Advances in Ecological Applications of Fengyun Satellite Data

基于风云气象卫星数据的生态应用研究进展

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  • In recent years, the remote sensing based on meteorological satellite observations has become an important tool for assessing global ecological conditions. Since the early 2000, Fengyun (FY) satellite data have been widely used to derive the key parameters of ecological environment in China. An 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 on. Considerable 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 settlement. In 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.
    卫星遥感应用已成为监测和评估全球生态状况的重要工具,极大地丰富了生态监测的理论和方法。风云气象卫星已广泛应用于生态环境关键参数反演。基于风云气象卫星建立的地球综合观测系统已用于实时的生态关键参数反演以及植被指数、陆表温度、净初级生产力和植被健康指数等长序列数据集的建设。同时,基于风云气象卫星资料,一些应用服务如空气污染防治、生态红线管控、 “一带一路”生态监测和人居环境监测等亦取得了重要进展。为了实现实时和动态全覆盖的生态关键参数监测,需进一步加强生态遥感应用技术研究,提供强有力的生态气象保障服务。
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  • Fig. 1.  Global distribution of the 10-day composite normalized difference vegetation index (NDVI; color shading) derived from the FY-3C Visible and Infrared Radiometer (VIRR) data on 10 July 2017.

    Fig. 2.  Distribution of the national eco-environmental condition in 2019; the higher (lower) the index (denoted by color shading, with color bar on the lower left of the figure), the better (worse) the ecological environment condition.

    Fig. 3.  Sensitivity assessment of rocky desertification in Guangxi, China in 2019.

    Fig. 4.  Spatial distributions of 0–5-cm averaged soil volumetric moisture content over Angola and Namibia, obtained from the Microwave Radiation Imager (MWRI) onboard FY-3D for (a) January, (b) February, (c) March, and (d) April 2021.

    Fig. 5.  Distribution of forest oxygen release over China in 2019.

    Fig. 6.  Monitoring of the urban heat island over the Pearl River Delta (PRD) based on the FY-3D Medium Resolution Spectral Imager (MERSI) data in July 2020.

    Table 1.  Information on the Fengyun (FY) series of meteorological satellites (the names of those satellites currently in orbit are italicized)

    SatelliteLaunch timeTypeDesign lifeStatus
    FY-1A1988-09-07Experiment2 yr39 days
    FY-1B1990-09-03Experiment2 yr158 days
    FY-1C1999-05-10Operation2 yr6.5 yr
    FY-1D2002-05-15Operation2 yr10 yr
    FY-3A2008-05-27Experiment3 yr10 yr
    FY-3B2010-11-05Experiment3 yr10 yr
    FY-3C2013-09-23Operation5 yrIn orbit service
    FY-3D2017-11-15Operation5 yrIn orbit service
    FY-3E2021-07-15Operation8 yrIn orbit test
    FY-2A1997-06-10Experiment2 yr10 months
    FY-2B2000-06-25Experiment2 yr8 months
    FY-2C2004-10-19Operation3 yr8.5 yr
    FY-2D2006-12-08Operation3 yr10 yr
    FY-2E2008-12-23Operation3 yr10 yr
    FY-2F2012-01-13Operation4 yrIn orbit service
    FY-2G2014-12-31Operation4 yrIn orbit service
    FY-2H2018-06-05Operation4 yrIn orbit service
    FY-4A2016-12-11Experiment7 yrIn orbit service
    FY-4B2021-06-03Operation7 yrIn orbit test
    Download: Download as CSV

    Table 2.  List of the remote sensing products from FY meteorological satellites

    Product typePolar-orbiting satelliteGeostationary satellite
    Cloud and
    radiation
    Albedo, cloud mask, cloud amount, cloud phase, cloud type, cloud top temperature, cloud top height, cloud optical depth, cloud effective radius, outgoing longwave radiation, surface upward longwave radiationCloud optical depth, cloud phase, cloud top height, cloud top pressure, cloud top temperature, cloud type, downward longwave radiation, surface outgoing longwave radiation, surface reflected shortwave radiation, top of atmosphere reflectance, surface solar irradiance
    AtmosphereAerosol optical depth, fog detection, precipitation, perceptible water, dust storm index, atmospheric total cloud liquid water, microwave rainfall rate, rain detection, vertical temperature profile, vertical humidity profile, total ozone, vertical ozone profile, ultraviolet aerosol index, atmospheric density profileAerosol optical depth, atmospheric correction image, atmospheric motion wind vector, convective initiation, fog detection, dust detection, total column precipitable water, vertical moisture profile, vertical temperature profile, lightning detection, liquid water profile, rainfall rate, tropopause folding, lightning density, lightning frequency
    Land and
    ecology
    Fire detection, land cover, albedo, normalized difference vegetation index, land surface temperature, leaf area index, evapotranspiration, fraction of photosynthetically active radiation, net primary productivity, soil moisture, drought indexFire/hot spot, land surface temperature, land surface emissivity, dust storm index, fog detection, dust detection, rainfall rate, evapotranspiration, aerosol optical depth over land surface
    CryosphereSea ice, snow cover, snow depth, snow water equivalent
    OceanSea surface temperature, sea surface wind speed, ocean color, water-leaving radianceSea surface temperature, aerosol optical depth over ocean
    Space weatherEnergetic electrons, surface potential, radiation dose rate, solar X-ray image, solar extreme ultraviolet image, geomagnetic field, Global Navigation Satellite System Radio Occultation Sounder (GNOS) electron density profile, critical frequency of F2 layer (foF2), ionospheric total electron content (TEC), peak height of F2 layer, electron differential directional flux, proton differential directional flux, airglowEnergetic electrons, energetic proton
    Download: Download as CSV

    Table 3.  Countries served by FY meteorological satellites by April 2021

    ServiceCountry
    Use of data service networkGlobal users; 117 countries in total
    Direct-receiving of FY-2 dataMongolia, North Korea, Nepal, Thailand, French Reunion, Australia, Mozambique,
    Kyrgyzstan, Oman (9 countries)
    Direct-receiving of FY-3 dataZimbabwe, Namibia, Iran (3 countries)
    Use of FY-3 satellite software packageThe United States, Germany, Russia, the United Kingdom, Australia, Indonesia, South Korea, Brazil, Thailand, Norway, Oman, Finland, Canada, Malaysia, Bolivia, Poland, the Netherlands, Mongolia, Greece, United Arab Emirates, Belarus, Japan, Niger, Sweden, Vietnam, France, Ukraine, Spain, the Philippines (29 countries, 55 users)
    Use of CMACast (a system that uses satellite
    Digital Video Broadcast technology to transmit meteorological data)
    Bangladesh, Indonesia, the Maldives, Nepal, Mongolia, Malaysia, Pakistan, Thailand, the Philippines, Uzbekistan, Tajikistan, Kyrgyzstan, Laos, Sri Lanka, North Korea, Vietnam, Myanmar, Iran, Kazakhstan (19 countries)
    Use of emergency support mechanism of FY satellites (FY-ESM)Laos, Myanmar, Iran, the Maldives, Thailand, the Philippines, Algeria, Malaysia, Uzbekistan, Tunisia, Mongolia, Nepal, New Zealand, Oman, Mozambique, Kyrgyzstan, Lesotho, Nigeria, Ethiopia, Guinea, Benin, Mauritius, Ghana, Portugal, Malawi, Armenia, Sri Lanka, the Solomon Islands, Vanuatu (29 countries)
    Download: Download as CSV
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Advances in Ecological Applications of Fengyun Satellite Data

    Corresponding author: Jun YANG, junyang@cma.gov.cn
  • 1. National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081
  • 2. Jiangsu Climate Center, Jiangsu Provincial Meteorological Bureau, Nanjing 210008
Funds: Supported by the National Key Research and Development Program of China (2018YFC1506500)

Abstract: In recent years, the remote sensing based on meteorological satellite observations has become an important tool for assessing global ecological conditions. Since the early 2000, Fengyun (FY) satellite data have been widely used to derive the key parameters of ecological environment in China. An 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 on. Considerable 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 settlement. In 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.

基于风云气象卫星数据的生态应用研究进展

卫星遥感应用已成为监测和评估全球生态状况的重要工具,极大地丰富了生态监测的理论和方法。风云气象卫星已广泛应用于生态环境关键参数反演。基于风云气象卫星建立的地球综合观测系统已用于实时的生态关键参数反演以及植被指数、陆表温度、净初级生产力和植被健康指数等长序列数据集的建设。同时,基于风云气象卫星资料,一些应用服务如空气污染防治、生态红线管控、 “一带一路”生态监测和人居环境监测等亦取得了重要进展。为了实现实时和动态全覆盖的生态关键参数监测,需进一步加强生态遥感应用技术研究,提供强有力的生态气象保障服务。
    • Ecological environment is related to the sustainable development of economy and society and therefore it is highly concerned by the public (Craw et al., 2016; Siepielski et al., 2017). At present, satellites provide observations at high spatiotemporal resolution and wide spectral coverage (Gong, 2012), making it possible to monitor the global ecological environment change in real time. As the most effective technical method of obtaining the changes in macro-ecological environment, satellite remote sensing has outstanding and irreplaceable advantages in rapid, quantitative, global, and periodic monitoring of the earth (Li et al., 2002; Xu et al., 2016).

      After more than 50 years of rapid development, the satellite remote sensing technology in China has progressed greatly in the field of ecological environment monitoring. Earth observing satellites covering meteorology, environment, resource, and marine, and Gaofen (GF) series, as well as associated application systems, have been established (Li, 2001; Shi and Lei, 2016; Meng et al., 2017). The role of satellite remote sensing technology in the field of ecological environment monitoring has become increasingly prominent. As the data sources increase, the remote sensing monitoring of ecological environment has turned from single element to multiple elements and gradually expanded from regional to global scales (Brown et al., 2017; Kim et al., 2018; Yue et al., 2018).

      Since the mid-1980s, the remote sensing monitoring of ecological environment in meteorological departments of China has developed rapidly, and has made great progress. Meteorological satellite remote sensing has been widely used in monitoring and assessing the atmospheric environment, terrestrial ecology, water environment, and marine ecological environment, which provides an effective technical support for air pollution prevention and control, water environment treatment, ecological conservation redline demarcation and control, etc.

      On the whole, with the increasing data resources for ecological remote sensing in meteorological departments of China, significant progress has been made in theoreti-cal research, technical breakthrough, system development, and application service in ecological remote sensing. With the implementation of national programs such as the Belt and Road Initiative, an all-factor and whole-process international service pattern of ecological remote sensing from the perspective of globalization is forming. However, some gaps and problems remain. There is still a mismatch between the acquisition ability of massive remote sensing data and the ability of data processing and application. The research on frontier issues in remote sensing science and technology is relatively weak, and the capability of global comprehensive observation and application should be improved. It is imperative to further enhance the research on monitoring and quantitatively assessing the ecological environment change by use of satellite remote sensing technology.

      This paper reviews the progress in ecological applications of Fengyun (FY) meteorological satellite data such as key ecological parameters inversion, long-term datasets construction, and technology development for ecological monitoring and evaluation. We introduce how meteorological departments in China use FY satellite data to serve the national ecological civilization construction and major development strategies. We also discuss the future development of FY meteorological satellite ecological remote sensing service. The purpose of this paper is to showcase applications of advanced meteorological science and technology to the ecological civilization construction in China.

    2.   Development of the FY series of meteorological satellites
    • The first polar-orbiting meteorological satellite FY-1A was launched in 1988. Since then, China has developed 4 series of FY meteorological satellites and successfully launched 19 such satellites, including 9 polar-orbiting and 10 geostationary (Table 1). At present, totally eight FY meteorological satellites operate in orbit, including three FY-2, three FY-3, and two FY-4, which have formed an observation network with both polar-orbiting and geostationary meteorological satellites. FY-2 is the first-generation geostationary meteorological satellite; FY-2H, FY-2G, and FY-2F are operating at 79°, 99.5°, and 112°E above the equator, respectively. FY-4 is the second-generation geostationary meteorological satellite; FY-4A has advanced earth observation capabilities and provides better temporal resolution than the FY-2 series. FY-3 is the second-generation polar-orbiting meteorological satellite. Compared with the first-generation polar-orbiting satellite, FY-3 series has more than 10 instruments and provides higher resolution data with more spectral coverage from the ultraviolet, visible, infrared, to microwave bands (Xian et al., 2021).

      SatelliteLaunch timeTypeDesign lifeStatus
      FY-1A1988-09-07Experiment2 yr39 days
      FY-1B1990-09-03Experiment2 yr158 days
      FY-1C1999-05-10Operation2 yr6.5 yr
      FY-1D2002-05-15Operation2 yr10 yr
      FY-3A2008-05-27Experiment3 yr10 yr
      FY-3B2010-11-05Experiment3 yr10 yr
      FY-3C2013-09-23Operation5 yrIn orbit service
      FY-3D2017-11-15Operation5 yrIn orbit service
      FY-3E2021-07-15Operation8 yrIn orbit test
      FY-2A1997-06-10Experiment2 yr10 months
      FY-2B2000-06-25Experiment2 yr8 months
      FY-2C2004-10-19Operation3 yr8.5 yr
      FY-2D2006-12-08Operation3 yr10 yr
      FY-2E2008-12-23Operation3 yr10 yr
      FY-2F2012-01-13Operation4 yrIn orbit service
      FY-2G2014-12-31Operation4 yrIn orbit service
      FY-2H2018-06-05Operation4 yrIn orbit service
      FY-4A2016-12-11Experiment7 yrIn orbit service
      FY-4B2021-06-03Operation7 yrIn orbit test

      Table 1.  Information on the Fengyun (FY) series of meteorological satellites (the names of those satellites currently in orbit are italicized)

      The capability of earth system monitoring was greatly enhanced after the second-generation polar-orbiting FY-3 satellites and the geostationary FY-4 satellites were launched. Meanwhile, the quality of the products generated from FY-3 and FY-4 is comparable to that of the Moderate Resolution Imaging Spectroradiometer (MO-DIS) products. At present, more than 100 products are derived from FY polar-orbiting and geostationary meteorological satellite data and released to the public (Table 2). The FY meteorological satellites have contributed to earth science and sustainability studies through an open data policy and stable product quality. FY satellite data have been utilized extensively in weather forecasting, climate prediction, climate change study; monitoring of environmental disaster, agriculture, and ecological environment, “Belt and Road” service, and so on.

      Product typePolar-orbiting satelliteGeostationary satellite
      Cloud and
      radiation
      Albedo, cloud mask, cloud amount, cloud phase, cloud type, cloud top temperature, cloud top height, cloud optical depth, cloud effective radius, outgoing longwave radiation, surface upward longwave radiationCloud optical depth, cloud phase, cloud top height, cloud top pressure, cloud top temperature, cloud type, downward longwave radiation, surface outgoing longwave radiation, surface reflected shortwave radiation, top of atmosphere reflectance, surface solar irradiance
      AtmosphereAerosol optical depth, fog detection, precipitation, perceptible water, dust storm index, atmospheric total cloud liquid water, microwave rainfall rate, rain detection, vertical temperature profile, vertical humidity profile, total ozone, vertical ozone profile, ultraviolet aerosol index, atmospheric density profileAerosol optical depth, atmospheric correction image, atmospheric motion wind vector, convective initiation, fog detection, dust detection, total column precipitable water, vertical moisture profile, vertical temperature profile, lightning detection, liquid water profile, rainfall rate, tropopause folding, lightning density, lightning frequency
      Land and
      ecology
      Fire detection, land cover, albedo, normalized difference vegetation index, land surface temperature, leaf area index, evapotranspiration, fraction of photosynthetically active radiation, net primary productivity, soil moisture, drought indexFire/hot spot, land surface temperature, land surface emissivity, dust storm index, fog detection, dust detection, rainfall rate, evapotranspiration, aerosol optical depth over land surface
      CryosphereSea ice, snow cover, snow depth, snow water equivalent
      OceanSea surface temperature, sea surface wind speed, ocean color, water-leaving radianceSea surface temperature, aerosol optical depth over ocean
      Space weatherEnergetic electrons, surface potential, radiation dose rate, solar X-ray image, solar extreme ultraviolet image, geomagnetic field, Global Navigation Satellite System Radio Occultation Sounder (GNOS) electron density profile, critical frequency of F2 layer (foF2), ionospheric total electron content (TEC), peak height of F2 layer, electron differential directional flux, proton differential directional flux, airglowEnergetic electrons, energetic proton

      Table 2.  List of the remote sensing products from FY meteorological satellites

      Since the beginning of the twentieth century, China has been using the FY satellite data to monitor the ecological environment. With the observation ability of FY meteorological satellites being improved significantly, application of the FY satellite data to ecological environment monitoring has undergone comprehensive and ra-pid development. A number of scientific algorithms for deriving the key parameters of ecological environment using the FY satellite data have been developed, and an integrated earth-observation system has been established, which is able to monitor the ecological environment in real time. The long time series datasets of ecological environment parameters based on the FY satellite observations have been developed and employed to evaluate the ecological environment change, the suitable place for human settlement, and the management and control of ecological conservation redline. Ecological environment monitoring systems at both national and provincial levels have been established, and the monitoring area has been extended to cover the whole globe, including the Belt and Road Initiative region. Prediction of ecological environment change based on the FY satellite data is also under development.

    3.   Remote sensing of key parameters for ecological monitoring
    • The ecosystem is composed of terrestrial and aquatic identities macroscopically, mainly including mountain, lake, forest, cropland, grass, city, ocean, and so on. The quality of a ecosystem can be comprehensively indicated in terms of the parameters in meteorology, air quality, hydrology, geology, soil, plant, animal and microorganism. Remote sensing technology and abundant data have become indispensable technical methods and data sources for regional ecosystem monitoring and assessment (Ouyang et al., 2015). With the development of remote sensing from qualitative to quantitative monitoring, more and more ecological parameters of vegetation, land surface, water environment, and atmospheric environment can be obtained by remote sensing algorithms. The remote sensing parameters of ecosystems are expanding gradually and are becoming important data sources for quantitative ecosystem monitoring and assessment.

    • Vegetation has functions of carbon fixation and oxygen release, climate regulation, water conservation, and wind-breaking and sand-fixing, and it plays an irreplaceable role in the maintenance of global ecological secu-rity and protection of the earth’s ecological environment. By using different data sources such as FY satellite multispectral data, hyperspectral data, microwave data, and lidar data, various remote sensing vegetation indices from different models have been established, which have greatly promoted the understanding of the earth’s biosphere on the macroscale (Ma et al., 2006; Brown et al., 2017). Based on various vegetation indices and combined with meteorological and biological observations, a series of estimation methods for vegetation coverage via statistical regression, mixed pixel, machine learning, and physical model have been developed and also widely used in practice (Liang et al., 2013; Du et al., 2014; Du et al., 2019). The contents of chlorophyll, nitrogen, and other biochemical substances in plant leaves can also be obtained by satellite remote sensing technology (Zhao et al., 2012; Chen et al., 2017; Zhang and Zhou, 2018). Vegetation net primary productivity (NPP) and biomass are the key components of the terrestrial carbon cycle and ecosystem process. The NPP values estimated by satellite remote sensing can better reflect the dynamic changes on the regional scale (Yuan et al., 2014; Zhang et al., 2019; Gao et al., 2020). Biomass estimation is usually carried out through analyzing the mathematical relationship between one or multiple vegetation indices, spectral characteristic parameters, and aboveground biomass of vegetation. On this basis, biomass inversion models via the vegetation index, spectral characteristic parameters, and regression methods are constructed (Yao et al., 2016; Zhao et al., 2016). There are some other applications of satellite remote sensing technology in the research of vegetation ecosystem, including the estimation of vegetation carbon storage (Pan and Wen, 2015; Zhou et al., 2016) and assessment of plant diversity.

      Land surface temperature (LST) and soil moisture are the key parameters affecting the earth–atmosphere energy exchange and heat exchange, and are widely used in the research of land surface energy balance, global climate change, and ecological environment monitoring. China’s FY-3 polar-orbiting meteorological satellite exhibits a high accuracy in retrieving both LST and sea surface temperature (SST) (Dong et al., 2012; Wang et al., 2014; Jiang et al., 2019). Based on the LST products of FY-2 geostationary meteorological satellite, the algorithms are developed for daily LST retrievals (Meng et al., 2019). Usually, the LSTs are retrieved from the thermal infrared data. Based on the infrared radiation transfer equation, a variety of inversion algorithms are developed for different sensors carried by different satellites (Meng et al., 2017; Wang et al., 2019).

      Similar to the LST, research on soil moisture monitoring based on satellite remote sensing was initiated rela-tively early. Various indices used in soil moisture monitoring, soil evapotranspiration models, and soil heat flux models (Shi et al., 2011; Liu et al., 2012; Parinussa et al., 2018; Wang et al., 2018; Wang C. M. et al., 2020), such as vegetation index, thermal inertia, crop water stress index, and water deficit index, were established based on visible–near-infrared, thermal infrared, microwave, and hyperspectral data, and have been widely put into operation. In recent years, with the development of artificial intelligence technology, artificial neural network, random forest, and other machine learning algorithms have gradually shown great potential in soil moisture estimation (Cui et al., 2016; Zhang et al., 2020). However, due to the complexity of the relationship among plant, soil, and water, all models have certain seasonal and regional limitations, and thus it is necessary to further improve the accuracy of soil moisture monitoring by remote sensing.

      The capabilities of monitoring the water body and water quality by remote sensing have been effectively improved due to the appearance of multisource and multiscale satellite data (Li et al., 2017). The remote sensing of water body and water environment parame-ters has made great progress. Associated application is not only limited to the monitoring of water surface area, water level, water storage, and their changes (Huang et al., 2019; Sun and Ma, 2020), but also extended to investigation of the interaction among the lakes/rivers, climate change, and ecosystem change based on analysis of the spatiotemporal variations of lakes and rivers (Gong et al., 2017). In terms of water quality, remote sensing algorithms are developed for monitoring water color, chlorophyll-a, suspended solids concentration, chromophoric dissolved organic matter, total phosphorus, total nitrogen, and other water quality parameters (Pan and Doerffer, 1996; Yin et al., 2005; Han et al., 2010). In order to effectively overcome the limitations in atmosphe-ric correction and sample data with some previous empi-rical and semi-empirical methods for remote sensing of ocean color, it is a trend to develop remote sensing water quality estimation model based on atmosphere–ocean coupled radiative transfer model and artificial intelligence machine learning algorithm (Zhang et al., 2016; Fan et al., 2020).

      Aerosols exert important impacts on global climate change and air quality (Li et al., 2019). Passive sensors onboard polar-orbiting or geostationary satellite platforms and spaceborne lidar can be used for the inversion of aerosol optical properties. At present, many satellites including FY-3 and FY-4 can provide global distribution of aerosol properties, the vertical distribution of aerosol optical properties (such as from the Cloud–Aerosol Li-dar with Orthogonal Polarization, i.e., CALIOP), and the aerosol absorption properties (such as from the Total Ozone Mapping Spectrometer and Ozone Monitoring Instrument, i.e., TOMS and OMI) (Wang et al., 2010; Tang et al., 2018). Atmospheric correction (AC) model based on atmosphere–ocean coupled radiative transfer model and machine learning algorithm can also obtain aerosol retrieval results (Fan et al., 2020). Contents of trace gases in the whole atmosphere including ozone, sulfur dioxide, nitrogen oxides, bromine oxide, and formaldehyde can also be obtained from satellite observations. So far, the analyses on numerous atmospheric trace gases are based on satellite data (Wang et al., 2010). At present, the load of China’s spaceborne atmospheric trace gas differential absorption spectrometer with wide wavelength range (240–710 nm) and large field of view (114°) can realize the 1-day global-coverage monitoring for various components of trace gases and aerosols mentioned above.

    • Ecological parameters obtained from satellite remote sensing are the basis of ecological monitoring and research, of which the key is the establishment of long-time series data of ecological parameters under unified data standards. According to the requirements by ecological civilization construction on ecological parameters, studies on inversion algorithms for satellite retrieval of ecological parameters have been carried out, and the inversion techniques of various parameters required for ecological applications have been developed and improved. So far, the long-time series datasets of historical ecological parameters derived from FY meteorological satellites have been established in various levels of meteorological departments in China.

      In the development of high-quality and long-term ecological parameters data, the main difficulty is to establish a scientific algorithm for the inversion based on the FY meteorological satellite data. At present, a scientific algorithm system of FY meteorological satellites has been established for retrieving ecological parameters, including high-accuracy cloud detection, atmospheric correction considering the influence of aerosols, various inversion methods of basic parameters required for improving the ecological applications, and multisource satellite data correction and coordination technology (Zhang et al., 2020). Cloud detection is the basis to most scientific algorithms for ecological parameter inversion with satellite data. A unified cloud mask algorithm for FY-3 meteorological satellites (UCM-FY3) have been developed. In this algorithm, cloud is detected at every pixel and the cloud mask at four levels are output for subsequent inversion (Han et al., 2020). In the atmospheric correction for optical data, an algorithm based on the back propagation (BP) neural network model has been developed (perso-nal communication). A unified linearized vector radiative transfer model (UNL-VRTM) was used to simulate the atmospheric radiation transmission process during the atmospheric correction, making the correction accuracy surpass that of the traditional atmospheric correction methods (Yang et al., 2020). A competition of scientific retrieval algorithms for FY meteorological satellites was held; the wisdom of all parties was gathered to provide scientific and technological support to meet the growing needs of ecological applications. Based on the long-time series of normalized difference vegetation index (NDVI) and LST products from NOAA/Advanced Very High Resolution Radiometer (NOAA/AVHRR, since 1988) and Earth Observing System/Moderate Resolution Imaging Spectroradiometer (EOS/MODIS, since 2000), which are widely used in the global and regional ecological monitoring of vegetation change, investigations on multisource satellite data correction and coordination technology of FY-3 Visible and Infrared Radiometer (VIRR), Medium Resolution Spectral Imager (MERSI) NDVI (Fig. 1), and LST products have been conducted (Han et al., 2020), and a convergence approach for multisource satellite products based on machine learning has been adopted. In addition, for ecological parameters inverted by the above-mentioned retrieval algorithms based on FY meteorological satellites, the validation of the relevant data has been performed to guarantee the accuracy of these parameters.

      Figure 1.  Global distribution of the 10-day composite normalized difference vegetation index (NDVI; color shading) derived from the FY-3C Visible and Infrared Radiometer (VIRR) data on 10 July 2017.

      In recent decades, meteorological departments of China have initially buit up long-time series datasets of ecological parameters, including fire and hot spots, water body coverages, cyanobacteria water bloom, vegetation index, LST, soil moisture, evapotranspiration, snow cover, aerosol optical depth (AOD), and so on. Because the long-time series datasets of some ecological parame-ters had been accumulated through daily operation, a unified and standardized inversion algorithm and data format were needed. The low accuracy in the datasets of ecological parameters cannot satisfy the rapid development of ecological meteorological service. In view of this problem, the National Satellite Meteorological Center (NSMC) of China Meteorological Administration (CMA) carried out investigations on exploring new satellite data to meet the specific needs of ecological application. Based on the established scientific algorithm system for retrieval of basic ecological datasets from FY meteorological satellites, remote sensing based ecological products have been continuously developed. In recent years, the basic data of national ecological monitoring from 2010 to 2019 have been gradually established, including NDVI and enhanced vegetation index (EVI), leaf area index (LAI), NPP, LST, soil moisture, snow cover, remote sensing evaluation index (RSEI), and vegetation health index (VHI) (Fan et al., 2020; Han et al., 2020; Wang L. et al., 2020; Zhang et al., 2020). Moreover, waterbody ecological datasets such as the water area of key lakes, cyanobacteria water bloom, and chlorophyll-a are also obtained. The current standardized and high-accuracy ecological datasets are critical to the ecological meteorological service.

    • Since the initiation of the CMA operational ecological remote sensing in 2018, a space–earth integrated observation system has been laid out, the ground-based ecological meteorological observation network has been further developed, and a number of characteristic ecologi-cal remote sensing application centers at national and pro-vincial levels have been further set up. Moreover, ecological consultation services at national and provincial ecological remote sensing centers under the associated meteorological departments have been provided, thus establishing a systematic service network to monitor and assess ecological environment based on mainly FY satellite data. The ecological environment evaluation indices have been discussed by researchers from different angles, and the key technologies in monitoring, evaluation, and regulation of ecological environment based on satellite remote sensing have been analyzed (Wang et al., 2003; Li et al., 2004). Besides, a series of corresponding ecological quality evaluation index systems have also been constructed for different ecological function areas (Yu et al., 2020; Chen et al., 2021; Zhou et al., 2021).

      At present, based on domestic FY meteorological satellites, a complete operational system is running, which generates more than 100 global remote sensing products describing the atmosphere, cloud and radiation, land and ecology, cryosphere, ocean, and space weather (Table 2), thus realizing the capability to monitor land surface, atmosphere, and marine environments on the global scale. In terms of atmospheric environment monitoring, the system can monitor the haze, dust, atmosphe-ric particulates, ozone, nitrogen dioxide, and methane. For the terrestrial ecology, operational monitoring of vegetation index, crop growth, LAI, and biomass by satellite remote sensing has been basically realized. As for water environment, the satellite remote sensing monitoring of chlorophyll-a, suspended solids concentration, transparency, and cyanobacteria water bloom has been achieved. Among them, the monitoring of cyanobacteria bloom in key lakes and reservoirs, water eutrophication, water quality of drinking water sources, and urban black and odorous water body has basically met the operatio-nal requirements. For the national development programs involving the ecological civilization construction, the ecological meteorological services for key ecological function areas, ecological fragile regions, ecological conservation redline, and the Belt and Road Initiative have been developed and made significant progress.

    4.   Advances in ecological applications of FY satellite data
    • Support of ecological civilization construction by meteorological service is one of the two main responsibilities of meteorological departments in China, who have unique advantages in accessing satellite remote sensing data resources, accumulating remote sensing monitoring data for meteorological elements, and providing observations of meteorological elements sensitive to ecological environment change. Besides, considerable work has been carried out on exploitation and utilization of climate resources, monitoring and early warning of meteorological disasters affecting the ecology, as well as protection, restoration, and utilization of the ecosystem. In order to meet the new requirements of ecological civilization construction in China, operational meteorological service in support of ecological civilization and ecologi-cal monitoring and assessment has been widely developed through applications of FY meteorological satellite data, in the form of ecological meteorological service, such as prevention of air pollution, management and control of ecological redline, facilitating of the Belt and Road Initiative, and evaluation of natural oxygen distribution in China. The ecological meteorological service has applied the wisdom of meteorological science and technology to the ecological civilization construction.

    • Air pollution is the most serious problem for many countries in the world. Although the emission of air pollutants in China has been declining rapidly in recent years, the total amount is still high (Zhou, 2017). In ear-lier years, satellite remote sensing was used by meteorological departments of China to monitor the greenhouse gases, trace gases, and aerosols in the atmospheric environment, and relevant results were obtained (Wang et al., 2010; Yan et al., 2016; Tang et al., 2018). In recent years, remote sensing monitoring of PM2.5 concentrations near ground has become an effective method for regional atmospheric environment monitoring. This is a new technology developed rapidly and also a hot topic for environmental remote sensing research in the world. The PM2.5 concentrations near ground are usually estimated through the AOD retrieved from satellite remote sensing. Based on the AOD products from FY and other satellites combined with meteorological factors such as boundary layer height, relative humidity, temperature, and wind speed, as well as the factors such as population density and land-use type, the spatiotemporal changes of PM2.5 concentrations can be comprehensively analyzed. On this basis, the remote sensing-based estimate model of PM2.5 concentrations is constructed (Jia et al., 2013; Tao et al., 2013; Ma, 2015; Chen et al., 2019). FY-3, the second-generation polar-orbiting meteorological satellites of China, has greatly enhanced our capability in monitoring the atmospheric environment quality in China, providing a new remote sensing data source for the large-scale monitoring of aerosols and particulates in the atmosphere. Some studies (e.g., Chen et al., 2018) have proved the ability of the FY-3B/MERSI satellite data in monitoring the aerosols and PM2.5 concentrations near ground.

      Meteorological service for the atmospheric environment has become a daily operation in Chinese meteorological departments, among which the satellite remote sensing technology provides strong technical support to the monitoring and control of air pollution. Based on the dynamic monitoring of the spatiotemporal distributions of national and regional AOD retrieved by FY satellite data, it is found that the AOD in China presents a declining trend in recent years (CMA, 2018). The retrieved AOD can be further used to monitor the spatial distribution and change process of atmospheric pollutants such as PM2.5 and PM10. Moreover, combined with meteorological observations and weather forecasts, the operational forecasting services for air quality and heavy pollution weather such as smog are carried out. Through FY satellites, the all-weather, continuous, and dynamic remote sensing monitoring of heavy pollution weather such as smog, sand, and dust has been realized (Xu, 2015; Qiu et al., 2018), and the monitoring of air pollution sources due to forest fire and straw burning has been basically realized in operation. In the future, the meteorological satellite observations, ground observations, and refined weather forecasts will be collectively utilized to improve the forecasting accuracy of heavy pollution weather such as smog. Analysis of the heavy pollution weather processes will be carried out to scientifically evaluate the contribution of meteorological conditions in the transition of air quality.

    • Demarcation and control of the ecological conservation redline (ECR) play a key role in the maintenance of national and regional ecological security and the sustainable development of China. ECR is a boundary imposed to prevent construction or other human activities within a specified area aimed at ecological protection of the area. ECR commits over one-fourth of China’s territory to varying degrees of protection for biodiversity, disaster mitigation and providing critical ecosystem services. This is an important measure to promote the ecological civilization construction and also an important innovation of the Chinese ecological environment protection policy (Yang et al., 2014). In China, ECR has a wide distribution with diverse topographic and geomorphic features as well as complex and changeable natural conditions. The demarcation, monitoring, and evaluation of ECR are based on the remote sensing data, including satellite remote sensing observations, aerial remote sensing observations, and ground observations (Wang et al., 2017; Han and Tang, 2018).

      Real-time monitoring of the types and distributions of the ecosystem such as the mountain, lake, forest, farmland, and grassland within the region of ECR, and the closely related meteorological elements, has been carried out through mainly using the FY satellite data and other remote sensing data. Based on multisource data from FY meteorological satellites, GF series satellites, and meteorological observations, the NSMC has established remote sensing based ecological indices such as vegetation index, vegetation coverage, land-use type, NPP, and the biodiversity index, considering the physical meaning and mathematical model of ecological service function. Through principal component analysis of the weight coefficient of each index, the indices for ecological functions of wind-breaking and sand-fixing, biodiversity maintenance, soil and water conservation, and water resource conservation, as well as the index of rocky desertification sensitivity, have been proposed and investigated, respectively (Zhou et al., 2021). On this basis, an comprehensive evaluation model for eco-environmental conditions has been constructed, realizing the quantitative evaluation of ecological environment (Figs. 2, 3).

      Figure 2.  Distribution of the national eco-environmental condition in 2019; the higher (lower) the index (denoted by color shading, with color bar on the lower left of the figure), the better (worse) the ecological environment condition.

      Figure 3.  Sensitivity assessment of rocky desertification in Guangxi, China in 2019.

      In view of the ecological function areas and ecologi-cal fragile areas within the range of ECR, the satellite remote sensing monitoring and meteorological service based on local demands are actively implemented in provincial meteorological departments, where special features and highlights are identified. For ecological fragile areas of rocky desertification in Guangxi Region of China, the ecological–meteorological integrated observation network is constructed and improved, and the collaborative monitoring as well as climatic assessment of rocky desertification based on satellite and surface meteorological observations is performed (Chen et al., 2021). In provinces in Northeast China such as Liaoning, for different types of wetlands such as lake, river, swamp, and artificial paddy fields, the observations from the polar-orbiting satellites including FY-3 are taken as the main data sources to establish the indicators for monitoring typical wetlands and an indicator model for evaluating the overall ecosystem environment in Northeast China (Yu et al., 2020). On this basis, dynamic monitoring and quantitative evaluation of wetlands in Northeast China by remote sensing are carried out, which provides scientific support to the protection, restoration, and reconstruction of wetlands as well as the improvement of the ecological environment in Northeast China. Nevertheless, the application of satellite remote sensing technology and meteorological service in the management and control of ECR still needs to be enhanced. From the aspects of ecosystem pattern, quality, and function, it is necessary to combine additional data sources and parame-ters such as environmental observations and ecosystem condition parameters, so as to provide basis to better management and decision-making associated with the practice of ECR.

    • The FY-3 polar-orbiting meteorological satellite series has a global observation ability and can provide informative, highly accurate, real-time, and dynamic monitoring. It is expected that more objective and accurate data can be obtained for monitoring and assessment of the ecological environment in the Belt and Road Initiative region. Multiscale and multisource satellite data are used to monitor the ecological environment of this region, such as the macro pattern of terrestrial ecosystem, condition of major vegetation ecosystem, terrestrial solar energy resources, terrestrial water budget, regional ecological constraints of main economic corridors, as well as the ecological environment associated with development of important node cities (Liu Y. H. et al., 2018). Through the land cover data retrieved by satellite remote sensing, combined with the social–economic data from the countries along the route, spatiotemporal dynamic variations of land cover along this route and related driving mechanism are analyzed (Fan and Li, 2019; Ge et al., 2019). Characteristics of regional climate, topography, soil, hydrology, vegetation cover, and terrestrial ecosystem are evaluated by an integrated application of remote sensing monitoring and statistical analysis (Wu et al., 2018).

      The FY meteorological satellite series in China, together with the NOAA series in the United States and the polar-orbiting meteorological satellite (MetOp) series in Europe, has become an important space infrastructure used to construct earth’s operational observation system. The FY satellites have been incorporated into the global operational meteorological satellite network by the World Meteorological Organization (WMO), becoming an important member of the global integrated observation system (Xian et al., 2021). In addition, FY meteorological satellite is a duty satellite of the International Charter Space and Major Disasters to support disaster response worldwide. The losses caused by disasters in related countries and regions along the Belt and Road Initiative are more than twice the global average, among which the largest part of losses are caused by meteorological disasters. Timely and efficient observation of extreme weather, climate, and environmental events in the global or regional range along the Belt and Road Initiative can be realized by using FY meteorological satellites. The countries using FY satellite data have increased to 117, including 83 countries along the Belt and Road Initiative (Table 3). In order to make the FY meteorological satellites better serve the related countries and regions along the Belt and Road Initiative in disaster prevention and mitigation, the CMA has adjusted the layout of meteorological satellites to realize the full coverage of geostationary satellites of FY series over the whole territory of China, related countries and regions along the Belt and Road Initiative, the Indian Ocean, and most African countries. In addition, in 2018, CMA established an emergency support mechanism for international users of FY meteorological satellites in disaster prevention and mitigation (FY-ESM). When natural disasters happen in the countries along the Belt and Road Initiative, intensified observations can be provided based on the needs for monitoring of the disasters. By 2020, there have been 29 registered countries for the emergency support mechanism. Since January 2018, the support service of disaster prevention and mitigation for international users of FY meteorological satellites has been activated nearly 20 times due to meteorological disasters, including typhoon, heavy rainfall, sandstorm, flood, wildfire, volcanic eruption, and severe drought (Fig. 4). In order to satisfy the need for ecological environment monitoring of the Belt and Road Initiative by remote sensing, it is necessary to focus on the key elements, regions, and types of ecological environment in future. The application of FY meteorological satellite remote sensing to ecological environment monitoring should be expanded. The monitoring, early warning, and research on the ecological environment and disasters prevention related to climate and vegetation, as well as the severe flood, wildfire, and snow within the region of the Belt and Road Initiative should be enhanced, so as to achieve dynamic monitoring of the ecological environment elements in these regions.

      ServiceCountry
      Use of data service networkGlobal users; 117 countries in total
      Direct-receiving of FY-2 dataMongolia, North Korea, Nepal, Thailand, French Reunion, Australia, Mozambique,
      Kyrgyzstan, Oman (9 countries)
      Direct-receiving of FY-3 dataZimbabwe, Namibia, Iran (3 countries)
      Use of FY-3 satellite software packageThe United States, Germany, Russia, the United Kingdom, Australia, Indonesia, South Korea, Brazil, Thailand, Norway, Oman, Finland, Canada, Malaysia, Bolivia, Poland, the Netherlands, Mongolia, Greece, United Arab Emirates, Belarus, Japan, Niger, Sweden, Vietnam, France, Ukraine, Spain, the Philippines (29 countries, 55 users)
      Use of CMACast (a system that uses satellite
      Digital Video Broadcast technology to transmit meteorological data)
      Bangladesh, Indonesia, the Maldives, Nepal, Mongolia, Malaysia, Pakistan, Thailand, the Philippines, Uzbekistan, Tajikistan, Kyrgyzstan, Laos, Sri Lanka, North Korea, Vietnam, Myanmar, Iran, Kazakhstan (19 countries)
      Use of emergency support mechanism of FY satellites (FY-ESM)Laos, Myanmar, Iran, the Maldives, Thailand, the Philippines, Algeria, Malaysia, Uzbekistan, Tunisia, Mongolia, Nepal, New Zealand, Oman, Mozambique, Kyrgyzstan, Lesotho, Nigeria, Ethiopia, Guinea, Benin, Mauritius, Ghana, Portugal, Malawi, Armenia, Sri Lanka, the Solomon Islands, Vanuatu (29 countries)

      Table 3.  Countries served by FY meteorological satellites by April 2021

      Figure 4.  Spatial distributions of 0–5-cm averaged soil volumetric moisture content over Angola and Namibia, obtained from the Microwave Radiation Imager (MWRI) onboard FY-3D for (a) January, (b) February, (c) March, and (d) April 2021.

    • Clean water, fresh air, secured food, and high-quality ecological products have become the expectation of people for a better life. More high-quality ecological products are needed to meet people’s ever-growing demands for a beautiful ecological environment. The gene-ral office of the State Council of China proposed that the integrated development of tourism with transportation, environmental protection, land, ocean, meteorology, and other industries be promoted, and the ecotourism area, natural oxygen zones, and meteorological parks be developed. It is necessary for meteorological departments of China to give a full play to their own advantages and actively use the satellite remote sensing technology to provide technical support to the establishment of natural oxygen zones (areas with high negative oxygen ion level, good air quality, superior climate and environment, and complete tourism facilities that are suitable for tourism, leisure, and health) and to the improvement of ecologi-cal environment for human settlement.

      To meet the needs of users, NSMC has developed new index products for evaluating the natural oxygen zones of China in addition to existing satellite remote sensing monitoring products. The new products are produced mainly based on the FY satellite data (Fig. 5), providing assistance to the establishment and evaluation of natural oxygen zones according to the relevant standards of China (CMSA, 2017). With the national ecological remote sensing products, satellite remote sensing-based indicators of 5 kinds covering 12 terms are studied and computed. They include the evaluation indices for the quantity of oxygen release (based on the NPP and the quantity of forest oxygen release), climate comfort level for human settlement (based on the satellite retrieved LST, relative humidity, intensity of urban heat island, and temperature–humidity index), atmospheric environment (based on the satellite retrieved air quality index, air quality index in periods suitable for tourism, proportion of the days with air quality at good and moderate levels, and proportion of the time range with air quality at good and moderate levels), percentage of forest cover, and regional water quality. In the evaluation of natural oxygen zones of China during 2017–2019, the above 5 kinds of indicators covering 12 terms provided support to a scientific, objective, and quantitative evaluation of the ecological environment of natural oxygen zones in China.

      Figure 5.  Distribution of forest oxygen release over China in 2019.

      The urban heat island effect has become one of the most serious problems in urban ecological protection, bringing about negative effects to the sustainable development of the city, the life quality of urban residents, and the improvement of human settlement. Based on FY and other sources of satellite data, real-time and dynamic monitoring of the intensity of urban heat island is conducted in meteorological departments of China, and the relationship between urban heat island and climate change is also analyzed (Liu Q. H. et al., 2018). In August 2019, the CMA launched the monitoring and evaluation services for national urban heat island using FY satellites, which is the first running operation in the integrated application system of national satellite remote sensing. The meteorological departments have been carrying out monitoring of the urban LST, and monitoring and evaluating of urban heat island intensity (Fig. 6). On this basis, an operational layout covering the country, province, city (prefecture), and county can be formed, to provide scientific basis for the improvement of ecologi-cal environment for human settlement, urban planning and construction, fine management, and so on. For a long time, satellite remote sensing mainly provides decision-making services for the government, but few for the public. To change this situation, Jiangsu Provincial Meteorological Bureau uses FY satellites and other satellites and ground observation data, constructs quantitative estimation model based on remote sensing technology and machine learning algorithm, and releases six public service products including the ecological heat, greenness, dryness, humidity, atmospheric turbidity, and water surface coverage, for use by the public. A platform for product making and releasing is developed, which realizes the operational running of public service for ecological remote sensing. The six remote sensing-based ecological products and derivative public service products make the ecological remote sensing a part of human life, and effectively expand the breadth and depth of meteorologi-cal support service for ecological civilization construction, thus attracting widespread attention in China.

      Figure 6.  Monitoring of the urban heat island over the Pearl River Delta (PRD) based on the FY-3D Medium Resolution Spectral Imager (MERSI) data in July 2020.

    5.   Future prospects
    • Building an ecological civilization has been given unprecedented attention by the Chinese government. The concept of the five-sphere integrated development and the international coalition with the Belt and Road Initiative as the core have put forward new requirements and directions for the development of satellite remote sensing technology. The meteorological satellites based remote sensing for monitoring and protection of ecological environment is facing both opportunities and challenges.

      To meet the stringent requirements for development of meteorological industry including the FY meteorological satellites, a development plan for 2021–2035 for China’s meteorological satellites and associated applications has been formulated. The new FY-3 polar-orbiting meteorological satellites to be developed and launched in the near future will form a global observation networking system including three polar-orbiting satellites and one slanting orbit precipitation measurement satellite, which will further enhance China’s global observation capability. The new FY-4 geostationary meteorological satellites to be launched in the future will be networked with FY-4A to form operational pattern of “dual satellite operation,” and further improve the global meteorological remote sensing operation system.

      With the development of satellite remote sensing technology, remote sensing for ecological environment is heading towards multisatellites, multisensors, multiplatforms, as well as high spatial, spectral, and temporal resolutions. Meanwhile, related operational application is also progressing towards dynamic and refined realization and globalization. In order to give a full and effective play of the scientific support by the satellite remote sensing technology to the meteorological service for ecological civilization construction, it is necessary to streng-then the remote sensing based meteorological service to ecological environment by using the FY satellite data more efficiently.

      In construction of the ecosystem monitoring system, it is necessary to build up a space–ground integrated monitoring network for ecological environment. Specifically, this monitoring network should be built with satellite remote sensing as the main part, airborne remote sensing as the supplementary part, and surface observation network for the verification. The unique advantages of satellite remote sensing, airborne remote sensing, and ground monitoring data should be fully utilized to realize the real-time, dynamic, and full-coverage monitoring of ecologi-cal environment elements. On this basis, the operational observation of the main ecosystem types and their representative elements could be realized, the long-time series datasets of ecological environment through FY satellites and multisource meteorological satellite data could be constructed, and an effective sharing mechanism of multisource data within the industry and among departments could be established.

      In application of the remote sensing technology to ecological investigations, the research and development of FY meteorological satellite positioning and calibration technology should be strengthened to further improve the quality of FY meteorological satellite data and ecological products. Verification of the eco-meteorological remote sensing products should be enhanced. In the national key ecological function areas, on-site field verification for all eco-meteorological remote sensing products should be built up to form an integrated verification system based on ground–aircraft–space observations. Studies on the high-accuracy correction of multisource satellite data and the assimilation and fusion technology of space–ground ecological meteorological monitoring data should be strengthened; the key meteorological indicators representing the quality level of main ecosystem types should then be determined. In addition, investigations on the reconstruction of ecological environment parameter time series should be conducted. Therefore, the technologies for refined and long-time prediction of ecological environment and risk early-warning could be further developed based on long-time series data of FY satellites and other multisource data. Moreover, quantitative evaluation of the meteorological service value for ecosystems and upper limit of capacity of ecological resources will be performed. The future application of the meteorological service for ecological environment will integrate the dynamic monitoring, assessment, and early warning as well as meteorological contribution analysis for ecological environment resources.

      Finally, it is necessary to promote the application of multisource remote sensing data, including FY satellites, GF series satellites, and other satellites, to improve the temporal and spatial resolutions in ecological civilization construction. Further, the ecological remote sensing monitoring and related meteorological service should be expanded to help determine the national ecological conservation redline, atmospheric environment, biodiversity, and the Belt and Road Initiative. Moreover, standards and specifications used in the meteorological support service need to be formulated. Such service platforms for national and provincial ecological civilization construction will be built up to strengthen the ecological monitoring using FY satellite data at provincial and even municipal levels according to the local climate and geographical features. On this basis, FY satellites will play a more important role in ecological environment monitoring, evaluation, and prediction; and the operational capability of the meteorological support service for ecological civilization construction could be comprehensively improved.

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