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Vegetation Products Derived from Fengyun-3D Medium Resolution Spectral Imager-Ⅱ

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Journal of Meteorological Research 2020年第4期34卷 775-785页

作者： xiuzhen han Jun YANG Shihao TANG Yang han National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081

The surface vegetation condition has been operationally monitored from space for many years by the Advanced Very High Resolution Radiometer(AVHRR) and the Moderate Resolution Imaging Spectroradiometer(MODIS) instruments. As these instruments are close to the end of their design life, the surface vegetation products are required by many users from the new satellite missions. The MEdium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) onboard the Fengyun(FY) satellite(FY-3 series;FY-3 D) is used to retrieve surface vegetation parameters. First, MERSI-Ⅱ solar channel measurements at the red and near-infrared(NIR) bands at the top of atmosphere(TOA) are corrected to the surface reflectances at the top of canopy(TOC) by removing the contributions of scattering and absorption of molecules and aerosols. The normalized difference vegetation index(NDVI) at both the TOA and TOC is then produced by using the same algorithms as the MODIS and AVHRR. The MERSI-Ⅱ enhanced VI(EVI) at the TOC is also developed. The MODIS technique of compositing the NDVI at various timescales is applied to MERSI-Ⅱ to generate the gridded products at different resolutions. The MERSI-Ⅱ VI products are consistent with the MODIS data without systematic biases. Compared to the current MERSI-Ⅱ EVI generated from the ground operational system, the MERSI-Ⅱ EVI from this study has a much better agreement with MODIS after atmospheric correction.

关键词： MEdium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) Fengyun(FY)satellite vegetation index(VI) atmospheric correction

Synthesis of True Color Images from the Fengyun Advanced Geostationary Radiation Imager

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Journal of Meteorological Research 2021年第6期35卷 1136-1147页

作者： Yuchen XIE xiuzhen han Shanyou ZHU School of Remote Sensing&Geomatics Engineering Nanjing University of Information Science&TechnologyNanjing 210044 National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081

The production of true color images requires observational data in the red,green,and blue(RGB)*** Advanced Geostationary Radiation Imager(AGRI)onboard China’s Fengyun-4(FY-4)series of geostationary satellites only has blue and red bands,and we therefore have to synthesize a green band to produce RGB true color *** used random forest regression and conditional generative adversarial networks to train the green band model using Himawari-8 Advanced Himawari Imager *** model was then used to simulate the green channel reflectance of the FY-4 AGRI.A single-scattering radiative transfer model was used to eliminate the contribution of Rayleigh scattering from the atmosphere and a logarithmic enhancement was applied to process the true color *** conditional generative adversarial network model was better than random forest regression for the green band model in terms of statistical significance(e.g.,a higher determination coefficient,peak signal-to-noise ratio,and structural similarity index).The sharpness of the images was significantly improved after applying a correction for Rayleigh scattering,and the images were able to show natural phenomena more *** AGRI true color images could be used to monitor dust storms,forest fires,typhoons,volcanic eruptions,and other natural events.

关键词： Advanced Geostationary Radiation Imager(AGRI) RGB true color random forest regression conditional generative adversarial networks Rayleigh scattering

First Look of Surface Vegetation from the Advanced Geostationary Radiation Imager(AGRI) onboard Fengyun-4B

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Journal of Meteorological Research 2023年第4期37卷 536-550页

作者： Shengqi LI xiuzhen han Yeping ZhanG Yachun LI Nanjing University of Information Science&Technology Nanjing 210044 National Satellite Meteorological Centre China Meteorological AdministrationBeijing 100081 Jiangsu Climate Centre Jiangsu Provincial Meteorological ServiceNanjing 210009

For many years, the status of surface vegetation has been monitored by using polar-orbiting satellite imagers such as Moderate Resolution Imaging Spectroradiometer(MODIS). However, limited availability of clear-sky samples makes the derived vegetation index dependent on multiple days of observations. High-frequency observations from the geostationary Fengyun(FY) satellites can significantly reduce the influence of clouds on the synthesis of terrestrial normalized difference vegetation index(NDVI). In this study, we derived the land surface vegetation index based on observational data from the Advanced Geostationary Radiation Imager(AGRI) onboard the FY-4B geostationary satellite. First, the AGRI reflectance of visible band and near-infrared band is corrected to the land surface reflectance by the 6S radiative transfer model. The bidirectional reflectance distribution function(BRDF) model is then used to normalize the AGRI surface reflectance at different observation angles and solar geometries, and an angle-independent reflectance is derived. The AGRI surface reflectance is further corrected to the MODIS levels according to the AGRI spectral response function(SRF). Finally, the daily AGRI data are used to synthesize the surface vegetation index. It is shown that the spatial distribution of NDVI images retrieved by single-day AGRI is consistent with that of 16-day MODIS data. At the same time, the dynamic range of the revised NDVI is closer to that of MODIS.

关键词： atmospheric correction normalized difference vegetation index(NDVI) spectral response function(SRF) bidirectional reflectance distribution function(BRDF)

Ecological Assessment of Oxygen Balance:A Case Study of China’s Natural Oxygen Bars

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Journal of Meteorological Research 2023年第3期37卷 404-416页

作者： Hao GAO xiuzhen han Xiurong WANG Yu FU Chen MIAO Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites National Satellite Meteorological Centre(National Centre for Space Weather)/CMA Innovation Centre for Fengyun Meteorological Satellites(FYSIC)China Meteorological Administration(CMA)Beijing 100081 Public Meteorological Service Centre China Meteorological AdministrationBeijing 100081 Carbon Neutrality Research Center Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 National Remote Sensing Center of China Beijing 100036

Ecological assessment plays a vital role in sustainable development of the environment,and thus exploration of specific and integrated ecological assessment methods has become a critical *** this study,based on the concept of oxygen balance and by accommodating both natural factors and socioeconomic elements,we establish an oxygen(O_(2))balance index(OBI),i.e.,the ratio of the O_(2)production from the ecosystem to the O_(2)consumption by human behavior,based on the net primary productivity(NPP),fuel consumption,gross domestic production(GDP),population data,and so *** results show that the spatial distributions of OBI in China are intimately correlated to the regional vegetation and socioeconomic *** estimated OBI values are then validated by statistical data from 27 counties in China,and it is found that the OBI reflects the ecological environment status ***,the average OBI values derived from 190 natural oxygen bars in China reveal extreme imbalance between O_(2)production and consumption in highly developed regions,especially in the cities,in contrast to good O_(2)balance in areas with high-quality ecological status and less *** findings from this study have quantitatively captured the regional ecological quality,providing guidance for sustainable natural and socioeconomic developments in local areas of China.

关键词： oxygen balance index(OBI) ecological environment remote sensing China’s natural oxygen bars meteorological satellites

Advances in Ecological Applications of Fengyun Satellite Data

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Journal of Meteorological Research 2021年第5期35卷 743-758页

作者： xiuzhen han Hao GAO Jun YANG Yachun LI Weicheng GENG National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081 Jiangsu Climate Center Jiangsu Provincial Meteorological BureauNanjing 210008

In recent years,the remote sensing based on meteorological satellite observations has become an important tool for assessing global ecological *** the early 2000,Fengyun(FY)satellite data have been widely used to derive the key parameters of ecological environment in *** integrated earth-observation system has been developed in China through using FY satellite data,including retrievals the key ecological parameters as well as to constructions of long-term data records of vegetation index,land surface temperature,net primary production,vegetation health index,and so *** progress has thus been made in the application and service for prevention of air pollution,management and control of ecological redline,ecological monitoring for the Belt and Road Initiative,and assessment of ecological environment for human *** order to monitor the ecological parameters in real time and with a full dynamic coverage,it is necessary to improve the technology in application of ecological remote sensing from meteorological satellites,and further enhance the ecological meteorological service.

关键词： Fengyun satellite ecological environment remote sensing assessing and monitoring operational service

Assessing the Ecological Quality of Nanjing during Its Urbanization Process by Using Satellite,Meteorological,and Socioeconomic Data

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Journal of Meteorological Research 2020年第2期34卷 280-293页

作者： Xin hanG Yachun LI Xiaochun LUO Min XU xiuzhen han Jiangsu Climate Center Jiangsu Meteorological BureauNanjing 210008 Jiangsu Meteorological Service Center Jiangsu Meteorological BureauNanjing 210008 National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081

Assessing the ecological status of different districts within a city undergoing urbanization is challenging given their complex surface types and fast pace of *** this study,we utilized satellite data obtained from Landsat5/TM(Thematic Mapper)and Landsat 8/OLI(Operational Land Imager)images in conjunction with meteorological and socioeconomic data to construct a remote sensing ecological index(RSEI)for monitoring the ecological quality of Nanjing,Jiangsu Province.A higher RSEI value corresponded to better ecological *** ratings were associated with RSEI values of city districts:very poor,poor,average,good,and *** Nanjing,the percentage of areas evidencing good RSEI ratings decreased from 55.9%in 2000 to 48.0%in 2018,whereas there was a slight increase in areas with very poor RSEI ratings during this *** the 11 city districts,16.8%,21.8%,and 61.4%respectively evidenced the increasing,decreasing,and stable ecological quality relative to their quality in *** the11 administrative districts in Nanjing,the main urban districts evidenced increased RSEI values in 2018 compared with those in 2000,with the improved areas exceeding the ones that had deteriorated in these ***,the ecological quality of new urban and ed because of the urban expansion,with areas that had deteriorated exceeding the improved *** the three protected ecological zones,the quality of Zijin Mountain National Forest Park was considerably better than that of Laoshan and ***,the urbanization rate and RSEI evidenced a high negative correlation coefficient value(-0.76).The urbanization process of Nanjing induced a declining trend for the ecological quality,indicating the need of strong protection measures for the maintenance or improvement of its ecological environment.

关键词： ecological quality remote sensing ecological index(RSEI) urbanization

Self-organizing feature map neural network classification of the ASTER data based on wavelet fusion

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Science China Earth Sciences 2004年第7期47卷 651-658页

作者： HASI Bagan MA Jianwen LI Qiqing han xiuzhen LIU Zhili Laboratory of Remote Sensing Information Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China Laboratory of Remote Sensing Information Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China Laboratory of Remote Sensing Information Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China Laboratory of Remote Sensing Information Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China Laboratory of Remote Sensing Information Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China

Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification results can be obtained with the neural network method through getting knowledge from environments and adjusting the parameter (or weight) step by step by a specific measurement. This paper focuses on the double-layer structured Kohonen self-organizing feature map (SOFM), for which all neurons within the two layers are linked one another and those of the competition layers are linked as well along the sides. Therefore, the self-adapting learning ability is improved due to the effective competition and suppression in this method. The SOFM has become a hot topic in the research area of remote sensing data classi-fication. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a new satellite-borne remote sensing instrument with three 15-m resolution bands and three 30-m resolution bands at the near infrared. The ASTER data of Dagang district, Tianjin Munici-pality is used as the test data in this study. At first, the wavelet fusion is carried out to make the spatial resolutions of the ASTER data identical; then, the SOFM method is applied to classifying the land cover types. The classification results are compared with those of the maximum likeli-hood method (MLH). As a consequence, the classification accuracy of SOFM increases about by 7% in general and, in particular, it is almost as twice as that of the MLH method in the town.

关键词： classification, wavelet fusion, self-organizing neural network feature map (SOFM), ASTER data.

An Improved Model for Evaluating Ecosystem Service Values Using Land Use/Cover and Vegetation Parameters

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Journal of Meteorological Research 2021年第1期35卷 148-156页

作者： Fang-Cheng ZHOU xiuzhen han Shihao TANG Xiaoning SONG Hao WANG National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081 University of Chinese Academy of Sciences Beijing 100049 Shandong Climate Center Shandong Meteorological BureauJinan 250031

It is crucial to maintain the balance of economic development and ecosystem protection. The value of ecosystem services is an indicator to help people understand the importance of ecosystem protection. Traditional models estimate ecosystem service values only according to land use/cover data while ignoring vegetation status differences in the same land use/cover. This study uses the normalized difference vegetation index(NDVI), the leaf area index(LAI),and net primary productivity(NPP) as vegetation status data to describe the differences in the same land use/cover type. The principal component analysis(PCA) approach is used to reduce the correlations among the three types of vegetation status data. Then, the calculated vegetation status index after PCA is input into the estimation model. The case study in China shows that the improved model has two major advantages. First, it can clearly distinguish the differences in ecosystem service values even for the same land use/cover type. Second, it can clearly describe the transitional zones between different land use/cover types through continuous changes in ecosystem service values. This improved model can provide a more detailed description of the distribution characteristics of ecosystem service values in China and help policymakers balance economic development and ecosystem protection.

关键词： ecosystem service values land use/cover data vegetation status principal component analysis(PCA)

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

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Journal of Meteorological Research 2022年第3期36卷 401-416页

作者： Yonghong LIU xiuzhen han Fuzhong WENG Yongming XU Yeping ZhanG Shihao TANG Earth System Modeling and Prediction Center China Meteorological AdministrationBeijing 100081 State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081 National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081 School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science&TechnologyNanjing 210044

Currently,the satellite data used to estimate terrestrial net primary productivity(NPP)in China are predominantly from foreign satellites,and very few studies have based their estimates on data from China’s Fengyun ***,despite their importance,the influence of land cover types and the normalized difference vegetation index(NDVI)on NPP estimation has not been *** study employs the Carnegie–Ames–Stanford approach(CASA)model to compute the fraction of absorbed photosynthetically active radiation and the maximum light use efficiency suitable for the main vegetation types in China in accordance with the finer resolution observation and monitoring-global land cover(FROM-GLC)classification ***,the NPP is estimated from the Fengyun-3D(FY-3D)data and compared with the Moderate Resolution Imaging Spectroradiometer(MODIS)NPP *** FY-3D NPP is also validated with existing research results and historical field-measured NPP *** addition,the effects of land cover types and the NDVI on NPP estimation are *** results show that the CASA model and the FY-3D satellite data estimate an average NPP of 441.2 g C m^(−2) yr^(−1) in 2019 for China’s terrestrial vegetation,while the total NPP is 3.19 Pg C yr^(−1).Compared with the MODIS NPP,the FY-3D NPP is overestimated in areas of low vegetation productivity and is underestimated in high-productivity *** discrepancies are largely due to the differences between the FY-3D NDVI and MODIS *** with historical field-measured data,the FY-3D NPP estimation results outperformed the MODIS NPP results,although the deviation between the FY-3D NPP estimate and the in-situ measurement was large and may exceed 20%at the pixel *** land cover types and the NDVI significantly affected the spatial distribution of NPP and accounted for NPP deviations of 17.0%and 18.1%,***,the total deviation resulting from the two factors reached 29.5%.These results show that

关键词： net primary productivity(NPP) Carnegie-Ames-Stanford approach(CASA)model maximum light use efficiency Fengyun-3D(FY-3D) finer resolution observation and monitoring-global land cover(FROM-GLC)land cover types Moderate Resolution Imaging Spectroradiometer(MODIS)