Chen Hong-Yan, Zhao Geng-Xing, Li Xi-Can, Zhu Xi-Cun, Sui Long, Wang Yin-Juan
College of Resources and Environment, Shandong Agricultural University, Tai' an 271018, Shandong, China.
Ying Yong Sheng Tai Xue Bao. 2011 Nov;22(11):2935-42.
A total of 60 soil samples with approximate contents of N, P, and K and greatly different content of organic matter were selected by statistical analysis. Through hyper-spectral detection and analysis, the first derivative spectrum of the soil logarithmic reflectance was obtained, and was decomposed by the Bior 1.3 wavelet function. The approximative signal of the lowest frequency and the noise signal of the highest frequency were removed from the input spectrum so as to obtain the characteristic spectrum corresponding to soil physical and chemical parameters. The sensitive bands of soil organic matter were selected by correlation analysis, and the forecasting models were built by multiple regression analysis, based on the sensitive bands and the characteristic spectrum, respectively. Through comparison analysis, the optimal wavelet decomposing resolution for extracting the characteristic spectrum of soil organic matter was ascertained, and the best forecasting model was established. The best wavelet decomposing resolution was 9, followed by 8 and 10. Based on the characteristic spectrum of wavelet decomposing of 9 resolutions, the model R2 reached 0.89, which was increased by 0.31 as compared to the model based on sensitive bands, and increased by 0.10 as compared to the model based on the original spectrum.
