The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE).
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.
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.
Xin
X
Tian
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China.
Zengyuan
Z
Li
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China.
Erxue
E
Chen
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
Qinhuo
Q
Liu
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.
Guangjian
G
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.
Jindi
J
Wang
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
Zheng
Z
Niu
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.
Shaojie
S
Zhao
Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, P.R. China.
Xin
X
Li
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China.
Yong
Y
Pang
Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands.
Zhongbo
Z
Su
Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands.
Christiaan
C
van der Tol
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, P.R. China.
Qingwang
Q
Liu
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
Chaoyang
C
Wu
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
Qing
Q
Xiao
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
Le
L
Yang
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.
Xihan
X
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.
Yanchen
Y
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.
Yonghua
Y
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.
Hongmin
H
Zhou
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
Shuai
S
Gao
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.
Linna
L
Chai
Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing, P.R. China.
Huaguo
H
Huang
Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, P.R.China.
Wenjie
W
Fan
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, P.R.China.
Shihua
S
Li
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, P.R. China.
Junhua
J
Bai
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.
Lingmei
L
Jiang
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, P.R.China.
Ji
J
Zhou
D003247:Conservation of Natural Resources; D058998:Remote Sensing Technology; D013686:Telemetry