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Remote estimation of the fraction of absorbed photosynthetically active radiation for a maize canopy in Northeast China

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Journal of Plant Ecology 2015年第4期8卷 429-435页

作者： Feng Zhang Guangsheng Zhou Christer Nilsson State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of Sciences20 NanxincunXiangshanHaidian DistrictBeijing 100093China Chinese Academy of Meteorological Sciences 46 Zhongguancun South StreetHaidian DistrictBeijing 100081China Landscape Ecology Group Department of Ecology and Environmental ScienceUmeåUniversityUmeåSE-90187Sweden

Aims accurate remote estimation of the fraction of absorbed photosynthetically active radiation(fAPAR)is essential for the light use efficiency(LUE)***,one challenge for the LUE models is lack of knowledge about the relationship between fAPAR and the normalized difference vegetation index(NDVI).Few studies have tested this relationship against field measurements and evaluated the accuracy of the remote estimation *** study aimed to reveal the empirical relationship between NDVI and fAPAR and to improve algorithms for remote estimation of *** to investigate the method of remote estimation of fAPAR seasonal dynamics,the CASA(Carnegie-ames-stanford approach)model and spectral vegetation indices(VIs)were used for in situ measure-ments of spectral reflectance and fAPAR during the growing season of a maize canopy in Northeast *** Findingsthe results showed that the fAPAR increased rapidly with the day of year during the vegetative stage,it remained relatively stable at the stage of reproduction,and finally decreased slowly during the senescence *** addition,fAPAR green[fAPAR_(green)=fAPAR_(green) -fAPAR_(green) LAI_(max))]showed clearer seasonal trends than *** NDVI,red-edge NDVI,wide dynamic range vegetation index,red-edge position(REP)and REP with sentinel-2 bands derived from hyperspectral remote sensing data were all significantly positively related to fAPAR green during the entire growing *** a comparison of the predictive performance of VIs for the whole growing season,REP was the most appropriate spectral index,and can be recommended for monitoring seasonal dynamics of fAPAR in a maize canopy.

关键词： fraction of absorbed photosynthetically active radiation hyperspectral remote sensing maize canopy spectral vegetation indices

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Estimation of chlorophyll content in maize canopy using wavelet denoising and SVR method

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International Journal of Agricultural and Biological Engineering 2018年第6期11卷 132-137页

作者： Haojie Liu Minzan Li Junyi Zhang Dehua Gao Hong Sun Liwei Yang Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of EducationChina Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural information acquisition technology Ministry of AgricultureChina Agricultural UniversityBeijing 100083China

In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy *** spectral reflectance of maize canopy was measured and processed following wavelet denoising and multivariate scatter correction(MSC)to reduce the noise ***,the signal to noise ratio(SNR)and curve smoothness(CS)were used to evaluate the denoising effect of different wavelet functions and decomposition *** a result,the Sym6 wavelet basis function and the 5th level decomposition were determined to denoise the original *** MSC method was used to eliminate the scattering effect after *** three spectral ranges were extracted by interval partial least squares(IPLS)including the 525-549 nm,675-749 nm and 850-874 ***,the chlorophyll content estimation model was developed by using support vector regression(SVR)*** calibration Rc2 of the SVR model was 0.831,the RMSEC was 1.3852 mg/L;the validation Rv2 was 0.809,the RMSEP was 0.8664 mg/*** results show that the SNR and CS indicators can be used to select the parameters for wavelet denoising and model can be used to estimate the chlorophyll content of maize canopy in the field.

关键词： maize canopy spectral reflectance wavelet denoising SVR model chlorophyll content

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