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Retrieval of land surface parameters from geostationary satellite data： An overview of recent developments

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：fanghl@lreis.ac.cn
  • Introduction：方红亮，1971年生, 男, 研究员， 研究方向为遥感建模、参数反演与不确定性研究。E-mail:fanghl@lreis.ac.cn
FANG Hongliang12

реферат

Since the beginning of this century, more and more attention has been given to using geostationary meteorological satellite data in the retrieval of land surface parameters. This paper gives an overview of the recent developments on the retrieval of land surface parameters from geostationary satellite data. Geostationary meteorological satellites have been developed in Europe (Meteosat), United Sates (GOES-R), Japan (Himawari), and China (FY series). The geostationary satellite data volume is usually high because of the high temporal revisit frequency, which poses great challenges to data storage, parameter retrieval and product distribution. Nonetheless, each satellite program has developed a series of land surface products to support near real-time applications.Various methods to estimate land surface parameters are described. The Meteosat SEVIRI is leading the development of land surface products, especially the unique long-term Thematic Climate Data Record (TCDR) products. The standard products include Land Surface Temperature (LST), longwave and shortwave radiances, albedo, fraction vegetation cover, leaf area index, the fraction of absorbed photosynthetic active radiation, and gross primary production, evapotranspiration, latent and sensible heat flux, wild fire, and snow cover. Among the standard products, the LST, radiance, and albedo are also distributed in the TCDR products. GOES-R and FY-4A are also releasing similar preliminary products, but their quality still need to be fully validated. The Himawai-8 products are still in the research stage. Further physical retrieval methods and various machine learning inversion methods can be explored to improve the accuracy of parameter inversion. It is also necessary to improve the quality of auxiliary atmospheric field data and model simulations to facilitate the land surface parameter retrieval.Multiple geostationary satellites can be integrated to provide long-term observations and global coverage. EUMETSAT has integrated all TCDR products generated from the first-and second-generation Meteosat data, and the National Oceanic and Atmospheric Administration (NOAA) has released all GOES level-1B data since 1979. These two dataset can be used to generate long-term continuous land surface products. GOES-R and Himawari-8 data have also been combined to generate high quality top-of-atmosphere reflectance and bright temperature products, which will greatly facilitate the generation of other land surface parameters. Geostationary satellite data can also be combined with the polar orbiting satellite data in land surface parameter retrieval; however, current studies are mostly carried out on small regional scales, whereas national and global studies are relatively deficient.Land surface parameters retrieved from geostationary satellites can be validated through comparison with concurrent ground measurements, polar orbiting satellite products and model simulations. However, simultaneous real-time surface data are lacking. On the other hand, current geostationary meteorological satellite data are in the kilometric spatial resolution and are limited for high resolution applications. With its high temporal and spatial resolutions (50 m), the GF-4 satellite launched by China provides a great potential for high resolution land surface monitoring and is worthy of further exploration.Future researches using geostationary satellite data to estimate land surface parameters include: (1) exploration of new techniques to improve the efficiency and accuracy of geostationary satellite data acquisition and processing; (2) integration of multiple geostationary and polar-orbiting satellite data to produce long term global land surface parameters; (3) exploration of automatic field measurement methods to enhance the validation of land surface parameters derived from geostationary satellite data.

ключеви́че слова́

geostationary meteorological satellite;land surface parameters;Climate Data Record (CDR);MSG SEVIRI;GOES-R ABI;Himawai-8 AHI;FY-4A AGRI

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