DocumentCode :
2143560
Title :
A method for estimating chlorophyll content of wheat from reflectance spectra
Author :
Xiang, Zhao ; Suhong, Liu ; Jindi, Wang ; Zhenkun, Tian
Author_Institution :
Res. Center for Remote Sensing & GIS, Beijing Normal Univ.
Volume :
7
fYear :
2004
fDate :
20-24 Sept. 2004
Firstpage :
4504
Abstract :
Chlorophyll and carotenoid, relating to the physiological function of leaves, are two pigments which can absorb the light energy during the process of plant photosynthesis. Among the pigments, the chlorophyll plays an important role in the photosynthesis, and its content, as a predictor of the nutritional status of vegetation, is one of the main factors to evaluate the environment and growth conditions for the winter wheat. This paper, based on the reflectance spectra of wheat in Xiao Tangshan County, in China, took PLS regression as the quantitative inversion method to have established the hyperspectral inversion model between chlorophyll content and the reflectance spectra of wheat. Through analysis, it indicated that the chlorophyll content of wheat was highly relative to the reflectance of hyperspectral from 350 nm to 1060 nm. The correlation coefficient between the prediction value and the measured value is as high as 0.9, and the RMSEP is lower than 0.4. The research provided an effective method to estimate the chlorophyll content using the quantitative inversion technology of hyperspectral RS
Keywords :
biochemistry; crops; least squares approximations; photosynthesis; physiology; reflectivity; regression analysis; vegetation mapping; 350 to 1060 nm; China; PLS regression; Xiao Tangshan County; carotenoid; chlorophyll content; correlation coefficient; environmental conditions; growth conditions; hyperspectral inversion model; hyperspectral reflectance spectra; leaves physiology; light energy absorption; nutritional status; physiological function; pigments; plant photosynthesis; quantitative inversion method; vegetation; winter wheat; Chemicals; Crops; Geographic Information Systems; Hyperspectral imaging; Hyperspectral sensors; Least squares methods; Pigments; Reflectivity; Remote sensing; Yield estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-8742-2
Type :
conf
DOI :
10.1109/IGARSS.2004.1370154
Filename :
1370154
Link To Document :
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