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Proceedings Paper

Sensor intercomparison of distributed surface radiation measurement system
Author(s): Baocheng Dou; Jianguang Wen; Xiuhong Li; Qiang Liu; Qing Xiao; Junhua Bai; Jingjing Peng; Xingwen Lin; Zhigang Zhang; Xiaodan Wu; Erli Cai; Jialin Zhang; Chongyan Chang
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Paper Abstract

The Wireless Sensor Networks of Coarse-resolution Pixel Parameters (CPP-WSN) was established to monitor the heterogeneity of coarse spatial resolution pixel, with consideration of different categories of land surface parameters in Huailai, Hebei province, China (40.349°N, 115.785°E). The observation network of radiation parameters (RadNet) in CPP-WSN was developed for multi-band radiation measurement and consisted of 6 nodes covering 2km*2km area to capture its heterogeneity. Each node employed four sensors to observe the five radiation parameters. The number and location of nodes in RadNet were determined through the representativeness-based sampling method. Thus, the RadNet is a distributed observation system with nodes work synchronously and measurements used together.

The intercomparison experiment for RadNet is necessary and was conducted in Huailai Remote Sensing Station from 5th Aug to 10th Aug in 2012. Time series observations from various sensors were collected and analyzed. The maximum relative differences among sensors of UVR, SWR, LWR, PAR, and LST are 4.83%, 5.3%, 3.71%, 11%, and 0.54%, respectively. Sensor/parameter differences indeed exist and are considerable large for PAR, SWR, UVR, and LWR, which cannot be ignored. The linear normalization and quadratic polynomial normalization perform similar for CUV5/UVR, PQS1/PAR, CNR4/SWR, and SI-111/LST. As for CNR4/LWR, quadratic polynomial normalization show higher accuracy than linear normalization, especially in node2, node4, and node5. Thus, the LWR measured by CNR4 is proved to be nonlinear, and should be normalized with quadratic polynomial coefficients for higher precision.

Paper Details

Date Published: 9 December 2015
PDF: 6 pages
Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98081F (9 December 2015); doi: 10.1117/12.2207628
Show Author Affiliations
Baocheng Dou, Beijing Normal Univ. (China)
Joint Ctr. for Global Change Studies (China)
State Key Lab. of Remote Sensing Science (China)
Jianguang Wen, Joint Ctr. for Global Change Studies (China)
State Key Lab. of Remote Sensing Science (China)
Xiuhong Li, Beijing Normal Univ. (China)
Joint Ctr. for Global Change Studies (China)
State Key Lab. of Remote Sensing Science (China)
Qiang Liu, Beijing Normal Univ. (China)
Joint Ctr. for Global Change Studies (China)
State Key Lab. of Remote Sensing Science (China)
Qing Xiao, Joint Ctr. for Global Change Studies (China)
State Key Lab. of Remote Sensing Science (China)
Junhua Bai, Joint Ctr. for Global Change Studies (China)
State Key Lab. of Remote Sensing Science (China)
Jingjing Peng, State Key Lab. of Remote Sensing Science (China)
Peking Univ. (China)
Xingwen Lin, State Key Lab. of Remote Sensing Science (China)
Zhigang Zhang, State Key Lab. of Remote Sensing Science (China)
Xiaodan Wu, State Key Lab. of Remote Sensing Science (China)
Erli Cai, Beijing Normal Univ. (China)
Jialin Zhang, Beijing Normal Univ. (China)
Chongyan Chang, Beijing Normal Univ. (China)


Published in SPIE Proceedings Vol. 9808:
International Conference on Intelligent Earth Observing and Applications 2015
Guoqing Zhou; Chuanli Kang, Editor(s)

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