最新刊期
卷 19 ， 期 6 ， 2015
- 摘要：Remote sensing is a modern geo-informatics technology with a high priority for practical applications. However,theoretical studies in this field are relatively not updated compared with its fast-expanding technical applications. Thus,establishing new theories to support further development and deep applications of remote sensing is important. This paper focuses on new theoretical exploration on the inherited cross time and space features in general remote sensing applications to guide future remote sensing applications. The historic development of remote sensing technology is reviewed,and existing composition and classification of remote sensing are analyzed. Then,the demand for a new classification method of remote sensing is examined. Cross time and space features commonly found in remote sensing applications are evaluated,and a new vision toward remote sensing applications is discussed. A new theory isproposed. A new method ology-based classification approach is proposed for remote sensing. This technique can provide the foundation for some new research directions in remote sensing. Cross time and space Remote Sensing（ CRS）theory is proposed,and its basic framework is described to provide a new vision toward future remote sensing applications. Three typical examples are used to illustrate the actual application of CRS theory in our previous study. CRS theory and its application are developed because of the strong demand for further development and applications of remote sensing. This study will serve as apractical guide for various future applications of this technology.关键词：remote sensing application;remote sensing theory;subject classification of remote sensing;cross time and space remote sensing;a new vision
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发布时间：2021-06-10 - 摘要：Global Navigation Satellite Systems（ GNSS）,including the United States Global Positioning System（ GPS）,Russian GLONASS,the European Union’s Galileo,and China’s Bei Dou,provide L-band microwave signals with high temporal resolution.The applications of GNSS are extended from positioning / navigation to remote sensing. The versatile refracted GNSS signals sound the atmosphere and ionosphere. The reflected signals,which involve measuring the reflections from the Earth,have recently shown their capacity for Earth observations over ocean or land surfaces. This newly developed technique is called GNSS reflectometry（ GNSS-R）. The structure of GNSS-R observations can be theoretically considered as a bistatic radar. Analyzing the principles and characteristics of GNSS-R,we define GNSS-R observations as two patterns,namely,Double-Antenna Pattern（ DAP） and SingleAntenna Pattern（ SAP）,by using signal-receiving approaches. DAP has two antennas that straightforwardly receive the direct and reflected signals. The main data processing procedure of the DAP is based on the bistatic radar equation. The SAP receives the interference of the direct and reflected signals by using a single antenna. The main data processing procedure of the SAP is based on theories of signal interference. Some special issues related to the DAP and SAP are also illustrated in this study; these issues include the multiple polarization,footprint,and locations of the specular points. An initial frame for GNSS-R applications is also proposed. The results of several ground-based or airborne experiments previously conducted by our group and collaborators are shown to further explain the proposed DAP and SAP patterns. These results include those from both soil moisture and vegetationwater-content retrievals. The next generations of GNSS systems,especially GPS III and Bei Dou II,are expected to demonstrate improved performance and increased capabilities to users worldwide. This study may serve as a reference for China’s future development of GNSS-R.关键词：GNSS-R;BeiD ou;GPS;observation pattern;interference;soil moisture
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发布时间：2021-06-10 -
Advances in canopy radiation and scattering characteristic three-dimensional joint simulation models
摘要：Canopy radiation and scattering signal contain high amounts of vegetation information. Biophysical parameters can be quantitatively retrieved by establishing a canopy radiation and scattering model and inverting this model. Thus far,models in visible / near infrared（ VIS / NIR）,thermal infrared（ TIR）,and microwave（ MW） regions have been developed. Each model suitable to a specific spectral（ frequency） domain is characterized by advantages and disadvantages in terms of parameter inversion. Joint simulation models can produce complementary advantages and improve inversion precision. Although joint simulation and inversion research area has been improved,studies are mostly based on semi-empirical models or analytical models. A three-dimensional model is more suitable to characterize multiple scattering caused by vegetation structures and specified distribution of different components.This article presents a review on advances in three-dimensional canopy radiation and scattering characteristic simulation models specifically used for VIS / NIR,TIR,and MW regions,as well as a joint simulation model. The three-dimensional VIS / NIR Bidirectional Reflectance Distribution Function（ BRDF） model can be divided into two categories,namely,Monte Carlo ray tracing and radiosity models. The model is improved in terms of the ability to simulate remote sensing images with kilometer pixels and to simulate non-lambert characteristic of components. The three-dimensional TIR directional radiation model can be extended from the three-dimensional BRDF model by considering self-emission. The three-dimensional MW backscatter model includes incoherent and coherent models. A coherent model can export phase information and is more accurate,especially at low frequencies,than an incoherent model. MW emission can be simulated by calculating bistatic scattering with a three-dimensional MW backscatter model. In addition to this VIS / NIR and TIR joint simulation model and the active and passive MW joint simulation model,VIS / NIR and active MW joint simulation models were discussed. These models were listed and described in this paper.Based on our analysis,three scientific issues and corresponding solutions were proposed using the three-dimensional joint simulation models. One issue involved scene unification. Models suitable for different spectral（ frequency） domains describe a specific scene in different details. A standard should be established to perform simulations based on the same scene. Another issue involves the unity of physical and chemical parameters of components. Driving parameters vary. Therefore,transformation is required,and some models（ e. g.,PROSPECT and dual-dispersion models） should be coupled to convert various parameters into few but unified parameters. The last issue is the choice of efficient inversion methods. three-dimensional models require more calculation than other models. Look-up table and neural network methods are two of the commonly used approaches. However,these methods should be improved to satisfy three-dimensional joint simulation models. Other prospects are developed for further advancements of the joint three-dimensional simulation model. Three-dimensional joint simulation systems should be developed and used by researchers in future studies,considering that remote sensing platforms of the mechanism models（ http: / /210. 72. 27. 51:83 / ModelC ategory / All /） developed by the State Key Laboratory of Remote Sensing Science are constantly improved.关键词：vegetation canopy;three-dimensional scene;multispectral（frequency） domain;radiation and scattering characteristic;joint simulation model- 183
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发布时间：2021-06-10 - 摘要：The continuously observed Leaf Area Index（ LAI） dataset from the ground station network is an important data source for the validation of remote sensing products. However,direct comparison introduces errors to the validation results if the stationobserved LAI cannot represent the pixel because of the scale mismatch between station and pixel observations. This study aims to present an approach to evaluate the spatial representativeness of station LAI observations. This proposed approach will be used to validate LAI products. Three evaluation indicators,including the Dominant Vegetation Type Percent（ DVTP）,the Relative Spatial Sampling Error（ RSSE）,and the Coefficient of Sill（ CS）,were established to determine the different levels of spatial representativeness for station observations. DVTP calculated by land-cover maps can evaluate the vegetation-type representativeness in the product pixel. RSSE and CS were calculated from LAI / normalized difference vegetation index high-resolution reference maps,which were used to describe the degree of representativeness for vegetation density in the pixel. The approach was applied to 25 stations from the Chinese Ecosystem Research Network（ CERN）,which includes croplands and forest in China. The threshold was set as 60% for DVTP and 20% for both RSSE and CS to determine the level of spatial representativenessat different observed dates and stations. Then,the variation between seasonal and inter-annual spatial representativeness was evaluated by comparing 2010 and 2011. Finally,the results of Moderate-Resolution Imaging Spectroradiometer（ MODIS） LAI product validation before and aftergrading station observations were compared to demonstrate the importance of spatial representativeness evaluation. The spatial representativeness level of station-observed LAI data with different dates was first determined on the basis of grading criterion. The seasonal level varied at different growth stages of vegetation,whereas the inter-annual level was consistent because the structure and pattern of vegetation were stable for the adjacent years. The root mean squared error between the MOD15A2 and observed LAI with the good spatial representativeness reduced from 1. 67 to 1. 16 compared with that of all observed LAI data. The combination of DVTP,RSSE,and CS is an effective approach to assess the spatial representativeness of station-observed LAI dataset. Moreover,the uncertainty of MOD15A2 validation significantly differsat different levels of spatial representativeness. Thus,the level of stationobserved LAI data at the product pixel scale should be determined,and high-level LAI observation should be chosen to reduce the error for validating LAI products. However,the station LAI observations that can represent the product pixel were not sufficient because of the influence of spatial heterogeneity. For example,the percentages of levels 0 to 3 for CERN station-observed LAI dataset were70. 9% and 8. 9% in 2010,respectively. Therefore,further studies should focus on increasing the number of validation dataset by two ways: collecting station LAI observations over many years at the global scale for various biomes and rectifying scale errors between station and pixel observations to fully utilize station-observed LAI data. Consequently,the LAI products can be comprehensively and reliably validated.关键词：spatial representativeness;LAI;validation;station observation;CERN
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发布时间：2021-06-10 - 摘要：Population problem is an important part of human-land relationship,which is crucial in geography. As the world’s largest nation with 1. 3 billion people,China accumulated many historical statistical data about population distribution throughout history. Hu Huanyong designated the line from Heilongjiang Province to Yunnan Province as the population density boundary（ Hu Line） of China in 1935. Population density,geographical structure,human lifestyle,and economic development significantly differ among different sides of Hu Line. To promote new-type urbanization,state leaders have considered whether or not the Hu Line can be broken and how to breakit. Basing from the proportion of definition by Hu Huanyong（ 3. 87% and 96. 13%） and Lorentz Curve Principle,this study characterized the Chinese population density boundary from 1935 to 2010. Boundary variations in recent 80 years were also analyzed. The first chapter of this thesis introduces the background and theoretical bases of the research. Then,a large number of population data collection and processing work were conducted. In addition,the methods and steps of accurate determination of population density boundary were described on the basis of Hu Line definition and Lorenz curve theory. Finally,changes in the boundary position and population proportion in each partition were studied. Since 1935,the population density boundary of China has migrated toward northwest. The observed migrations in Gansu and Ningxia provinces are the most dramatic,followed by those in Jilin,Inner Mongolia,Shanxi,Yunnan,and Southeast Sichuan. However,the boundary basically remains unchanged in the northeast Sichuan Province. In general,the population growth rate from 1935 to 2010 is higher on the northwest side of the population density boundary than on the southeast side. Hence,the population distribution in mainland China is unequal. The temporal and spatial distribution patterns of population in mainland China are revealed. Thus,this paper not only provides a scientific basis for the implementation of the urbanization strategy but also has practical significance to the implementation of national macro strategies.关键词：mainland China;population density boundary;Hu line;Lorenz curve;spatial pattern of population distribution
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发布时间：2021-06-10 - 摘要：Mesoscale eddies play an important role in the transportation of substance and energy throughout the oceans. Several extraction / identification algorithms have been proposed by scholars during the last few decades. Algorithms based on remotely sensed data are generally acknowledged,but defects are still inevitable when extracting eddies to date. To improve extraction performance,a new Universal Kriging Algorithm based on altimetric remotely sensed sea level anomaly（ SLA） datasets has been proposed to identify mesoscale eddies. SLA fields are transformed to general amplitude fields to ensure rapid and effective implementation. Eddy attributes such as polarity,radius,area,and amplitude can also be acquired using this method.Variograms are generated to determine the window width and lag distance which are used to compute variance fields. Such field data are virtually planar variance grids with each pixel number indicating the variance between one central pixel and pixels at specific lag distances and directions from it on SLAs. Variance fields are defined as"general amplitude fields",assuming that all pixels are potential eddy cores consisting of both true and false ones. The Universal Kriging interpolation is utilized to eliminate false signals and data noises of variance fields,acquire pure signal fields of general amplitudes and discriminate useful signals from noises derived from the variance calculation. Variances of true cores are equivalent to real amplitudes,whereas those of false cores differ from the real amplitudes. However,the features of some of the false cores are connected with eddy boundaries,which are sufficient to determine the characteristic isolines for identifying eddies. The deduced isolines are generated on the general amplitude fields to extract eddy boundaries and attributes from background sea surfaces. The values of these isolines are determined using specific equations of true and general amplitudes of eddy boundaries on the basis of amplitude statistics. They are separated into three latitudinal zones,namely,0° N—30° N,30° N—45 ° N,and 45° N—60° N.Northern Pacific was set as the study area. Eddies were extracted followed by quantitative precision tests using four AVISO SLA datasets in April,2012（ 4th,11 th,18th,and 25th）. A total of 841 eddies were identified,including 450 cyclones and 391 anticyclones. Three multi-core eddies were also captured,with one lasting for at least 15 days（ from 4th to 18th）. Compared with other remote-sensing oriented methods with complex criteria,the Universal Kriging Algorithm achieved a successful detection rate of approximately 90%（ 88. 00%,89. 18%,88. 04%,and 87. 92% with a maximum of 89. 18%） and an excess detection rate of less than 20%（ 11. 50%,14. 95%,18. 66%,and 16. 91% with a minimum of 11. 50%）. Results were acceptable allowing for the spatial resolution of SLAs. Error thresholds were less than 0. 25 degree.The Universal Kriging Algorithm has three noteworthy advantages. First,this algorithm is time saving. In particular,isolines are directly generated on the general amplitude fields,thereby simplifying identification procedures. The method significantly accelerates the extraction with a magnitude of 10 s in the core algorithm routines. Second,the algorithm is stable. Variance calculations along with Universal Kriging’s elimination of noises are used to extract eddies at a relatively constant accuracy based on deduced characteristic isolines on general amplitude fields. Third,the algorithm is self-adaptive. That is,it is applicable to the real-time identification of mesoscale eddies throughout oceans and seas only depending on a relatively small quantity of essential data. Further plans of our research include revealing latent spatial information in marine data fields（ especially in remotely sensed datasets） and exploring the application of the methodology in other ocean-element fields,such as the sea surface temperature,to improve the flexibility of the Universal Kriging Algorithm.关键词：northern Pacific Ocean;mesoscale eddies;sea level anomaly;Universal Kriging Algorithm;variogram;variance field;general amplitude
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发布时间：2021-06-10 - 摘要：For airborne or satellite polarimetric atmospheric detection,the reflected signal of the underlying surface is a noise that should be considered. This signal affects the retrieval precision of aerosol parameters. Therefore,research on the polarized reflected property of underlying surface is crucial for earth-atmosphere decoupling. Such a research can improve the precision of the aerosol optical depth retrieval.To design two fly height（ i. e.,3000 m and 480 m） airborne experiments,the measurement data of the underlying nature surface（ i. e.,vegetation and water） are obtained. In addition,the polarimetric reflected data of the soil are measured in the laboratory. Combining with the theory of polarized radiative transfer and the law of Fresnel,the polarized reflected characteristic is analyzed to determine the polarized reflected characteristics of calm and glitter waters based on micro facet theory.The curve of the polarized reflectance of vegetation and soil slightly changes at different bands,and the linear fitting slope of the polarized reflectance of both visible and infrared bands（ 2250 nm） is close to 1. The correlation coefficient is 0. 95,and the polarized reflectance of vegetation is not sensitive to the band. Comparative analysis of the polarization properties of calm and glitter waters is conducted at 670 nm and 2250 nm. The polarized reflectance of glitter water is approximately two times larger than that of calm water. This phenomenon agrees well with Fresnel theory. Finally,the polarized reflectance of red and river sandy soils is not sensitive to the band,and the linear fitting slope is only at the 10- 5level.Analysis of airborne-polarized remote sensing data of underlying nature surface shows that the polarized reflectance of vegetation is not sensitive to the band,which can use the polarized reflectance of the 2250 nm band to express the polarized reflectance of670 nm and 1610 nm for the vegetation and soil surface. The polarized reflectance of glitter water is approximately two times larger than that of calm water. The polarized reflectance of water is changed with acute geometric conditions. The research on the polarized property of underlying nature surface efficiently decouples atmosphere-surface and improves the retrieval precision of aerosol parameters.关键词：airborne remote sensing;polarization;underlying surface;sensitivity
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发布时间：2021-06-10 - 摘要：Hyperspectral target detection is based on the spectral characteristic difference between the target and the background.Generally,the finer the resolution is,the higher the accuracy will be. Yet the spatial and spectral resolution can barely meet the need simultaneously,for technical obstacles. A tradeoff between the two factors is needed for effective target detection and the choice of appropriate remote sensing data for target detection has always been concentrated nowadays. Most previous studies focused on the precision assessment of the target detection in a particular case using current image spectrometer data,but failed to provide a quantified criterion for either the spatial or the spectral resolution appropriate for the data in that case. This study focused on the quantification of the scale impact of spectral and spatial resolution on target detection precision,with respect to situations where the target is small and has a similar spectrum to the homogenously distributed background.Here we proposed a technical method for spectral and spatial resolution assessment for target detection,setting green context and sparse grass as the target and background,respectively. Through the down sampling processing of the high spatial hyperspectral image from Field Imaging Spectrometer System（ FISS） together with Constrained Energy Minimization（ CEM） detection algorithm and Receiver Operation Characteristic（ ROC） evaluation method,this study analyzed the relationship between the spatial and spectral resolution and the detection accuracy. And then it proposed the optimal spatial and spectral scale for target detection.Results revealed that:（ 1） With the decline of spatial resolution,the detection accuracy experienced three stages of descending rates: gently-dramatically-gently. The corresponding spatial resolution before the second stage is the effective scale for detection. Using FISS data（ 4—7 nm spectral resolution and 1. 4 nm sampling interval）,the required spatial resolution for target detection was about within twice the size of the target;（ 2） When the spectral resolution was finer than 40 nm,two main features: the reflection peaks and basic trend differences,associated with the target and the background,could be identified. The detection accuracy would reach 0. 94 or above within the spatial resolution of 0. 85 cm. When the spectral resolution was coarser than 40 nm,the differences of reflection peaks disappeared since they were 20 nm apart and the detection accuracy decreased;（ 3） Given spectral resolution insufficiency（ > 40 nm）,the basic R,G,B bands added the yellow and red edge bands appeared the optimal combination for target detection with respect to current multispectral remote sensors.It was concluded that the quantitative analysis method and results of spatial and spectral scales for target detection would be of great significance for both data source selection and studies on other target-background combinations under similar conditions.关键词：hyperspectral;target detection;spatial resolution;spectral resolution;scale effect;suitable scale
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发布时间：2021-06-10 - 摘要：Land Surface Temperature（ LST） is a significant surface biophysical variable. This parameter is also important in vari-ous fields such as urban thermal environment,agricultural monitoring,surface radiation,and energy balance. Data from Landsat satellites are vital remote sensing data for LST retrieval since the 1980 s. The present Landsat 8 Thermal Infrared Sensor（ TIRS）imagery provides a new data source for LST retrieval. Landsat 8 TIRS is improved compared with the Landsat 6 Thematic Mapper.Landsat 8 data are extensively applied,so the mono-window algorithm should be updated with new sensor characteristics. Therefore,we aim to explore an adaptive method with more reliable accuracy to retrieve LST using Landsat 8 TIRS data.In this paper,a relation model（ TIRS10 SC） was established between LST and several parameters,namely,brightness temperature,mean atmospheric temperature,atmospheric transmittance,and land surface emissivity. The model was based on the radiative transfer equation and characteristics of Landsat 8 TIRS10. The LSTs of the study area were retrieved by initially deriving the atmospheric transmittance from MODIS data and MODTRAN simulation results. Then,the mean atmospheric temperature was obtained using empirical formulas,and land surface emissivity was retrieved from the Landsat 8 OLI data using image classification-based method. Finally,the LSTs of the study area were retrieved from the processed data. The algorithm and retrieval results were assessed by simulated and measured data. Meanwhile,the sensitivity of variables in themono-window algorithm was analyzed.Results show that the mono-window algorithm can perform well for Landsat8 TIRS data for LST retrieval. The LSTs of different land-cover types in study area varied. The LSTs of bare soil and cements were evidently higher than those of the vegetated areas.The LST of the former varied between 24. 12 ℃ and 32. 25 ℃,whereas that of latter ranged from 10. 72 ℃ to 19. 79 ℃. Furthermore,compared with the measured data,the average error and correlation coefficient of retrieved LSTs were 0. 83 ℃ and 0. 805,respectively. The accuracy of the algorithm was also assessed using simulated data,which showed that the error in the LST data in the majority of cases ranged between 0. 2 ℃ and 0. 3 ℃. The retrieval results agree with the assessed temperature data. Results from the analysis of the sensitivities of land surface emissivity,atmospheric water vapor content,and average temperature showed that the TIRS10SC algorithm can obtain more reliable results with higher sensitivities for the former two para meters and lower sensitivity for the latter one. The proposed algorithm can be applied for the fast retrieval of LST using Landsat 8 TIRS data.关键词：thermal remote sensing;land surface temperature retrieval;mono-window algorithm;Landsat 8 TIRS;MODIS
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发布时间：2021-06-10 - 摘要：As the next generation earth observing satellite launched by America,National Polar-orbiting Partnership（ NPP） satellite is going to take over the Terra and Aqua satellite which is in extended service. Visible Infrared Imaging Radiometer containing Suite（ VIIRS） is one of the five earth observation instrument which are taken in this satellite. The VIIRS channel for aerosol is inherited from MODIS on board the Terra and Aqua satellite. The application with the VIIRS data for the atmospheric pollution monitoring in China needs to be carried out urgently. In this paper,the terrestrial Aerosol Optical Depth（ AOD） with 750 m resolution retrieved from the VIIRS data is studied.The Dark Dense Vegetation（ DDV） method is used to retrieve the AOD in this research. First of all,the cloud pixels are checked and removed through the VIIRS cloud product. According to Normalized Difference Vegetation Index（ NDVI） of the infrared wavelength the dark pixels are identified. The Look Up Table（ LUT） is built by the 6S radiative transfer model,ant then,the AOD is interpolated from the LUT. Finally,the AOD at different altitudes are obtained after the altitude correction.The North Plain of China,which includes Beijing,Tianjin,Hebei region,Shandong,Henan and Shanxi province,is selected as the area of the experiment. The result of September 1,2013 is presented. The result shows that the VIIRS data can monitor the distribution of aerosol very well. The result is validated by ground-based sun photometer measurements in the Beijing site of AERONET. The VIIRS AOD inversed in this study and the ground-based measurements agrees well,and the correlation coefficient is0. 7920. Also,the retrieved VIIRS AOD,which is resampling to 10 km resolution through the three convolution interpolation,is compared with the MODIS AOD product. The comparing result shows that their distribution trend is consistent,and the correlation coefficient is 0. 7059.In this study,AOD with 750 m resolution is obtained from the NPP-VIIRS data based on the DDV method. The retrieved AOD has high correlation coefficient with the ground-based measurements and the MODIS aerosol product. The result shows that the VIIRS data can monitor the terrestrial aerosol well. It will provide fine data source for the atmospheric aerosol particulates monitoring.关键词：NPP VIIRS;terrestrial aerosol;Optical depth;Dark Dense Vegetation（DDV）;remote sensing inversion
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发布时间：2021-06-10 - 摘要：Sea ice edge is an important index for illustrating changes in the Arctic sea ice. This parameter is important for navigation safety and sea ice disaster warning in Chinese northern coast. Sea ice edge is often previously obtained through long-term artificial judgment or in default equivalent to a certain isoline of sea ice concentration,which neglects the influence of large floes on sea ice edge. This study aims to develop a more accurate and faster automatic method for monitoring sea ice edge compared with conventional monitoring techniques. A novel method based on morphology is proposed. Connected Component Analysis（ CCA） and image closing operation are performed. The main ice region,main water region,and floes are distinguished using CCA twice.Then,some large floes are reserved and merged into the principal ice region. Finally,sea ice edge is retrieved by a changeable image closing operation with self-adaptive structural element. This method can be extensively applied to ice-water binary data,such as sea ice concentration,satellite image,and aerial image. The proposed technique is applied on the reflectance data produced from band 1 and band 2 of MODIS（ Moderate-resolution Imaging Spectroradiometer）. The new method is used to retrieve sea ice edge from sea ice concentration data from advanced microwave scanning radiometer for EOS data covering ten regions in the Arctic ocean. Compared with the 15% isoline of sea ice concentration in the regions covered by many large ice floes,the sea ice edge obtained by the new method is more reasonable for monitoring large-scale sea ice. This advantage is caused by the reservation and merging of slightly large floes into the principal ice region. Rapid monitoring of sea ice in the Chinese northern coast without the restriction of chosen data formatis feasible because the method is automatic and can be widely applied. Meanwhile,the dramatic change in the Arctic sea ice can be quantified using the sea ice edge obtained by the proposed method. A regional and integral Arctic sea ice edge dataset can be built for further Arctic studies.
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发布时间：2021-06-10 - 摘要：Soil Organic Matter（ SOM） is one of the key variables in agronomy and environmental management. It controls soil fertility and has a significant impact on atmospheric CO2 concentration. Carbon sequestration in soil can not only reduce the emissions of the greenhouse gases but also improve the quality and productivity of soils. Therefore,accurate estimation of SOM distribution at large scale is needed for policy making,sustainable soil utilization and management. The aims of this study were to predict the SOM across the Northeast and North Plain in China using a model trees method with a large number of satellite-derived data and soil vis-NIR spectroscopy data. A total of 1078 soil samples were collected to estimate spatial variation of SOM in Northeast and North Plains,China. Remote sensing data,including MODIS,TRMM and STRM,and soil spectroscopy data were used as environmental predictors. 306 soil samples were used as external validation dataset and the others were used to build models. Decisiontree-based M5 algorithm was introduced to construct the prediction models between SOM and the environmental predictors throughthe modelling tool Cubist. The method converted to a series of rules,each with an associated linear model,that partition the data into regions with similar conditions defined by the characteristics of the predictor variables. Prediction models between SOM and predictors with different number of samples were tested and it was found that the optimal number of training samples is 300. For the evaluation on the validation dataset,the model showed an R2 of 0. 69,RMSE of 7. 25 g·kg- 1and RPD of 1. 53. According to the S = f（ s,c,o,r,p,t） function,it was noted in the predicted model that soil spectroscopy and climate predictors were the dominant factors in controlling the spatial variation of SOM,while organism predictors showed less importance and terrain factors had least impact. The predicted map showed a significant increasing trend of SOM content from southwest to northeast. Compared with the map produced by National Soil Survey Office,the predicted map presents similar pattern of the spatial variation of SOM in Northeast and North Plain in China. Nevertheless,the area of high SOM and low SOM decreases in about two decades due to the human activities and tillage in the area. The methodology in this study combines remote sensing with proximal soil spectroscopy using a rule-based soil mapping framework. The result shows that predicting SOM at large area is acceptable through Cubist. The climate factors and soil spectroscopy were the most dominant factors among the environmental factors while terrain factors contributed least.It is found that the spatial pattern of SOM generated by Cubist is consistent with that of the second national soil survey of China produced in early 1980 s,meanwhile the area of high SOM and low SOM decreases.关键词：digital soil mapping;soil organic matter;remote sensing;soil spectroscopy;data mining
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发布时间：2021-06-10 - 摘要：Remote measurement and diagnosis of the plants nutritional status is an important means for efficient easily and simple management system,and high-yield and high quality cultivation. So far,there is not yet much research on the nutrition diagnostic of fruit trees through low-altitude remote sensing data. We carried out the following experiments in order to provide a theoretical basis and technical support for the research and development of nutritional diagnosis technology of fruit trees based on low-altitude remote sensing data. In this work,the multi-spectral image information of ‘Hamlin ’orange plant canopies were obtained by a multi-spectral camera array mounted on the eight rotor Unmanned Aerial Vehicle（ UAV） at an altitude of 100 m above the canopyat 11: 00—13: 00 on a sunny day in spring. Then,the multi-spectral images were pre-processed by Pixel Wrench 2 of tetracam,average spectral reflectance of the whole canopy were individually extracted based on ENVI 4. 7. Twenty leaves from the mature spring shoots were collected from around crown of every tree. Total nitrogen,chlorophyll a,chlorophyll b and carotenoids contents of each plant were measured in the laboratory. The characteristic wavelengths were extracted by means of the correlation analysis of the average spectra of the plants with the nutrition content. A total amount of 88 citrus trees were collected and randomly grouped into two sets of samples: 66 plants for the calibration set and 22 plants for the prediction set. The two kinds of spectral pre-processing methods（ Multiplicative Scatter Correction（ MSC） and Standard Normal Variable（ SNV）） were adopted and four kinds of modeling methods（ Partial Least Squares（ PLS）,Multiple Linear Regression（ MLR）,Principal Component Regression（ PCR）and Least Squares Support Vector Machine（ LS-SVM）） were employed to estimate total nitrogen,chlorophyll a,chlorophyll b,and carotenoids content in canopy leaves. The results showed that the prediction accuracy of the MLR model based on SNV spectral pre-processing methods was the best for the prediction of total nitrogen,chlorophyll a and chlorophyll b content,correlation coefficients of prediction（ Rp） were 0. 8036,0. 8065,0. 8107,and Root Mean Square Error of Prediction（ RMSEP） were 0. 1363,0. 0427 and 0. 0243,respectively. The LS-SVM model based on SNV spectral pre-processing methods for the carotenoids content of crown was the best,which is with Rp= 0. 8535,RMSEP = 0. 0117. The results demonstrated that the airborne multi-spectral image information of citrus plants canopy could be used to estimate total nitrogen,chlorophyll a,chlorophyll b and carotenoids content in canopy leaves. This research results would provide a new way for accurate,efficient prediction of plants nutrition status of largescale citrus orchards.关键词：sweet orange;canopy;photosynthetic pigments;nitrogen;low-altitude remote sensing
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发布时间：2021-06-10 - 摘要：Persistent Scatterer Interferometry（ PSI） is widely used to monitor the slowly accumulated land subsidence in urban areas. However,a few explorations concentrated on monitoring the significant subsidence by PSI and its applicability in such areas.In this study,typical industrial towns（ subsidence troughs） located around the boundary between Tianjin and Langfang cities were chosen as the study areas for subsidence detection and analysis with multiband Synthetic Aperture Radar（ SAR） images to evaluate the potential and applicability of PSI to monitor significant ground subsidence. Land subsidence was extracted using PSI through phase spatial correlation analysis and three dimensional phase-unwrapping approaches. In total,23 L-band（ wavelength is23. 6 cm） Phased Array type L-band Synthetic Aperture Radar（ PALSAR） images acquired between 2007 and 2010,and 23C-band（ wavelength of 5. 6 cm） Advanced Synthetic Aperture Radar（ ASAR） images acquired between 2007 and 2009 were selected for multiband data processing. The results derived from the two types of SAR images were comprehensively and mutually compared and validated. In addition,the reliability of the PSI method for significant subsidence detection was explored by considering the different sensitivities to subsidence of the PALSAR and ASAR systems. The spatiotemporal distribution of the subsidence in the study area was also analyzed with respect to the geological settings and groundwater exploitation information. The derived subsidence in the study area presents a significant inhomogeneous pattern,and the subsidence trend derived from the two types of SAR images ranges between 0 and 210 mm / a. The root mean squared error is 6. 5 mm / a between the two types of PSI subsidence results in the subsidence bowl area. Further exploitation indicates that such significant subsidence is mainly caused by excessive exploitation of groundwater. Moreover,the subsidence magnitude is highly related to different land-use categories,such as factories,residential quarters,railways,highways,and farmland,indicating anisotropy of groundwater usage in these areas. Comparison analysis demonstrates that the PALSAR and ASAR PSI results are in good agreement and are consistent with previous studies,although the two data sets have different spatial and temporal resolutions,wavelengths,incidence angles,and time spans. The above results and analysis indicate that the PSI method used in our experiment is applicable to different SAR systems. The PSI method shows good reliability and applicability to monitor long-term significant ground subsidence in areas with sparse PS distributions. Therefore,the PSI method used in this study can provide scientific and effective technical assistance for ground subsidence monitoring in such areas. The results（ subsidence rate and time series） of the PSI method can serve as assistant information when constituting measures to control groundwater exploitation.关键词：persistent scatterer interferometry;phase spatial correlation analysis;detection and analysis of significant land subsidence;multi-platform data;cross validation
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发布时间：2021-06-10 - 摘要：With the formulation of different policies,special economic zones dramatically expanded in the past 40 years. Basing from remote sensing and geographic information system technology,we performed long-term and high-frequency monitoring of urban expansion in special economic zones by using multi-source remote sensing images between 1973 and 2013. Shenzhen,Xiamen,and Haikou were selected as research subjects. All spatial expansion information was obtained through human-computer interactional digital interpretation. Basing from established-interpretation symbols of urban lands,researchers with experience in visual interpretation referred to Google Earth and topographic maps to ensure the accuracy of monitoring results above 90%. This study selected expansion speed,influences on land use,compact ratio,and centroid shift as indicators,and combined the effects of natural and man-made elements to analyze the similarities and differences of spatiotemporal characteristics among Shenzhen,Xiamen,and Haikou. Four major results were obtained. First,the expansion speed of special economic zones in the past 40 years experienced one low-speed stable stage,two acceleration stages,and two deceleration stages. The expansion speed of Shenzhen was the fastest,followed by Xiamen and then Haikou. This situation fully reflects the significance of national and local policies,as well as social and economic development. Second,1370. 61 km2 nonurban lands around Shenzhen,Xiamen,and Haikou were converted to urban land between 1973 and 2013. Arable land was the first land source of special economic zone urban expansion. Other main land sources of urban expansion include forest land,water body,rural settlement,industrial and traffic lands,and sea area. Grassland and unused land had minimal contribution to urban expansion. Rural settlement and industrial-traffic land was the second land source of Xiamen and Haikou urban expansions,and the third land source of Shenzhen urban expansion. Forest land was the second land source of Shenzhen urban expansion but produced a contribution rate of < 10% to Xiamen and Haikou. Third,the compact ratio of special economic zones decreased. Before 2004,land resources around special economic zones were relatively adequate,urban expansion was fast,land-use efficiency was low,and compact ratio considerably reduced. After 2004,urban expansion space was limited,expansion speed slowed down,and compact ratio stabilized. Fourth,marine reclamation engineering appeared during urban expansions in Shenzhen,Xiamen,and Haikou. Under the combined effects of the policies and marine reclamation engineering,the centroid of special economic zones tended to migrate toward the coastline. Affected by the comprehensive influence of expansion area and compact ratio,Shenzhen and Haikou had the large stand smallest centroid shift distances,respectively. Remote sensing monitoring of expansions in special economic zones can provide support for the future projection and policy formulation of special economic zones.关键词：remote sensing;monitoring;urban expansion;special economic zones;contrastive analysis
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发布时间：2021-06-10 - 摘要：<正>由美国电子与电气工程师协会（IEEE）地球科学与遥感分会（GRSS,Geoscience and Remote Sensing Society）主办,中国科学院国家空间科学中心承办的2016年国际地球科学与遥感大会（IGARSS 2016,International Geoscience And Remote Sensing Symposium 2016）将于2016年7月10日至15日在北京召开。地球科
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发布时间：2021-06-10
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