Snow cover is of great significance to both global water balance and the climate system, due to its significant variability, as well as snow albedo feedback. Current grid snow depth datasets of long time series are mainly derived from passive microwave remote sensing and reanalysis, but apparent inconsistencies exist among them. Nevertheless, the assessment on multi-source snow depth datasets is still inadequate, especially assessment on their spatial characteristics. Therefore, 5 snow depth datasets, including AMSR-E, WESTDC, GlobSnow, ERA-Interim and MERRA2, were evaluated against ground observations on their spatial uncertainties and relative performances. Preliminary results were: (1)Due to the fine performances at northwest and northeast part of China, WESTDC is quite suitable in snow depth study over northern China; (2)MERRA2 shows general good performances in northwest and northeast part of China, while it lacks of detailed information due to the coarse resolution, it is recommended to conduct statistical analysis over large regions. (3)AMSR-E performs best in middle to southeast part of China, which makes it a good choice in snow depth analysis over mid-southeast part of China.
Snow albedo is one of the important surface parameters affecting the balance of ground-air radiation energy. Based on the characteristics of snow deposition in the Qinghai-Tibet Plateau, this paper studies the spatial differences of albedo from 28 observation points in the Qinghai-Tibet Plateau from February to March 2018, and analyzes the surface snow parameters affecting the albedo. The results show that during the snowmelt period of the Qinghai-Tibet Plateau, the average albedo of snow cover is 0.72. The albedo of the observation points in Qinghai area of the northeastern part of the plateau is higher than that in southern Tibet. The internal differences of snow albedo in different areas are different. The source of water vapor in southern Tibet and the faster snow melting process leads to the albedo in this area being lower than that in Qinghai. The observation points covered by patchy snow on the surface have lower albedo values (less than 0.5). In cloudy weather, short-term cloud occlusion has little effect on albedo, which remains almost unchanged.
The change of glacier area is the embodiment of the accumulation and ablation of glaciers, and it is closely related to the change of climate. The method of remote sensing provides reliable approach for monitoring glacier outlines and area, but the common method of optical remote sensing is easily affected by the changeable weather conditions in glacier area. Synthetic Aperture Radar (SAR) is not affected by the weather conditions, especially the high-resolution SAR imagery can provide abundant details on glacier surface and monitor the change of glacier better. In this paper, phase congruency method and fast marching method are used simultaneously to extract the glacier outlines and surface texture. The error of glacier area calculated from the extraction of glacier outlines was less than 5%, so it indicated that the method could extract glacier area accurately. Meanwhile, the extraction of glacier surface texture can be used to monitor effectively the supraglacial rivers, which is hard to visible in optical imagery, but is visible in high-resolution SAR imagery. Supraglacial rivers are closely related to medium and long term ablations of glacier, and they offer a new perspective for glacier monitoring.
In the context of global warming, the area and thickness of Arctic sea ice is gradually decreasing, which provides the possibility for Arctic navigation. As an important transportation hub of sea transportation, the Arctic Strait has a direct impact on the opening of the Arctic Channel. In this study, the sea ice density in the Northeast Passage and the Northwest Passage of the Arctic region in recent 35 years was used as the research object, and the sea ice concentration was clustered by using Kernel K-means clustering. The trend of time series of sea ice intensity under different clustering models is analyzed by Empirical Mode Decomposition (EMD), and the heterogeneity of ice regime changes in important straits is explored. Then taking the summer melting ice age as the study period, the cluster and heterogeneity analysis were carried out, and the following conclusions were drawn: ①The sea ice concentration of 14 straits in the North Pole showed three spatio-temporal clustering models. The variation of sea ice concentration in the same clustering model has strong consistency, and the variation of sea ice concentration is quite different among different models. All the straits with low sea ice concentration are in the Northeast Passage. ②The sea ice density of the other straits except the Bering Strait and the Dimitri Laptev Strait shows a decreasing trend in the whole year. The two straits with a decreasing trend are the straits with low sea ice concentration. ③The variation trends of sea ice concentration in each strait during the summer melting ice period are various. in addition to the simple increasing and decreasing trends, there are also various fluctuating trends, including the U-shape trend.
Based on the L2 level altimeter data in the SARIn mode of the CryoSat-2 from 2010 to 2016, combined with the glacier vector data of the Qinghai-Tibet Plateau and the global GEDM data, use the overlapping footprint method, pseudo-plane fitting method, and new method curved surface fitting to extract the height variation of typical glacier ice caps in the Qinghai-Tibet Plateau . The results show that: (1) CryoSat-2 altimetry data is feasible by using these three methods to extract height changes of mountain glaciers. (2) The three methods for extracting height changes have their own advantages and disadvantages, which provides the basis for the method selection for extracting the height variation of mountain glaciers. (3)The above three methods were used to obtain the quantitative height change results of typical glacier ice caps in the Qinghai-Tibet Plateau from 2010 to 2016. The results show that most of the glaciers are in ablation and the height change results of the three methods are almost the same. In the West Kunlun Mountains, the Zhongfeng Glacier, Guliya, and Tuzegangmu Glaciers have gentle reduce, while the Mushitage Glacier in the Palmy Plateau has shrunk slightly, but both are balance. And Laigu and Qiaqing glaciers in the Nianqing Tanggula have the fastest rate of change.
Snow cover products can provide temporal and spatial information on snow cover distribution,which is an important data source for snow monitoring. The accuracy validation and contrastive analysis of the remote sensing products are helpful to the further development and application of snow cover data products. the survey project of snow cover characteristics and distribution in China are taken for validating the applicability of snow cover products in Northeast China,The inversion accuracy of FY-3B snow depth product, AMSR-2 snow depth product and GlobSnow snow equivalent product under two underlying surface of Broadleaf Forests and Croplands were verified by using the 25 km typical quadrats and snow path survey data in Northeast China. The results show that the precision of GlobSnow snow water equivalent product has the highest accuracy and the maximum deviation and root mean square error without distinguishing the underlying surface are respectively 10.87 cm and 12.53 cm.The inversion accuracy between GlobSnow snow equivalent product and FY-3B snow depth product under two underlying surfaces is very little and the difference of deviation and root mean square error between two products is respectively smaller than that of 2.11 cm and 3.46 cm.The inversion accuracy of AMSR-2 products under two kinds of underlying surfaces is significant different and the difference of deviation and root mean square error between the two underlying surfaces is greater than that of 9.94 cm and 7.19 cm. For the three snow products, The inversion accuracy of snow depth on the underlying surface of croplands is higher than that of broadleaf forests on the underlying surface.
The satellite radar altimeter calibration progress as of now is mainly summarized in this paper. The paper presents a short history of the altimeter development and the error sources of the altimeter. Then the paper comes to the Three methods of in-situ altimeter absolute calibration for error correction and get the Bais. Those three methods are the GPS buoys observation, the mooring tide gauge observation and the coastal tide gauge observation. The paper also discusses the history of the altimetry calibration and the international situation of the altimeter calibration sites. The 4 service calibration sites in the world including the Harvest Platform, the Bass calibration site, the Corsica Island calibration site and the Gavdos calibration site are introduced. Besides the paper introduces the dedicated calibration site for HY-2 series in Wanshan, Zhuhai. By introducing the methods of absolute calibration and the international calibration sites, the paper provides references for the HY-2 series calibration sites for China.
The Satellite-borne Microwave Sounder is an important payload of a meteorological satellite. It can provide important atmospheric parameters such as vertical profile of temperature and humidity and rainfall in all weather conditions, which can provide important data for meteorological forecast, climate change research and disaster monitoring. Calibration is the key basis for the quantitative application of the Satellite-borne microwave sounder.In this paper, the basic calibration principle of the Satellite-borne microwave sounder is described in detail. The definitions about the calibration are also given. There are two calibration phases of the Satellite-borne microwave sounder, prelaunch Thermal/Vacuum calibration and on-orbit calibration and validation. The technology and method of each calibration phase are summarized, and the key points are all pointed out. To sum up, a standard for Satellite-borne microwave sounder calibration is established, which is of great significance to apply the unified calibration and quantitative application of the Satellite-borne microwave sounder.
FY-3 is Chinese second-generation sun-synchronous meteorological satellite series. The Microwave Humidity Sounder (MWHS) is a five-channel microwave radiometer in the range of 150~191 GHz onboard FY-3 satellite for atmosphere humidity sounding. To verify the load radiation performance, an aeria-test has been finished during Sep. and Nov. 2007 before launching, transferring around Qinghai, Dunhuang, and Puer. In this paper, the principle of airborne radiometric calibration for MWHS has been explained, the aeria-test calibration data has been analyzed, and the results has been compared with the synchrotron radiation correction measurement results. According the outcome, we consider the FY-3/MWHS is good enough to meet the requirements of design specifications. The aeria-test results make a firm foundation for MWHS’s operational application, and the model of airborne radiometric calibration for MWHS supported the operational preprocessing follow-up.
The Spaceborne Microwave Humidity Sounder is an important payload of a meteorological satellite, which can detect the atmospheric humidity. The long-term observation data of several sounders can provide important support for meteorological forecast, data assimilation and global climate change monitoring. Due to the lack of absolute reference for microwave radiation and the difference between the characteristics and the calibration method between each sounder, re-calibration is an important technology for obtaining long-term consistent and stable observation data. Since 2008, China has launched 4 spaceborne microwave humidity sounder onboard the Fengyun-3 series meteorological satellites. There are more than 10 years observation data need to be recalibrated for valuable application. For this purpose, this paper summarized the research status of the spaceborne microwave humidity sounder domestic and overseas at first. Then the existed recalibration method for these sounders are summarized in detail. In addition, the plan of the recalibration for Chinese spaceborne microwave humidity sounders are given, which will provide the key reference for the future re-calibration processing.
The AgileDARN radar is an agile ground-based coherent high-frequency radar based on the all-digital phased array technology, enabling the detection of mid-high latitude ionosphere. The amplitude and phase imbalance among channels may lead to the beam pointing error, the elevation of the side lobe level and the decrease of the antenna gain, and ultimately affect the radar detection accuracy. Aiming at the imbalance in the AgileDARN radar, a real-time internal calibration method based on FPGA and C++ was proposed. Before observing ionosphere, receiver calibration and transmitter calibration are done first. During the calibration, the same calibration signal is sent to all transceivers. Then the variance of the outputs illustrates the imbalance among the transceivers and can be used to implement calibration. The measured data shows that the amplitude imbalance among channels after calibration is less than 0.2 dB, and the phase imbalance is less than 1°.On the premise of meeting the system consistency requirements, the method not only ensures real-time amplitude and phase calibration, but also saves radar system costs.
Microwave remote sensing can provide large scale soil moisture information, and even further derive soil moisture products at global scale. Due to impacts of observation configuration and retrieval method etc., different soil moisture products feature different accuracies and reliabilities. Based on Triple-Collocation method, this study analyzes the uncertainties among AMSR2, SMAP and SMOS soil moisture products at two spatial scales in Naqu study area, i.e., 0.25°× 0.25° and 1.0°×1.0°, and further performs data fusion based on analyzed random errors to obtain more reliable soil moisture product. The uncertainty analysis indicates that the three products have distinct random error distribution in spatial. In this case, the traditional arithmetic mean method may not be appropriate. Hence, data fusion is performed by proposed optimizing weighting method based on the analyzed uncertainties. In comparison with the three original soil moisture products, the fusion result shows a more effective data size and improved accuracy. When different original products present similar errors, the fusion products of equal weighting and optimized weighting methods show the similar performance. Oppositely, the uncertainties analysis based fusion method is superior to equal weighting method in terms of effective data size and accuracy.
The estimation of leaf area index using remote sensing observation data depend on canopy radiative transfer models is a reliable and robust method. However, the information deficiency contained in the remote sensing data derived from the limitations of the surface heterogeneity, remote sensing observation and self-correlation, which cannot fully support the retrieval of surface parameters (e.g. LAI) and easily bring about the retrieval become ill-posed. The problem can be solved or alleviated effectively by introducing prior knowledge. This paper come up with an approach to extract priori information of Leaf Area Index (LAI) from the remote sensing data, and the information is utilized to construct cost function, PROSAIL radiative transfer model and genetic algorithm are coupled to retrieve LAI at 500 m and 250 m scales. Then the 10 m spatial resolution LAI is upscaled to 500 m and 250 m respectively to verify the corresponding LAI result, and evaluate effects of introduction of prior information on improving LAI accuracy. The comparison of performance between LAI result using MODIS LAI as prior information at 500 m scale with one without prior information indicates thatR2 increased from 0.55 to 0.65 and RMSE decreased from 1.29 to 0.38. The LAI result using 500 m optimal LAI result as prior information at 250 m scale is better than the estimation result with MODIS LAI priori knowledge, verification result shows thatR2 increased by0.08, RMSE decreased by 0.18. It is shown that LAI retrieval accuracy can be enhanced by auxiliary of LAI prior information, besides prior information quality also affects the LAI result to some extent. Multi-resolution LAI retrieval method developed in this paper has potential to estimate spatial and temporal LAI on large scale.
When the density of LiDAR point cloud data in urban area is high, there is so much data redundancy that a series of problems such as large computation, low efficiency, inconvenient display and so on arise, making the application of 3D visualization and 3D reconstruction of buildings more challenging. To solve this problem, a thinning algorithm suitable for LiDAR point cloud data in urban area is proposed, which combines the terrain adaptive features of Poisson disk sampling in geodesic space. Poisson disk sampling randomly add points whose geodesic distance is larger than a certain threshold to the sampling point set, and repeat this process until there are no new sampling points can be added anymore. On this basis, according to the characteristics of LiDAR point cloud data, a new weighted geodesic distance related to the height standard deviation of the points around the selected point is defined to improve the Poisson disk sampling algorithm. This method can effectively adjust the sampling rate of urban buildings, so as to keep the original features of buildings as much as possible, and keep good visualization effect at the same time. The experimental results of four sets of data demonstrate the applicability and efficiency of the algorithm.
The shadow detection accuracy in the high-resolution remote sensing images is easily disturbed by water, vegetation and so on. This study proposed a shadow detection method based on object-oriented method and established characteristic components by analyzing the spectral characteristics of typical features in GF-2 satellite images．The following components were constructed to detect shadow information: first principal component (PC1), brightness component I, Normalized Difference Vegetation Index (NDVI) and Water Index (WI). And then, we normalized each characteristic component to establish a rule set containing features such as band mean, standard deviation. Brightness I and PC1 were chosen as the main data source for multi-resolution segmentation, at last, performed object-oriented method on the segmented images to detect shadow. Selected different areas of GF-2 images for the proposed method, and experimental results show that the proposed method could extract complete shadow patches and effectively reduce the influence of water bodies and vegetation compared with pixel-based method.
In order to improve the precision, efficiency and robustness in complicated terrain conditions, a CSF-modified filtering method was presented based on Cloth Simulation Filtering(CSF). Firstly, with the method of dynamic grids based on slope, we established a gird index for the point cloud of which the gross errors are eliminated. Secondly, the terrain surface equation is produced by the nadir in every girds to fit elevation value of every point. And the difference between real and fitted value is the elevation normalized. Finally, with CSF algorithm to simulate gradually filtering process to refine the cloth and ground points are obtained by the final cloth and limited threshold. We verify the presented algorithm and the original CSF algorithm with the same filtering parameters in the complicated test area. The accuracy increases from 88.9% of original CSF algorithm to 95.19% of the CSF-modified algorithm. And error of type I and type II decrease from 9.71% and 1.39% to 4.57% and 0.24% respectively. In addition, the filtering duration is shortened from 164 s to 60.9 s. The result shows that on the basis of ensuring the accuracy of filtering in a wide range of complicated terrain, the modified method is not only adaptable for complicated situation areas but also helpful to enhance the efficiency of filtering computation.
Remote Sensing Data (RSD) and corresponding information extraction technologies are widely used in urban planning, ecological environment modeling, change detection, etc. Optical and radar remote sensing data, due to different imaging mechanisms, have complementary advantages for information extraction and applications. In order to improve the limitations of combining optical and SAR data for land surface information extraction. Strategies for Land Use and Land Cover (LUCC) classification based on collaborative processing optical, SAR RSD and topography information was proposed. Firstly, Digital Elevation Model (DEM) information was extracted from multi-temporal Radarsat-2 SAR images using InSAR technique. Then integrated model was constructed for information extraction based on inputs from topography data, Landsat optical RSD, Radarsat-2 SAR RSD. Finally, LULC information was extracted by random sample selection and machine learning algorithms (e.g. Random Forest (RF) ensemble learning method, Support Vector Machine (SVM) and Decision Tree (DT)). The results demonstrate that (1) when 10% training sample was selected, advantages come from combination of DEM extracted from Radarsat-2 SAR and Landsat data compared with combination of ASTER GDEM and the corresponding optical dataset; (2) Comparison results among different algorithm models by averaging 50 times classification accuracy of each model, demonstrate the robust and advantage of RF than DT and SVM. In this research, the combination advantage of optical and SAR remotely sensed data are explored, which can provide a new approach for making full use of optical and SAR data in the process of LULC classification.
Earth observation technology provides massive multi-temporal, wide-coverage and three-dimensional remote sensing data resources for Earth System science, making it possible to observe, understand, simulate and predict the complex behavior of earth systems. However, large redundant information from the existing remote sensing data retrieval systems fails to directly meet the diverse research needs and also increases the high-quality data cleaning cost. In this study, a newly retrieval results optimization method was established based on Landsat 8 satellite metadata database in 2017. The method is to select three indicators as a data quality evaluation system, including spatial integrity, temporal proximity and cloud cover, to filter out the high-quality datasets. The simplest and customized experimental results show that this method can improve the efficiency and accuracy of data retrieval and no changes for the existing data service system.
The earthquake will bring huge losses to the human society. After the earthquake, accurate and effective earthquake rescue and relief information can be obtained timely to provide auxiliary information for relevant departments to formulate rescue plans, which can minimize casualties and economic losses, and is of great significance to rescue and emergency command. In order to obtain information to assist disaster relief quickly after the earthquake, weibo data in jiuzhaigou were crawled by Python, classification and location information were obtained, VGI data and remote sensing images were aggregated to do the analysis of service area and the closest facility point path, and the landslide area was extracted by using the image change. The results show that the aggregation analysis of effective data at the first time after the earthquake can effectively obtain the service area and the optimal accessible path of facility point, forming complementary geographic information and playing a great role in disaster emergency response. The research proves that the aggregation analysis of VGI and remote sensing image data to obtain earthquake relief information is feasible in emergency rescue.
In order to solve the problem of in-corrected results for preliminary matching. This study proposed an image matching correction algorithm which combines least squares and phase correlation. The method is based on the existing preliminary matching. First, the preliminary matching results are processed using least squares, and a series of image windows in different size are selected to carry out the least squares matching, such that the matching results are obtained and the correlation coefficients of each matching window are calculated. Then, the phase correlation matching are carried out using image windows with different sizes so as to record the matching results. At the same time, the correlation coefficients are calculated. In order to verify the algorithm, the Unmanned Aerial Vehicle (UAV) images in the Shanxi mountain area are selected ad the experimental data. After experiments, it is concluded that the image matching correction algorithm combined least square and phase correlation in window series can significantly improve the precision of preliminary matching results so as to achieve the matching refinements.
Spectral variability is an important factor which influences the accuracy of spectral analysis in hyperspectral images. Multiple endmembers spectral mixture analysis is an effective method to solve this problem. In order to reduce the time complexity of spectral mixing analysis and improve the accuracy in the same time, a multiple endmember spectral mixture analysis algorithm based on corse-to-fine scheme is proposed. Based on the extended endmember set for each pixel, the proposed algorithm firstly make fully-constrained spectral mixing coarse analysis to determine the initial set of end-members containing all land cover material. On this basis, the algorithm further conducts fine spectral mixture analysis, iterative spectral mixture analysis to build end-member subsets and the optimal end-member set is finally determined according to the variation of reconstruction error. The experimental results show that compared with the iterative spectral mixture analysis method and the hierarchical multi-endmember spectral mixture analysis algorithm, the proposed algorithm reduces the error of inversion abundance and improves computational efficiency greatly.
Considering the problem of false detection caused by the parallax in building change detection of urban high resolution remote sensing images, a new method of building change detection is proposed. Firstly, the classical method of bimodal splitting is used to extract the shadows of buildings with small improvements, and then the seed area is selected automatically by estimating the possible roof position with the help of shadows, and the roof is extracted by the region growing method. Finally, the change of building in two-phase image is analyzed by using parallax, parallax azimuth, area, etc.. Through the change detection of WorldView-2 images and Ikonos images, the average accuracy rate reached 89.6%. Experimental results show that in sparse building areas with uniform roof spectrum and obvious building shadows, the proposed method can effectively solve the difficult problems of automatic selection of roof seed areas and false detection caused by the parallax in building change detection.
Urban ground subsidence monitoring is one of the important means to ensure the safe construction and healthy development of the city, and the traditional settlement monitoring method can not reflect the ground deformation information on a large scale. In view of the large-scale subsidence phenomenon in Tianjin in recent years, Sentinel-1A data is used to carry out large-scale settlement monitoring research in urban areas based on the interferometry technology of permanent scattering body and analyze the causes of ground subsidence. The results show that many places in Tianjin have ground subsidence, and the severe settlement area concentrates the Wuqing District, Beichen District and the suburban township combined area of Wang Qingxuan Town, Shengfang Town, zuogezhuang town, Jinghai Town and Daji Town, the largest settlement funnel is located in Wang Qingxuan Town, the settlement rate is -63.2mm/a. The analysis shows that the ground subsidence in Tianjin is related to overexploitation of groundwater, migration and construction of large industrial areas and geological activities of active fault zones.
Modeling ancient architectures by traditional methods can hardly obtain high precision 3D spatial information and the texture information of the target object at the same time. This study proposes to combine the 3D laser scanning technology and tilt photography measurement technology to quickly restore the 3D information of ancient architectures. This method fuses multi-source data by feature matching and establishes the complete 3D model of the ancient architecture (inside and outside). The validation using the Li's family temple in a traditional Chinese village in Fujian province, China, shows that the relative mean error of the established 3D model is 5 mm. Furthermore, this method increases the modeling efficiency greatly. In addition, the constructed model is used for 3D printing and indoor and outdoor display in different scales, and the availability of model is further verified. This research will provide technical support for the restoration and protection of ancient architectures.
