GNSS World of China

Volume 46 Issue 6
Dec.  2021
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XIAO Xingxing, LYU Weicai, ZHONG Chen, CAO Jinjin, LI Yanjie, ZHANG Can. Iversion of normalized difference vegetation index based on GPS-IR[J]. GNSS World of China, 2021, 46(6): 118-124. doi: 10.12265/j.gnss.2021060901
Citation: XIAO Xingxing, LYU Weicai, ZHONG Chen, CAO Jinjin, LI Yanjie, ZHANG Can. Iversion of normalized difference vegetation index based on GPS-IR[J]. GNSS World of China, 2021, 46(6): 118-124. doi: 10.12265/j.gnss.2021060901
shu

Iversion of normalized difference vegetation index based on GPS-IR

doi: 10.12265/j.gnss.2021060901
  • 1.

    School of Spatial Information and Surveying Engineering, Anhui University of Science and Technology, Huainan 232001, China

  • 2.

    Coal Industry Engineering Research Center of Mcning Area Environment and Disasters Collaborative Monitoring, Anhui University of Science and Technology, Huainan 232001, China

  • 3.

    Key Laboratory of Anhui-Aerospace-Ground Cooperative Monitcring and Early Warning of Cool Mining-Induced Disasters of Anhui Higher Education Institutes, Anhui University of Science and Technology, Huainan 232001, China

  • 4.

    Anhui Guanghang Construction Engineering Co.Ltd., Wuhu 241002 China

  • Received Date: 2021-06-09
    Available Online: 2021-12-17
    Abstract
  • In view of the fact that GPS receiver is sensitive to the water content of surrounding vegetation when receiving L-band signal, the normalized difference vegetation index (NDVI) of the station is retrieved by using the change of GPS reflected signal. In this paper, the normalized microwave reflection index (NMRI) calculated from the continuous observation data of two GPS reference stations in recent five years is used to construct a univariate linear model for NDVI inversion. The correlation coefficients R of the inversion results are 0.626 53 and 0.625 73 respectively, and the root mean square error root mean square error (RMSE) are 0.051 29 and 0.055 08 respectively. The correlation coefficients of the inversion results are increased by 2% and 6% respectively by using BP neural network model, which indicates that the regional NDVI inversion results of GPS-interferometric reflectometry (GPS-IR) have high reliability. This study provides a theoretical support for obtaining accurate position, real-time continuous and high-resolution NDVI.

     

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