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Monitoring The Chlorophyll Fluorescence Parameters In Rice Under Flooding And Waterlogging Stress Based On Remote Sensing

文献类型： 外文期刊

作者： Gu, Xiaohe 1 ; Xu, Peng 1 ; Qiu, He 3 ; Feng, Haikuan 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Xian Univ Sci & Technol Xian, Coll Geomat, Xian 710054, Peoples R China

关键词： rice;flood and waterlogging stress;chlorophyll fluorescence parameters;neural network

期刊名称：2014 WORLD AUTOMATION CONGRESS (WAC): EMERGING TECHNOLOGIES FOR A NEW PARADIGM IN SYSTEM OF SYSTEMS ENGINEERING

ISSN： 2154-4824

年卷期： 2014 年

页码：

收录情况： SCI

摘要： Flood and waterlog disaster is one of the most serious catastrophes for rice in China. Timely and accurately monitoring waterlogging damage can provide quantitative damage assessment and support for after-flood field management. Chlorophyll fluorescence (CF) is directly related to the waterlogging stress. This paper aims to establish models to monitor the change of chlorophyll fluorescence parameters (FPs) at different growth stages under waterlogging stress based on hyperspectral data. Waterlogging stress was simulated in experimental environment. Back Propagation Neural Network (BPNN) model were proposed by analyzing the relationship between chlorophyll fluorescence parameters (FPs) and spectra absorption feature parameters, which were extracted from continuum removal spectra (550nm-750nm) to represent absorption features. The experimental results indicated that absorption feature parameters and BPNN can improve the estimation accuracy of FPs under flooding and waterlogging stress.

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