最新刊期
卷 19 ， 期 5 ， 2015
- 摘要：Continuous observations from ground station networks are important in the validation of remote sensing products and algorithms. However,owing to the spatial mismatch between the point observation of ground stations and the pixel observation of remote sensing products,a direct comparison tends to incur scale errors. Therefore,the evaluation of the representativeness of ground station measurements for the reasonable validation of remote sensing products is necessary. At present,various observation stations are situated all over the world,and many representativeness evaluation methods have been developed. To maximize the representativeness evaluation methods for the reasonable validation of remote sensing products,we review the present methods for evaluating observation representativeness. The indicators for evaluating observation representativeness can be divided into two classes: point-to-area consistency indicator and spatial heterogeneity indicator. The first indicator evaluates observation representativeness by analyzing the consistency between ground point observations and observations in the pixel area. Generally,three specific classes of methods calculate the point-to-area consistency indicator. The first class is based on physical models,such as the footprint model,and is used for evapotranspiration measurements. The second class evaluates representativeness based on distribution maps. The last class determines observation representativeness by combining multi-stations and multi-observations in the computation of the average difference between a specific station and other stations. The spatial heterogeneity indicator is applied to evaluate ground observation representativeness by assessing spatial variations. Generally,two classes of methods obtain spatial heterogeneity: first-order statistics and semivariogram. The application of representativeness evaluation methods in validating remote sensing products is reviewed. In the validation of evapotranspiration,surface albedo,radiation,LAI products,etc.,the representativeness of ground observations is evaluated to obtain the accurate validation data. A case study is then conducted in the middle reach of the Heihe River Basin for the LAI observations. The point-to-area consistency indicator SSE and the spatial heterogeneity indicator Sill are calculated at the product pixel scale. Result shows that the degree of representativeness characterized by the SSE and Sill is inconsistent for different pixels at the 1 km scale. Thus,the indicator for evaluating the representativeness of various stationobserved parameters should be properly selected. This paper reviewed the methods for evaluating the representativeness of ground station observations and their application in remote sensing product validation. The widely used indicators and the methods for calculating indicators were reviewed first,and the advantages and disadvantages of the methods were summarized. The applications of the methods in the validation of remote sensing products were analyzed,and a case study on the difference of the two indicators in evaluating spatial representativeness for LAI validation was performed in the middle reach of the Heihe River Basin. The review revealed that current indicators and methods are usually suitable for specific parameters and that individualized research on such parameters is necessary. Only a few studies on the evaluation of global ground station networks have been conducted for global product validation. Currently available methods for evaluating observation representativeness must be improved further.关键词：station representativeness;heterogeneity;validation;spatial scale;remote sensing products
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发布时间：2021-06-10 - 摘要：Sensors of Landsat mission satellites have systemically acquired moderate-resolution multispectral images of the Earth for more than 40 years since Landsat 1 was launched in 1972. The images have been extensively applied,such as in global change,agriculture,forestry,geology,resource management,geography,cartography,and coastal research. These successive images are important to characterize and detect changes in land cover and land use worldwide. Many studies consider these data to perform long-term monitoring of the quantitative information of surface on medium-resolution scales to reflect refined climate change in localor even global scale. The premise of these studies is to conduct radiometric calibration of long sequential data over decades and ensure the consistency of sequential data radiation through cross calibration of different satellites. Radiometric calibrations are constantly updated from Landsat 1 to Landsat 8. Moreover,sensor performance and data acquisition abilities are improved,which involve preflight calibration,internal lamp calibrator,full aperture solar calibrator,cross calibration method,and vicarious calibration based on test sites.This review classified the sensors of Landsat mission satellites based on their performance. Their imaging modes were summarized and compared to determine the advantages and disadvantages of each mode. Then,the development of various radiometric calibration methods was reviewed. The merits and demerits of these methods,as well as the impacts on the accuracy of calibration results,were analyzed. During prelaunch,the calibration of various sensors was mainly based on calibrated integrating sphere. The difference in the time between the integrating sphere calibration and that of sensor calibration caused calibration uncertainty to a certain degree. The consistency of calibration time improved calibration accuracy by approximately 10%. The main calibration equipment comprised lamps and solar calibrator systems during in-orbit. With the advancement from Landsat 4 to Landsat 5,the number of lamps on TM increased from 2 to 3,and the solar calibrator system was accordingly refined,which expanded to full aperture on L7 / ETM +. Vicarious calibration between Landsat 4 and Landsat 5,Landsat 5 and Landsat 7 also provided important calibration reference. With the advent of Landsat 8,the holistic assessment system was incorporated into ground station Internet Authentication Service（ IAS）,which firstly finished in Landsat 7,and specific calibration parameters were stored in calibration and bias parameter files. IAS was expanded toward previous data of Landsat 5 TM,Landsat 4 TM,and L1-L5 MSS.To widen the quantitative application of remote sensing data,radiometric calibration methods were varied along with the development of remote sensors. Moreover,the cross calibration and validation of multi-source remote sensing data and the perfection of the entire radiometric calibration process were realized. In specific,preflight,on-orbit,and vicarious calibrations were performed.Any individual calibration step affected the calibration accuracy; therefore,subsequent research should attempt to reduce the error in each process. Moreover,basing from the calibration situation in China,the authors emphasized the construction of standard radiation transfer and research,strengthened on-orbit calibration methods and calibration equipment,accelerated the construction of the ground radiometric calibration field,and attached importance to the normalization of multi-remote sensing data.关键词：Landsat missions;optical remote sensor;radiometric calibration methods;calibration accuracy;vicarious calibration
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发布时间：2021-06-10 - 摘要：The main restriction on surface parameter inversion from remote sensing data with 30 m resolution is the limited number of observations. Nevertheless,a network or multiple-sensor method can efficiently increase the number of observations. In this study,a multi-sensor database was generated from HJ-1 / CCD and Landsat 8 / OLI from June 2013 to August in 2013 in the middle reach of Heihe River Basin. Characteristics,including proportion of valid observations,distribution of observation angles,bidirectional reflectance distribution function,and data consistency among sensors after preprocessing,of the multi-sensor dataset were analyzed. Difference in observation quality from different sensors is a major issue regarding Leaf Area Index（ LAI） inversion from a multi-sensor dataset. Therefore,an observation quality control criterion was initially designed. Multi-sensor observations that satisfied the quality control requirements were used to inverse LAI based on a look-up table built by the unified model. The synthesis LAI over 10 days was set as the mean of LAI inversion from each sensor observation because of limited observation number. Analysis and validation were performed based on LAI products produced in the middle reach of the Heihe River Basin. Results show that the percentage of valid LAI inversion significantly increased from 6. 4% to 49. 7% of the single-sensor inversion to 75. 9% of the multi-sensor inversion. Validated results show that the average RMSE between field measurements and LAI inversion was 0. 71.The network of HJ-1 / CCD and Landsat 8 / OLI sensors with 30 m spatial resolution can generate LAI products with reasonable accuracy and continuous temporal resolution.关键词：multi-sensor dataset;middle reach of the Heihe River Basin;leaf area index;HJ-1 / CCD;Landsat 8 / OLI
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发布时间：2021-06-10 - 摘要：The Bidirectional Reflectance Distribution Function（ BRDF） qualifies variations in reflectance patterns as a function of illumination and viewing geometry. Statistically,directional reflectance in a given direction is considered as the mean. The variation in that direction can then be characterized by its variance. The BRVF is a useful method for characterizing reflectance anisotropy.In this study,we first explored the variance in bidirectional reflectance through the use of not only a BRVF technique based on the MODIS BRDF model but also an error propagation method. Then,we performed a method validation by using the MODIS BRDF / Albedo product from selected EOS Land Validation Core sites（ LVCS） with underlying typical IGBP land cover types and NDVI threshold. Finally,as an initial application of the BRVF,we applied the aforementioned theoretical results in the simulation result derived from the collected 69 field measurements.Major results demonstrate that the spatial distribution patterns of the BRVF are linear combinations of the first-degree term and the second-degree term of geometric optical kernels（ i. e.,K geo） and volumetric scattering kernels（ i. e.,K vol）. Validation results based on the MODIS BRDF / Albedo product are consistent with the theoretical derivations. In general,the BRVF spatial distribution pattern reaches its peak value at the hotspot direction. Beyond the large viewing angle（ VZA > 60°）,the BRVF tends to increase along with the increase in VZA. The BRVF value in the near-infrared band is greater than that in the red band,thereby indicating that for the MODIS sensor,the BRVF is a band-dependent variable. The initial application of the BRVF to the simulation result derived from the collected 69 field measurements shows that at a large VZA where observations are not available,the modelextrapolated BRVF value increases prominently,thereby affecting the accuracy of surface albedo retrievals. The maximum relative error for the white sky albedo retrieval in the red band is about 38. 26%.This study is very helpful in understanding the uncertainty of surface albedo retrieved by a narrow VZA sensor. The results are also necessary in understanding a priori knowledge applications for the retrieval of surface albedo and other biophysical parameters.关键词：BRVF;BRDF;MODIS;land cover types;EOS land validation core sites;error propagation;albedo;kernel-driven BRDF model
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发布时间：2021-06-10 - 摘要：A large percentage of the world’s population and commercial centers are in the vicinity of coastal environments. Understanding near shore habitats is critical for management decisions,disaster planning,reduction of environmental impacts,and resolution of challenges resulting from growing populations and rising sea levels. The goal of this study is to predict the maximum detectable bottom depth for CZMIL in the Yellow Sea,the East China Sea region. The coastal waters of China,being considerably polluted,represent typical coastal Case Ⅱ waters with high sediment load and complicated optical properties. Therefore,using satellite algorithms like the ratio of water-leaving radiances at different wavelength to estimate water optical properties may be practically impossible and even provide incorrect results. Hence,an approach for the calculation of diffuse attenuation coefficients at490 nm exclusively for the coastal waters of China is chosen. The map of the CZMIL maximum depth in the Yellow Sea is then shown.The methods used in the study are summarized as follows. First,samples of the Case II water color test collected from the Yellow Sea in the spring of 2003 are used to verify the diffuse attenuation coefficient at 490 nm; the algorithm which is proposed by Wang. et al.,is employed in the verification. Owing to the laser of CZMIL working at a wavelength of 532 nm,the diffuse attenuation coefficient at 490 nm should be recalculated to obtain the diffuse attenuation coefficient at 532 nm. Data processing demonstrates good correlation regression between the diffuse attenuation coefficient at 490 nm and the diffuse attenuation coefficients at other wavelengths（ 412,443,510,520,555,and 565 nm）. Thus,the regression between the diffuse attenuation coefficient at532 nm and the diffuse attenuation coefficient at 490 nm are obtained.In the next step,the CZMIL maximum penetration depth for the coastal waters of China is estimated as the diffuse attenuation coefficient at 532 nm divided by 3. 75,which is a specification of CZMIL provided by Optech Incorporation. The equation is valid for bottom reflectivity above 15%. Then,the CZMIL maximum penetration depth of the entire Yellow Sea and East China Sea region is estimated using the remote measurement data of the NASA space satellite Aqua-MODIS（ hereafter referred to as NASA data） for the said region. The diffuse attenuation coefficient at 490 nm for the NASA data is first calculated using remote sensing reflectance at 490,555,and 670 nm. The diffuse attenuation coefficient at 490 nm is then converted to the diffuse attenuation coefficient at 532 nm. Lastly,the maximum penetration depth is calculated as the diffuse attenuation coefficient at 532 nm divided by3. 75.Two maps show the CZMIL maximum depth and the depth penetration isobaths for the region of interest. The range of CZMIL maximum depth was divided equally into 36 bins,and the detectable area inside each bin was calculated and presented in a pie chart. With the two maps and pie chart as basis,we arrive at the following preliminary conclusions.（ 1） The maximum detectable depth along the Jiangsu coast may vary from 0 m to 10 m.（ 2） The CZMIL maximum detectable depth for the waters near the Yangtze River Delta does not exceed 12 m and extends as far as 200 km from the coastline.（ 3） Within 200 km of the coastline from the continental shelf,the CZMIL maximum detectable depth in the Yellow Sea ranges from 0 m to 50 m,and the area accounts for the 76. 2% of the region of interest.关键词：Yellow Sea;East China Sea region;airborne bathymetric laser system;coastal zone mapping and imaging Lidar;diffuse attenuation co
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发布时间：2021-06-10 - 摘要：Compressed Sensing（ CS） in sparse microwave imaging has been extensively investigated. Inverse Synthetic Aperture Radar（ ISAR） imaging problem can be transformed into a sparse signal representation problem. However,the performance of current CS-based ISAR imaging algorithms is usually limited in terms of reconstruction speed and accuracy; as such,existing algorithms are inconvenient for practical applications. Accelerated Iterative Hard Thresholding（ AIHT） can be used to establish a trade-off between low computational complexity and strong performance guarantees in CS. Considering this condition,we determined the mechanism by which AIHT algorithm is applied to ISAR sparse imaging.In ISAR imaging,cross-range resolution is dependent on the total rotation angle of a target relative to the radar line of sight during observation. In a short observation time,high cross-range resolution is difficult to obtain using a conventional ISAR imaging algorithm because target-radar orientation variation is usually small. The CS method can be applied in ISAR imaging to obtain highresolution images with limited measurements because space distribution of point scatterers is sparse. After an azimuth Fast Fourier Transform（ FFT） is obtained,an echo signal can be expressed with sparse discrete points in a Doppler frequency domain. Based on CS theory,this study establishes an ISAR sparse imaging model,which contains an undersampling echo data in azimuth. AIHT is robust to noise to some extent; thus,this algorithm can obtain enhanced reconstruction results in a few number of observations.The AIHT method is also simple to implement and does not require computation,storage,and repeated use of matrix inverses.Therefore,this method provides an advantage in numerous CS applications characterized by measurement matrix that is often based on fast transforms,such as wavelet and Fourier transform. This study also compares various CS algorithms and rationally analyzes the specific features and adaptive mechanism of the AIHT algorithm applied to ISAR sparse imaging problem.Experimental results based on simulated and measured data show that the proposed algorithm maintains a more efficient balance between computation load and reconstruction signal sparsity than existing algorithms. The echo data in simulation experiments are added to Gauss white noise with 4 d B SNR; thus,half of the azimuth data are randomly selected and missing data are zero padded. A one-dimensional range profile is obtained through range alignment and phase correction; a two-dimensional ISAR image is then obtained through the azimuth processing. Results suggest that this method can effectively achieve imaging with few measurements of a complex baseband echo signal. This method is also focused on a large variation range of pulse number and signal-to-noise ratio. The advantage of reconstruction speed is apparent compared with other CS algorithms.This study shows that the ISAR sparse imaging method based on AIHT not only significantly reduces the time of imaging reconstruction but also improves the precision of imaging reconstruction. AIHT also exhibits a great advantage of processing real-time imaging or large-scale and high-dimension problems. Step selection and setting threshold strategy should be improved in further studies to maintain a more robust algorithm and avoid local minimum.关键词：accelerated iterative hard thresholding（AIHT）;inverse synthetic aperture radar（ISAR）;sparse imaging;compressed sensing（CS）;finit
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发布时间：2021-06-10 - 摘要：Anomaly detection algorithms are based on the assumption that the background follows a multivariate normal distribution. An anomaly emerges when a deviation occurs in the distribution of the background model. Another assumption is that the spectral features of the background and the anomaly are uncorrelated. However,clutter background often has spectral features that are too complex to be accurately described by a background model. In fact,the complex spectral features of different ground objects are correlated with the features of anomaly targets to some extent. Hence,background information may be contaminated by other anomalous and noisy signals. Moreover,the false alarm rate of detection results increases because noise or background pixels may be wrongly detected as anomalies. In other words,the anomaly detection method based on hypothesis testing is sensitive to anomalies.Two main factors explain high false alarm rates in detection results. One factor is the limited capability of models to characterize backgrounds. An effective background model must reduce the correlation between backgrounds and anomalies. The other factor is the judgment of uncertain areas,which differ from backgrounds but are not sufficiently anomalous to be marked as anomalies.Inspired by the biological theory of fly vision,we proposed an anomaly detection algorithm for spectral anomaly targets in remote sensing images. A parallel multi-aperture model was constructed to adaptively and separately model the spectral features of multiple kinds of background objects. Depending on the significance of an anomaly,the anomaly,background,and uncertain area were marked by the relative Mahalanobis distance. Hence,the proposed algorithm can reduce the influence of correlation between anomalies and backgrounds and remedy the disadvantages of traditional hypothesis testing methods,which cannot distinguish uncertain areas and the absence of anomalies. The detection results from the multi-aperture model were then fused to obtain the anomaly detection results. The simulated experiment focused on the anomaly detection of clutter areas containing various background objects.Both synthetic data and real data were applied to verify the effectiveness of the proposed method. Experiment results show that the proposed algorithm achieves better performance compared with other classic algorithms. Under clutter background,the proposed method can still detect anomalies,as well as the shape and size of a certain target. The proposed algorithm only marks the most significantly abnormal areas as targets. An uncertain area detected by one aperture is estimated again through fusion.The multi-aperture model reduced the correlation of spectral features between the background and anomaly to some extent. The background-sensitive algorithm can distinguish uncertain areas and backgrounds and precisely detect the shape and size of targets,especially those with distributed densities. A soft judgment is deemed robust to background models; hence,the proposed algorithm is robust to clutter backgrounds.关键词：anomaly detection;fly vision;clutter background;multi-aperture structure;adaptive
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发布时间：2021-06-10 - 摘要：Mapping autumn crop distribution via remote sensing is influenced by the same growth period of crops. Identifying autumn crops using low temporal resolution or low spatial resolution remote sensing data is difficult because the spectral signatures of different crops are similar. An effective approach is the use of remote sensing data with high temporal-spatial resolution in solving the problem of"foreign bodies with spectrum,"which lowers the accuracy of autumn crop distribution mapping.In this study,the Spatial Temporal Data Fusion Approach（ STDFA） is employed to generate remote sensing data with high temporal-spatial resolution,namely,Red,NIR,and NDVI. The Red and NIR data are smoothened using the TIMESAT program.Meanwhile,the phenology indices from the time-series NDVI data are calculated by the filtered Red and NIR data. The four data types,namely,Red,NIR,NDVI,and phenology indices,are used to construct fifteen kinds of 30 m resolution simulated remotely sensed data for the identification of autumn crops. The applicability of the different dimensions of the data in autumn crop identification is then verified using a support vector machine. The test data are derived from the visual interpretation of the results of unmanned aerial images.A high mapping accuracy is achieved with the autumn crop classification results from the different data sets. The crop classification results of the generated remote sensing image data and the corresponding bands of Landsat 8 and MODIS are compared. The analysis of precision and crop spatial distribution reveals that the Red + phenology data set effectively identifies autumn crops in terms of spatial position and distribution details. The data set achieves accuracies of 91. 76% and 82. 49% for paddy producers and users,respectively,and 85. 80% and 74. 97% for corn produces and users,respectively. The overall accuracy achieved for both paddy and corn reaches 86. 90%.The Red,NIR,NDVI,and phenology data sets generated by the STDFA can effectively distinguish the type of autumn crops.The increase in the dimension of high spatial-temporal data and the accuracy of classification are not positively correlated,with the former showing a slight correlation with stability to some extent. Compared with the classification results of the MODIS data,the remote sensing images with high spatial-temporal resolution show higher classification precision and better crop spatial distribution.The findings of the study can therefore serve as preliminary experimental basis for utilizing remote sensing images with high spatialtemporal resolution in the identification of autumn crops.
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发布时间：2021-06-10 - 摘要：The North Pacific Ocean has a complex circulation system and is thus sensitive to global atmospheric and oceanic changes. Mesoscale eddies are widely found in this region. Research on the distribution characteristics and motion features of mesoscale eddies is very important in explaining the energy change and air-sea interaction in the region. In this study,the merged altimeter data obtained from AVISO are used to identify and trace the mesoscale eddies in the North Pacific（ 100° E—77° W,0° N—70° N）from 1993 to 2012. The attribute characteristics（ such as amplitude,radius,and speed）,spatial distribution,and transmission characteristics are statistically analyzed. The seasonal,interannual,and decadal variabilities are also studied. The SLA-basedmethod is used to identify mesoscale eddies based on the outermost closed contour of sea level anomalies. This algorithm is widely used because of its high accuracy. The tracking method applied is a combination of the nearest distance method and the similaritybased method. If no trace is observed for two weeks,the eddy is considered to have disappeared. The tracking process needs to last at least four weeks. The average amplitude and speed of the eddies in the North Pacific are 8. 44 cm and 6. 4 cm / s,respectively.An eddy can continue for about 6. 9 wk. Unlike those of cyclones,the amplitude and lifetime of anticyclones obviously change in different zonal bands. As latitude increases,the propagation speed of eddies gradually reduces. Eddies are abundant in California,the Gulf of Alaska,and especially in the Kuroshio Extension. Most of these eddies propagate west nonlinearly. Anticyclones are prone to poleward deflections,whereas cyclones are prone to equatorward deflections. Eddies occur frequently in spring and summer and occasionally in autumn and winter. Seasonal variations are apparent in California. The interannual variabilities of the number of eddies are closely related with the ENSO phenomena. In 1993 to 2002,the number of eddies was positively relative to the EMI index,but in 2003 to 2012,it was negatively relevant. We focused on the spatio-temporal distribution characteristics of mesoscale eddies in the North Pacific and analyzed the distribution differences from 1993 to 2002 and from 2003 to 2012. The differences were mainly observed in the subtropical countercurrent area and in the open ocean in the Northeast Pacific. However,the mechanisms of eddy genesis and the causes of the differences in temporal and spatial distribution must be analyzed with a physical ocean model. This aspect should be implemented in future research.关键词：satellite altimeter;mesoscale eddy;North Pacific;temporal and spatial characteristics;ENSO
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发布时间：2021-06-10 - 摘要：Sea Surface Temperature（ SST） is an important parameter of the sea water environment,especially that of the Yangtze estuary. An accurate retrieval of SST is helpful in studying the hydrodynamic environment of estuaries and marine phenomena,such as upwelling and temperature fronts. Affected by both land runoff and ocean currents,estuarine water is strongly mixed,making it different from ocean water in terms of thermal infrared radiation properties and atmospheric conditions. Thus,the algorithm for ocean water is not tuned for estuarine water. The above issue is addressed in this study with the development of a new Yangtze Estuarine Sea Surface Temperature（ YESST） algorithm. The proposed algorithm was made applicable to estuarine environments by basing it on the Qin split-window algorithm and by optimizing its calculation of two key parameters,namely,atmospheric transmittance and sea surface emissivity. The YESST algorithm was applied to the Terra-MODIS L1 B data and validated with both in situ water temperature data and the standard MODIS SST product. In terms of bias and RMSE,analysis showed that the results of YESST algorithm have improved accuracy compared with the standard MODIS SST products. The bias was reduced by 0. 23 ℃,and RMSE was reduced by 0. 62 ℃. The YESST algorithm was used to retrieve the 13-year SST dataset（ 2000—2012） on the Yangtze estuarine waters,as well as 574 images. The SST spatial distribution and the seasonal and interannual SST variations in the research area were revealed according to the dataset. The SST of the Yangtze estuarine water presents a stepwise change spatially from the upstream to the lower reaches of the estuary,which is dominated by solar radiation. The SST outside the river mouth is higher than the SST in the inner estuary in winter; this condition is reversed in summer. On January 29,2007,for example,the SST of the research area ranged from 3 ℃ to 13 ℃. On July 29,2007,the SST was as high as 24 ℃ to 35 ℃. Meanwhile,the temperature gradient from the upstream to the lower reaches of the estuary is higher in winter than in summer. This result indicates that the SST of estuarine areas is the result of the interaction between land runoff and sea currents and that terrestrial water is a heat source in summer and a cold source in winter. In 2003,the Three Gorges Reservoir began regulating the Yangtze water discharge.The increased runoff in winter season,during which large amounts of cold water flow into the estuary,has more a significant cooling effect on the SST outside the river mouth than on the SST in the inner estuary,as determined from the daily SST data retrieved.However,decreased land runoff in the summer season when solar radiation plays a decisive role in the SST of estuarine waters does not obviously affect SST.关键词：MODIS;sea surface temperature;runoff;the Yangtze Estuary
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发布时间：2021-06-10 - 摘要：As the second most important greenhouse gas after carbon dioxide（ CO2）,methane（ CH4） plays a major role in photochemical reactions at global and regional scales and significantly affects energy balance and climate change. Although observations of near-surface CH4 via the Atmospheric Infrared Sounder（ AIRS version 6. 0） of the EOS / Aqua platform have been published,they have yet to be documented in the context of China. Analysis of near-surface CH4 concentration in China using thermal infrared sensor data is still in its initial phase. Ground-based observation data from Waliguan（ WLG） in Qinghai,Taiwan Lulinshan（ LLN）,and Ulaan Uul in Mongolia（ UUM） are employed to validate the near-surface CH4 concentration obtained via AIRS V6. 0.Results show a consistent trend for the WLG,LLN,and UUM data,with the error being less than 2% and the correlation coefficients being 0. 68,0. 5,and 0. 69,respectively. These data sets can be effectively applied in the analysis of the spatial and temporal distribution characteristics of near-surface CH4 concentration. In this paper,the spatial and temporal distribution characteristics of the near-surface CH4 concentration from 2003 to 2013 in China are discussed according to region,seasonal variation,and interannual variation. The following results are obtained.（ 1） The minimum near-surface CH4 concentration is observed in Tibet（ 1800ppbv）,and the maximum is observed in northern Xinjiang,Inner Mongolia,and northern Heilongjiang（ 1920 ppbv）.（ 2）Through the analysis of the 11-year AIRS data（ 2003—2013） on near-surface CH4 concentration synthesis products,we find that the near-surface CH4 concentration is low in the south and high in the north,a trend that is consistent with that in the middle and high latitude regions.（ 3） Regional statistics and the overall seasonal variation demonstrate that the seasonal change is significant,especially in the western region where the increase is observed from 1838 ppbv in April to 1882 ppbv in September. The lowest value is observed in April and May,and the highest value is observed in August and September. These results indicate high near-surface CH4 concentrations in summer and autumn and low concentrations in winter and spring. In the northwest,high near-surface CH4 concentrations are observed in December.（ 4） From 2003 to 2013,the national average CH4 concentration near the ground showed a basic growing trend,except for the slight decrease in 2006 and 2010. The concentration values in the northwest and east regions vary consistently. A rapid increase is observed in northeast China,whereas the growth in the south is not obvious. Interannual fluctuations are bigger in the northwest than in the northeast.（ 4） In the south,the overestimated value of near-surface CH4 concentration by AIRS is higher than the ground-based CH4 products in LLN in summer.
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发布时间：2021-06-10 - 摘要：In recent years,the time and spatial resolution of satellites have been effectively improved with the development of satellite remote sensing technology. Thus,research on convection has focused on improving the detection of Convective Initiation（ CI） and Rapidly Developing Convection（ RDC）,which is indicative of impending disastrous weather conditions. In this study,we aim to identify ways to utilize satellite remote sensing technology in capturing RDC information for RDC detection. RDC detection using satellite data is possible with the operation of the rapid scan mode of China’s meteorological satellite FY-2F. This satellite provides multi-channel rapid scan data with a time resolution of 6 min and a spatial resolution of 5 km.A method for RDC detection is designed according to the characteristics of the multi-channel rapid scan data of the FY-2F satellite. A multi-channel identification of Cloud-Top Cooling rate（ CTC） and CTC filtration with three tests（ elimination of cloud movement,acquisition of BT minimum value,and filtration according to identified RDC） are also performed. After the conduct of the three tests,the local BT minimum value of RDC can then be filtered,and the surrounding pixels can be filled using the flood fill method to determine the RDC.The developed method is then employed in RDC detection using the scan data of the 94° E—129° E,11° N—26° N area in August 2013. Artificial statistics with a visual analysis of the accuracy rate of RDC detection is used in the experiment. Experimental results show that the POD,FAR,and CSI of RDC detection using our method are 0. 89,0. 15,and 0. 77,respectively.In this study,we propose a method for RDC detection and apply the rapid scan data of the FY-2F satellite. The method is proven effective in accurately detecting RDC. However,the 15% false alarm rate of our method indicates the need to narrow down the scope of RDC identification conditions. In the future,we will focus on identifying ways to use the 1 km VIS channel data of the FY-2F satellite to achieve highly accurate detection.关键词：rapidly developing convection;FY-2F;convective initiation;cloud-top cooling rate
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发布时间：2021-06-10 - 摘要：In this study,we put forward an MRF-ν-SVM classification system that integrates spatial contextual information with SVM while taking advantage of the good classification performance of SVM for small samples and its effective capacity to model the spatial correlation of MRFs. The system performs classification at the regional level instead of at the pixel level. To protect the image edge structure,we set up a dual threshold for local edge intensity and refine the MRF-based spatial contextual model to a contextual model for fuzzy edges. The bias of the original problems of SVM is then optimized to improve the optimal hyperplane.Sea ice is an important component of the global climate system. As a unique coherent imaging system,the Synthetic Aperture Radar（ SAR） provides high-resolution images and serves as an important tool for monitoring sea ice. Sea ice image classification via SAR is thus an effective method for monitoring sea ice. This study integrates the spatial correlation based on MRF into the support vector classifier. Using the spatial contextual information based on MRF can improve scene interpretation accuracy. Meanwhile,SVM has good generalization ability and efficient classification performance,as well as high robustness to the Hughes phenomenon. MRF is thus used to model space interaction in the SVM framework.The watershed segmentation algorithm is used to obtain a number of homogeneous areas and the edges of closed regions. The image is then classified at the regional level. We extract the gray tone and three GLCM texture statistics,namely,entropy,contrast,and correlation,as the regional feature vector of the samples. For the characteristics of the sea ice image obtained via SAR,we use a window of a certain size to collect training samples and to record the edge information of each window image. The spatial contextual model based on the neighborhood system is optimized to the contextual model of the edge,which is introduced to the bias of the SVM,to structure the contextual bias of the edge and then correct the optimal hyperplane. To improve classification accuracy,we set up a double threshold for the strength of the edge. The labels of the regions for the strong edge are reserved,the edge label for the weak edge is cleared,and the contextual information for the fuzzy edge is computed.When the spatial correlation of images is not considered,several speckle-like regions can be observed in the classification maps of the KNN and ν-SVC algorithms. The regional MRF classification demonstrates weak noise resistance,and the classification result of the post SVM-MRF algorithm cannot accurately identify the regions with similar features. Experiments show that the proposed algorithm can effectively improve classification accuracy,enhance speckle noise resistance,and accurately classify the regions with similar features but different labels.The MRF-ν-SVM classification system that integrates spatial contextual information with SVM can effectively classify SAR images. The proposed algorithm has more advantages than the other four algorithms tested in the study. Specifically,it can accurately classify SAR images,reduce interference from speckle noise,and recognize regions with similar features but different labels.关键词：Synthetic aperture radar;ν-Support vector machines;Markov random field;sea ice;classification
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发布时间：2021-06-10 - 摘要：SAR sea ice image segmentation is essential for climate variation research and navigation safety. However,current region-level MRF-based algorithms have been widely used in SAR sea ice image segmentation. Speckle noise is a phenomenon of the SAR imaging system produced by inherent defects of the system. This phenomenon seriously affects the accuracy of polarimetric SAR image interpretation and subsequent segmentation,classification,target detection,and treatment. Thus,polarimetric SAR image speckle should be inhibited. To effectively suppress the interference of speckle noise and preserve edge information,We proposed a new segmentation algorithm in this paper.A novel segmentation algorithm called polarimetric SAR sea ice image with noise suppression（ NS-RMRF） is introduced.Speckle Reduction Anisotropic Diffusion（ SRAD） filtering is used in polarization total power span before splitting the original image. The simulated overflow watershed segmentation algorithm is applied to generate initialized areas. After the initialized segmentation,a region adjacency graph is constructed. With the difference between the area and the adjacent area,the difference degree is introduced to the region MRF model based on the Wishart distribution. Simulated annealing is the optimization algorithm used to minimize the objective function.Two polarimetric SAR of sea ice image data obtained by RADARSAT-2 and SIR-C were used to verify the effectiveness of the proposed method. The classical polarimetric segmentation ML method of Lee,the region-WRMF segmentation method of Wu,and the Polar IRGS segmentation method of Yu were compared with the NS-RMRF method. The result indicates that the proposed algorithm exhibit advantages over other image segmentation methods.In a subjective perspective,the results of the NS-RMRF segmentation algorithm can indicate the true distribution of surface features. In an objective perspective,the proposed algorithm achieves better segmentation results than the three other methods based on the overall accuracy and Kappa coefficient.The segmentation algorithm of NS-RMRF is proposed in this study and a new calculation method that measures the regional difference is also presented. Noise-reduction filtering algorithm is used to establish a valid initial segmentation and maintain edge information by considering the difference between regions in a spatial context model. The proposed algorithm can effectively capture and maintain details and smoothen homogeneous areas more effectively than ML,region-based WMRF,and Polar IRGS algorithms.However,the proposed method are limited by deficiencies; thus,the proposed algorithm should be improved.关键词：sea ice;polarimetric SAR;MRF;noise suppression;regional difference
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发布时间：2021-06-10 - 摘要：Synthetic Aperture Radar（ SAR） has become a tool for the effective and convenient monitoring of sea ice. It plays a significant role in both scientific research and business activities,such as ship navigation. Owing to the large volume of SAR sea ice data,the automated interpretation of SAR sea ice images is very important. Unfortunately,such interpretation involves numerous imaging characteristics and environmental factors. Hence,a strong change in the scattering coefficient of SAR sea ice can make sea ice images non-stationary. Under such condition,the segmentation of SAR sea ice images becomes challenging.Previous studies use the traditional method based on Markov Random Field（ MRF）,which can effectively improve the accuracy of SAR sea ice image segmentation. However,the improvements of the MRF method are only based on local edge strength. The scale dependence of sea ice scenes must still be considered. Owing to the non-stationary scale of complex SAR sea ice images,local weight must be immediately integrated into the adaptation of complex scenes in the segmentation algorithm. To improve the accuracy of complex SAR sea ice image segmentation,we propose a segmentation method with a hierarchical binary tree structure on the basis of region splitting and adaptive adjustment.The global iterative weights based on the MRF model are used to complete the initial region merging,and the merging process is described in the form of a binary tree. In the proposed hierarchical clustering algorithm,a positive correlation exists between the scale of the object in a scene and the number of binary tree nodes. The subsequent refinement of region splitting does not generate new regions but only reverts to a previous configuration. The scale weight of the spatial contextual model is adjusted adaptively according to the complexity of the objects in different regions in a scene. The updated weights renew the regional merging.The segmentation result obtained by the proposed algorithm is compared with that of three other algorithms,namely,C-MRF,V-MRF,and IRGS. The result of the C-MRF algorithm is too smooth and ignores many details. Meanwhile,the V-MRF algorithm cannot accurately identify large-scale regions. As for the IRGS algorithm,it is difficult to use when identifying regions with sea ice of varying complexities. The proposed algorithm considers both edge details and regional consistency. The overall accuracy and kappa coefficient of the proposed algorithm are higher than those of the other three algorithms. Experiment results show that the proposed method effectively improves the accuracy of SAR sea ice image segmentation,particularly for images of complex scenes.Compared with the existing single MRF method,the proposed method obtains better visual effects. Despite several studies on multi-scale random fields and continuous state modeling methods,the problem of modeling discrete fields with multi-scale structures remains unresolved. To address this gap,we use a single MRF to model a discrete field with spatial dependence and non-stationary structure. Such model can satisfy ice class business specifications by drawing on the natural ways of efficiently representing the spatial structure of different dimensions to avoid the low efficiency of several schemes.关键词：SAR ice image;regional splitting;two-fork tree;Markov Random Field（MRF）;spatial context model
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发布时间：2021-06-10 - 摘要：<正>为促进中国与中亚国家的科技及全方位合作,为国家丝绸之路经济带规划提供宏观战略咨询服务,为丝绸之路经济带沿线国家提供空间信息与环境现状信息支持,为丝绸之路经济带沿线省、区城镇化发展及环境资源布局提出建议,2014年6月,"丝绸之路经济带资源环境格局与发展潜力"中国科学院学部咨询评议项目启动。该项目由中国科学院遥感与数字地球研究所郭华东院士主持。在吉尔吉斯斯坦举行的"第二届干旱半干旱区对地观测国际会议"
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发布时间：2021-06-10
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