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PloS one
2015;10 (9): e0137545.

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE).

Xin Tian, Zengyuan Li, Erxue Chen, Qinhuo Liu, Guangjian Yan, Jindi Wang, Zheng Niu, Shaojie Zhao, Xin Li, Yong Pang, Zhongbo Su, Christiaan van der Tol, Qingwang Liu, Chaoyang Wu, Qing Xiao, Le Yang, Xihan Mu, Yanchen Bo, Yonghua Qu, Hongmin Zhou, Shuai Gao, Linna Chai, Huaguo Huang, Wenjie Fan, Shihua Li, Junhua Bai, Lingmei Jiang, Ji Zhou
Author Information
  1. Xin Tian: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China; Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands.
  2. Zengyuan Li: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China.
  3. Erxue Chen: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China.
  4. Qinhuo Liu: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  5. Guangjian Yan: State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, P.R. China.
  6. Jindi Wang: State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, P.R. China.
  7. Zheng Niu: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  8. Shaojie Zhao: State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, P.R. China.
  9. Xin Li: Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, P.R. China.
  10. Yong Pang: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China.
  11. Zhongbo Su: Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands.
  12. Christiaan van der Tol: Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands.
  13. Qingwang Liu: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China.
  14. Chaoyang Wu: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  15. Qing Xiao: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  16. Le Yang: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  17. Xihan Mu: State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, P.R. China.
  18. Yanchen Bo: State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, P.R. China.
  19. Yonghua Qu: State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, P.R. China.
  20. Hongmin Zhou: State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, P.R. China.
  21. Shuai Gao: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  22. Linna Chai: State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, P.R. China.
  23. Huaguo Huang: Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing, P.R. China.
  24. Wenjie Fan: Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, P.R.China.
  25. Shihua Li: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, P.R.China.
  26. Junhua Bai: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  27. Lingmei Jiang: State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, P.R. China.
  28. Ji Zhou: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, P.R.China.
PMID: 26332035 DOI: 10.1371/journal.pone.0137545

Abstract

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques.

References

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MeSH Term

Conservation of Natural Resources
Remote Sensing Technology
Telemetry
Journal Article Research Support, Non-U.S. Gov't

Word Cloud

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