最新刊期
卷 20 ， 期 3 ， 2016
- 摘要：Four Wide-Field-Viewing（WFV） cameras are taken onboard the GF-1 satellite, which is a newly launched earth-observing satellite from China. The satellite is employed to monitor land use, environmental parameters, and agriculture, among others. However, a highaccuracy Atmospheric Correction（AC） algorithm is imperative to process the GF-1 WFV data for quantitative applications. The key problems in the AC of WFV cameras include the following: large amount of data, lack of auxiliary data, and aerosol and molecular variations. In the paper, an AC algorithm for GF-1 WFV data is introduced. Based on radiance transfer theory, the fast AC algorithm for WFV data was established as follows:（1） The radiometric calibration was completed in four seasons by cross-calibration method using Landsat 8 data. The apparent reflectance in all four bands of the WFV camera was received at the solar zenith angle, and the solar irradiance was obtained at the top of atmosphere.（2） The sun and viewing zenith angles were calculated at 1km resolution with the use of the auxiliary WFV data, including projection information, satellite passed time, view zenith angle at nadir, and pixel position.（3） Rayleigh scattering was corrected for each pixel in an image with the use of altitude data and the second simulation of the satellite signal in the solar spectrum（6S） in the same view geometry.（4） Aerosol Optical Depth（AOD） was derived from the apparent reflectance in the blue band by the deep blue algorithm at 10 km resolution with the use of MODIS 8-day surface reflectance product.（5） In every 10 km ×10 km block of WFV image, the retrieved AOD was inputted into the6 S, and the three atmospheric parameters were determined. Then, from the apparent reflectance in the four bands, the surface reflectance in the four bands was retrieved using the atmospheric parameters in every block. After all the blocks were processed, the AC of the WFV image was completed. The AC module for GF-1 WFV data was developed using interactive data language and our AC algorithm. Three GF-1 WFV images over North China Plain acquired on September 27, 2013, March 13, 2015 and May 25, 2015 were selected to conduct the experiments that will verify the performance of our AC algorithm and module. The results show that our algorithm has significantly removed the atmospheric influences, including molecular and aerosol scattering and absorption. However, if the aerosol layer is thick, the influence of the atmosphere cannot be completely removed. From these images, we select three typical surfaces for further study, including vegetation, soil, and urban.Then, the reflectance after AC is compared with that before AC. The reflectance after AC was close to the spectrum of these surfaces, and the corrected normalized difference vegetation index reflects the character of the typical surface. In this paper, a new AC algorithm based on the aerosol retrieved from the deep blue algorithm was built for GF-1 WFV data. The scattering and absorption of molecules and aerosols in the GF-1 WFV data were well corrected using the proposed algorithm, which also allowed for the rapid acquisition of surface reflectance. However, our algorithm may still be improved in terms of robustness against high-concentration aerosol, such as haze, and against adjacency effect over non uniform surface.关键词：remote sensing;atmospheric correction;GF-1;deep blue;aerosol
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发布时间：2021-06-10 - 摘要：Retrieval of Normalized Difference Vegetation Index（NDVI）with high spatial and temporal resolutions from remote-sensing data is important for monitoring the dynamic changes in vegetation. However, owing to the impact of weather, a single satellite cannot provide a time series with high-spatial-resolution NDVI data. An effective solution to this problem is to fuse the NDVI with various resolutions retrieved from different sensor data to produce a dataset that has both high spatial and high temporal resolutions. In this study, the middle east part of North China Plain is selected as the experiment area, and HJ-1 CCD and MODIS NDVI data are used. The spatial and temporal adaptive reflectance fusion model is improved in two ways:（1）The difference of the spectral responses between different objects is considered-using the confidence interval（ x ±2σ） to filter the data and consequently reduce classification errors, and the linear regression method is used to modify the spectral distance weight according to different objects.（2） The predicted radius is defined to predict the missing HJ-1satellite data whose unavailability is caused by external influences. Results indicate that the distribution of high and low NDVI values of the six studied dates is highly consistent between the observed and predicted images. According to the results of the correlation analysis, the scatter points concentrate along the line of y=x. The correlation coefficients of the six pairs of observed and predicted NDVI images are 0.957, 0.962, 0.935, 0.964, 0.913, and 0.933, which are all significant at 0.01 levels, and all RMSEs are less than 0.07, which indicates good fusion results. According to the difference images and their histograms, the averages of the six difference images are-0.02, 0.001,-0.012,-0.025, 0.023, and-0.02, with standard deviations of 0.049,0.044, 0.049, 0.036, 0.056 and 0.043, respectively. The pixel values concentrate around 0, and 90% of the pixels have absolute values smaller than 0.1. The percentages of pixels that have absolute values smaller than 0.2 are 99.80%, 99.97%, 99.95%, 99.98%, 99.79%, and 100%for the six images. The accuracy of results tends to decrease as the time intervals increase between the base and prediction dates. The comparison of the results of IR shows that the prediction accuracy of the improved algorithm is higher than that of the original algorithm. The results also show that the base and predicted images have similar seasonal characteristic. If the time interval is extremely long,however, similar seasonal characteristics are not obvious or exhibit significant differences, which reduce the accuracy and precision of the prediction results. Therefore, an appropriate reference image should be selected. In summary, the improved method is employed to reconstruct NDVIs with high temporal and high spatial resolutions by combining the spatial details of HJ-1 CCD data and the temporal variation of MODIS data to effectively supplement the missing HJ-1 CCD NDVI dataset.关键词：NDVI series;spatial and temporal fusion;HJ-1 CCD;MODIS;STARFM
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发布时间：2021-06-10 - 摘要：Plant canopy temperature and soil temperature can be used to estimate canopy transpiration. The sensible heat flux and latent heat flux of the soil are crucial in predicting drought and estimating crop yield. In reality, land vegetation has multiple types with different shapes and spatial distributions; thus, a general model suitable for variety of vegetation types is important to investigate the thermal radiation directivity of the surface covered by vegetation. Based on the theory of bidirectional gap probability, a three-dimensional simulation model for hermal radiation directivity of a nouniform canopy was established in this study. The ideal plant given the special distribution was employed to form the canopy observation scene. The ideal plant is the basic unit for describing the observation scene, which possesses the statistical averages of the parameters of all the plants in the observed area. The multi-angle thermal infrared field data of different growth periods of a corn crop was used to verify the results of the simulation model proposed in this paper. Encouraging results have been obtained. The results show that the model can accurately simulate the change trend in the canopy temperature distribution with only a small deviation, which may be attributed to the following factors:（1） The deviation in the parameters used to establish the ideal plant affects the simulation result.（2）The roughness of the soil layer and the shape of the leaf are not considered in the process of simulation.（3） The model does not consider multiple scattering in the canopy body. Moreover, the quantity of the plants in the observation area is limited, and the spatial distribution of the plants differs to a certain extent from the statistical average used in the simulation. Given the parameters of the ideal plant and its spatial distribution, the model can calculate the area ratios of the four components in asparse vegetation scene, dense vegetation scene, and a mixed vegetation scene. The results combined with the Li-Strahlergeometric-optical model could be applied to the study of the surface albedo under complex surface conditions.关键词：thermal radiation;three dimensional model;ideal plant;gap probability;component temperature
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发布时间：2021-06-10 - 摘要：Land Surface Emissivity（LSE） is a key parameter for determining land surface temperature because it depends on the features of the surface composition, is related to surface roughness, dielectric constant, and water content, and changes with vegetation fraction. Auxiliary data are used to calculate emissivity through single-channel algorithms, e.g., classification-based, NDVI threshold, and vegetation cover methods. However, these techniques present several limitations. For example, the classification-based method LSE is insensitive to land cover change on barren surfaces.Moreover, MODIS C5 LST products underestimate LST in an arid area of Northwest China because of the over estimate of LSE. Therefore, the precision of land surface emissivity in arid and semi-arid areas must be improved before the retrieval of land surface temperature. In this study, we developed a method for improving the accuracy of land surface emissivity for barren surface by using the latest ASTER Global Emissivity Database（GED） and Vegetation Cover Method（VCM）. ASTER GED was used to determine bare soil emissivity. The spectra of soil in ASTER spectral library were selected to fill small gaps of bare soil emissivity according to different land cover types. The vegetation emissivity of ASTER spectral library and Fractional Vegetation Cover（FVC） product were used in VCM method estimate land surface emissivity. This method can not only maintain the high precision of ASTER GED for barren surface but can also effectively characterize seasonal characteristics of vegetation emissivity over time; hence, the proposed technique an provide reliable input data for retrieval of land surface temperature. This method was evaluated with 11 scenes of ASTER land surface emissivity products of Zhangye region in 2012 and in situe missivity measurements. Results show that the biases of bands 10-12 are within 0.015 and RMSEs are less than 0.03.The biases of bands 13 and 14 are less than 0.005 and RMSEs are less than 0.01 compared with those in ASTER LSE products. The retrieved emissivity is close to the in situ measured emissivity with minor errors. One scene of Landsat 8 TIRS data was also used to analyze the influence of the proposed method on the accuracy of land surface temperature retrieval. The results show that the method can obtain more reasonable and accurate land surface emissivity than NDVI threshold method, especially for barren surface. Hence, the method can be used to produce high-precision land surface temperature products for other thermal infrared sensors, e.g., Landsat 8 TIRS,HJ-1B IRS, and FY-3 MERSI. In this study, an improved method for estimating land surface emissivity from ASTER GED products and VCM was proposed to improve the accuracy of land surface emissivity for barren surfaces. This method was evaluated with ASTER LSE products and in situ emissivity measurements. One scene of Landsat 8 TIRS data was also used to analyze the influence of this method on the accuracy of land surface temperature retrieval. The results in Zhangye city show that the proposed method can obtain reasonable and accurate land surface emissivity. However, further analysis is required for other areas. Although ASTER GED is relatively stable on bare surfaces, Hulley et al.（2010） indicated that the soil moisture at 11 and 12 micron can increase LSE by up to 0.03 times. In this regard, the effects of soil moisture on land surface emissivity must be considered in future research.关键词：ASTER GED;land surface termpera ture;land surface emissivity;fractional vegetation cover;temperature emissivity separation
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发布时间：2021-06-10 - 摘要：Image retrieval is a key technology for data acquisition and knowledge transformation under the background of remote sensing data. Remote sensing images with high spatial resolution provide Object details and diverse structures of land features, thereby making differences in local visual feature as evident. Most existing retrieval methods represent and model image contents on the basis of low-level visual features of the image, leading to limited retrieval performance of high-resolution remote sensing image. This paper presents a novel retrieval method for high-spatial resolution remote sensing image; the proposed method utilizes abundant information about the spatial distribution and structure of land features. In the proposed method, data on low-level visual features and land-feature relations are utilized to represent the content of remote sensing images. Firstly, Quin+ tree is used to decompose the original large-sized image into a feature block sequence with a fixed size. Lowlevel visual features and land-feature relation descriptions are then extracted from the corresponding feature block. Feature histograms for candidate blocks are constructed according to the descriptions of the feature blocks. In each feature block, low-level visual information is represented using color and texture histograms. Moreover, land-feature spatial relation information is modeled as Object–direction and category co-occurrence histograms. Finally, the similarity between the query template and all candidate blocks is determined according to the feature histograms. Candidate blocks with high similarity values are selected as the final retrieval results. Several high-resolution remote sensing images of Quick Bird and ZY-3 are used in the experiments to confirm the effectiveness of the proposed method. Based on the retrieval results of the proposed method, the average retrieval precision of water, farmland, buildings, and other categories are higher than 0.75. In addition, the proposed method is compared with two typical CBIR methods. Quantitative evaluation indicates that the proposed method yields optimal results. The proposed method can significantly improve the retrieval performance because it considers the description of space relationship information among different land features.关键词：Remote sensing image retrieval;Quin+ tree;Spatial symbiotic relation;Spatial direction relation;histogram matching
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发布时间：2021-06-10 - 摘要：Promoting the accuracy of hyperspectral image classification is a crucial and complex issue. Hyperspectral image provides details of spectral variation of land surface with continuous spectral data. On the one hand, this characteristic is widely utilized to analyze and interpret different land-cover classes. On the other hand, the availability of large amounts of spectral space introduces challenging methodological issues, such as curse of dimensionality. Subspace ensemble systems, such as random subspace method（RSM）, significantly outperform single classifiers in classifications involving hyperspectral image. However, two issues should be addressed to improve robustness and overall accuracy of the system. The first issue is diversity within subspace ensemble systems, and the second one is the classification accuracy of individual subspaces. In this paper, we adopt Support Vector Machine（SVM） as base classifier and proposed a novel subspace ensemble method, namely, optimal subspace SVM Ensemble, for hyperspectral image classification to improve the performance of RSM. Based on random subspace selection as the initial step, a two-step procedure is designed to avoid similarity within ensemble systems during the optimization of individual subspace accuracy. Instead of maximizing the diversity of ensemble by using a specific diversity measure, the first step employs the k-means cluster procedure according to the similarity of SVM patterns to classify random base classifiers. Second, an optimization process is implemented with Jeffries–Matusita（J-M） distance as criterion by selecting the optimal subspace from each group in the formal phase. The final label is decided based on majority voting of optimal subspaces. Experiments on two hyperspectral datasets reveal that the proposed OSSE obtains sound, robust, and overall accuracy compared with RSM and random forest method. In the first hyperspectral image, namely, the Pavia university data set, the maximum increases in Kappa coefficient and overall accuracy are about 0.04 and 2.64%, respectively, compared with those in RSM and about 0.15 and 12.75%, respectively, compared with those in random forest method. In the second hyperspectral image, namely, the Indian Pines data set, the maximum increases in Kappa coefficient and overall accuracy are about 0.02 and 1.00%, respectively, compared with those in RSM and about 0.13 and11.12%, respectively, compared with those in random forest method. The combination of optimal subspaces improves the diversity of subspace system and the accuracy of individual classifiers and thus exhibits better performance, particularly when using limited samples, which is common in hyperspectral image classification. Basing on the results of different parameter settings in OSSE, we found two interesting issues related to the number of clustering and initial size of random subspaces. First, the optimal number of clusters in OSSE is stable when using specific hyperspectral remote sensing data. Hence, the optimal number of cluster could be assessed using the characteristics of remote sensing images. Second, similar to RSM, increasing the number of random subspaces minimally contributes to the improvement of classification accuracy in OSSE. Consequently, to decrease the time cost of computing, we should avoid selecting numerous random subspaces.关键词：hyperspectral image classification;random subspace;optimal subspace;support vector machine
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发布时间：2021-06-10 - 摘要：Impervious surfaces, such as houses, cement or asphalt roads, parking lots, and other artificial surfaces, can be used as indicators for environment monitoring. Several methods have been proposed to estimate impervious surfaces by using remote sensing images.However, accurate extraction of impervious surfaces remains challenging because of the diversity of urban land covers. Thus, this paper presents an improved hyperspectral remote sensing algorithm for impervious surface extraction by combining spatial-spectral kernel and Support Vector Regression（SVR）. The composite kernel support vector regression model estimates impervious surface abundance by fitting an optimal approximating hyper plane to a set of training samples. Basing on the hyperspectral image, we first extract spatial-spectral feature and then select 10% pixels as training data. Spectral features include reflectivity of each pixels, NDVI, greenness and brightness of tasseled cap transformation, soil-adjusted vegetation index, and normalized difference built-up index. Gray level co-occurrence matrix approach is employed to extract spatial features. The first and second moments are identified as effective texture measures. The window size is set as 3×3 pixels for hyperspectralimage. SVR method generally uses a single kernel. In this study, instead of using only one single kernel, spatial and spectral kernels are integrated into a kernel framework. Basic kernels include Gaussian, poly, and linear kernels. Using a linear weighted summation kernel as composite kernel combination method, we set the composite kernel SVR model in a manner that combines spatial and spectral features. The values of unknown pixels are predicted using the composite kernel SVR model. Finally, we evaluate the results of the experiments. Two accuracy indices, namely, root mean square error and coefficient of determination, are employed to assess the accuracy of impervious surface extraction. To test the performance of the composite kernel support vector regression model, we conducted experiments on simulated and real hyperspectral datasets. We also compared the performance of the proposed composite kernel SVR with single kernel SVR. On the experiment of simulation range, the root mean square error of the proposed algorithm is lower whereas than the determination coefficient is higher than those of single kernel method（1.4% and 0.6%, respectively）. On the Hyperion data experiment, the root mean square error of the algorithm is lower but the determination coefficient is higher than those of single kernel method(1.8% and 11.7%, respectively. On the two kinds of hyperspectral data experiments, the proposed algorithm can obtain spatially explicit results. Furthermore, a distortion phenomenon is observed in the results of single-kernel SVR algorithm. We propose a composite kernel support vector regression model for impervious surface extraction using a hyperspectral image. The results indicate that our algorithm can effectively extract urban impervious surface and exhibits higher accuracy than the single-kernel SVR model. In our future work, we will focus on multisource remote sensing fusion through multiple kernels for impervious surface extraction.关键词：support vector regression;spatial-spectral kernel;feature extraction;impervious surface;hyperspectral
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发布时间：2021-06-10 - 摘要：Space resection is the method of acquiring the exterior orientation parameters of a camera based on three ground control points（GCPs） at least and the corresponding image points. The traditional least squares method of space resection needs good initial values of exterior orientation parameters. However, good initial values are difficult to obtain in the oblique photogrammetry condition. The objective of this study is to compute accurate exterior orientation parameters automatically to provide good initial values for the least squares method of space resection. Solving the space resection problem needs three GCPs and the corresponding image points at least. This study initially starts from three GCPs and then derives a direct solution model of space resection. The three GCPs must be coplanar and the corresponding image points must also be coplanar. Thus, the homography matrix can be used to describe the geometric relationship between a set of coplanar points and another set of coplanar points. This study transforms the collinearity equation into a homography matrix model and derives two constraints based on the orthogonality of the rotation matrix. When only three GCPs exist, the space resection problem can be transformed into a set of binary quadratic equations. The binary quadratic equations have four solutions at most. An additional GCP is necessary to decide the unique solution. When three ground control points exist, the unique solution can be directly computed based on a set of linear equations, which are derived from the homography matrix model. After computing the homography matrix solution, the exterior orientation parameters can be obtained using the relationship between the homography matrix and collinearity equation. Three experiments tested the effectiveness and reliability of the proposed method. The first experiment aimed to test the performance of the proposed method when only three GCPs exist. The experimental data comprise done oblique image of the Yangjiang area and four evenly distributed GCPs. Three of the GCPs were used to compute the exterior orientation parameters, and the remaining one was used to decide the unique solution. The proposed method was compared with the traditional range and imaging equation models. In the first experiment, the proposed method showed the best back substitution accuracy, which reached as high as 9.908010E-9 pixels. However, the back substitution accuracies of the range and imaging equation models were merely 5.891172E-6 pixels and 9.285811E-4 pixels, respectively. The second experiment aimed to show the influence of the proposed method on the least squares result of space resection in different camera angle conditions. Two different datasets were used in the second experiment. The first dataset was still the oblique image of the Yangjiang area. The initial values using the proposed method were compared with the values from the traditional method and the man-made methods. In the large camera angle condition, the least squares iteration based on the traditional method was unable to converge. The accuracy of the least squares results based on the proposed method was as good as that of the man-made initial values. Both methods had back projection accuracies of 0.03592 pixels. However, the convergence rate of the proposed method was good. The second dataset was an aerial image of the Toronto area. Given that the camera angle was small, the traditional method achieved good initial values. Accordingly, the proposed method was compared with the traditional method.In the small camera angle condition, the back projection accuracy of the proposed method was as good as that of the traditional method. The accuracy of both methods was 0.02525 pixels, but the convergence rate of the proposed method is better than the traditional one. The third experiment compared the proposed method with currently popular direct solution models of space resection in the case of multiple coplanar GCPs. Camera calibration data were used in the third experiment. The accuracy of the proposed method was better than that of the coordinate transformation and polynomial models, and was as good as the accuracy of the virtual GCP model. This study proposes a new direct solution model of space resection based on the homography matrix. It transforms the space resection model into a set of binary quadratic equations and acquires the direct solution of exterior orientation parameters. When multiple coplanar GCPs exist, the proposed method is able to transform the collinearity equation into a set of linear equations. Experimental results show that the proposed method can provide good initial values for the least squares method of space resection, which can be applied in oblique photogrammetry, close-range photogrammetry, and so on. Future studies will focus on acquiring direct solutions in the case of non-coplanar GCP s to improve the universality of the proposed method.关键词：space resection;homography matrix;closed-form solution;unit quaternions;co-planar points
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发布时间：2021-06-10 - 摘要：Considerable noise is present in some multispectral images acquired by remote-sensing satellites. The current traditional de-noising methods not only fail to completely remove the noise, but also cause image blurring and spatial-resolution degradation. This study aims to mitigate the tradeoff between the removal of noise and the reservation of information. To solve this problem, we propose an improved and high-performing sparse representation approach that processes the high-frequency portions in the difference images based on the initial image and the Gaussian-filtered image to remove the noise. In this study, sparse representation is applied to the information in a remote-sensing image to accurately represent important information, which includes edge and texture. By contrast, the noise that is mainly concentrated in the high-frequency portion cannot be represented. We used data sparsity to reconstruct the high-frequency portion without noise. The algorithm completely preserves the low-frequency information and reconstructs the high-frequency information by sparse representation based on whether or not such information can be represented by fewer atoms from the over-complete dictionary. Theoretical analysis and experimental results show that the proposed method outperforms the traditional de-noising methods and the sparse representation method. In terms of visual quality, the proposed method reconstructs the image with clear color and apparent structure.The results of the objective assessment show that the proposed method can achieve a higher peak signal-to-noise ratio than the other methods and provide a feasible solution to remove noise effectively and considerably highlight the details of the original images.关键词：remote sensing images;sparse representation;image de-noising;K-SVD;quality evaluation
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发布时间：2021-06-10 - 摘要：Land surface temperature and emissivity are essential parameters for quantitative thermal infrared remote sensing. These parameters can be accurately determined by converting at-sensor radiance into at-surface radiance through removal of effects related to atmospheric radiation and absorption. Therefore, obtaining accurate atmospheric parameters is the fundamental of quantitative thermal infrared remote sensing. Atmospheric parameters are typically obtained using a hyperspectral image through（1） Autonomous Atmospheric Compensation（AAC） and（2）In-Scene Atmospheric Compensation（ISAC） algorithms. Through simulation studies, we found that the noise immunity of the AAC algorithm was weak. When this algorithm is applied to images derived from Thermal Airborne hyperspectral Imager（TASI）, results of atmospheric inversion calculation are uncertain. However, the ISAC algorithm can select blackbodies by linear regression,thereby resolving "AAC uncertainty." Based on the combination of these two algorithms, an improved algorithm is proposed to obtain accurate atmospheric parameters for TASI data analysis. Pixel brightness temperature of each TASI data channel was calculated, and the channel with the highest temperature was selected. The ratio of the number of pixels with the highest brightness temperature to the total number of pixels in the channel was then determined, and the channel with the highest ratio was used as reference channel. The maximum pixels of this channel were selected as test pixels to determine surface temperature. The obtained surface temperature and Planck function were used to build a linear regression model for extracting atmospheric transmittance and path radiance data based on the Kolmogorov-Smirnov statistic.The model was used to easily obtain transmittance radio（Tr） and path radiance difference between strong and weak absorption channels（Pd）.MODTRAN was then applied to simulate atmospheric spectra based on the condition of the study area. The simulated pseudo TASI32-band spectra were resampled. Basing on the resampled atmospheric spectra, we determined empirical formula coefficients, goodness of fit,and root mean square error. The empirical formula was employed to obtain atmospheric transmittance and path radiance. The diversity of the two parameters（Tr and Pd） is effectively controlled using the proposed algorithm. The inversion results are more stable than that obtained through the AAC algorithm, particularly at the spectral position of absorption peaks. Moreover, compared with the retrieved data from the improved algorithm, the simulated results by MODTRAN are less reliable because they could not ascertain differences in time and space.We applied the inversion result of the improved algorithm and the simulated result of MODTRAN to experiments on Temperature and Emissivity Separation（TES） by using the ASTER-TES algorithm to determine the emissivity spectra. The emissivity spectrum recovered from the improved algorithm is more similar to the spectrum measured in field by using MORDTRAN. The improved algorithm, similar to the ISAC algorithm, can select blackbodies by linear regression to restrain the diversity of Tr and Pd in the AAC algorithm. Despite the accuracy of the retrieved atmospheric spectra or the recovered emissivity spectra, the improved algorithm performs better than the AAC algorithm and MODTRAN when applied to TASI data. Future studies must investigate whether the improved algorithm can be applied to other types of data.关键词：Autonomous Atmospheric Compensation（AAC）;In-Scene Atmospheric Compensation（ISAC）;combination;TASI;noise immunity
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发布时间：2021-06-10 - 摘要：Remote-sensing technology features and the environmental elements of surface complexity together determine mixed pixels in remote-sensing images. Many mature methods of hyper spectral mixed-pixel decomposition are available, but research on the multispectral decomposition of mixed pixels are rare. The purpose of this study is to decompose mixed pixels based on their multispectral imaging characteristics.Hyperspectral images with high spectral resolution may benefit from the spectral unmixing of end-members.By contrast,FY3 multispectral（MERSI）image shavea lower spectral resolution but a higher temporal resolution. Thus,MERSI-EVI time series is introduced in this paper to decompose mixed pixels. The basic parameters of the experiment areas are as follows: study area: Hebi City, Henan Province, China; data: 79 MERSI images acquired from May 1, 2013 to October 15, 2013（89 days had no data） and a Landsat 8 OLI image of the year; purpose: extraction of 2013 corn acreage from the data images. First, the remote-sensing images were processed, and the support-vector-machine classification method was used to extract information on farmlands with the use of a Landsat 8 OLI image. Then, SG-filtered MERSI time-series images were used to calculate EVI; the EVI growth curves of the mixed pixels and the crop end-numbers were then generated. The end-members were determined by field investigation. Corn is the main crop in the area. A total of 14 corn end-members were evenly selected in the space.Then, using the traditional method, the 14 corn end-members were combined with other end-members for unmixing. Finally, the spectral angle matching（SAM） method was used to improve the accuracy of the decomposition and adaptively select the most similar corn end-member with mixed pixels. In this case, a growth curve was used instead of a spectral curve. The results of the traditional decomposition methods vary widely; the extracted corn acreage ranges from 191.90 km2 to 574.83km2,whereas the generated corn acreage of the new decomposition method is 589.95 km2.The 2013 summer corn acreage in Hebi City is780.39 km2. Thus, compared with the best result generated by the traditional methods, the relative accuracy of the new method is improved by 2%. This study shows that using vegetation growth curves to decompose mixed pixels is effective for multispectral images.Of course, this study focused on plains, where crop planting structure is relatively simple. For areas with complex geographical environments and/or planting structures, the performance of the proposed method has yet to be confirmed.
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发布时间：2021-06-10 - 摘要：Using satellite-derived NO2 column data from Ozone Monitoring Instrument（OMH）, we analyzed the characteristics and factors affecting the spatio-temporal distribution of tropospheric NO2 column density in Beijing-Tianjin-Hebei Region from 2005 to 2014. Results demonstrated that（1） tropospheric NO2 column density considerably fluctuated on the temporal scale and increased at an annual rate of3.35%, with the highest column density in 2011; NO2 level increased during 2005 to 2011, ignoring changes in 2008, and decreased from2012 to 2014.（2） The spatial distribution of tropospheric NO2 column density significantly changed, with the lowest distribution found in the northwest part and the highest in the southeast part. Tropospheric NO2 column density was low in Zhangjiakou and Chengde in north Beijing-Tianjin-Hebei Region but high in Beijing-Tianjin-Tangshan and Shijiazhuang-Xingtai-Handan.（3） Beijing-Tianjin-Hebei is surrounded on three sides by mountains in north and is not conducive to NO2 distribution. Precipitation exhibited a highly negative correlation with NO2 concentration because of atmospheric wet deposition.（4） Pollution sources were highly determined by industrial and energy structures.The tertiary industry is dominant and increases steadily in Beijing, where coal consumption is low, but car ownership increases 1.5 times; as such, the main source of NO2 in Beijing is motor vehicle exhaust emissions. The second industry of Tianjin is slightly higher than the third industry; in this area, coal consumption is twice higher than that in Beijing but car ownership is only half of that in Beijing; thus, industrial emissions and motor vehicles are a common source of NO2 in Tianjin. A high proportion of secondary industry is found in Hebei, where coal accounted for 80.6% of that in the Beijing-Tianjin-Hebei Region; hence, Hebei industrial emissions are the main source of NO2, although vehicle emissions have increased with increased vehicle ownership in the recent years.关键词：Tropospheric NO2 column density;OMI;satellite remote sensing monitoring;spatial-temporal change;impact factors;Beijing-Tianjin-Hebei Region
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发布时间：2021-06-10 - 摘要：Vegetation index is an important parameter that reflects the status of vegetation in an area. Analyzing the relationship between climatic factors and the vegetation index is helpful to fully understand the impact of climate change on vegetation. However, some conclusions on the relationship between the vegetation index and climatic factors are inconsistent across various time scales. Thus, this issue is addressed in the present study to enhance our understanding of the relationship between vegetation and climatic factors. With the use of the Normalized Difference Vegetation Index（NDVI） data of moderate resolution imaging spectroradiometer（MODIS）during the growing seasons from 2000 to 2009, the monthly climatic factors（i.e., mean air temperature, accumulated temperature above0 °C, and monthly precipitation） of three observations in the northern Tibetan Naqu were combined, and the within-growing-season and cross-growing-season correlations between the NDVI and the climatic factors were analyzed. First, we preprocessed the data. To eliminate the interference of human factors, especially the urban buildings in the near site, we obtained the NDVI values outside the radius of 25 km around the meteorological station. Second, we calculated the correlation coefficient between the NDVI and the monthly mean air temperature. Similarly, the correlation coefficient between the mean air temperature for the month ahead and NDVI was also calculated using the NDVI（4–9 months） and the monthly mean temperature series（3–8 months）. The same process is applied to the two months ahead and the other factors. Third, we calculated the correlation coefficient between the NDVI and the mean air temperature of the month. Similarly, the correlation coefficient of the mean air temperature for the month ahead and the NDVI for April was calculated using the NDVI for April and the mean air temperature for March. The same process is applied to the two months ahead and the other factors. The within-growing-season correlations between the NDVI and the temperature and precipitation factors were highly and positively significant, and the lag effects of the climatic factors on NDVI were most obvious for the one-month lag. By contrast, the inter-growing-season correlation between NDVI and precipitation was not significant, and the lag effect was much weaker than the within-growing-season lag effect. Therefore, the correlations between the NDVI and climatic factors vary between the within-growing-season and the inter-growing-season. Such a variation can be attributed to two aspects: the within-growing-season correlation fully considered the synchronization of the rainfall and temperature, whereas the inter-growing-season did not; the difference in sample sizes resulted in different results. In this paper, the relationship between NDVI and climatic factors is discussed at different time scales. Results show differences in some aspects. At present, most of the studies are based on the relationship between vegetation changes and climate factors in the growing season.The analysis of the relationship between vegetation development and climatic factors are more scientific and persuasive. In conclusion, much more attention should be paid to the different approaches to obtain the various correlations between NDVI and climatic factors. spects: the within-growing-season correlation fully considered the synchronization of the rainfall and temperature, whereas the inter-growing-season did not; the difference in sample sizes resulted in different results.
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发布时间：2021-06-10 - 摘要：Due to the frequent mining activity and increasing mine geological disasters in our country, the long term dynamical monitoring and analysis of the mining area are of great importance to prevent the potential geological damage in mining area. The Permanent Scatterer Interferometric Synthetic Aperture Radar（PSIn SAR）, a newly developed ground deformation monitoring technique, which may not be influenced greatly by the spatial and temporal deccorelation, has been widely applied in study on regional displacement, including the deformation of urban area, terrain area and mining area. During the step of spatial unwrapping in PSIn SAR algorithm, stable points or external known GPS points are necessary. The procedure of selecting the reference stable points is obviously uncertain and the external GPS data is difficult to obtain. Due to the shortcomings of traditional spatial unwrapping in PSIn SAR algorithm, a new method of spatial unwrapping based on periodic function is developed. Since Corner Reflector（CR） point can be installed easily, which can be applied in the area without external constrained data（such as GPS data） and avoid the uncertainty of choosing reference point in the PS parametric adjustment network, the subsidence rates calculated on CR points are used as constraints for PS network while the spatial unwrapping is performed using the parametric adjustment method. With the improved method, the PSIn SAR is applied in the inversion of time series ground deformation in mining area.With 14 ALOS PALSAR images from February 2007 to February 2010, the deformation inversion experiment is carried out. The colliery dense distribution area, around Baisha reservoir in Henan province, is chosen as the study area in the experiment. 6546 PS points except CR points are detected during the experiments. The linear velocities calculated out through traditional spatial unwrapping method are compared to that of the developed method. The algorithm achieves the integration of CR data and PSIn SAR algorithm for the first time. The authors succeed to inverse the time series of subsidence from February in 2007 to February in 2010, using the periodic function to simulate the linear and nonlinear components of the deformation. The results show that there appears obviously time series subsidence around the reservoir, with the max value over 10 cm in the colliery distribution area, due to mining activities. The subsidence mainly performs to be linear subsidence. The nonlinear subsidence only appears to be a little obvious in the northeast of the reservoir. In order to validate the result of the experiment, deformation monitoring with leveling was also carried out in the area. With comparison to the deformation result of leveling, the accuracy of ± 2.1mm is calculated.It can be concluded from the good accordance that the method has the following advantages:（1） CR point can be installed easily, hence we can choose the study area freely, which can be applied in the area without external constrained data（such as GPS data）;（2） CR Point can be taken as constraining data for the spatial unwrapping of PS network and increase the redundancy number of parametric adjustment model which can make the solutions more stable;（3） Corner reflectors have high reflectivity which can be identified easily on the SAR image, thus the inaccuracy within the step of coordinate transformation can be avoided and the accuracy of the solutions can be improved.（4） It can avoid the uncertainty of choosing reference point in the PS parametric adjustment network.关键词：permanent scatters;Corner Reflector（CR）;constraint adjustment;mining subsidence;deformation monitoring
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发布时间：2021-06-10 - 摘要：SSM/I can receive radiation information of surface and near-surface penetrating through clouds. Compared with crossing scanning sensors, such as AMSU-A, SSM/I that utilizes conical scanning can avoid limb effects on AMSU-A measurements. Thus, this SSM/I measurement can be applied to analyze spatial and temporal variations in polar climate. Spectrum, wavelet, and Empirical Orthogonal Function（EOF） analyses were used to analyze the SSM/I measurements from 1998 to 2008 in a polar area. Results of spectrum and wavelet analyses show significant Four-Month Oscillations（FMO） in SSM/I channel 19V/H and 37V/H measurements. The FMO of the brightness temperature peaked at the beginning of March, July, and November in the Arctic and at the middle of April, August, and December in the Antarctic. The intensity of FMO varied inter-annually. The intensity was stronger in 1999, 2002, and 2005 than that in the other years in the Arctic and in 1998, 2001, 2005, and 2008 in the Antarctic. FMO was also detected in reanalysis of ERA-Interim surface skin temperatures and sea ice area. The mean brightness temperature of the surface varied with increasing surface skin temperature in the Arctic from December to May of next year, when sea ice area almost remains the same. From June to October, surface skin temperature was nearly invariable,and the mean brightness temperature varied with increasing sea ice area in the Arctic. When the FMO of the mean surface temperature and sea ice area were relatively high, the FMO of the mean brightness temperature peaked. Conversely, when both FMO of the mean surface skin temperature and sea ice area were relatively low, the FMO of the brightness temperature went to the valley value. Compared with the surface skin temperature, melting and freezing of sea ice exhibited greater impacts on the brightness temperature. The EOF results showed the spatial characteristics of FMO in the Arctic and Antarctic. Most areas in the Arctic presented the same FMO, but the phase differed in Weddell Sea（20°W–60°W, 60°S–75°S） and Lazarev Sea（20°W–30°E, 60°S–70°S） in the Antarctic. The phase of 60°S–70°S latitude band were opposite from that of 70°S–80°S latitude band within 90°W–180° W around the Antarctic area. Significant FMO signals exist in the brightness temperature of SSM/I channel 19 V/H and 37 V/H. The FMO of surface climate variables in the Arctic and Antarctic was confirmed by combining FMO with ERA reanalysis temperature. The intensity of the FMO varied inter-annually. The FMO of the brightness temperature reflected the combined effect of variation in the surface skin temperature and the melting and freezing process of sea ice. For the spatial characteristics of the brightness temperature, FMO presented synchronous variations in the Arctic and varied from region to region in the Antarctic.
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发布时间：2021-06-10 - 摘要：<正>高分4号卫星是中国第1颗地球静止轨道高分辨率对地观则卫星,轨道高度为36000 km。卫星配置了可见近红外相机和中波红外面阵相机各一台,兼具可见光和红外线全天候成像能力,其可见光谱段分辨率为50 m,中波红外谱段分辨率为400 m,实现了高时间分辨率和较高空间分辨率的有机结合。
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发布时间：2021-06-10
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