title: [Hyper spectral estimation method for soil alkali hydrolysable nitrogen content based on discrete wavelet transform and genetic algorithm in combining with partial least squares DWT-GA-PLS)].
abstract: Taking the Qihe County in Shandong Province of East China as the study area, soil samples were collected from the field, and based on the hyperspectral reflectance measurement of the soil samples and the transformation with the first deviation, the spectra were denoised and compressed by discrete wavelet transform (DWT), the variables for the soil alkali hydrolysable nitrogen quantitative estimation models were selected by genetic algorithms (GA), and the estimation models for the soil alkali hydrolysable nitrogen content were built by using partial least squares (PLS) regression. The discrete wavelet transform and genetic algorithm in combining with partial least squares (DWT-GA-PLS) could not only compress the spectrum variables and reduce the model variables, but also improve the quantitative estimation accuracy of soil alkali hydrolysable nitrogen content. Based on the 1-2 levels low frequency coefficients of discrete wavelet transform, and under the condition of large scale decrement of spectrum variables, the calibration models could achieve the higher or the same prediction accuracy as the soil full spectra. The model based on the second level low frequency coefficients had the highest precision, with the model predicting R2 being 0.85, the RMSE being 8.11 mg x kg(-1), and RPD being 2.53, indicating the effectiveness of DWT-GA-PLS method in estimating soil alkali hydrolysable nitrogen content.
authors.affiliation: National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, Shandong, China. chenhy@sdau.edu.cn
authors.forename: Hong-Yan
authors.initials: HY
authors.lastname: Chen
authors.affiliation: National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, Shandong, China.
authors.forename: Geng-Xing
authors.initials: GX
authors.lastname: Zhao
authors.affiliation: College of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, Shandong, China.
authors.forename: Xi-Can
authors.initials: XC
authors.lastname: Li
authors.affiliation: Kenli County Bureau of Land and Resources, Kenli 257500, Shandong, China.
authors.forename: Xiang-Feng
authors.initials: XF
authors.lastname: Wang
authors.affiliation: Qihe Bureau of Agriculture, Qihe 251100, Shandong, China.
authors.forename: Yu-Ling
authors.initials: YL
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000468:Alkalies / Q000737:chemistry; D002681:China; D004784:Environmental Monitoring / Q000379:methods; D006868:Hydrolysis; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D009584:Nitrogen / Q000032:analysis*; D012987:Soil / Q000737:chemistry*; D013057:Spectrum Analysis / Q000379:methods*; D058067:Wavelet Analysis
chemical_list: D000468:Alkalies; D012987:Soil; D009584:Nitrogen