolSAR), which focuses on emitting and receiving complete polarized radar waves to characterize observed targets, is an advanced form of SAR. PolSAR data have unique advantages in obtaining land cover information in comparison with optical remote sensing data, thereby resulting in their wide use in many areas. However, the SAR data are inherently affected by speckle noise. The presence of speckles complicates the PolSAR image interpretation and land surface parameter inversion. Therefore, despeckling is an essential procedure in most cases before using SAR images to obtain land cover information. In the traditional methods based on the linear minimum mean square error (LMMSE) filter, a group of homogeneous image pixels is first selected in a local window to obtain precise filter parameters in the LMMSE estimator. The LMMSE estimator is then generated from the values of the selected pixels and is saved as the filtered value of the pixel being processed. These methods assume that all of the selected pixels are absolutely homogeneous pixels with respect to the processed pixel, which is insufficiently reliable. In addition, most of these methods have limitations, such as a limited selection range of pixels, which are only compared with the characteristics of their own, thereby possibly producing a biased or inferior estimation of the filter parameters in the estimator. In this study, we proposed a similar patch matching and LMMSE filter combined with a PolSAR despeckling method based on the 3D block matching-based algorithm. The main idea behind the proposed method is to select additional similar pixels in the nonlocal area to improve the performance of the LMMSE estimator. The main process of the proposed method is as follows: first, the similar patches in a nonlocal window for each target patch are selected to form a patch group, and the LMMSE filter is used to filter all the pixels in the group. Second, an aggregation step is utilized to estimate the pixels that have been clustered into several groups. The patch matching process is used again to group similar patches by considering the information of the original and basic estimated images. Finally, a collaborated LMMSE filter and an aggregation step are undertaken to filter the image. The experiments on the simulated PolSAR and two real PolSAR images acquired by the GF-3 satellite revealed the positive despeckling performances of the proposed method. The speckle is reduced to a large degree, and the image details, such as the edges and strong point targets, are effectively preserved.关键词：polarimetric synthetic aperture radar;speckle filtering;similar patches matching;linear minimum mean square error filter;nonlocal means
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发布时间：2021-06-07 - “在热带气旋强度预测领域，专家基于静止红外卫星云图和相关向量机RVM构建了有眼TC客观定强模型，通过高斯平滑去噪、GAC模型分割眼壁、提取亮温梯度信息等方法，有效估计TC强度，为减轻灾害影响提供解决方案。”
摘要：A Tropical Cyclone (TC) is an influential natural hazard in the world. Thus, the forecasting accuracy of intensity and track is important to reduce the impacts of this disaster. In recent years, a remote sensing satellite cloud image gradually becomes the main means of monitoring a TC. In this study, an eyed TC intensity objective estimation model based on geostationary infrared satellite cloud image and Relevance Vector Machine (RVM) is proposed. First, the infrared satellite cloud image is denoised through a Gaussian smoothing method. Second, a TC eye area is segmented using a Partial Differential Equation (PDE) based on a Geodesic Active Contour (GAC) model. Third, the brightness temperature gradient data of the eyewall are obtained, and the maximum of brightness and different mean brightness temperature gradients with various probabilities are calculated. Thus, the characterization factors that are closely related to the TC intensity are structured. Finally, the TC intensity objective estimation model based on the RVM is established (single and multiple characterization factors) to study the influence of different characteristic dimensions on a TC intensity estimation error. Experimental results show that the estimation model that is established using the mean brightness temperature gradient of the TC eyewall with a probability of 95% performs better than the other models in studying intensity estimation model that is established using a single characterization factor. The error of the intensity estimation model established using multiple characterization factors is lower than that of the single characterization factor. That is, the multiple characterization factors contain additional characteristic information that is related to the TC intensity. In the study of the intensity estimation model established using multiple characterization factors, the estimation model performs better in the two characterization factors (the maximum of brightness and mean brightness temperature gradients with a probability of 95%) than in the three characterization factors. Therefore, not all “more” is better. Hence, the dimension of the characterization factor should be reasonably selected. Three types of RVM kernel functions are used in the experiments. Results show that all RVM kernel functions are efficient in estimating the TC intensity. Cauchy kernel function is suitable for the estimation model established using multiple characterization factors. The RVM model proposed in this study has a favorable high-dimensional nonlinear processing capability. Therefore, this model can effectively estimate the TC intensity.关键词：tropical cyclone;infrared satellite image;image segmentation;relevance vector machine;machine learning;intensity objective estimation- 513
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发布时间：2021-06-07 - “在遥感领域，专家建立了统一的遥感数据空间尺度分级模型，为多源数据集成与综合应用提供解决方案。”摘要：The progress of Internet and remote sensing technologies has increased the amount of available remote sensing satellite resources. Thus, extraction ability of remote sensing information becomes increasingly strong, and social application need for such data becomes increasingly diverse. Comprehensive application of multi-source remote sensing data has become an inevitable trend. Given that remote sensing data are spatiotemporal data, their comprehensive application needs to be carried out in a certain spatial scale. A unified spatial scale set for remote sensing data is a basic requirement for the integration and comprehensive application of multi-source data. Many popular spatial scale sets are available, and each of them exhibits its own advantages and disadvantages. However, none of them is currently applied as a standard. The lack of a standard spatial scale set restricts the comprehensive application of multi-source remote sensing data. Thus, popular spatial scale sets should be evaluated to establish or optimize a reasonable spatial scale set as a standard. Information density depends on the amount of information in a unit area. Thus, the spatial resolution of remote sensing images is an effective way of expressing the information density of remote sensing data. Map scale refers to the ratio of the distance on the map to that on the field, which imposes strong information requirements. A large map scale requires high information density, which indicates high spatial resolution. High image spatial resolution is necessary to obtain considerable surface details from a large map scale. If the image spatial resolution is insufficiently high, then the map will present inferior quality or will even be unavailable for use. However, high image space resolution is not always ideal for a specific scale. Excessively high resolution data indicate redundant data, which require high computational cost and storage and network resource. All these factors may affect the data display. The national primary scale is a widely used mapping scale grading system and is a perfect reference system to evaluate an optimal spatial scale set for remote sensing data application. Apart from map scale, visual precision is also an important index that affects the spatial resolution requirements. Visual precision requirements to remote sensing image application involve two levels. One is high visual precision requirement, which is suitable for application types of quantitative calculation, scientific analysis, and human–computer interaction. The other is low visual precision requirement, which is suitable for remote sensing data exhibition in long distance, such as image displays in a large screen and a wall map. In this study, the spatial resolutions required by the national primary scale in two visual precision levels were analyzed. Then, the optimal spatial resolution scale of current popular spatial scale sets was built by matching the requirements. The popular spatial scale sets included OGC popular scale sets “GlobalCRS84Pixel” and “GoogleMapsCompatible”, the grid system of NASA WorldWind, Google Map, Baidu Map, Map World, and the “five-layer fifteen-level” remote sensing data organization model. Thereafter, the data redundancy rates of these popular spatial scale sets were calculated and compared from the optimal resolutions to the required spatial resolutions of the national primary scale in two visual precision levels. Results show that, compared with other popular spatial scale sets, the “five-layer fifteen-level” remote sensing data organization model best matches the requirements of the national primary scale. In particular, the model presents the lowest average redundancy rates in the two visual precision levels among others. Therefore, the “five-layer fifteen-level” remote sensing data organization model is a suitable standard spatial scale set for the multi-scale comprehensive application of multi-source remote sensing data.关键词：spatial scale set;spatial resolution of remote sensing image;the national primary scale;comprehensive application of multi-sources remote sensing data;five-layer fifteen-levels
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发布时间：2021-06-07 - “资源三号02星搭载的中国首个对地观测试验性激光测高载荷，通过激光测高数据辅助卫星立体影像进行成像几何模型精化处理的通用理论，有效提高了卫星立体数据测图精度。”摘要：ZY-3 02, which was launched on May 30, 2016, is the second satellite in the ZiYuan3 series and mainly used for developing the civil space infrastructure in China. The laser altimeter, which is a core payload in ZY3-02, is the first laser altimeter experiment instrument of China for earth observation. The evaluation of the accuracy of this instrument is a crucial factor for stereoscopic mapping and occasionally even more difficult to improve than the planar. The basic conventional block adjustment was combined with the increasing accuracy of laser altimeter data and the characteristics of the laser altimeter data in the ZY3-02 satellite. The geometric imaging model of refinement general theory of satellite stereo images aided with the laser altimeter data was proposed in this study. First, high-precision tie-point matching in the conventional block and free network adjustments without constraints was utilized to obtain high-precision relative accuracy and absolute precision, which was not worse than that in original imaging geometric model. Second, the reference image point coordinates, which were used to map the target image point coordinates with the geometric model, could be acquired on the basis of the 3D coordinates of laser altimeter data and refined imaging geometric model of the reference data. Third, high-precision homonymous image points could be determined in the target images after the refinement process through the tie-point matching algorithm. Finally, the block adjustment with the homonymous point as the elevation control was performed to further process the imaging geometry model and compute the high-precision compensation parameters. The experiments in Hubei and Qinghai indicated that the elevation accuracy of the satellite geometry model refined using the laser altimeter data could reach 1.97 and 3.23 m. The comparative experiments in Hubei and Xinjiang areas presented that the system error became larger given the weakening of the control strength. The experiments discussed above demonstrated that the proposed method could effectively improve the accuracy of satellite stereo mapping. The application of laser altimeter data was subject to topographic relief. It could be more effective in the flat area but requires several necessary preprocesses in the mountain area before utilization. In practical applications, the laser data should be distributed evenly and no more than one track in the space interval.关键词：ZY-3 02 satellite;laser altimetry;geometry model refined;stereo images
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发布时间：2021-06-07
Technology and Methodology
- “在东北黑土区的侵蚀沟识别领域，专家构建了遥感影像的训练样本集，提取了不同层次的特征，形成了一套特征提取与识别方法，为耕地保护提供有力支持。测试结果表明，利用卷积神经网络提取的深层特征，其识别精度达到了95.5%，同时大大提高了自动化程度。”摘要：Northeast China is a major commodity grain base in China. The protection of cultivated lands in Northeast China is crucial for safeguarding the food security in China. The recognition of erosion gullies is an important means of monitoring soil erosion. Furthermore, remote sensing technology is extensively used in this field given the multiple advantages of this technology. However, the traditional methods based on remote sensing mostly depend on manual interpretations. Therefore, the degree of the automation and the efficiency are relatively low. In this study, multi-level features were extracted, thereby effectively describing the specific objects by using machine and deep learnings, and erosion gullies were identified based on these features to improve the accuracy and efficiency of recognizing erosion gullies. In this study, we first cut the remote sensing images in a fixed size and labeled these images manually to create datasets as training samples that consist of two categories, namely, farmland and erosion gully. Second, we extracted spectral and textural features based on this dataset as low-level features, encoded SIFT features through ScSPM as middle-level features, and extracted high-level features by using CNN. Third, a linear SVM and a softmax classifier were applied to classify the remote sensing images based on the multi-level features to identify the images with erosion gullies. Finally, we completed a set of methods to extract the feature and recognize the erosion gully, thereby providing a robust support for protecting arable land in the black soil area of Northeast China. The multi-level features extracted through the proposed method demonstrate specific capabilities in identifying erosion gully images. In the test phase, results show that the recognition outcome based on low-level features exhibits the lowest accuracy (91.1%), whereas the recognition accuracy based on middle-level features is the highest (98.5%). However, both features require a manual design. Hence, the degree of automation is limited. By contrast, the CNN can extract high-level features and automatically achieve an “end-to-end” learning, which highly improves the degree of automation of erosion gully recognition. Furthermore, the recognition accuracy based on high-level features is 95.5%, which satisfies the expectation of this study. The recognition accuracy is slightly lower in the validation phase than in the test phase because the images typically contain several irrelevant objects in the practical application, thereby preventing the improvement of the accuracy. However, the proposed method can generally identify the erosion gullies in the images with a reasonable practicality. Low-level features demonstrate several advantages, such as simple calculation and low time consumed. However, the capability to describe the erosion gully is relatively poor, thus resulting in low recognition accuracy. By contrast, the methods based on middle- and high-level features can identify nearly all the erosion gullies in the images, although these methods are time-consuming during the early training phase. Specifically, the method based on high-level features can automatically recognize the erosion gully. This study shows that deep learning has a great potential in remote sensing image application. If the sample size is continuously increased and the network structure expanded, then the recognition accuracy of erosion gully can be further improved.关键词：erosion gullies;training samples;low-level features;middle-level features;high-level features;convolution neural network
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发布时间：2021-06-07 -
Research on saturation correction for long-time series of DMSP-OLS nighttime light dataset in China AI导读
“在夜间灯光数据的社会经济问题研究中，本文提出了一种新的饱和校正方法，通过年际校正和NDVI数据辅助，区分不同城市聚类分区进行校正，有效消除了饱和效应，提高了数据的可靠性和空间异质性，为长时间序列的社会经济研究提供了新的解决方案。”摘要：Night Time Light (NTL) data have been verified to be a favorable proxy for socioeconomic activities. However, saturation correction is necessary to make the results credible and reliable when detecting the multitemporal socioeconomic changes by using time-series analysis of the NTL data. This study is aimed at presenting a new method for correcting the saturation effects of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) stable light images based on normalized differential vegetation index (NDVI) data. First, the different regions and years are selected as references for conducting intercalibration, which is different from the conventional invariant region method. A TNDVI indicator, which shows a significant positive correlation with the Digital Numbers (DNs) of the NTL data, is built based on the original NDVI data after the intercalibration. Second, a K-mean value is utilized to divide the cities into four types and then correct the saturation effects based on the various characteristics of the NTL and NDVI data of various regions. Third, the saturation threshold of the NTL dataset is accurately identified during the saturation correction. Furthermore, the saturated and unsaturated portions are analyzed to construct a saturation correction model. Finally, the relationship between the sum of the NTL brightness and Gross Domestic Product (GDP) before and after the saturation correction is compared to verify the effect of this new saturation correction method. In this research, unsaturated and saturated portions can be found in the NTL dataset, with a saturation threshold of 30, that is, 0—30 are unsaturated, whereas 31—63 are saturated. The functions between the DN values of the two portions and the corresponding original data are different, that is, the unsaturated portions comply with the linear model, whereas the saturated portions comply with the growth model. The various cluster partitions must be distinguished, and the saturation effects, especially the saturation correction for NTL dataset in large areas with remarkable regional differences, must be corrected. On the basis of the TNDVI data, the DN values of the NTL images after the saturation correction by using the growth model for the saturated portions are remarkable, that is, the DNs of F182013 are from 2.717 to 245.673 after the saturation correction, the spatial heterogeneity is enhanced, the fitting relationship with the regional GDP is improved, and the saturation effects caused by the satellite sensor set attributes are appropriately removed. Therefore, the data can appropriately reflect the intensity and spatial distribution of human socioeconomic activities without the saturation effects. Hence, the new saturation correction method is confirmed to be effective.关键词：nighttime light data;TNDVI;urban cluster partition;saturation threshold;saturation correction- 561
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发布时间：2021-06-07 - “在京津冀地区地表沉降问题日益严重的背景下，本文利用Sentinel-1A数据，针对TOPS模式影像间存在多普勒中心不一致问题，借助外部高精度POD轨道和DSM数据进行频移滤波迭代配准，并集成优化了基于点目标时序分析的大区域地表形变监测方法，为大范围地表沉降调查监测提供了可靠的应用精度。”
摘要：In recent years, a differential subsidence of the Beijing-Tianjin-Hebei Plain has become an increasingly significant issue. The safety of the country’s infrastructures, such as highways and railways, is seriously threatened, thereby arousing considerable attention at home and abroad. The time-series differential synthetic aperture radar interferometry (TS-DInSAR) has a wide-area deformation measurement relative to traditional measurement methods, such as GPS and leveling, and has been applied extensively. Therefore, the current research focuses on accurately extracting a large-scale surface deformation by using high-potential image data. An integrated method is optimized in this research to apply Synthetic Aperture Radar interferometry (SAR) images of the Terrain Observation by Progressive Scan (TOPS) mode and accurately extract a large-scale surface deformation based on a novel Sentinel-1A source released by the ESA and time-series analysis of interferometric point targets (TSA-IPT). The present research uses an external high-precision digital surface model and POD and applies a spectral shift filtering iteration to suppress the phase jump caused by spectral decorrelation between bursts, thereby allowing the TOPS mode Sentinel-1A data to satisfy the requirements of registration accuracy of TS-DInSAR. Meanwhile, setting the regression analysis parameters and designing the processing flow are discussed using the TSA-IPT to determine the deformation of a large area. Based on the method presented in this research, 29 Sentinel-1A images obtained from June 2015 to August 2016 are selected for processing. The subsidence result in East Beijing, Langfang, and West Tianjin is obtained. The maximum subsidence rate of the study area at Wang Qingtuo town in Tianjin is 224 mm/a, and its maximum subsidence is 265 mm. In the Hei Zhuanghu Town in Beijing, the maximum subsidence rate is 159 mm/a, and its maximum subsidence is 265 mm. In the Dong Fengwu Village in Langfang, the maximum subsidence rate is 161 mm/a, and its maximum subsidence is 191 mm. In the city district of Langfang in Yanjiao, the maximum subsidence rate is 108 mm/a, and its maximum subsidence is 120 mm. In the Yanjiao City in Hebei Province, the maximum subsidence rate is 104 mm/a, and its maximum subsidence is 121 mm. The spatiotemporal distribution and cause of subsidence are thoroughly analyzed and combined with population density, industrial distribution, and surface coverage. A significant subsidence in the study area can be found on the basis of the monitoring results, which tend to concatenate. The main factors of subsidence are population density, development of industrial distribution parks, and cultivation of thirsty crops. The growth of thirsty crops is the main subsidence factor of a city suburb. Numerous differential settlement regions are found along a high-speed railway, which is in the boundary region of the city district and suburb. Results of this research show that using the TSA-IPT is effective for Sentinel-1A images. Therefore, Sentinel-1A images can be applied to monitor land subsidence over a large area in the future.关键词：Sentinel-1A;registration;time series analysis of interferometric point target;Beijing-Tianjin-Hebei area;subsidence analysis- 836
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发布时间：2021-06-07 - “采用葵花8号卫星数据，提出一种针对云和沙尘混合状况的识别方法，引入0.46 μm和0.51 μm反射率差值RDI，有效区分中高云和沙尘，为沙尘遥感监测提供新方向。”
摘要：Dust, as the main component of aerosols, has numerous effects on a climate system. Simultaneously, dust is harmful to human health as an environmental pollutant. The dust weather generally erupts in spring, thereby significantly affecting the production and life of most regions in Northern China. In the past, many studies have been conducted to identify dust by remote sensing. However, the traditional method has a poor effect and can hardly recognize dust in several complex situations, such as cloud–dust mixing. The dust process is typically accompanied by clouds, which are the main interfering factors in identifying dust. The judgment of pure dust is improved in the thermal infrared band, but the effect is poor for the dust mixed with clouds. In terms of microwave, the sensor is carried mostly by a polar orbit satellite, which time and space resolutions are low. This sensor cannot display real-time dust monitoring and warning. In this study, a new method was proposed using the data from the Himawari-8 satellite. In fact, the dust mixed with clouds demonstrated better successive distribution characteristics in space than in medium clouds and fractocumulus. Thus, the dust mixed with clouds could be identified. A difference in the reflectivity of 0.46and 0.51 μm in a certain range could properly exhibit the continuity characteristics of dust and effectively distinguish clouds and most surfaces with dust by analyzing several visible channels of the Himawari-8. A threshold less than 10—15 could cover most dust mixed with clouds in accordance with the experimental statistics. However, an RDI value of broken cumulus was mainly distributed between 5 and 15, which was similar to the RDI value of dust. Therefore, we introduced the entropy of brightness. In this study, pure dust was identified using a BTD value that is less than 0, and the dust mixed with clouds was identified through the new method. In the spring and summer of 2017, several types of dust accumulated in Inner Mongolia in China and its surrounding areas. We used the satellite data for April 16, May 4, and August 2 combined with visual interpretation and ground observation results to analyze and verify the proposed method. In the two dust processes of April 16 and August 2, we selected three typical regions with mixed cloud and dust. The dust storm on May 4 was large. Therefore, this dust storm was used to analyze and validate the algorithm on a large scale. The verification of dust on May 4, 2017, showed that the observations of the 22 stations were consistent with the results of 27 stations located in the cloud–sand mixing region. The algorithm proposed in this study achieved a significant result in dust recognition under various cloud–sand mixing conditions. A new method based on the brightness entropy of RDI was proposed in this study. The method could effectively identify dust mixed with clouds in comparison with the results of traditional methods or using visual interpretation and ground observation. This method compensates for the limitations of existing algorithms and data to a large extent. However, significant complications in recognition of certain floating dust and large-thickness cloud still exist. The accuracy of recognition will also be affected by the complex condition of the surface. This study reveals that the method exerted a certain effect on pure dust, which can be further discussed in the other research. In addition, this method is still limited when identifying dust at night.关键词：gray level entropy;BTD;dust;visible band;Himawari-8- 538
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发布时间：2021-06-07
Remote Sensing Applications
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Asia-Oceania GEOSS: A GEO initiative to implement GEOSS for sustainable development in Asia-Oceania AI导读
“亚洲大洋洲区域综合地球观测系统，为亚大区域国家提供地球观测技术应用需求，保障空间信息精准获取与服务，增强地球观测技术支撑区域可持续发展的综合能力。”
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Ecological environment monitoring for sustainable development goals in the Belt and Road region AI导读
“最新《全球生态环境遥感监测年度报告》发布，涵盖土地利用、植被生长、作物产量等研究领域，为生态环境监测提供重要数据支持。”
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Ecological environment monitoring for sustainable development goals in the Belt and Road region AI导读
“在"一带一路"区域的生态环境保护方面，专家利用多尺度、多源遥感数据，监测和分析了2015年的生态环境状况，为实现2030年可持续发展目标提供了基础。研究涉及宏观生态系统格局、植被状况、太阳能资源分布、水资源平衡等多个重要生态问题，为政策决策提供了科学依据。” - “在生物多样性监测领域，中国科学院建立了中国生物多样性监测与研究网络(Sino BON)，推动了生物多样性监测核心指标EBV的标准化和全球化，为数据共享和大尺度生物多样性变化评估奠定了基础。”摘要：Biodiversity is the variability of ecological organisms and their environment. It is also the foundation of ecosystem services as the material basis for humans. Climate change and irrational human activities have resulted in unprecedented rapid loss of biodiversity. Governments and relevant international organizations have been actively engaged in global action for monitoring and protection of biodiversity. To better understand the current status and changes of biodiversity, biodiversity monitoring has evolved from a single site to observatory networks. At the global scale, the Group on Earth Observations Biodiversity Observation Network (GEO BON) is representative and primarily develops and refines Essential Biodiversity Variables (EBVs). At the regional scale, the European Union has established the EU-BON, and the Asia-Pacific region has set up AP-BON. At the national scale, Switzerland, the United Kingdom, and Japan have established a national biodiversity monitoring network. The Chinese Academy of Sciences has built the China Biodiversity Monitoring and Research network (Sino BON) during the Twelfth Five-Year Plan. The monitoring network focused on forests, grasslands, deserts ecosystem, mammals, birds, amphibians, reptiles, fish, insects, soil fauna, and microorganisms. Biodiversity monitoring not only relies on traditional manual surveys but also uses new technologies, such as genetic barcoding, camera traps, and drones. Remote sensing can provide continuous biodiversity information at a large scale and is thus expected to be an important method for observing biodiversity. GEO BON established the “Ecosystem Structure” workgroup to develop RS EBVs for measuring or modeling globally with the integration of remote sensing and in-situ observations. Sino BON also established a forest tower crane network to monitor the biodiversity of canopies and introduced unmanned aerial vehicle lidar and hyperspectral remote sensing for biodiversity monitoring at a large scale. The development of China GEOSS is expected to enable integration of the ground observation provided by Sino BON into satellite data. This integration will achieve biodiversity monitoring from space to ground and will benefit the biodiversity conservation and evaluation in China.关键词：biodiversity;monitor network;field observation;remote sensing;Sino BON
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发布时间：2021-06-07
AOGEOSS Progress
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