最新刊期
卷 23 ， 期 5 ， 2019
- 摘要：Curiosity is the source of science and technology development, and continuous innovation. Application shows how humanity and society benefit from the development and innovation of curiosity-driven. There is no exception that the development of Synthetic Aperture Radar (SAR) technology and inversion of aboveground biomass (AGB) in forested environments around the globe have been continuously driven by both curiosity and application. With the rapid climate change worldwide today, the balance of curiosity and application acts is of ever significance. The areal extents and AGB of trees in forested areas around the world are fundamental for the assessment of the global carbon stock and emission, and their variation. Of successful curiosity- and application-driven examples, the AGB estimation has been strongly coupled with quad-pol SAR, interferometric SAR (InSAR), and tomography SAR (TomoSAR) techniques and their datasets. With the reflection of the past and the balance of curiosity and application, the following four approaches are recommended for the assessment of the global AGB in the future. They are the spaceborne dual-wavelength SAR systems and allometric equations, the light detection and ranging (LiDAR) and allometric equations, the combination of the first two approaches, and the exploratory one.关键词：radar backscatter;synthetic aperture radar (SAR);Interferometric SAR (InSAR);aboveground biomass (AGB) of tree;forest stand;global AGB of forests
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发布时间：2021-06-07 - 摘要：Evapotranspiration (ET), including evaporation from soil surface and vegetation transpiration, is an important component for water and energy balances on the Earth’s surface. The quantification of ET at daily or long time scales is significant in modeling the global hydrological cycle, studying climate change, and managing water resources. However, current remote sensing-based ET models can generally only provide snapshots of ET at the time of a satellite overpass and do not satisfy the expectations of hydrologists, irrigation engineers, and water resources managers concerned with practical applications. In this paper, a comprehensive overview of the methods for estimating daily ET from remotely sensed instantaneous observations is presented. These methods include the constant upscaling factor methods (constant evaporative fraction, constant decoupling factor, radiation-energy derived, constant reference evaporative fraction, and constant surface resistance) and data assimilation method. The commonly used approaches are compared with a discussion regarding the main merits, limitations, and accuracies. The problems and uncertainties of the temporal upscaling of ET, including the evaluation of model applicability, the daily variation of cloud, the spatial interpolation accuracy of meteorological parameters, nighttime ET, the uncertainties from the temporal upscaling methods and ET models, and the approaches of accuracy assessment, are discussed. To improve the accuracy of daily ET estimation from remotely sensed instantaneous observations, several suggestions for future research are proposed as follows: First, research on the continuous surface meteorological data at remote sensing image pixel scale should be enhanced because large-area applications of the temporal upscaling methods are hampered by the lack of appropriate ground-based observations and the spatial heterogeneity causes low accuracy of the spatial interpolation methods of meteorological parameters. Second, the accuracy of ET estimation using remotely sensed data can significantly affect the accuracy of the temporal upscaling of ET. As ET estimation models have not been perfected yet, the methods for the temporal upscaling of ET can be combined with those for ET estimation to reduce the influence of accumulated errors. Third, to weaken the influence of unstable upscaling factor during cloudy days, research on the relation between the constant upscaling factor and cloud (e.g., the appearing time, thickness, and duration of cloud) should be enhanced. Therefore, developing a robust method for the temporal upscaling of ET during cloudy days is vital. Fourth, research on the physical mechanisms of each commonly used method and development of an improved upscaling factor that can be independent of the variation in atmospheric variables and can incorporate the horizontal advection are essential. Fifth, numerous methods for the temporal upscaling of ET can only accurately provide daytime ET, whereas the daily ET is closely concerned with practical applications. Thus, research regarding the temporal upscaling methods should be enhanced in consideration of nighttime ET and its physical mechanisms. Finally, enhancing the research on the new technology and methods of accuracy assessment for ET can weaken the uncertainty of verification.关键词：remote sensing;evapotranspiration;temporal scale;upscaling methods;uncertainty analysis
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发布时间：2021-06-07 -
摘要：A fundamental subject in planetary exploration and sciences is the determination of mineral abundance and size distribution from visible/near-infrared spectra. Such knowledge can help better understand what geologic processes have been active on the lunar and planetary surface. The imaging spectrometer carried by the Yutu rover of the Chang’E-3 mission measured the reflectance spectra of lunar soil at a height of approximately 1 m, providing a new insight for understanding lunar surface. A new method was proposed to retrieve mineral abundance and particle size distribution and apply the results to Yutu rover in situ measurement. A methodology combining Hapke radiative transfer model and sparse unmixing algorithm was proposed in this study to retrieve mineral abundance and particle size distribution. The imaginary part of the refractive index of each endmember was first calculated by solving the Hapke model. The single-scattering albedos of each endmember with different particle sizes were obtained based on the Hapke slab model, and then the endmember library was constructed. Finally, the single-scattering albedo of the mineral mixture, which was computed using Hapke bidirectional equation, was unmixed using sparse unmixing algorithm with the aid of the endmember library. Laboratory measurements collected from the Reflectance Experiment Laboratory were used to validate the methodology. Results showed that the methodology has good performance in retrieving the abundance and particle size from mineral mixtures. Finally, the methodology was applied to Yutu rover measurement. The values of average abundance of pyroxene, olivine, plagioclase, agglutinit, and ilmenite at four observation points were 28.1%, 4.5%, 39%, 28%, and 0.4%, respectively. The average particle sizes of pyroxene, olivine, plagioclase, and fused glass were 166.02, 8.34, 196.31, and 44.21 μm, respectively, possibly indicating the different response of each component to space weathering in this site.关键词：radiative transfer model;sparse unmixing;abundance;particle size distribution;Yutu rover;lunar- 572
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发布时间：2021-06-07 -
摘要：The Microwave Radiometer Imager (MWRI) onboard FY-3C satellites was successfully launched on December 23, 2013. MWRI observes the Earth’s atmosphere and surface at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with dual polarization and can provide an important initial field for Numerical Weather Prediction (NWP). However, the O-B (observation minus simulation) of MWRI shows a clear bias difference between ascending and descending orbits. The magnitude of this ascending–descending bias is approximately 2 K for all channels, thereby restricting its operational application in NWP data assimilation systems. This research analyzes the causes of the bias and makes appropriate corrections. The parameters of the calibration equation were analyzed, including physical temperature of the warm load, brightness temperature of the hot reflector’s back-lobe, physical temperature of the hot reflector, physical temperature of the cold reflector, receiver channel instrument temperature, warm load radiometric counts, cold space radiometric counts, antenna brightness temperature calibration scale, antenna brightness temperature calibration offset, and a sensitivity analysis of each term of the calibration equation was conducted. Results indicated that high values of the hot load reflector are the main causes of the bias. The reflector was heated periodically by incident solar radiation and emitted a variable radiation with space and time, which caused the ascending–descending bias. Thus, the brightness temperature was simulated using the basic atmospheric parameters of ERA5 in conjunction with the radiative transfer model known as RTTOV. With the principle that the probability density difference between the O-B of ascending and descending orbits is minimum, the emissivity of the hot load reflector is estimated. Results show that before adjusting the emissivity of the hot reflector, the probability density plot of the O-B of ascending and descending orbits was separated. After correction, the bias difference between the ascending and descending orbits were clearly reduced, thereby identifying the main error source of the ascending–descending bias. Such identification can guide the development of future instruments and provide the condition for direct assimilation of MWRI radiance data. Although the accuracy of NWP fields, the radiative transfer model, calibration, and cloud detection are not the main error source of the ascending–descending bias, they may affect the estimation accuracy of the emissivity of the hot load reflector. Thus, strict quality control should be carried out in the future, and after the samples of greater uncertainty are eliminated, more accurate on-orbit emissivity of the hot load reflector can be estimated.关键词：remote sensing;FY-3C;microwave imager;calibration;difference between the ascending and descending orbits- 549
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发布时间：2021-06-07 - 摘要：Digital Beam Forming (DBF) in elevation plays a crucial role in spaceborne Synthetic Aperture Radar (SAR) by realizing the high-resolution wide-swath (HRWS) imaging mode. However, when dealing with echo signals in mountainous and relief areas, the traditional scan-on-receive (SCORE) approach in elevation always leads to the problem of beam mispointing. This problem leads to the reduction of system receiving gain, which affects the imaging performance of SAR systems. To address the problem of beam mispointing, this work proposes a DBF processing approach in elevation based on Digital Elevation Map (DEM). This work presents a detailed analysis on the steering direction of the DBF receiving beam. A processing procedure for the proposed approach consisting of three main steps is elaborated. First, on the basis of spaceborne SAR imaging geometry, the height of the target is computed for each range cell in SAR imagery by using the DEM data and satellite orbit parameters. Second, the direction of arrival (DOA) angle of the target is calculated for each range cell with the geometry model of earth. Third, according to the relationship between DOA angle of target and each range cell, the DBF weighted coefficients are computed for each range cell. This step enables the receiving beam to point to the signal source of the real target, thereby improving the receiving gain of echo signals. Finally, an X-band spaceborne SAR system is introduced for simulation experiments. Simulation results show that when the ground height is more than 1.9 km, the amplitude of the target signal processed by the traditional SCORE method is attenuated more than 2.8 dB. After the proposed method is used, the target signal amplitude is attenuated less than 0.4 dB. Thus, the proposed method is superior to the traditional SCORE method. The influence of DEM error on the DBF processing method in elevation is also analyzed. As the acquired DEM data have adequate height accuracy, DEM errors have minimal influence on the proposed approach. Taking full consideration of spaceborne SAR imaging geometry model and DEM data of the ground scene, the proposed method can correct the DBF receiving beam pointing deviation in mountainous areas. The proposed method has the potential to promote the application of DBF processing in spaceborne SAR.关键词：synthetic aperture radar;high-resolution wide-swath;digital beamforming;digital elevation map;receiving gain
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发布时间：2021-06-07 - 摘要：Image registration is key to high-resolution phase extraction and height inversion for interferometry synthetic aperture radar (InSAR). Recently, the registration method with high accuracy and efficiency has been one of the widely discussed issues in High-Resolution Wide-Swath (HRWS) InSAR applications. Different regions of a large scene will cause the pixel offsets of InSAR images to change dramatically. Traditional algorithms of InSAR image registration are based on maximum coherence require substantial block and interpolation processing, which may suffer from huge computational complexity and low precision. An efficient image registration algorithm for InSAR large scenes via DFT model is proposed in this study. In the scheme, a DFT model of InSAR complex image registration is constructed based on the minimum mean square error criteria. Then, the efficient sub-pixel registration for InSAR complex images is achieved via quadtree block and matrix multiplication DFT registration. Simulation and experimental results are presented to confirm the effectiveness of the algorithm. Results demonstrate that the algorithm can achieve subpixel image registration of InSAR large scenes and has high computational efficiency, usually more than thrice that of traditional FFT-based registration methods.关键词：remote sensing;InSAR;complex image registration;quadtree block;maximum coherence criterion;DFT model
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发布时间：2021-06-07 -
摘要：The water index can suppress background noise and increase the separability of surface water. Thus, it has been widely used for surface water extraction. Traditional FCM clustering algorithm considers the uncertainty of ground objects without neighborhood spatial information, which is sensitive to background heterogeneity. On the basis of the shortcomings of traditional FCM clustering algorithms, this study proposed a regional FCM clustering algorithm and applied it to extract city surface water in complex environment regions using GF-2 remote sensing imagery. The main steps of the method include (1)Calculating the normalized difference water index after the removal of shadows; (2) Presenting a regional FCM clustering algorithm;(3)Proposing the urban surface water automatic extraction algorithm by combining the water body index and the regional FCM clustering algorithm. Finally, the proposed method was carried out on two GF-2 high-resolution remote sensing image data located in Guangzhou and Wuhan. The experimental results showed that the proposed method has better accuracy and water boundary than state-of-the-art methods. The proposed method also retains regional integrity and local details of surface water objects while effectively inhibiting noise from urban surface water in the complex background, thereby reducing the " salt and pepper” phenomenon found in traditional FCM clustering algorithm.关键词：remote sensing;GF-2;urban surface water;normalized difference water index;Fuzzy clustering algorithm;FCM algorithm;region FCM clustering algorithm- 794
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发布时间：2021-06-07 -
Investigation on earthquake-induced landslide in Jiuzhaigou using fully polarimetric GF-3 SAR images
摘要：Conventional landslide identification model is only suitable for multispectral images. Optical images can directly reflect the information of ground objects and provide people a real and intuitive feeling. Earthquake areas are often hit with inclement weather after the occurrence of earthquakes. Effective optical images are difficult to obtain in the presence of clouds and fog, thus leading to the incomplete recognition of landslide information. Given that SAR satellite technology is not affected by clouds and breaks through the limitations of optics, SAR data have gradually become the mainstream data for earthquake disaster response and assessment. With the development of SAR sensors, SAR has developed into a multiband, multi-polarization, multi-angle, and variable working model from the first single mode of operation. SAR data guarantee flexibility in its application to seismic landslide identification. Existing SAR image landslide identification methods mainly use single characteristics of texture features or polarization features of landslide information in SAR images. Multi-source features in SAR images have not been fused, especially the characteristics of multi-polarization SAR image data. Thus, landslide survey accuracy is low and cannot meet actual application needs. Taking the Jiuzhaigou earthquake as an example, this study adopts the first C-band multi-polarization GF-3 satellite data with a resolution of 1 m as the data source. Polarization and texture features of the image are extracted based on an in-depth analysis of the characteristics of multi-polarization image data. Afterward, GF-2 satellite data obtained post-earthquake are carefully registered with the GF-3 satellite data. Typical landslide samples are selected from the GF-2 images, which are used as training samples for classification. Finally, back propagation neural network is used to extract the landslide in the whole area through the comprehensive utilization of polarization characteristics, texture, and terrain feature information based on the training samples. To meet the urgency of the disaster, identification accuracy can be improved as much as possible while meeting the efficiency of information identification. The findings can provide a reference for the restoration, reconstruction, and scientific exploration of the Jiuzhaigou earthquake. A comparison of the results of visual interpretation of GF-2 optical images and the unmanned aerial vehicle images revealed that the overall extraction accuracy of the landslide is 92.8% and the Kappa coefficient is 0.715. The scattering characteristics of the ridge on the image are easily confused with the bright spots formed by the landslide because these characteristics are more obvious. The spatial distribution feature of the slope can eliminate partial landslide information and eliminate the influence of the ridge. Taking the Jiuzhaigou earthquake as an example and using the homemade GF-3 full polarization SAR satellite data, this study proposes a fully polarimetric data seismic landslide automatic recognition method based on integrated polarization features, texture features, and terrain features. The method is used for a general investigation of landslides in the entire earthquake area of Jiuzhaigou. The extraction results meet the requirements of earthquake emergency, post-earthquake recovery, and reconstruction. The method also promotes the application of GaoFen satellites in the earthquake prevention and disaster reduction industry.关键词：remote sensing;GF-3;landslide;full polarization;SAR;Jiuzhaigou earthquake;neural network- 642
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发布时间：2021-06-07 - 摘要：Land Use/Land Cover (LU/LC) classification of urban areas is of great significance to urban studies and has become a highly important research direction. However, with continuous urbanization and more types of inner cities diversified, single remote sensing image has been unable to meet the requirements of high precision. Therefore, urban LU/LC classification by data fusion has emerged. In this study, hyperspectral images are widely used in urban LU/LC classification because of their abundant spectral information. However, an objective limitation is that similar spectral characters with different elevation cannot be distinguished. LiDAR data can obtain accurate elevation information. Therefore, such data will obtain better classification maps when merged with hyperspectral images. This work proposes an urban LU/LC classification method based on the multi-level fusion of hyperspectral imagery and LiDAR data by using the complementary of their advantages. First, the spectral, spatial, and elevation information extracted from two images are stacked to achieve level fusion. Then, the classification is divided into two frameworks. One framework classifies all pixels of the feature images, while the other uses LiDAR data to extract the building mask and classify the off-building area. Classification maps of this framework are obtained by combining the classification map of the latter framework and the off-building area. The classification results are then obtained by voting the classification results obtained by the two frameworks to complete the decision-level fusion. Finally, the conditional random fields are processed to smoothen the image and remove noise. The data set of 2013 IEEE GRSS data fusion contest was experimented on to verify the effect of the proposed algorithm. The OA was 93.22%, and Kappa was 0.93. The accuracy of the proposed method exceeded 90% in most categories, while the classification accuracy of synthetic grassland, soil, tennis court, and running track was 100%. Experiment results showed that the proposed algorithm greatly improved the classification of buildings, roads, and parking lots. In this study, hyperspectral imagery and LiDAR data are applied to classify LU/LC in urban areas. It also combines feature level and decision level and achieves good results. The following problems will be considered in future works: increasing the accuracy of building extraction to improve the effect of feature-level fusion, considering the increasing intensity of LiDAR point cloud data in feature-level fusion, and increasing the number and diversity of classifiers when using the multiple classifier classification.关键词：remote sensing;hyperspectral;LiDAR;data fusion;urban LU/LC classification;multi-feature
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发布时间：2021-06-07 -
摘要：Hyperspectral remote sensing data have a wealth of spectral information that can describe objects in detail. However, redundancy occurs due to the high correlation of adjacent bands in the narrow-band continuous spectrum space. This redundancy leads to high computational complexity and dimension disaster in data analysis. As an important means of dimension reduction, band selection can reduce these negative effects. The goal of band selection is to retain the relevant information needed in practical applications with as few bands as possible. Therefore, two aspects of discussion are involved: selection criteria and selection methods (search methods). A new band search method based on band subspace is proposed to improve band searching efficiency. This method needs to input the required number of bands without any other parameter settings. First, the spectral space of hyperspectral data is partitioned according to the block characteristics of band correlation coefficient matrix image and the adjacent transitive correlation, which is partitioned as a first subspace. Then, on the basis of the actual demand band number, the first subspace is divided secondary according to the proportion of the subspace size, and the final band subspace is obtained. Second, a band is selected according to certain rules (e.g., the maximum standard deviation) in each final band subspace to form an initial band subset. Lastly, after the objective function (e.g., the average correlation of the band subsets, the best index, the overall classification accuracy) is set, bands are replaced by each subspace to increase (or decrease) the value of objective function until no replacement can improve the goal further, which is the final band subset we pursued. The other three band selection methods are compared with our method on two opened hyperspectral data to verify the validity of the proposed method. Experimental results show that as a fast search strategy, the computational time of the proposed method is much less than the exhaustive band combination. The proposed method has faster search efficiency and convergence than the artificial bee colony algorithm for all kinds of objective functions. Moreover, compared with band selection method based on spectral clustering and adaptive band selection, this method can flexibly transform the target function for specific applications and obtain more suitable band subsets for different requirements. The correlation of spectral space of hyperspectral data is flexibly used in this study. It substantially reduces the computational complexity of the search algorithm in the band selection that combined the subspace partition with band search. Moreover, few parameters are used to simplify the complexity of the model and reduce the time spent in parameter tuning. For different application requirements, the proposed method flexibly transforms the objective function so that the searched bands’ combination becomes suitable for these requirements.关键词：remote sensing;hyperspectral images;band selection;subspace partition;criterion function;classification, search strategy- 647
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发布时间：2021-06-07 -
摘要：Performing rainfall-runoff simulation and prediction in a lake basin is a time-series analysis problem in complex systems. Existing mechanistic and identification methods for model selection have advantages and disadvantages. Mechanism model has a clear physical explanation, but it requires professional data for support and the model-solving process is complicated. Identification model is flexible and its solution is simple, but it has difficulty building a universal model and the accuracy of the model is low. Existing simulation models are also based mostly on static data, which cannot effectively use real-time observation data from sensor networks to improve simulation uncertainty. This study aims to improve the problem of traditional identification models being incapable of effectively using timing information, which results in low simulation accuracy. Moreover, it establishes a simulation and simulation framework for dynamic feedback and adaptive adjustment between observation and numerical simulation. This research proposes a dynamic data-driven model based on deep recurrent neural network, named dynamic data-driven time sequence model, which consists of a multilayered long short-term memory loop body and a fully connected layer. The proposed model incorporates runoff remote sensing rainfall data and ground station observation rainfall data as static data input and recent ground station actual runoff observation data as dynamic data input to simulate catchment runoff process. Several cases of multiple sub-river runoff into Poyang Lake indicate that in static data-driven mode, with TRMM_3B42_V7 precipitation as input, the ENS accuracy of DTSM is 10 percentage points higher or more than that of the mechanism model. The cases also indicate that in static data-driven mode, with the fusion precipitation from TRMM_3B42_V7 and ground station as input, the ENS accuracy of DTSM is 29 percentage points higher or more than that of the mechanism model. Lastly, the dynamic data-driven model can further improve the accuracy of simulation compared with the static-driven model, and the improvement is substantial in the basin with lower accuracy in static data-driven mode. The model based on deep recurrent neural network can effectively extract the timing information from the data. The dynamic data-driven model can make adaptive adjustments to improve simulation uncertainty. Based on the above two reasons, the DTSM proposed in this paper can achieve the same or a better simulation accuracy than existing representative mechanism models. At the same time, DTSM is flexible and the solving process is simple.关键词：remote sensing;data-driven simulation;deep learning;runoff simulation;TRMM rainfall;sensor network- 574
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发布时间：2021-06-07 -
摘要：Downward Surface Shortwave Radiation (DSSR) estimated from satellite measurements is crucial in climate change study and clean energy applications. The Advanced Himawari Imager (AHI) onboard the new generation geostationary satellite Himawari-8 provides an unprecedented opportunity for the near real-time estimation of DSSR, with a spatial resolution of 5 km and a temporal resolution of 10 min over the full disk regions. To meet the requirements of fast and accurate estimation of DSSR from Himawari-8, this study proposed a Look-Up Table (LUT) method to estimate the DSSR from Himawari-8/AHI level 2 (L2) atmospheric products. We first investigate the sensitivities of DSSR to solar geometry (solar zenith angle), atmosphere conditions (aerosol optical depth, cloud optical depth, and cloud effective radius), and surface condition (surface albedo) basing on the atmospheric radiative transfer model. Then, LUTs for clear and cloudy skies are generated based on the sensitivity results. Finally, the DSSR is estimated with the inputs of Himawari-8 L2 aerosol and cloud products released by JAXA on the basis of LUTs previously created. As an experiment, DSSR results are estimated using our algorithm at 02:00 UTC on April 1, 2016 and compared with the JAXA Himawari-8 L2 DSSR product. The comparison shows that our DSSR estimates are consistent with the operational DSSR results over the full disk regions. To further validate our DSSR estimates, we compare our results and the operational DSSR results with ground-based measurements at Yonsei site (land) and 0n_165e site (sea) in April, July, October, and December 2016. The correlation coefficients (R) derived from ground measurements and these two DSSR results are larger than 0.88 for all types of sky conditions. With the scattering properties of non-spherical (hexagon) ice cloud particles included, the biases of our DSSR estimates in the validation with ground measurements at two sites are lower than the operational DSSR results. This study developed an LUT-based method to estimate DSSR with inputs of Himawari-8 L2 atmospheric products (including aerosol and cloud products, and other auxiliary data such as solar zenith angle in L1 product). The estimated DSSR was validated against both land and sea sites of ground observed DSSR, with RMSEs of 94.13 Wm−2, 62.92 Wm−2, and 110.60 Wm−2 for all types of sky conditions at Yonsei, and RMSEs of 123.86 Wm −2, 105.33 Wm−2 and 151.44 Wm−2 for all types of sky conditions at sea site 0n_165e. Furthermore, the correlation coefficients (R) of our DSSR estimation from the land and sea sites were greater than 0.88 for all sky conditions. These validation results suggested that our DSSR estimation with Himawari-8 atmospheric products works well and can thus be further used in land surface radiation budget research and solar energy application after improvements on the current algorithm.关键词：Himawari-8;Downward Surface Shortwave Radiation;radiative transfer theory;LUT- 752
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发布时间：2021-06-07 -
A model for the fusion of multi-source data to generate high temporal and spatial resolution VI data
摘要：Vegetation Index (VI) data with high spatial and temporal resolution are highly important in the use of remote sensing technology to observe the earth. Owing to technological limitations, obtaining VI data that exhibits both high spatial and temporal resolution is impossible. The arrival of multi-source remote sensing data spatial and temporal fusion model enables the retrieval of data with high spatial and temporal resolution. The commonly used model does not have the ability to capture the intermediate change process of pixel value, and a certain regularity occurs in the change of the VI of the landmark. This study proposes a new multi-source data fusion model (FCMVISTFM) based on Fuzzy C-Mean algorithm (FCM). Making pixels that group together have similar VI values and law of VI changes throughout the period. FCMVISTFM uses FCM to divide land-cover types into certain categories based on multi-phase VI data, which are defined as subclasses of each land-cover class. Each land-cover class average value is calculated by using the linear unmixed model, and subclass average value is calculated by the law between land-cover class and subclass. The VI data are fused by Landsat8 OLI and MODIS data based on the assumption that the average VI value of subclass S is the same as the VI value of pixels that belong to subclass S. Results show that FCMVISTFM can achieve relatively high accuracy. The average values of correlation coefficient (R), RMSE, ERGAS, and variance are 0.9057, 0.0674, 1.9795, and 0.0045, respectively. With this level of accuracy, VI data can be used for vegetation research and observations of the earth. Commonly used line unmixed models, spatial and temporal adaptive reflectance fusion model (STARFM), and its improved models have the problem of uncertain ability to capture the intermediate change process of VI. Thus, FCMVISTFM is more accurate compared with STDFA and ESTARFM. FCMVISTFM is developed for obtaining high spatial and temporal resolution VI data, making it easier to capture the intermediate changes of VI, which can be applied where high spatial and temporal resolution VI data are needed. In this study, the accuracy of the multi-source data fusion model can be increased by the following aspects. (1) The models based on line unmixed model, regardless of pixel classes or subset S average value calculations, are based on the entire image. However, in the STARFM model and improved models based on STARFM, the data fusion based on high and low resolutions pixels in a certain window, cloud cover only affects the calculation of pixels near its coverage area. The acquisition of multiphase cloudless images is especially difficult when the study area is large. In this case, the STARFM model and improved models based on STARFM have more application advantages. (2) All of the multi-source remote sensing data fusion models are based on certain assumptions, though these assumptions are based on a certain theoretical and have certain rationality. Errors are mainly caused by assumptions. A complete model assumption is proposed as the main way to improve the accuracy of the multi-source remote sensing data fusion model. (3) The time sequence laws of the VI of various landmark are not disordered, but a certain regularity, such as the specific laws of the crop’s VI, occurs. If the multi-source remote sensing data fusion model is established based on these laws, then it can also improve the accuracy of fusion results to some extent.关键词：remote sensing;Vegetation Index (VI);data fusion;temporal and spatial resolution;fuzzy c-means algorithm;linear unmixed model- 685
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发布时间：2021-06-07 -
摘要：Studying seawater optical properties is of great importance in global climate change and material cycle. lidar has the ability to retrieve the profiles of seawater’s optical properties. A single-scattering lidar equation is typically a useful and effective model of lidar return. However, lidar return depends on the volume scattering function at 180° scattering angle and lidar attenuation coefficient, which makes retrieval from an equation difficult. The Fernald method is often used to retrieve backscatter lidar return with the assumption of lidar ratio. High Spectral Resolution Lidar (HSRL) can retrieve optical properties without assumption. A shipborne traditional lidar was developed to detect the vertical profile of seawater optical parameters. Experiments on seawater were conducted in the nearshore and offshore regions of the Yellow Sea. The lidar system was fixed on the front deck of a scientific survey boat. In situ optical measurements were also performed in the two regions. A simple quasi-single-scattering approximation was employed to calculate a modeled lidar return with inherent optical properties derived from the in situ measurement. The comparison of oceanographic lidar returns with modeled lidar returns using nearly coincident in situ optical properties were in perfect agreement with the nearshore and offshore regions, indicating that lidar can effectively detect seawater optical parameters. The difference between the inverse lidar attenuation coefficient and the in situ diffusion attenuation coefficient were analyzed based on the Fernald method with different lidar ratios. With the use of the calibrated lidar ratio, the lidar attenuation coefficients while sailing were obtained by using the Fernald method. A fusion algorithm based on traditional lidar data and in situ backscatter coefficient was also proposed to simulate HSRL. Then the accuracies of the Fernald method and fusion algorithm were compared. In the nearshore water column, diffuse attenuation coefficient varied from 0.15 m −1 to 0.28 m−1, and the maximum error of both methods was below 11%. As for the offshore water column, diffuse attenuation coefficients changed little through depths, approximately 0.1 m−1 to 0.16 m−1. The maximum error of the two methods was nearly 17%. The statistical analysis showed that diffuse attenuation coefficient can be well employed both by fusion algorithm and the Fernald method (with calibrated lidar ratio). This paper described the applications of lidar for profiling the properties of upper ocean. To overcome the assumption of lidar ratio in the future while retrieving water column information from traditional lidar, the HSRL without assumption has a great advantage in the field of seawater optical parameter detection.关键词：remote sensing;ocean optics;optical remote sensing;lidar;diffuse attenuation coefficient;lidar attenuation coefficient- 651
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发布时间：2021-06-07 - 摘要：The Universal Normalized Vegetation Index (UNVI) is an improved Vegetation Index (VI) based on the Universal Pattern Decomposition Method (UPDM). However, UPDM-based UNVI involves the calculation of a complex coefficient matrix, which is inconvenient for users. We reformulated the computation of the coefficients of the UPDM without changing its main mathematical formulation to generalize the UNVI in a user friendly manner. We derived new matrices and developed a UNVI software using IDL to facilitate the convenient calculation of the UNVI based on data from MODIS and Lands at TM, ETM, and OLI satellite sensors. Vegetation information derived from satellite data is highly significant to the operational monitoring of the Earth’s land cover. VIs are determined traditionally by calculating directly the algebraic combinations of the reflectance at different bands, that is, from the visible to the SWIR spectral range. These VIs (e.g., NDVI and EVI) were calculated by limited reflectance bands and might cause loss of information. All available data are considered as input variables in UPDM-based UNVI in the calculation of VIs. We provided the code and the coefficient matrices in this study to make UNVI usable for all users and multiple sensors. For the UPDM-based UNVI, the spectrum of each pixel is expressed as the linear sum of three fixed standard spectral patterns (i.e., water, vegetation, and soil), along with a supplementary one (i.e., yellow leaves), associated with particular objects found on land. The goal of UPDM is to transform the reflectance values of the n bands of a target pixel into three standard coefficients, along with a supplementary one, using standard spectral decomposition patterns. We derived the matrices to facilitate the convenient calculation of the UNVI based on data from the MODIS and Lands at TM, ETM, and OLI satellite sensors. We also provided the software and coefficient matrices of UNVI. We assessed the capabilities of the UNVI to evaluate the Gross Primary Production (GPP) of vegetation compared with the GPP data derived from the flux tower sites. The GPP estimated by UNVI used in this model was GPP∝PAR×VI×VI. The GPP estimated by UNVI has a higher correlation with the GPP obtained from the flux sites. The R2 between the GPP from the flux sites and that estimated by UNVI is above 0.79 for the mixed forest and deciduous broad-leaved forest vegetation types. This result is consistent with the strong correlation between UNVI and vegetation physicochemical parameters. Thus, UNVI could be applied in estimating vegetation GPP. In this study, we reformulated the computation of the coefficients of UPDM without changing its main mathematical formulation and provided the index, which was termed UNVI. We also derived new matrices to facilitate the convenient calculation of the UNVI based on data from MODIS and the Landsat-TM, ETM, and OLI satellite sensors. The UNVI could be obtained directly by multiplying the coefficient matrix M and the surface reflectance, which would result in a user friendly computation of UNVI. We developed a software using IDL to facilitate the calculation of the UNVI from different remote sensing images. We applied UNVI in the GPP estimation to introduce the operation of the UNVI software. The results show that the UNVI-based GPP estimation has a high correlation with the GPP obtained from the flux sites, with coefficient R2 above 0.79. Thus, UNVI can be used for vegetation monitoring. The UNVI software provides the important technical support for studies and applications for the remote sensing inversion of vegetation physicochemical parameters and estimation of vegetation GPP.关键词：Landsat;MODIS;vegetation index;UNVI;IDL;GPP
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发布时间：2021-06-07 -
摘要：Crop mapping is an important component of agriculture monitoring. Accurate information on crop area coverage is vital for food security and the agricultural industry, and the demand for timely crop mapping is high. Previous research indicated that remote sensing technology is a practical and feasible method for agricultural crop area extraction. In this study, the north area of the Yellow River in the North China Plain is chosen as the study area, where the main crops are winter wheat, maize, cotton, and soybean. To obtain the distribution information of crops, the yearly four-day composite MODIS time-series Leaf Area Index (LAI) with 500 m spatial resolution is collected. A total of 92 MODIS LAI images obtained yearly from 2007 to 2016 are used to build time-series LAI curves. To avoid the edge effect of the time-series LAI caused by the Savitzky–Golay filter, the last two phases of LAI images in the last year and the first two phases of LAI images in the next year are added to build the time-series LAI in a year. The Savitzky–Golay filter is then applied on the yearly time-series LAI pixel by pixel to minimize effects of anomalous values caused by atmospheric haze, cloud contamination, and so on. Fourier transform method based on reconstructed LAI is further employed to extract the key parameters. The 11 parameters, including the amplitudes of 0–5 terms and the phases of 1–5 terms, are taken as the features for crop identification. The training samples and verification samples of various crops are obtained through ground investigation and Google Earth images. On the basis of the training samples of various crops, online dictionary learning algorithm is applied to construct the dictionary used to identify the crops. With the dictionary, the orthogonal matching pursuit algorithm is further applied on samples under testing to obtain the sparse representation coefficient. Then the crops are identified according to the minimum reconstruction error, which can be calculated by the dictionary and the coefficient. Therefore, the areas planting winter wheat, spring maize, summer maize, cotton, and orchard from 2007 to 2016 are extracted in the study area. Lastly, the accuracy of the identification results is evaluated yearly by a confusion matrix. Results show that the reconstructed time-series LAI curves are smooth and consistent with crop growth and development characteristics. Overall identification accuracy reaches 77.97% with a Kappa coefficient of 0.74 from 2007 to 2016. User accuracies for individual crops are as follows: winter wheat and summer maize, 90.60%; spring maize, 73.40%; early summer maize, 81.80%; cotton, 69.40%; and orchard, 81.60%. Annual overall accuracies from 2007 to 2016 range between 70.57% and 83.71% and Kappa coefficients range from 0.66 to 0.81. In conclusion, combining the harmonic characteristics of the time-series LAI with the sparse representation can effectively identify the areas for planting different crops. The approach developed in this study is feasible for extracting information on main crop distribution in the study area.关键词：remote sensing;leaf area index;sparse representation;Savitzky-Golay filter;North China Plain;crops;identification- 633
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发布时间：2021-06-07 -
摘要：Baiyangdian Wetland is located in Xiong’an New Area, with important ecological functions and special strategic position. For a long time, under the combined effects of human activities and climate change, the Baiyangdian Wetland has gradually shrunk and even dried up. It is of great significance to further study the carrying capacity of ecological and environmental of the Xiong’an New Area by understanding the change characteristics and ecological functions of the Baiyangdian Wetland ecosystem. Currently remote sensing based analysis of changes of this region has insufficient in research periods, classification system and information interpretation of land use / land cover of wetland. This work studied spatiotemporal characteristics of changes of Baiyangdian Wetland from 1975 to 2018. Images of Landsat MSS/TM/ETM+/OLI during 1975—2018 and GF-2 PMS in 2017 and 2018 were acquired to analyze land use / land cover change. First, image interpretation symbol were built based on field survey of wetland types and their image features. Then, all images in ten periods were interpreted visually and land use / land cover were mapped out during 1975—2018. Last, we analyzed spatiotemporal characteristics of changes of Baiyangdian Wetland from changes of areas, types and landscape patterns. Results showed that areas of the Baiyangdian Wetland decreased 68.20 km2 (24.83%) from 1975 to 2018. Different characteristics were at different time periods. The wetland area was basically stable from 1975 to 1990, but decreased continuously from 1990 to 2011. In the period of 2011—2018 wetland area showed an increasing trend. The inter-transformation areas between types of wetland and non-wetland were mainly distributed in the transition region of water-aquatic plants-cultivated land-construction land, in the southern, western and northern parts of the Baiyangdian Wetland. In the past 43 years, the landscape pattern of Baiyangdian Wetland has become fragmented, complex and heterogeneous. Main uncertain factors affecting the analysis results include acquired time, such as month and year, of satellite images, classification systems and methods of land use / land cover. Changes in natural factors, such as climate, hydrology, as well as in human factors, such as water consumption from industrial, agricultural and urban, water resources projects in headwaters of baiyangdian basin, and groundwater exploitation, are the causes of the reduction and drying of the Baiyangdian Wetland.关键词：wetland changes;remote sensing;Landsat;GF-2;land use / land cover;Baiyangdian wetland- 704
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发布时间：2021-06-07 - 摘要：In recent years, geological disasters like landslides have often occurred within the Markam area, which is located in the southeastern part of Tibet Autonomous Region. The stability of these landslides is affected by factors such as physical geographical/meteorological conditions and anthropogenic activities. These factors pose great threats to power grid construction/operation, major transportations, and public security. Hence, effective technologies are needed to detect potential landslide hazards within this area and provide key supports for decision making regarding disaster prevention and reduction. This study employed the small baseline subset InSAR technique to process archived SAR data stacks acquired by ALOS PALSAR and ENVISAT ASAR covering the Markam area. A few suspected landslide hazard sites are identified along National Highway 318 and the Valley of Jinsha River. Spatial distribution and temporal evolution patterns of the surface displacements upon these unstable slopes are characterized.关键词：remote sensing;landslide detection;InSAR;SBAS;time series analysis;displacement characters
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发布时间：2021-06-07 -
摘要：Improving ocean interior observation resolution via satellite remote sensing is essential because of the limitation and sparsity of ocean interior observation. Retrieving the multi-temporal and large-scale thermal structure information of the subsurface ocean on the basis of satellite remote sensing is of great importance in understanding the complex and multi-dimensional dynamic processes within the ocean. This task requires a robust model with strong spatiotemporal applicability to provide technical support based on satellite observations. This study adopts a random forest regression model, namely, an advanced machine learning algorithm, to predict global Subsurface Temperature Anomaly (STA) at different depth levels (upper 1,000 m) in different seasons in 2010 from multisource sea surface parameters (sea surface height anomaly, SSHA; sea surface temperature anomaly, SSTA; sea surface salinity anomaly, SSSA; sea surface wind anomaly, SSWA) based on satellite observations. We use the in-situ Argo data for performance measurement and accuracy validation by combined use of the root mean square error (RMSE), normalized root-mean-square error (NRMSE) and coefficient of determination (R2) at global and ocean basin scales. For model accuracy, the results show that the average R2 and NRMSE of 16 depth levels are 0.53/0.60/0.54/0.66 and 0.051/0.031/0.043/0.044 for global ocean in spring/summer/autumn/winter. With the evolution of seasons, the model performance promotes first, declines, and then promotes, a trend that may be caused by the El Niño and La Niña phenomena and the transformation between them. The best performance of the model occurs in the Indian Ocean with the average R2 and RMSE of 0.71 and 0.18 °C, respectively, whereas accuracy in the Atlantic is the lowest, with averageR2 and RMSE of 0.46 and 0.25 °C at different depth levels in different seasons. This study suggests that the random forest model is suitable for retrieving ocean subsurface temperature anomalies in different seasons and can achieve good performance in different ocean basins. STA has distinctive variation signal in the upper ocean (above 300 m) and spatial heterogeneity is considerable in different seasons. However, in the subsurface and deeper layers (below 300 m), STA variation signal is weak over different seasons. This study can provide a basis for remote sensing estimation of STA and further promote the reconstruction of long-term and large-scale ocean internal parameter information (such as thermohaline structure). It can also help develop the subsurface and deeper ocean remote sensing technique.关键词：remote sensing;global ocean;subsurface temperature anomaly;Random Forest;remote sensing inversion;seasonal-spatial variation- 547
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发布时间：2021-06-07
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