Wang Y.-C.,Shandong University of Science and Technology |
Wang Y.-C.,Chinese National Engineering Research Center for Information Technology in Agriculture |
Wang Y.-C.,Key Laboratory of Information Technology in Agriculture Ministry of Agriculture |
Yang G.-J.,Chinese National Engineering Research Center for Information Technology in Agriculture |
And 6 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2014
For improving the estimation accuracy of soil organic matter content of the north fluvo-aquic soil, wavelet transform technology is introduced. The soil samples were collected from Tongzhou district and Shunyi district in Beijing city. And the data source is from soil hyperspectral data obtained under laboratory condition. First, discrete wavelet transform efficiently decomposes hyperspectral into approximate coefficients and detail coefficients. Then, the correlation between approximate coefficients, detail coefficients and organic matter content was analyzed, and the sensitive bands of the organic matter were screened. Finally, models were established to estimate the soil organic content by using the partial least squares regression (PLSR). Results show that the NIR bands made more contributions than the visible band in estimating organic matter content models; the ability of approximate coefficients to estimate organic matter content is better than that of detail coefficients; The estimation precision of the detail coefficients fir soil organic matter content decreases with the spectral resolution being lower; Compared with the commonly used three types of soil spectral reflectance transforms, the wavelet transform can improve the estimation ability of soil spectral fir organic content; The accuracy of the best model established by the approximate coefficients or detail coefficients is higher, and the coefficient of determination (R2) and the root mean square error (RMSE) of the best model for approximate coefficients are 0.722 and 0.221, respectively. The R2 and RMSE of the best model for detail coefficients are 0.670 and 0.255, respectively.
Xu P.,Xi'an University of Science and Technology |
Xu P.,Chinese National Engineering Research Center for Information Technology in Agriculture |
Xu P.,Key Laboratory of Information Technology in Agriculture Ministry of Agriculture |
Gu X.-H.,Chinese National Engineering Research Center for Information Technology in Agriculture |
And 5 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2013
In order to provide the foundational theoretical support for flood loss estimation of rice with RS, the change of leaf area index (LAI) and canopy spectral response during four developmental stages and three waterlogging depths were studied, and the LAI estimation model was established with spectra characteristics parameter using regression analysis method. The results show that LAI value decreases as water depth increases in tillering, jointing and heading stages, and LAI value under complete submergence decreased by 36.36% than CK in jointing stages. "Double-Peak" presented in the canopy first derivative spectra of 680~760 nm where the red edge parameters existed, and the main peak is located in the 724~737 nm with 701 and 718 nm exhibiting secondary peak. With water depth increasing, "Triple-Peak" emerges especially. The red edge position moves to long-wavelength direction in each developmental stage. Blue shift of red edge amplitude and red edge area was detected in tillering, jointing and filling stages, while red shift appeared in heading stage. The relationship between spectra characteristics parameters and LAI were investigated during 4 growth stages, results were not consistently significant at any wavelengths, and the leaf area indices were significantly correlative to the spectra parameters before heading stage, so the spectra parameters before heading stage can be used to estimate the leaf area indices, and a regression model based on parameter Dλ737/Dλ718 was recommended. Therefore the variation range of LAI for rice could response to the stress intensity directly, and the regression model LAI=3.138(Dλ737/Dλ718)-0.806 can precisely estimate the leaf area index under flooding and waterlogging stress.