Hyperspectral estimation of soil organic carbon content in the west lakeside oasis of Bosten Lake based on successive projection algorithm - NIU Fangpeng; LI Xinguo; MAMATTURSUN•Eziz; ZHAO Hui (浙江大学学报. 农业与生命科学版, 2021)
Taking the west lakeside oasis of Bosten Lake as the study area, using the measured soil organic carbon content and hyperspectral data, the successive projection algorithm (SPA) was used to filter the characteristic variables from the full-band spectral data, and then the full-band and characteristic bands were used to construct partial least square regression (PLSR) and support vector machine (SVM) models to estimate soil organic carbon content. The results showed that: 1) The soil organic carbon content varied from 0.75 to 48.13 g/kg, with an average value of 13.31 g/kg, showed moderate variability, with a coefficient of variation of 63.19%. 2) The soil organic carbon content and the original spectral reflectance showed a negative correlation, with －0.62＜correlation coefficient (r)＜－0.07. After the bands were preprocessed by Savitzky-Golay-standard normal variate-first derivative (SG-SNV-1st Der), the number of bands that passed the extremely significant test (P＜0.01) were 414, mainly concentrated in 487-575, 725-998 and 1 464-1 514 nm. The correlation between 788, 800 and 1 768 nm was the highest, with the correlation coefficients of more than 0.80. 3) After the spectra were preprocessed by SG-SNV-1st Der, the coefficient of determination (R2) of validation set of PLSR model constructed by SPA was 0.79; root mean square error (RMSE) was 3.58 g/kg; residual prediction deviation (RPD) was 1.99; and ratio of performance to interquartile distance (RPIQ) was 2.23. However, the validation set constructed by SPA combined with SVM was R2=0.81, RMSE=3.16 g/kg, RPD=2.25, RPIQ=2.53. It shows that the model constructed by SPA combined with SVM can better estimate soil organic carbon content in the study area.