20 June 2019, Volume 34 Issue 3

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  • Yan Wei, Zhou Wen, Yi Lilong, Tian Xin
    Remote Sensing Technology and Application. 2019, 34(3): 445-454. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0445
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    Forest is one of the main vegetation type in the terrestrial ecosystem,and using remote sensing technology on discriminating and change monitoring forest types are of great significance importance for the global carbon cycle study and sustainable development of forest resources.This article reviewed the classical remotely sensed classification methods forest remote sensing classification methods,including pixel-based,object-oriented,red-edge spectral information based and deep learning methods,separately.We also introduced the details and individual advantages of these methods in the some specific applications.Finally,the limitations of the current study on forest remote sensing classification and change monitoring on forest types were indicated in order to provide reference for the dynamic supervision of forest resources under the new situation.
  • Ji Menghao, Tang Bohui, Li Zhaoliang
    Remote Sensing Technology and Application. 2019, 34(3): 455-466. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0455
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    Solar-Induced Chlorophyll Fluorescence (SIF) is a by-product of photosynthesis that provides direct information about vegetation photosynthesis and provides a new way to track photosynthesis and gross primary production.In recent years,there are many tower-based long-term fluorescence observation systems have been installed for studying the relationship between SIF and GPP.The quantitative remote sensing estimation of SIF is critical for terrestrial ecosystem carbon cycling,GPP,and drought monitoring.This paper systematically reviews the current status of satellite-derived SIF methods.Those methods are roughly grouped into two categories:fraunhofer line based method and atmospheric absorption band based method,according to the channels used for SIF retrieval;The problems of satellite SIF retrieval and application are discussed,including the instrumental effect,the daily variation of cloud effect,directional effects,the approaches of accuracy assessment,downscaling and the instantaneous to daily scale conversion;Finally,directions for future research to improve the accuracy of satellite-derived SIF are suggested.
  • Guo Jian, Liu Liangyun, Liu Xinjie, Hu Jiaochan, Jing Xia
    Remote Sensing Technology and Application. 2019, 34(3): 467-475. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0467
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    Tower-based spectral observation is an important connecting bridge between flux sites and satellite remote sensing data,and the effect of atmospheric absorption and scattering between horizontal surface and tower-based platform on the atmospheric absorption band such as O2-A is difficult to ignore.Firstly,the influence of atmospheric radiation transfer on the up-welling radiance and down-welling irradiance of the tower-based platform is analyzed,and the atmospheric correction method of based on upward and downward transmittance is established,that is,the influence of the upwelling radiance and down-welling irradiance is corrected by the direct transmittance and the total transmittance.Secondly,using the simulation data of MODTRAN model,the influence of AOD550 and radiative transfer path length on atmospheric transmittance is quantitatively analyzed,and the LUT of AOD550 is established based on the ratio of down-welling irradiance of near-infrared and red bands and solar zenith angle,as well as the upward and downward atmospheric transmittance LUT based on the AOD550 and the radiative transfer path length.Finally,using the canopy spectral data of different growth stages observed by the tower-based platform,the difference of the apparent reflectance between the inside and outside of the O2-A band absorption line before and after atmospheric correction was analyzed.The results show that the atmospheric correction method based on LUT of AOD550 and radiative transfer path length proposed in this paper can better correct the influence of upwelling radiance and down-welling on the O2-A absorption band of the tower-based platform,and provides important method support for applications such as SIF observation on the tower platform.
  • Wang Siheng, Huang Changping, Zhang Lifu, Gao Xianlian, Fu Anmin
    Remote Sensing Technology and Application. 2019, 34(3): 476-487. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0476
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    Solar-Induced Chlorophyll Fluorescence (SIF),which is emitted by photosystem during photosynthesis under natural illumination,carries important information of actual photosynthesis of plants.Spaceborne remote sensing of SIF provides an unprecedented opportunity for monitoring global photosynthesis at regional to global scales.Up to date,in-orbit operational spaceborne sensors that are available for SIF retrieval are originally designed for atmosphere monitoring.The hyperspectral sensor onboard Chinese Terrestrial Ecosystem Carbon Inventory Satellite (CTECS) is expected to be the first operational spaceborne sensor that is specifically designed for sensing SIF from space (scheduled to be launched around 2020,2 years before the Fluorescence Explorer (FLEX) Mission).Data-driven approach has been selected as the main algorithm for far-red SIF retrieval for CTECS,but is to be refined and assessed according to sensor specifications (e.g.spectral resolution and signal-to-noise ratio).In this context,this study aims to improve the designment of far-red SIF retrieval method for CTECS.based on end-to-end simulation,we evaluate the precision and accuracy of SIF retrieval in several potential windows.We then analyze the sensitivity of SIF retrieval to number of features (nv) and fluorescence spectral shape function (hF) in the forward model in different windows.Results show that a broader fitting window increases retrieval precision,but is accompanied with lower accuracy and stronger sensitivity to nv and  hF.Considering both retrieval precision and accuracy,the window of 735~758 nm with nv set to 6 and hFset as single peak Gaussian function (μ=740 nm and σ=30 nm) is selected as optimal fitting window for CTECS.SIF retrieval results based on proximal and airborne remote sensing data demonstrate the feasibility and reasonability of the designed method.Our results provide an important reference for far-red SIF retrieval for CTECS.
  • Xi Lei, Shan Nan, Yang Shenbin, Zhang Yongguang
    Remote Sensing Technology and Application. 2019, 34(3): 488-499. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0488
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    Sun-induced Chlorophyll Fluorescence and GPP Simulations
    with SCOPE Model for Paddy Rice Under Different Growing StagesCompared with traditional greenness-based vegetation indicators,Sun-induced Chlorophyll Fluorescence (SIF),as one of the most effective tools in monitoring Gross Primary Production(GPP),can be used to directly reflect the dynamic changes of photosynthesis.The SCOPE (Soil Canopy Observation,Photochemistry,and Energy Fluxes) model has been widely used to simulate the SIF and GPP across multiple scales.However,the accuracy and ability of the SCOPE model on the simulations under different growth stages and weathers remains unclear.In this study,we investigated the performance of SCOPE in SIF760 and GPP based on the physiological parameters and meteorological data of paddy rice in 2016.Then we compared the simulations with the measured SIF760and GPP under different growing stages and weather conditions.The results showed that the SCOPE model could simulate well for SIF760 and GPP at the seasonal scale (R2=0.44 and R2=0.67).However,the SCOPE model had different performances under different growth stages at the diurnal scale A better performance was obtained in maturity stage of rice (R2=0.99 and R2=0.96),while a lower performance was at the heading-flowering stage.During the whole growth period,the SIF760 simulated by the SCOPE model was lower than the measured SIF760while the simulated GPP was higher.In addition,weather conditions significantly affect the simulations from the SCOPE model.The accuracy of the SIF760 simulations on sunny days was better than that in cloudy days (R2=0.64 and R2=0.46,respectively).Our quantitative assessment of the SCOPE model supported its usefulness for interpreting SIF and GPP under different growth stages.These results provided the model evidence for remote sensing of SIF to monitor crop photosynthesis and its response on environmental factors.
  • Liu Ouyang, Liu Liangyun, Hu Jiaochan, Liu Xinjie, Jiang Jinbao
    Remote Sensing Technology and Application. 2019, 34(3): 500-510. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0500
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    In the visible and near-infrared region,at a spectral resolution of 1 nm,the solar irradiance spectrum exhibits four absorption features:the HαFraunhofer line (656.4 nm),H2O absorption at 719 nm,the O2-B (687 nm) and O2-A (761 nm) bands.Although the H2O band is located between the chlorophyll fluorescence emission peaks at about 690 and 740 nm,it has never been investigated for SIF retrieval.In this paper,the potential of the H2O absorption band at 719 nm for SIF retrieval is investigated using different Fraunhofer line discrimination (FLD) methods based on the FluorMOD simulations and field data taken by an ASD FieldSpec Pro spectrometer (3 nm resolution).Firstly,the SIF retrieval performance using the H2O band was examined with different FLD methods at a spectral resolution of 1 nm.the results obtained using the HO band are better than for the O2-B band,and the associated RMSE is 0.154 W/m2/μm/sr.Then,the sensitivities and uncertainties of the SIF retrieval using the improved FLD (iFLD) method were calculated for the three atmospheric absorption bands.the total SIF estimation error and its contribution to the theoretical error in the two correction coefficients are found to be smaller using the H2O band than using the O2-B band,but significantly larger than that achieved using the O2-A band.Finally,the SIF retrieval using the iFLD method in the three atmospheric absorption bands is also examined in a field experiment.the SIF retrieval using the H2O band at 719 nm is found to have a similar performance to that using the O2-B and O2-A bands at canopy level.Finally,the SIF retrievals using the iFLD method in the three atmospheric absorption bands were also examined using field experiments.the SIF retrievals using the H2O band at 719 nm are similar to those at O2-A and O2-B oxygen absorption band,showing high values in backward and hot-spot directions and low values in forward and dark directions,and high SIF values at noon and low SIF in the morning and afternoon.Therefore,the H2-O absorb band provides a new band for retrieval of canopy SIF at near-ground platform.
  • Chen Siyuan, Jing Xia, Dong Yingying, Liu Liangyun
    Remote Sensing Technology and Application. 2019, 34(3): 511-520. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0511
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    Detection of wheat stripe rust is important for agriculture management and decision,this paper aims to improve detection accuracy of the disease severity of wheat stripe rust by integrating the advantages of reflectance spectroscopy in the detection of crop biochemical parameters and the advantages of chlorophyll fluorescence in photosynthetic physiology diagnosis.Firstly,the solar-induced chlorophyll fluorescence (SIF) at O2-A band (760 nm) was calculated using the 3FLD algorithm,and seven spectral indices sensitive to wheat stripe rust were investigated for estimating the disease severity.Then,three classic statistical modelling methods,including Support Vector Machine (SVM),Stepwise Regression (SR) and BP neural network (BP),were used to quantitatively investigated the performance of the spectral indices and SIF for detection of winter wheat stripe rust severity.The results show that:(1) there is a significantly negative correlation between SIF and the severity of wheat stripe rust.The relationship between SIF and DI can be effectively applied to detect wheat stripe rust.(2) the spectral models based on SIF combined with spectral indices are more accurate than those based on spectral indices.SIF can significantly improve the detection accuracy of the disease severity of winter wheat stripe rust.(3) compared to the SVM and SR methods,the training model constructed by the BP neural network has the highest prediction accuracy whether using the spectral indices or SIF combined spectral indices.However,the verification results show that the disease severity prediction model constructed by SVM and SR method have a better prediction.
  • Lin Zhongli, Xu Hanqiu
    Remote Sensing Technology and Application. 2019, 34(3): 521-530. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0521
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    “Stove city” is the most direct description of the Urban Heat Island (UHI) effect,which is an important manifestation in the urban microclimate.This paper focuses on the quantitative evaluation and comparative analysis in the four “stove cities”,Nanjing,Hangzhou,Fuzhou and Guangzhou,from 1990s to 2010s.The study shows that the value of Urban-Heat-Island Ratio Index (URI) of Fuzhou increases from 0.553 in 1994 to 0.689 in 2016,suggesting the UHI effect in Fuzhou has been increasing significantly.In contrast,the UHI effect in Nanjing is relieved to some degree.The result reveals the quantitative relationships between land surface biophysical components,e.g.,built land and vegetation,and land surface temperature.The analysis shows that the increase and amalgamation of built-land patches,reduction and fragmentation of vegetation are the main factors contributing to the formation of urban heat islands.
  • Cheng Zhifeng, He Qisheng
    Remote Sensing Technology and Application. 2019, 34(3): 531-539. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0531
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    Obtain regional ecological environment quality and its change distribution rapidly and accurately is of great reference value for regional ecological environment monitoring and governance,urban construction planning and other issues.Based on this,taking the Su-Xi-Chang city group as the research area,the multi-temporal Landsat images were selected to extract the four indexes of humidity,greenness,heat and dryness,respectively,and calculate the RSEI (Remote Sensing Ecological Index) through principal component analysis to quantitatively evaluate the regional Ecological quality changes from 2001 to 2018.The results show that:(1) The ecological quality of the Su-Xi-Chang Region descend first and then pick up from 2001 to 2018.After falling 17.69% in 2008,it recovered 7.69%.Among them,Suzhou and Wuxi have rebounded more than Changzhou.(2) The trend of regional ecological deterioration has been significantly curbed in the decade after 2008.The growth rate of ecological deterioration area has decreased from 5.85% to 2.06% annually,and the ecological quality of the old urban area has been significantly improved.(3) The raise of the proportion of building objects is one of the most important reasons of ecological deterioration,while the greenness has the maximum weight of the four indexes in stepwise regression analysis and the dryness weight were negatively correlated,indicating that the recovery of vegetation coverage is the key to improve the regional ecological quality.
  • Li Qiongqiong, Liu Yunlong
    Remote Sensing Technology and Application. 2019, 34(3): 540-546. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0540
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    To explore the possibility of using soil spectral reflectance to estimate soil heavy metal content in urban residential area,this study chooses 30 soil samples of Cu,Pb and Zn in Minhang Residential area,Shanghai Province.Through the spectral factor transform to highlight its eigenvalues,constructed Multiple Linear Stepwise Regression(MLSR) model and Partial Least Squares Regression(PLSR) model based on spectral reflectance of soil heavy metals.The results show that the reciprocal first-order and the logarithmic first-order differential transformation can effectively enhance the heavy metal soil spectral characteristics.The best characteristic bands of Cu,Pb and Zn are 1 042.7 nm、706.84 nm and 1 404.8 nm.In terms of model stability and accuracy,PLSR model is better than MLSR model.The RMSE of Cu and Zn were only about 10% of the mean value of heavy metals in the study area,and the accuracy of the model was high.Compared with Cu and Zn,the R2 of Pb is between 0.64~0.88 which with higher model stability.By preprocessing the spectral data,the partial least-squares regression can effectively improve the accuracy of estimating the heavy metal content in urban residential areas.
    〖WTHZ〗Key words:〖WT〗
    Urban residential area;Soil heavy metals;Hyperspectral;Multivariate Linear Stepwise Regression(MLSR) model;Partial Least Squares Regression(PLSR) model
  • Song Minghui
    Remote Sensing Technology and Application. 2019, 34(3): 547-552. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0547
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    It is of great significance to study the method of extracting urban features from GF-2 remote sensing data.Taking the urban area of Jixi City as the study area,and the GF-2 image is used as the data source.The image is divided into multiple scales,the classification rules of the corresponding objects are established,and the object-based classification method of the rule set is used to classify the objects.Compare with SVM supervised classification results.The results show that the overall accuracy of object-oriented classification is 92.52%,and the Kappa coefficient is 0.91,which is significantly higher than the SVM supervised classification.Using the object-oriented classification method to classify the GF-2 image is better and the precision is higher.Object-oriented classification method based on GF-2 data is an effective method for extracting urban land use classification.

  • Tian Huihui, Feng Li, Zhao Menmen, Guo Song, Dong Jiwei
    Remote Sensing Technology and Application. 2019, 34(3): 553-563. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0553
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    Taking a university campus in Jiangning District of Nanjing as the study area,we used an Unmanned Aerial Vehicle (UAV) mounted with a thermal infrared imager to map the land surface temperature of the study area for analysis of thermal variance and its change over different typical urban surface patterns.6 typical urban underlying surfaces (water,shrubs,grass,brick pavement,marble pavement and asphalt pavement) were identified in the study area.Thus,the objective of the study is to analyze the variation of the obtained land surface temperature among the surface patterns in the study area and to reveal the detailed characteristics of the LST changes under different weather conditions within a day and a month.The estimation of sensible heat release was conducted to quantitatively describe the fine characteristics of the surface temperature of different underlying surfaces.The influencing factors of surface temperature changes were investigated by correlating surface temperature with meteorological factors.The results showed that under different weather conditions,there were differences in the characteristics of surface temperature and sensible heat release on underlying surfaces.In sunny days,the diurnal variation of different underlying surfaces fluctuated greatly,the temperature of asphalt pavement was relatively high,and the sensible heat release was the largest,which had the greatest impact on the thermal environment,followed by the marble and brick pavement.The water and shrub almost had no sensible heat release during the day,and the remission effect on the thermal environment was obvious.In cloudy days,the diurnal variation of surface temperature on different underlying surfaces was not obvious and the artificial was still the main body of sensible heat release.Referring to meteorological factors,the solar radiation and the air temperature were positively correlated with the surface temperature of the underlying surface which had a warming effect on the underlying surface.The air humidity was negatively correlated with the temperature of the underlying surface,which played a role of cooling the land surface.This research will provide a new idea and method for the study of urban micro\|thermal environment and a theoretical basis for the research of urban micro-thermal environment based on remote sensing.
  • Wang Runke, Wang Jian, Li Hongyi, Hao Xiaohua, Ma Jiapei
    Remote Sensing Technology and Application. 2019, 34(3): 571-582. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0571
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    Land surface temperature plays an important role in drought monitoring and Simulation of surface heat flux.In arid and semi\|arid regions,the Two\|Source Energy Balance model (TSEB) is commonly used to calculate the heat flux of the earth’s surface.Taking the typical irrigated area of the middle reaches of Heihe as the research area,the four Landsat\|7 ETM+ remote sensing images are selected.The soil surface temperature and canopy temperature were retrieved by combining vegetation index with TSEB model.The decomposition algorithm of soil surface temperature and vegetation canopy temperature is mainly discussed.The results showed that soil surface temperature and vegetation canopy temperature had good temporal and spatial consistency.The inversion accuracy of soil surface temperature and vegetation canopy temperature is indirectly verified by surface net radiation and surface heat flux.The calculated values of surface net radiation and surface heat flux correlate well with the observed values,and the correlation coefficient is greater than 0.92.The linear regression analysis of surface net radiation and surface heat flux shows that the fitting accuracy is high.The soil surface temperature and canopy temperature obtained by surface temperature decomposition are feasible for monitoring drought in typical areas and simulating surface heat flux.
  • Yun Zengxin, Zheng Guang, Ma Lixia, Wang Xiaofei, Lu Xiaoman, Lu Lu
    Remote Sensing Technology and Application. 2019, 34(3): 583-594. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0583
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    Natural forests have the vertical three\|dimensional structure of canopy and understory vegetation (shrubs,grasslands,and bare soil).Accurate and quantitative separation of understory vegetation is of great scientific significance and practicality on improving the precision of inversion of forest canopy leaf area index.value.Due to the limitations of traditional passive optical remote sensing data on directly acquiring 3D information,this study intends to combine active and passive ALS and HyperMap data with the Washington Botanic Garden as the key research area.On the basis of individual tree segmentation,the vertical stratification of the forest (forest canopy and undergrowth vegetation layer) is achieved.On this basis,the forest canopy laser point cloud data was used to remove the understory information from the optical image data.By comparing the results of the forest effective leaf area index obtained from aerial optical images and ground measurements,it was found that:(1) forest canopy density has a significant impact on the penetration of ALS data;(2) removal of understory information can effectively improve the forest crown accuracy of LAIe estimated.The correlation between Normalized Difference Vegetation Index (NDVI) and ground surface measured effective leaf area index increased from 0.087 to 0.591.In addition,the optical remote sensing image based on the removal of understory vegetation information was compared with the Simple Ratio vegetation index (SR) (the correlation increased from 0.209 to 0.559) and the simplified simple Ratio vegetation index (RSR) (the correlation increased from 0.147 to 0.358).The NDVI was most sensitive to changes in canopy leaf area index (correlation increased by 0.5).The method of quantitatively separating understory vegetation with the combined active and passive remote sensing data proposed in this study can effectively improve the accuracy of inversion of forest canopy leaf area index,and provide a solid foundation for quantitative and accurate estimate of forest biophysical parameters and study of carbon and water cycle processes.
  • Cong Ming, Duan Chenxi, Xu Miaozhong, Tao Yiting
    Remote Sensing Technology and Application. 2019, 34(3): 595-601. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0595
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    Simulating the psychological experience of human vision,a road extraction model based on the format tower is proposed to extract the road in the high resolution remote sensing image from the perspective of morphology.Firstly,based on the spectral and texture information,the suspected road targets are extracted by using segmentation technology.Then these targets are classified according to their reliability and extract the road targets for each category.Finally,three types of identified road information are verified and merged,and the continuous smooth road extraction results are obtained.Experiments on real high resolution images show that the results are consistent with the visual perception of the human eye,and the overall classification accuracy is higher,indicating that the algorithm is effective and feasible and has good use value.
  • Feng Chanying, Wang Zihao, Zheng Chengyang
    Remote Sensing Technology and Application. 2019, 34(3): 602-611. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0602〖
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    In recent years,remote sensing technology has been widely used in the field of surface energy,thermal environment,and climate research in small and medium-sized regions.The demand for high resolution,high precision albedo products has increased.In view of this,a physical-based downscaling method is proposed for efficiently and accurately generating high-resolution albedo results.First,under the Lambert hypothesis,the primary albedo of Landsat 8 could be obtained based on Landsat 8 reflectance at 30m resolution.On the 500 m scale,it is found that after classification,the primary albedo of Landsat 8 has a better correlation with the broad-band albedo of MODIS MCD43A3 product.Therefore,a linear regression function based on surface classification is established to integrate the high-resolution primary albedo of Landsat 8 with high-precision MCD43A3 albedo to obtain downscaled albedo.Compared to MODIS albedo,the downscaled albedo provided more rich details.Verification experience based on SURFRAD observation data shows that Bias of the albedo downscaling is 0.01,and standard deviation is 0.012,which has good adaptability for different surface categories.It shows that the algorithm has great application value for producing high-resolution albedo products.
  • Liang Lijuan, Huang Wanli, Zhang Rongyan, Lin Guangfa, Peng Junchao, Liang Chunyang
    Remote Sensing Technology and Application. 2019, 34(3): 612-621. https://doi.org/:10.11873/j.issn.1004-0323.2019.3.0612
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    Sentinel-2 satellite sensors acquire three kinds of optical remote sensing images with different spatial resolutions.How to improve the spatial resolution of lower spatial resolution bands by fusion method is one of the problems faced by Sentinel-2 applications.Taking the Sentinel\|2B image as the data source,a high spatial resolution band was generated or selected from the four 10m spatial resolution bands by four methods:the maximum correlation coefficient,the central wavelength nearest neighbor,the pixel maximum and the principal component analysis.We fused the one high spatial resolution band produced and six multispectral bands with 20 m spatial resolution by the five fusion methods of PCA,HPF,WT,GS and Pansharp to produce six multispectral bands with 10 m spatial resolution and the fusion results were evaluated from three aspects:qualitative and quantitative (information entropy,average gradient,spectral correlation coefficient,root mean square error and general image quality index) and classification accuracy of fused images.Results show that the fusion quality of Pansharp with the maximum correlation coefficient is better than other fusion methods,and the classification accuracy is slightly lower than the GS with the pixel maximum of the highest classification accuracy and far higher than the original four multispectral image with 10 m spatial resolution.According to the classification accuracy of experimental data,different fusion methods have different advantages in extraction of different ground objects.In application,appropriate schemes should be selected according to actual research needs.This research can provide reference for Sentinel-2 satellite and similar satellite data processing and application.
  • Zhang Haitao, Jin Yan, Liu Wanjun
    Remote Sensing Technology and Application. 2019, 34(3): 622-629. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0622
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    For the traditional remote sensing image registration method,there are too many pairs of registration error matching,and the efficiency of registration is low.In order to further improve the accuracy and efficiency of remote sensing image registration,a remote sensing image registration method based on wavelet was proposed.Firstly,the feature extraction of the reference image and the image to be registered using the Marr wavelet in scale space theory.Then use Euclidean distance to perform initial registration of the feature points of the reference image and the image to be registered.Again consistent with the random sampling method,the registration results for early registration for fine.Experimental results show that this method can effectively eliminate false matching points compared with SIFT and other improved SIFT algorithms.Improve registration accuracy,while improving the efficiency of more than double registration.Conclusion:for traditional remote sensing image registration methods,registration mismatches have many pairs of points and the efficiency is low.This paper presents an accurate remote sensing image registration method.The experimental results show that this method can effectively improve the accuracy of registration and reduce the time of registration.

  • Lu Jing, Jia Li, Zheng Chaolei, Hu Guangcheng
    Remote Sensing Technology and Application. 2019, 34(3): 630-638. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0630
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    To obtain the potential of remote sensing\|based water budget for regional water resources estimation,this study analyzed the correlations of the difference between remote sensing\|based precipitation and evapotranspiration and water resources from the statistical data in China at the water resources sub-region and provincial scales.The results concluded that there is a strongly positive linear relationship between remote sensing-based water budget and statistic-based water resources,but the estimation from remote sensing is generally lower than the statistic-based values.The underestimation at Hai river basin is more serious,then is Huai river basin and southwest river basin,while the northwest river basin is overestimated.For those regions with a large amount of groundwater use demands,e.g.,Hai river basin,groundwater exploitation is not considered as the source of remote sensing-based water resources,which is the main reason leading to the underestimation of remote sensing-based water resources.The underestimation of remote sensing-based precipitation and the overestimation of remote sensing-based evapotranspiration also lead to the remote sensing-based water resources lower than statistic-based water resources.There is an insignificant increasing trend for the water resources of the whole China.However,the water resources of North China region significantly decrease because of the large demands of crop and people for water,which will be adverse for economy development and people living of this region.
  • Li Jing, Li Hao, Wang Shudong, Yang Yingying, Wu Taixia
    Remote Sensing Technology and Application. 2019, 34(3): 639-646. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0639
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    The five Central Asian countries are located in the inland areas with a serious shortage of water resources and the lakes are the main support for the ecosystem in the area.Due to the lack of long-term information support both in the year and in the intervening years,it is difficult to fully understand the characteristics of the major lakes in the five Central Asian countries.In this study,MODIS 8-day reflectivity products were used as data sources from 2001 to 2016,and the Normalized Difference Water Index (NDWI) method was used to extract the water body area.The annual and inter-annual water surface of 9 major lakes in Central Asia Change Characteristics and Key Influence Factors.The results show that:(1) Lake water surface changes in the five Central Asian countries are affected by anthropogenic disturbance and climate change.The annual and interannual time series of the lake tail and alpine enclosed lake show different trend and fluctuation characteristics;(2) the changes of the Aral sea in the south and north are most obvious.The surface area of  the South Aral Sea has decreased obviously in recent 15 years,reducing the area by more than 70%,while the North Aral Sea fluctuates with the variation of precipitation.(3) the total area of  the sub-main lakes has been declining.The area has shrunk by 23.51% over the past 15 years.The South Aral Sea is a contributor to the statistical decrease of all lakes.However,the surface area of other lakes shows an increasing trend.
  • Xu Mengzhu, Xu Jia, Deng Hongru, Yuan Chunqi
    Remote Sensing Technology and Application. 2019, 34(3): 647-654. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0647
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    There are numerous islands with abundant resources in China.Due to the limited information included in common polarization features and the poor effect of traditional classification methods when there are few samples,nine polarization features are analyzed and classification is carried out using active deep learning.Firstly,multiple features are extracted from an original image.Then,the original features can be extracted by anto\|encoder and the initial classifier is trained and fine-tune the whole model with a small number of labeled samples.Finally,the most uncertain samples are selected to label with active learning algorithm and added to the training samples.Experiment comfirms that active deep learning can effectively improve the classification accuracy with less labeled samples and entropy shannon is a more effective feature to distinguish between seawater,mudflats and beaches.

  • Ma Jiapei, Li Hongyi, Wang Jian, Shao Donghang
    Remote Sensing Technology and Application. 2019, 34(3): 655-666. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0655
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    It is a significant way to improve the accuracy of hydrologic simulation in the area having sparse observation sites by using the regional climate model precipitation driving hydrological model.As a result,a critical issue arises,which is how to correct the precipitation data coming from Regional Climate Model (RCM) based on the observation in the hydrology research in cold region.However,no systematic studies have been conducted to compare different precipitation correction methods and evaluate their impact on the hydrologic simulation in the cold region yet.Due to this,two kinds of mainstream regional climate model precipitation correction methods,Quantile Mapping (QM) and the Optimal Interpolation (OI),have been compared and evaluated in Manas River basin between 2004 to 2009.The results show that both methods have their own advantages and disadvantages in statistical significance.The correction result of QM is good in low precipitation value and the average annual precipitation is more reasonable at spatial distribution.But when comes the high precipitation value,the result is not stable.Moreover,the correlation coefficient (R) and Root Mean Square Error(RMSE) doesn’t improve.Compared to R 0.37 and RMSE 2.80 mm/d,the modified R is 0.36 and RMSE is 2.70 mm/d;OI can improve R and RMSE significantly,one increases to 0.85 and the other reduces to1.46 mm/d after the correction.Despite that,OI also has its limitations.It gets more tiny precipitation relative to observation and the improvement of spatial distribution is not obvious.Using precipitation data before and after the correction to drive the hydrological models in a same set of model parameters.The results show that QM improves the simulation slightly,Nash-Sutcliffe efficiency coefficient (NSE) changes from 0.63 to 0.65 compared before,while OI is comparatively better,NSE increases to 0.71.This study is helpful to solve the problem of quality optimization in the preparation of hydrological simulation precipitation data and improve the precision of hydrologic simulation in cold region.

  • Xu Wenxin, Zhou Yuke, Liang Juanzhu
    Remote Sensing Technology and Application. 2019, 34(3): 667-676. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0667
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    Due to the fragile ecosystem and unique geographical environment on the TP,the vegetation strongly responds to climatic shifts.Therefore,it is of great significance to discuss the spatiotemporal trend shift of vegetation,to evaluate the climate change of the plateau and to predict regional ecological development.Using the GIMMS NDVI3g dataset from 1982 to 2012 to extract the NDVI information of the TP,as well as establishing seasonal trend model to classify research through the seasonal trend analysis and breakpoints detection method,reveals the spatiotemporal pattern of the trend shifts of plateau vegetation at both ends of the breakpoints combining the classification of land cover.The results shows that conclusions.(1) The seasonal trend model can effectively identify the breakpoints of vegetation time series,moreover the time span of the breakpoints were large and the spatial heterogeneity were strong.(2) The trend of vegetation degeneration in the western part of the Tibetan Plateau was small,vegetation degeneration in the south and northeast regions was obvious,and vegetation in the central and eastern regions has improved.58.93% of the vegetation status tends to be stable.The area where the vegetation status changes significantly accounts for about 32.3% of the entire plateau.(3) In the area where the vegetation status is generally or significantly changed,the vegetation improvement of monotonous trend and interruption trend were more than that of degradation,and the degenerative situation in the reverse trend were more than the improvement.Monotonous trend changed in 3.14% of the regions,58.36% of the regions occurred interruption trend changes,and 38.50% of regions occurred reverse trends.The time distribution of the monotonous trend and the interruption trend were more concentrated,while the reverse trend covered the entire time series.(4) The vegetation improvement and degradation in different land cover types were various conditions.The type with the highest rate of improvement was desert(53.30%),and the type with the highest rate of degradation was sparse vegetation(60.14%).Overall,the vegetation in Tibetan plateau tends to be greening,but the spatial heterogeneity remains significant.
  • Zhao Lu, Ren Hongyan, Yang Linsheng
    Remote Sensing Technology and Application. 2019, 34(3): 677-684. https://doi.org/10.11873/j.issn.1004-0323.2019.3.0677
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    Timely and accurately acquisition of the area and spatial distribution of greenhouse in the agricultural regions using remote sensing technique is a novel solution,which would be valuable for the local authorities taking measures to adjust regional agricultural structure and to prevent and control environmental pollution.In this study,the nearest neighbor method based on object\|oriented thought is used to extract greenhouses in Guantao County of Handan City with GF-2 satellite image.The random verification shows that the accuracy of extraction in greenhouses is 95.65%,and the area of the greenhouse is 21.11 km2.Since auxiliary facilities around greenhouses were also included in the area of greenhouses issued by local authority,the extraction results need to be revised by calculating the ratio of greenhouse in the greenhouse area.As a result,the final area of greenhouses is 33.68km2with the area accuracy of 87.80% (compared with the official statistics:30 km2).Greenhouses in Guantao County were obviously spatially clustered in some zones along traffic arteries and main rivers,especially around the Zhaizhuang village (about 0.93 km2).Using Chinese high-resolution satellites images to extract information of greenhouses can be effective and feasible with suitable method,and can provide technical support for decision makers to the spatial planning and management of agricultural greenhouse and the supervision and control of agricultural pollution.