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Technique system of remote sensing product generation and validation of GF common products

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

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

  • Email：liuqh@aircas.ac.cn
  • Introduction：柳钦火，研究方向为定量遥感。E-mail： liuqh@aircas.ac.cn
LIU Qinhuo112,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

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

WEN Jianguang112,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

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

ZHOU Xiang112,  
  • Affiliation：

    Earth Observation System and Data Center, China National Space Administration, Beijing 100101, China

ZHAO Jian2,  
  • Affiliation：

    Research Institute of Forest Resource Information Techniques, Chinese Academy of Foresty, Beijing 100091, China

LI Zengyuan3,  
  • Affiliation：

    National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China

LI Xin4,  
  • Affiliation：

    Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China

MA Mingguo5,  
  • Affiliation：

    Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

WANG Weizhen6,  
  • Affiliation：

    Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

LIAO Xiaohan7,  
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

LIU Shaoming8,  
  • Affiliation：

    Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China

FAN Wenjie9,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

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

XIAO Qing112,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

ZHONG Bo1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

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

LI Jing112,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

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

XIN Xiaozhou112,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

LI Li1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

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

JIA Li112,  
  • Affiliation：

    Research Institute of Forest Resource Information Techniques, Chinese Academy of Foresty, Beijing 100091, China

GAO Zhihai3,  
  • Affiliation：

    Geovis Technology Company Limited, Beijing 101399, China

JIN Jiadong10,  
  • Affiliation：

    Piesat Information Technology Company Limited, Beijing 100195, China

LIANG Shi11,  
  • Affiliation：

    Earth Observation System and Data Center, China National Space Administration, Beijing 100101, China

XIN Jin2,  
  • Affiliation：

    Earth Observation System and Data Center, China National Space Administration, Beijing 100101, China

LIAO Chujiang2,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

WU Yirong1

реферат

GF-1—GF-7 satellite series with 19 major payloads has been launched with the continuous implementation of the high-resolution Earth Observation System (referred to as GF) in the past decade. This progress is vital in forming the multispectral and multimode observation capability of China’s Earth Observation System. Remote sensing data with high spatial, temporal, and spectral resolution have been obtained and widely used in scientific research and remote sensing applications. However, obtaining high-quality remote sensing information products from the original satellite data is a complicated scientific issue and faces huge challenges. Hence, the conversion chain from GF data to information must be urgently set up to reduce the remote sensing application threshold and improve the effectiveness of application services.The errors of remote sensing quantitative products are determined by accumulating a series of errors, such as sensor imaging error, calibration error, remote sensing data processing error, and quantitative inversion error. Thus, improving the accuracy of quantitative remote sensing products is a complex system engineering. Completing the whole process, including data processing, retrieval algorithm development, product generation, and validation independently, is challenging. Remote sensing algorithm test and product validation are the two crucial ways for the quality improvement of remote sensing products. Hence, this study proposes the technique system of GF common product generation and validation to improve the quality of GF remote sensing products further, thereby guaranteeing the improvement of the application quality and the extensive application area of GF remote sensing products. Lastly, the current progress of the GF common product validation and algorithm determination system platform is introduced and discussed.GF common products are required by more than two thematic remote sensing products. They can be validated using in situ observations. According to the GF common product system, the number of 39 + 6 products in seven categories are sorted out for the common requirements of multiple users, including geometric products, basic radiation products, land cover and land type products, energy balance products, vegetation products, water products, and atmosphere products. This study presents the technique flowchart of GF common product algorithm determination and product generation. The key technologies of algorithm testing, algorithm optimization, product generation, and validation are developed. Eleven national standards for remote sensing product validation are issued and implemented. Other group standards, such as GF common product generation, ground in situ observation, and validation of GF common remote sensing products, are being designed and compiled. Based on these validation technologies and the in situ data from the national network of GF remote sensing product validation field sites, the GF common product validation platform and product algorithm determination system platform can ensure the high quality of GF common products.Building such a technical system for GF common product generation and validation has great relevance for ensuring high accuracy and high quality to improve the efficiency of application services further. It requires the cooperation of multiple researchers from different units to research and develop common product retrieval algorithms. Moreover, the algorithm should be continuously tested to improve the accuracy of common products.

ключеви́че слова́

GF satellite;remote sensing retrieval;common product;algorithm test;ground truth;product validation

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