IGSNRR OpenIR
estimatingfractionofphotosyntheticallyactiveradiationofcornwithvegetationindicesandneuralnetworkfromhyperspectraldata
Yang Fei1; Zhu Yunqiang1; Zhang Jiahua2; Yao Zuofang3
2012
Source Publicationchinesegeographicalscience
ISSN1002-0063
Volume022Issue:001Pages:63
AbstractThe fraction of photosynthetically active radiation (FPAR) is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles. Based on ground-measured corn hyperspectral reflectance and FPAR data over Northeast China, the correlations between corn-canopy FPAR and hyperspectral reflectance were analyzed, and the FPAR estimation performances using vegetation index (VI) and neural network (NN) methods with different two-band-combination hyperspectral reflectance were investigated. The results indicated that the corn-canopy FPAR retained almost a constant value in an entire day. The negative correlations between FPAR and visible and shortwave infrared reflectance (SWIR) bands are stronger than the positive correlations between FPAR and near-infrared band re- flectance (NIR). For the six VIs, the normalized difference vegetation index (NDVI) and simple ratio (SR) performed best for estimating corn FPAR (the maximum R2 of 0.8849 and 0.8852, respectively). However, the NN method esti- mated results (the maximum Rz is 0.9417) were obviously better than all of the VIs. For NN method, the two-band combinations showing the best corn FPAR estimation performances were from the NIR and visible bands; for VIs, however, they were from the SWIR and NIR bands. As for both the methods, the SWIR band performed exceptionally well for corn FPAR estimation. This may be attributable to the fact that the reflectance of the SWIR band were strongly controlled by leaf water content, which is a key component of corn photosynthesis and greatly affects the absorption of photosynthetically active radiation (APAR), and makes further impact on corn-canopy FPAR.
Language英语
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/127398
Collection中国科学院地理科学与资源研究所
Affiliation1.Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences
2.The Laboratory of Remote Sensing and Climate Information,Chinese Academy of Meteorological Sciences
3.中国科学院
Recommended Citation
GB/T 7714
Yang Fei,Zhu Yunqiang,Zhang Jiahua,et al. estimatingfractionofphotosyntheticallyactiveradiationofcornwithvegetationindicesandneuralnetworkfromhyperspectraldata[J]. chinesegeographicalscience,2012,022(001):63.
APA Yang Fei,Zhu Yunqiang,Zhang Jiahua,&Yao Zuofang.(2012).estimatingfractionofphotosyntheticallyactiveradiationofcornwithvegetationindicesandneuralnetworkfromhyperspectraldata.chinesegeographicalscience,022(001),63.
MLA Yang Fei,et al."estimatingfractionofphotosyntheticallyactiveradiationofcornwithvegetationindicesandneuralnetworkfromhyperspectraldata".chinesegeographicalscience 022.001(2012):63.
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