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The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE).
Tian, Xin; Li, Zengyuan; Chen, Erxue; Liu, Qinhuo; Yan, Guangjian; Wang, Jindi; Niu, Zheng; Zhao, Shaojie; Li, Xin; Pang, Yong; Su, Zhongbo; van der Tol, Christiaan; Liu, Qingwang; Wu, Chaoyang; Xiao, Qing; Yang, Le; Mu, Xihan; Bo, Yanchen; Qu, Yonghua; Zhou, Hongmin; Gao, Shuai; Chai, Linna; Huang, Huaguo; Fan, Wenjie; Li, Shihua; Bai, Junhua; Jiang, Lingmei; Zhou, Ji.
Afiliação
  • Tian X; 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.
  • Li Z; Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China.
  • Chen E; Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China.
  • Liu Q; The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  • Yan G; 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.
  • Wang J; 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.
  • Niu Z; The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  • Zhao S; 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.
  • Li X; Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, P.R. China.
  • Pang Y; Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China.
  • Su Z; Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands.
  • van der Tol C; Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands.
  • Liu Q; Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China.
  • Wu C; The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  • Xiao Q; The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  • Yang L; The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  • Mu X; 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.
  • Bo Y; 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.
  • Qu Y; 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.
  • Zhou H; 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.
  • Gao S; The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  • Chai L; 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.
  • Huang H; Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing, P.R. China.
  • Fan W; Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, P.R.China.
  • Li S; School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, P.R.China.
  • Bai J; The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
  • Jiang L; 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.
  • Zhou J; School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, P.R.China.
PLoS One ; 10(9): e0137545, 2015.
Article em En | MEDLINE | ID: mdl-26332035
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Telemetria / Conservação dos Recursos Naturais / Tecnologia de Sensoriamento Remoto Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2015 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Telemetria / Conservação dos Recursos Naturais / Tecnologia de Sensoriamento Remoto Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2015 Tipo de documento: Article