Wireless Sensor Network of Typical Land Surface Parameters and Its Preliminary Applications for Coarse-Resolution Remote Sensing Pixel. (26th April 2016)

• Record Type: Journal Article
• Authors: Dou, Baocheng; Wen, Jianguang; Li, Xiuhong; Liu, Qiang; Peng, Jingjing; Xiao, Qing; Zhang, Zhigang; Tang, Yong; Wu, Xiaodan; Lin, Xingwen; You, Dongqin; Li, Hua; Li, Li; Zeng, Yelu; Cai, Erli; Zhang, Jialin
• Other Names: Liang Yao Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2016)
• Publication Date: 2016-04-26
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2016/9639021
• Languages: English
• ISSNs: 1550-1329

Abstract

Abstract : How to obtain the "truth" of land surface parameter as reference value to validate the remote sensing retrieved parameter in heterogeneous scene and coarse-resolution pixel is one of the most challenging topics in environmental studies. In this paper, a distributed sensor network system named CPP-WSN was established to capture the spatial and temporal variation of land surface parameters at coarse-resolution satellite pixel scale around the Huailai Remote Sensing Station, which locates in the North China Plain. The system consists of three subnetworks that are RadNet, SoilNet, and VegeNet. Time series observations of typical land surface parameters, including UVR, PAR, SWR, LWR, albedo, and land surface temperature (LST) from RadNet, multilayer soil moisture and soil temperature from SoilNet, and fraction of vegetation cover (FVC), clumping index (CI), and leaf area index (LAI) from VegeNet, have been obtained and shared on the web. Compared with traditional single-point measurement, the "true" reference value of coarse pixel is obtained by averaging or representativeness-weighted averaging the multipoint measurements acquired using the sensor network. The preliminary applications, which validate several remote sensing products with CPP-WSN data, demonstrate that a high quality ground "truth" dataset has been available for remote sensing as well as other applications.