26 August 2017, Volume 32 Issue 4
    

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  • Research of Satellite Precipitation Data Assimilation on WRF Model
    Remote Sensing Technology and Application. 2017, 32(4): 593-605. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0593
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    Weather research and forecasting model and four\|dimensional variational(4Dvar)data assimilation system were used to assimilate Tropical Rainfall Measuring 3B42 precipitation dataset(TRMM 3B42)，Global Precipitation Measurement dataset(GPM)and FY\|2G precipitation dataset during 1 July to 4 July 2015.The results showed that：(1)assimilation of the satellite precipitation datasets does improve the forecasting of precipitation，because all assimilation precipitation RMSE are in(0，1)，and assimilating GPM dataset is superior than others；(2)the results of  2 m relative humidity from all experiments underestimated real observations，and 2 m relative humidity RMSE(units %)were in(10，50).Moreover，assimilating GPM provides an advantage in estimating various air moisture conditions;(3)Although the impact of assimilating precipitation datasets were complex for simulating 10 m wind speed，results of 10 m wind speed experiments were overestimated\|the real observation and the RMSE were in 1.5~3 m/s.In conclusion，GPM precipitation datasets assimilation was good for simulating precipitation，relative humidity and 10 m wind speed.
  • Improving Soil Moisture Estimation by Joint Assimilationof MODIS Land Surface Temperature and Airborne L band Microwave Brightness Temperature
    Remote Sensing Technology and Application. 2017, 32(4): 606-614. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0606
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    In this work，a novel soil moisture data assimilation scheme was developed，which was based land surface model (CoLM，Common Land Model)，microwave radioactive transfer model (L MEB，L band Microwave Emission of the Biosphere)，and data assimilation algorithm (EnKS，Ensemble Kalman Smoother).This scheme is used to improve the estimation of soil moisture profile by jointly assimilatingMODIS land surface temperature and airborne Lband passive microwave brightness temperature.The groundbased data observed at DAMAN superstation，which is located at Yingke oasisdesert area in the middle stream of the Heihe River Basin，are used to conduct this experiment and validate assimilation results.Three LAI products are used to analyze the influence of LAI on soil temperature.Three assimilation experiments are also designed in this work，including assimilation of MODIS LST，assimilation of microwave brightness temperature，and assimilation of MODIS LST and microwave brightness temperature.The results show that the uncertainties in LAI influence significantly soil temperature simulations in different soil layers.MODIS LAI product is seriously underestimated in this study area，which results soil temperature overestimation about 4~6 K.Three assimilation schemes can improve soil moisture estimations to different extend.Joint assimilation of MODIS LST and microwave brightness temperature achieved the best performance，which can reduce the RMSE of soil moisture to 31%~53%.
  • Data Assimilation Experiment ofPoor Quality of RemoteSensing Image in Critical Phenology
    Wang Yiming，Meng Jihua，Cheng Zhiqiang
    Remote Sensing Technology and Application. 2017, 32(4): 615-623. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0615
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    In the process of data assimilation，influenced by the water vapor and cloud cover in the study area，the qualities of remote sensing images are poor in the key crop phenological phases.This will cause not to get the perfect remote sensing images for a long time.So we try to solve this problem by using an improved EnKF method to assimilate the WOFOST crop growth model and the terrible quality of remote sensing images to forecast the maize’s yield in the Red Star Farm in Heilongjiang province.In order to improve the accuracy of simulated time series curve of the LAI and yield production results，the consideration on quality evaluation of the remote sensing images is introduced by using expansion coefficient and adjustable factor.The results shows based on the improved EnKF method，time series curve of the LAI keeps a normal tendency of LAI rather than negative fluctuations，and it also avoids the serrated fluctuation to a certain extent.In addition，compared with the original EnKF method，in the field level R2 can increased to 0.67 from 0.59，RMSE is reduced to 92.23 kg/hm2 from 240.57 kg/hm2 and in the farm level R2 can increased to 0.61 from 0.52，RMSE is reduced to 122.44 kg/hm2 from 310.94 kg/hm2 between simulated yield and measured yield.
  • Overview onInversion Algorithm for Precipitation Measurement with Synthetic Aperture Radar
    Xie Ya’nan， Zhou Mingliang， Liu Zhikun
    Remote Sensing Technology and Application. 2017, 32(4): 624-633. https://doi.org/10.11873/j.issn.1004-0323.2017.4.024
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    Global precipitation which is an important intermediary in the entire atmospheric energy transportation plays an irreplaceable role in meteorological，hydrological research and human’s daily life.With the rapidly development of the high resolution spaceborne microwave remote sensing technology，including the synthetic aperture radar technology，a great opportunities are provided to improve the measurement precision degree of the rainfall rates，therefore a good study to make use of the rainfall inversion algorithms based on the SAR is a very meaningful guide for people’s daily life.Firstly，this paper introduced the basic rainfall rates theories including the normalized radar cross section model，and then analyzed the Model\|oriented statistical inversion algorithm，the Volterra integral equation inversion algorithm，the Surface\|scattering reference attenuation inversion algorithm，the Modified regression empirical algorithm and the Model\|oriented statistical and Volterra integration statistical inversion algorithm.Meanwhile，a detailed explanation for these above algorithm’s applications were illustrated simultaneously.Finally，the existing problems combined with the current SAR measuring rainfall inversion algorithm were summarized，and a promising direction of the future work about this crucial issues that a reduction algorithm simulation errors and an improvement of the rainfall measurements accuracy based on the SAR technology were also put forward.

  • Current Status and Development Trend of Surface Penetrating Radarin the Application of Lunar and Deep Space Exploration
    Li Haiying，Wen Weibin，Wang Fang，Fu Qiang，Xiao Yuan
    Remote Sensing Technology and Application. 2017, 32(4): 634-642. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0634
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    Surface Penetrating Radar(SPR)，a kind of geophysical method to survey the internal structure and change of properties of lossy dielectric material by using electromagnetic wave，is only able to penetrate the desolate surface of the planet for finding hidden signs of water ice.In addition，SPR is also the most eligible nondestructive method for imaging planetary shallow layer soil structure and rock changes caused by electromagnetic discontinuous.This paper reviews the decades history of SPR in the application of extraterrestrial planetary exploration，and summarizes the scientific objectives，performance and results of SPRs configured in lunar and deep space missions.Then on this basis，combined with the latest news on radar technology，the main development trend of SPR in lunar and deep space exploration is given.

  • Estimation of Surface Solar Radiation Using MODIS Satellite Data and RSTAR Model
    Li Weiwei，Husi Letu，Chen Hongbin，Shang Huazhe
    Remote Sensing Technology and Application. 2017, 32(4): 643-650. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0643
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    The MODIS aerosol product and cloud product data are combined with the atmospheric radiation transmission model RSTAR to calculate the surface solar radiation under sunny and cloudy conditions.The results are compared with the ground observed values of Xianghe integrated radiation station.It shows that the simulated value and observed value have good correlation.The R2 and RMSE are 0.95 and 38.8 W/m2 in the clear skies，while 0.88 and 88.2 W/m2 in the cloudy skies.The results show that more cloud\|aerosol mixed states is in Xianghe station，while MODIS can only invert single microphysical parameter of cloud layer，which leads to error of model input parameter，bringing error to the calculation result of surface solar radiation.In order to quantitatively analyze the effect，the RSTAR radiation model was used to calculate the radiance values，and invert f different cloud and aerosol optical thickness，calculating surface solar radiation.The results show that the error of estimation of surface solar radiation is 1.29%~1.56% when the aerosol optical thickness (AOD)is 0.1，compared with the single layer.With the increase of AOD，the effect of AOD increased obviously.The relative error was 17.79%~18.38% when AOD was 1.2.For the heavily polluted areas of North China，it is important to analyze the influence of aerosol on the surface solar radiation under cloud cover，which is very important to improve the calculation accuracy of surface solar radiation under cloud conditions.

  • Derivation and Comparison of Water Vapor between Infrared Channels and Near Infrared Channels from FY\|3
    Remote Sensing Technology and Application. 2017, 32(4): 651-659. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0651
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    Atmospheric water vapor content has important significance for radiometric correction of satellite image，understanding of atmospheric micro\|physical process，precipitation prediction and so on.We will retrievie atmospheric column water vapor based on Moderate Resolution Imaging Spectroradiometer (MERSI)near infrared channels and Visible and Infrared Radiometer (VIRR)thermal infrared channels datas of FY\|3A，respectively.Comparing the retrieval results of MERSI，VIRR and the observations of sounding ground stations respectively，we find :① the observed correlation between retrieval results of MERSI and observations is 0.763，while retrieval results of VIRR has poor correlations with observations，which is0.169.What’s more，The retrieval accuracy of MERSI (1.108 g/cm2)is higher than that of VIRR (1.894 g/cm2)；②The average atmospheric column water vapor with three channels of MERSI has higher retrieval accuracy than channel 17th(1.133 g/cm2)，18th(1.424 g/cm2)，19th(1.827 g/cm2).The main reason is that three channels have different sensitivities of water vapor，and utilizing three channels to retrieve atmospheric column water vapor content can reach the effect of perfection.
  • Simulation and Analysis on the Influence of Soil Background of Wheat Farmland on NDVI under Different Vegetation Coverage
    Remote Sensing Technology and Application. 2017, 32(4): 660-666. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0660
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    It is quite confusing to effectively monitor and precisely evaluate growing conditions of wheat by using normalized differential vegetation index (NDVI)which is based on pixel scale as they are significantly different when acquired by the same growth status wheat with different background of soil types.This paper selects 9 typical soil types in our country as background with the wheat canopy spectrum is fixed which means the NDVIc is a constant value to study the influence of different soil background types on NDVI of wheat and analyze the sensitivity of NDVI of wheat to the vegetation coverage simulated by diverse liner mixed ratio of wheat canopy and soil background.The results show that：(1)wheat NDVI of farmland increases along with the increase of vegetation coverage under the same of soil background type，and vice versa；(2)wheat NDVI of farmland vary greatly with different soil background types，and the difference decrease while the vegetation coverage exceed 25%；(3)NDVI sensitivity also shows a quite difference to vegetation coverage under the diverse soil background types.With the increase of vegetation coverage，NDVI sensitivity decreases with the lower\|reflectance soil background while it increases monotonously with the higher reflectance soil background.It provides the foundation for the times of calculating the remote sensing’s NDVI information of all wheat growing periods under different types of soil background.
  • Estimations of Chlorophyll Content from Vegetation Indicesby Using PROSPECT and DART
    Zeng Yuyan，Shi Runhe，Liu Pudong，Wang Hong
    Remote Sensing Technology and Application. 2017, 32(4): 667-673. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0667
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    With the aid of a wellknown leaf optical model PROSPECT and a canopy scale model DART (Discrete Anisotropic Radiative Transfer)，sensitivities between chlorophyll content and six different vegetation indices were investigated by simulating eucalyptus，one of a dominant fastgrowing tree in China，as an example.Vegetation indices used here include Normalized Difference Vegetation Index (NDVI)，Structure Insensitive Pigment Index (SIPI)，Colouration Index (COI)，Simple Ratio Index (SR)，Cater Index (CAI)，and Rededge Position Linear Interpolation (REP_Li).Results indicate that at the leaf scale，COI and SIPI are sensitive to the LCC (Leaf Chlorophyll Content)as the Chlorophyll Content changes.Meanwhile，no obvious saturation phenomenon is observed for these two indices compared to other indices.Further investigations show that all these vegetation indices are incapable of estimating LCC at the canopy scale，due to significant influences from LAI(Leaf Area Index).Nevertheless，it suggests that SIPI and COI can be applied to estimate the CCC (Canopy Chlorophyll Content).
  • Surface Emissivity Image Simulationfor Atmospheric Absorption Bands based on Spectral Mixing
    Liu Yao，Zhang Wenjuan，Zhang Bing，Gan Fuping
    Remote Sensing Technology and Application. 2017, 32(4): 674-682. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0674
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    A simulation method based on spectral mixing is proposed for surface emissivity image generation in atmospheric absorption bands，in order to provide surface input data for the corresponding endtoend image simulation.First，endmember selection is conducted on data source to acquire image endmember spectra.Then，substitute endmembers are selected from surfacemeasured spectra by spectral matching with image endmembers，and used for abundance inversion.Finally，using emissivity spectra of substitute endmember in the absorption bands and abundance maps，emissivity images are simulated based on linear spectral mixing model.In the simulation experiment，Landsat8 OLI images were used as data source，and JHU and USGS spectral library data were assumed to be ground spectra of the test case.Since actual emissivity images in absorption bands are unavailable，accuracy analysis is conducted by comparing OLI reflectance images with its simulations generated by the proposed method.Total RSMEs of simulated OLI images are 0.045 and 0.049，respectively;which shows the image simulation method is feasible and can produce images with high accuracy.

  • The Study on Oceanic Vector Wind Field Retrieve Technique based on Neural Networks of Microwave Scatterometer
    Chen Kuntang，Dong Xiaolong，Xu Xing’ou，Lang Shuyan
    Remote Sensing Technology and Application. 2017, 32(4): 683-690. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0683
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    The neural networks are used to retrieve wind fields for microwave scatterometer data，especially for data gained by the scatterometer onboard HY\|2A satellite (HSCAT)under high wind speed conditions.The retrieval of wind speed is based on Back Propagation (BP)neural network，while multiple solutions of wind direction inversion is realized by Mixture Density Network (MDN)neural network.During the process，Gaussian kernel function is employed.The wind field used in network training is from corresponding European Centre for Medium\|range Weather Foresting (ECMWF).It is proved that wind fields retrieved in this paper could get results meeting the accuracy requirement for HSCAT by comparison with ECMWF wind fields.Results are also compared with the L2B wind field products distributed by the National Satellite Oceanic Application Service，it is shown that the method in this paper gave results with closer values than L2B products.
  • Algorithm of Object Recognition from Hyperspectral Remote Sensing and its Application in Lithologic Feature Extraction
    Tang Chao，Shao Longyi
    Remote Sensing Technology and Application. 2017, 32(4): 691-697. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0691
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    The study aiming at the problems of the distinction of spectrum waveform characteristics，operation speed，spatial detail spectral features for the improvement of the algorithm in Hyperspectral remote sensing feature recognition，On the basis of this puts forward the algorithm of the fractal signal.The performance，efficiency，etc of the algorithm itself has been tested by using CASI hyperspectral data，hyperspectral remote sensing image lithologic characteristics of the study area also has been extracted.The initial value of the signal，the iteration step length and other characteristics of fractal signal of the hyperspectral remote sensing data were discarded in this study.To a certain extent，the fractal signal algorithm can refine the distinguishability of the similar characteristics of hyperspectral，and the algorithm used for feature extraction in CASI data of lithology achieves the purpose to accurately extract the surface lithology of bedrock exposed areas.
     
  • An Anti noise Identification Methodfor Winter Wheat based on Satellite Time series Data
    Jiang Tao，Zhu Wenquan，Zhan Pei，Tang Ke，Cui Xuefeng，Zhang Tianyi
    Remote Sensing Technology and Application. 2017, 32(4): 698-708. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0698
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    China As one of the major crops in the world，the spatial distribution information of winter wheat plays an important role in monitoring winter wheat growth，assisting economic decisionmaking and addressing regional food security under climate change.This paper proposed a new antinoise identification method for winter wheat identification based on the 250 m MODISNDVI timeseries dataset during the period from September 30，2014 to June 26，2015.With the method，the spatial distribution of winter wheat in Henan province was extracted based on the analysis of winter wheat phenology.Results indicated that the total identification accuracy of winter wheat was 93.0%，94.0% and 86.0% for the whole study area，fragmentary land area and regular land area，respectively.Compared with the traditional identification method for winter wheat based on satellite timeseries data，the identification accuracies with the proposed method in different filtering scenarios were not only high but also similar to each other.It strongly proved that the new method had a good performance in noise immunity and stability and can be applied to the rapid extraction of winter wheat in a large scale based on satellite timeseries dataset.This new method provided a new technical support for the operational extraction of winter wheat.

  • SAR Sea Ice Image Segmentation Method based on Low Rank Sparse Representation and Improved MRF Model
    Zhang Baohua，Zhou Wentao，Lu Xiaoqi
    Remote Sensing Technology and Application. 2017, 32(4): 709-713. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0709
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    Accurate segmentation of Synthetic Aperture Radar (SAR)images is the premise of interpreting the distribution information of sea ice.However the existing segmentation methodsare seriously interfered by speckle noise，which leads to high segmentation error and low reliability interpreting results.In this paper，a novel sea ice SAR image segmentation method based on low rank sparse representation is proposed，firstly sparse components are extracted from the source image by using robust principal component，and then bilateral filter is used to enhance the image details.Due to the MRF segmentation model based on fixed potential function cannot accurately reflect the relevance between the areas，MRF segmentation model based on interactive potential function is built to segment the sea ice image accurately.A series of Radarsat satellites data are tested to validate performance of the proposed method，the results show that compare with traditional segmentation algorithms，the proposed method algorithm can not only maintain the connectivity of the image better，but also has higher segmentation accuracy.

  • Research on Vegetation Extraction and Fractional Vegetation Cover of Spartina Alterniflora Using UAV Images
    Zhou Zaiming，Yang Yanming，Chen Benqing
    Remote Sensing Technology and Application. 2017, 32(4): 714-720. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0714
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    In this study，vegetation extraction and fractional vegetation cover of Spartina alterniflora (S.alterniflora )was studied in an experimental region of Sansha bay，a typical coastal wetland area in Fujian Province，China.A new vegetation index visible\|band modified soil adjusted vegetation index (V\|MSAVI)was constructed and the fractional vegetation cover was calculated subsequently based on the NDVI model.Results showed that，the S.alterniflora extraction results by V\|MSAVI had a satisfactory precision.Most of the fractional vegetation covers of S.alterniflora were in medium\|level coverage (40%~60%)and a high\|level coverage (60%~80%).An accuracy analysis based on the visual interpretation indicated the overall extraction results accuracy of 0.89，and a kappa coefficient of 0.77.Root mean square error (RMSE)of fractional vegetation cover between the estimation value and the true value was 0.06，and the determination coefficient R2 was 0.92.
  • A Reducing Saturation Method for DMSP/OLS Nighttime Light Image Combining Landsat Data
    Ni Yuan，Zhou Xiaocheng，Jiang Wei
    Remote Sensing Technology and Application. 2017, 32(4): 721-727. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0721
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    Defense Meteorological Satellite Program/Operational Linescan System(DMSP/OLS)is one of the data sources，which can effectively reflect human activities of earth surfaces.During the past decade，DMSP/OLS had been extensively applied in urban extraction and extension study.In the recent year，the Vegetation Adjusted NTL Urban Index(VANUI)has been proposed and had proven to be a simple，convenient and high precision desaturation index to extract urban area.In VANUI method，negative values of imagery were directly eliminated to remove water body，which not only removed the bridge over the river and building but also extracted the aquaculture areas along the coast，thus，this method reduced the extraction accuracy.This paper proposed a new index\|RwNTLI，combining DMSP/OLS nighttime light data and the vegetation index (NDVI)and water index (MNDWI)which were constructed by Landsat data.In this study，Guangzhou was taken as experimental area.By comparing the VANUI index with the ability to identify ground objects as well as the ability to alleviate saturation regions，the result showed RwNTLI index could effectively solve the problem of VANUI as well as eliminate saturation effect of nighttime light imagery.Among them，the correlation between RwNTLI index and RCNTL is better than that of VANUI index and RCNTL.Therefore，RwNTLI index is a simple and effective index of luminous desaturation，which has more advantages than VANUI index in describing the characteristics of night lights of urban areas and will have higher application value in urban built\|up areas in the future.
  • CropStraw Fire Remote Sensing Monitoring and Its Algorithm Implementation
    Xu Qingyun，Gu Weiwei，Xie Tao，Liu Rui
    Remote Sensing Technology and Application. 2017, 32(4): 728-733. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0728
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    in order to obtain the information and achieve the effective control of crop straw fire spatial distribution in Central China Region.The MODIS L1B remote sensing datasets during 2014 for the main data source in this article，and combined with land use data，the farmland of Central China Region was taken as study region.Based on the enhanced contextual fire remote sensing detection algorithm，and make full use of the theoretical knowledge of quantitative remote sensing and Geospatial Data Abstraction Library (GDAL)and other technical means，to achieve the crop straw fire recognition in Central China Region.Using Ministry of Environmental Protection of the People’s Republic of China release the daily newspaper of crop straw fire in China and the standard fire products (MYD14)of MODIS for the comparative analysis of the quantitative and spatial.The results indicate that the algorithmof this paper can achieve crop straw fire remote sensing monitoring of this study region effectively，and the parameters can be adjusted in real time based on the characteristic of the study region，and improve the automation and working efficiency of crop straw fire monitoring.
  • Analysis on Effectiveness of SO2 and NO2 Emission Reduction in North China Plain by OMI Data during the Military Parade 2015
    Zhang Han，Yu Chao，Su Lin，Wang Yapeng，Cheng Liangfu
    Remote Sensing Technology and Application. 2017, 32(4): 734-742. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0734
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    During the military parade 2015(September 3rd)，emission control measures were implemented in Beijing and surrounding areas.To evaluate the effect of these measures，we use theplanetary boundary layer SO2 column densities and tropospheric NO2 column densities observed from the satellite instruments OMI in this study.We preprocessed satellite data and calculated the average of time and space from August 1 to September 20 in Beijing，JingJinJi region and surrounding regions (Shandong，Henan，Shanxi and Inner Mongolia).Finally，we plotted SO2 and NO2 space distributions of Beijing and North China Plain.We analyzed the space distribution of North China Plain SO2 and NO2 concentration during the period of the military parade 2015.It shows that both SO2 column densities and NO2column densities reach the minimum during the period of the acrosstheboard emission reductions.Compared to the past three years (2012-2014)，NO2 and SO2 column densities were found to manifest significant reductions over North China Plain.During the period of emission reductions (Aug. 20Sep. 3)，the study showed about 18% and 46% reduction of NO2and SO2 concentration in the JingJinJi region and decrease of NO2 by 10% and SO2 by 37% in the surrounding regions.However，air quality plummeted after the lifting of local and regional joint emission source control measures.Compared to the past three years，it showed increase of NO2 by 19% and SO2 by 1% in the JingJinJiregion.SO2 concentration increased by 30% over Beijing especially.It suggests the effectiveness of regional joint emission source control measures.Our study indicates that the emission source control measures were effective in Beijing and surrounding provinces during the military parade.With the advantage of realtime and largescale，observation from space could be applied to atmospheric pollution monitoring and assessment of emission control measures.
  • Citrus Recognition Methods of Hyperspectral Remote Sensing Image based on Spectral Unmixing Model
    Li Hengkai，Ou Bin，Liu Yuting，Qiu Yubao
    Remote Sensing Technology and Application. 2017, 32(4): 743-750. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0743
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    In order to monitor the citrus planting information timely and accurately，We take Huichang County of Jiangxi Province as the research area，using EO\|1 Hypersion hyperspectral remote sensing (HRS)image as a datasource to build a citrus recognition methods of hyperspectral remote sensing image based on spectral unmixing.First of all，the EO\|1 Hyperion hyperspectral remote sensing image has 242 bands，and it has a wide spectrum rang.It can extract the spectral curve of typical objects in the study area，which is based on the image pre\|processing including the band selection，the atmospheric correction and so on.Then，we use the fully constrained linear spectral mixture model of spectral unmixing to decompose the mixed pixels of the image，and then extract the abundance value of citrus.Finally，we construct the relationship between citrus abundance and the actual cultivation of citrus based on the high resolution remote sensing image.The results indicated that the unavoidable error in the extraction of the typical objects and the differences of the citrus canopy coverage can lead to the corresponding relationship between the citrus plant accurate identification and the citrus abundance threshold value.Under the condition of repeated experiments，the study area of citrus abundance thresholds in the range of 0.30~0.45，the overall accuracy can reach more than 90%，and it can meet the requirements of identification of citrus.
  • The Comparison of Reflectance based on Different Terrain Correction
    Lü Lili，Xie Yaowen，Dong Longlong
    Remote Sensing Technology and Application. 2017, 32(4): 751-759. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0751
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    Selecting the Xinglong Mountain which locates in the southeast of Lanzhou city，GanSu province as an example.Using the Landsat8 satellite image as the data source，the Cosine method，C and the modified Minnaert methods were used for each band in the study area.Comparing with the results of the field measurement reflectance and the statistical characteristics of image，the result showed that the cosine method has a perfect correction in the visible and short wave infrared wavelength，the C correction has a serious over\|correct，however.In the Near Infrared Wavelength，the better result obtained by C correction，and the cosine method has over\|correct otherwise.Comparing with the correct effect of whole bands，the modified Minnaert method has an ideal correct effect.The comparison of before and after correction we found that there is a smaller difference for three methods in the smaller slope，and the over correct is mostly in the south.What’s more，with the increase of the slope，the over correct is more obviously，but，there is a little over correct which used the modified Minnaert method in the whole area，it’s more suitable in large slope and the complicated area.
  • PlantingInformation Extraction of Winter Wheatand Rape based on GF-1 Images
    Ji Zhonglin，Zhang Yueping，Li Qiaoxuan，Liu Shaogui
    Remote Sensing Technology and Application. 2017, 32(4): 760-765. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0760
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    Successive emission of high resolution satellite has created new opportunities for the application of domestic high resolution remote sensing data.In order to explore the feasibility of GF data in the field of small and medium scale crop remote sensing monitoring and to establish a suitable technical system，with Yangzhou as an example，using decision tree model and object oriented classification method to research the feasibilityon crop planting information extraction of GF wide field viewdata.And explore the method to improve the accuracy.The results showed that，sub\|regionpretreatmentcan reduce the adverse effects of crop spatial distribution on the extraction of the planting area.The overall accuracy of winter wheat was 97%，the Kappa coefficient was 0.93;the overall accuracy of rape was 96%，the Kappa coefficient was 0.84.Research shows thatdomestic GF\|1 WFV images can be applied to the crop planting informationextraction，and toprovide an important reference and decision support for adjusting crop spatial and optimizing management of gain producing areas.


  • Remote Sensing based Comparison of the Sea Reclamation and Coastline Utilization in Different Projects of China
    Sun Fengqin，Xu Hanqiu，Tang Fei，Wang Meiya，Yang Lijuan
    Remote Sensing Technology and Application. 2017, 32(4): 766-772. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0766
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    Three groups of high resolution SPOT\|5/6 remote sensing images were used to interpret and compare the sea reclamation and coastline utilization among Tianjin Central Fishing Harbor，Ningde Nuclear Power Plant and Zhuhai Coal Terminal.The results indicated that Tianjin Central Fishing Harbor mainly extended seaward and this style benefited the coastline conservation. Both of its  reclamation intensity and coastline growth  were several times more than that of Ningde and Zhuhai. According to all indicators, Tianjin harbor was most efficient in sea reclamation and coastline utilization.Ningde Nuclear Power plant and Zhuhai coal terminal mainly extended longshore with similar horizontal\|to\|vertical ratios，but Ningde got much lower reclamation intensity，about only one\|third to that of Zhuhai，for its huge coastline loss due to the island\|besieged reclamation.The reclamation style，horizontal\|to\|vertical ratio reclamation efficiency and coastline configuration could be optimized by the engineering graphic design,to benefit the coastal resources utilization and conservation.
  • Mono-window Algorithm Applicability in Fujian Sea and Its Surrounding Areas Sea Surface Temperature Retrieval
    Chen Xuejiao，Chen Yunzhi
    Remote Sensing Technology and Application. 2017, 32(4): 773-779. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0773
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    On the basis of four quarters HJ\|1b thermal infrared remote sensing images during 2013~2014，each of the spring，summer，autumn and winter，mono\|window algorithm was adopted to retrieve Sea Surface Temperature(SST).To verify the feasibility and accuracy of this algorithm，the derived results were compared with the measured SST data，show that the average absolute error is 0.86 ℃ and the correlation coefficient R2 is 0.971 5.The different levels influence on derived results caused by the uncertainty of water vapor and atmospheric temperature is analyzed，indicate that if the water vapor error ranges between -2~0 g/cm2and the temperature variation is between -2 ℃~2 ℃，the sea surface temperature error will be within 5%，the high retrieving accuracy can still be achieved；The sensitivity of the water vapor in winter shows higher than in summer，while the sensitivity of atmospheric temperature demonstrates lower than in summer.Therefore，mono\|window algorithm is good applicable in the SST retrieval in Fujian sea and its surrounding areas，which is of great significance to Fujian environmental monitoring.
  • Extracting Built\|up Areas from TerraSAR-X Data Using Object-oriented Classification Method
    Wang Suyun，Sun Zhongchang，Guo Huadong，Shen Wei
    Remote Sensing Technology and Application. 2017, 32(4): 780-786. https://doi.org/10.11873/j.issn.1004-0323.2017.4.0780
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    Urban sprawl stands for one of the most dynamic process in the context of global land use changes.Currently developing countries are going through the tide of urban expansion，represented by China and India.The constantly increasing loss of land resources due to growing settlements comes along with various ecological and socioeconomic challenges such as air pollutant，water contamination，urban heat island effect and urban waterlog disaster.In order to prevent these negative consequences，effective methods and strategies for a sustainable development of urban planning is the availability of accurate and up\|to\|date geo\|data on the location，shape，and dynamics of built\|up areas.Based on single\|polarized TerraSAR\|X，the approach generates homogeneous segments on an arbitrary number of scale levels by applying a region\|growing algorithm，which takes the intensity of backscatter and shape\|related properties into account.The object\|oriented procedure consists of three main steps:firstly，the analysis of the local speckle behavior in the SAR intensity data，leading to the generation of a texture image；secondly，a segmentation based on the intensity image；thirdly，the classification of each segment using the derived texture file and intensity information in order to identify and extract build\|up areas.In our research，the distribution of BAs in Dongying City is derived from single\|polarized TSX SM image (acquired on 17th June 2013)with average ground resolution of 3m using our proposed approach.By cross\|validating the random selected validation points with geo\|referenced field sites，Google Earth high\|resolution imagery，confusion matrices with statistical indicators are calculated and used for assessing the classification results.The result of kappa coefficient is 0.85，OA coefficient is 92.89%.We have shown that connect texture information with the analysis of the local speckle divergence，combining texture and intensity of construction extraction is feasible，efficient and rapid.
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