Estimating soil total nitrogen contents from visible/near-infrared reflectance based on successive projections algorithm

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Shu, X.M.^1 ; Shi, T.Z.^2 ; Liu, Y.^1 ; Peng, H.W.^3 ; Gao, W.X.^4 ; Zhang, C.^1

Changjiang Waterway Survey Center, 430010, Wuhan, China^1

Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, 430079, Wuhan, China^2

Second Artillery Command College, 430012, Wuhan, China^3

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China^4

关键词: Calibrated model;    ITS applications;    Multiple regression methods;    Soil total nitrogen;    Successive projections algorithm;    Successive projections algorithms (SPA);    Support vector machine regressions;    Vis/NIR spectroscopy;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012046/pdf
DOI  :  10.1088/1755-1315/17/1/012046

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

The Visible/near-infrared reflectance (Vis/NIR) spectroscopy combined with multiple regression methods have been employed to assess various soil properties quantitatively. However, useless or irrelevant information in some bands worsen the performances of the calibrated models. Successive projections algorithm (SPA) is an effective technique for selecting informative variables, and its application potential in estimating soil contents using visible/near-infrared reflectance (Vis/NIR) spectroscopy has not been explored. This paper aimed to explore the effect of SPA combined with support vector machine regression (SVMR) on soil total nitrogen (TN) contents estimation using Vis/NIR spectroscopy. For this purpose, SPA-SVMR was compared with full spectrum based SVMR (FS-SVMR) for soil total nitrogen (TN) estimation, respectively. The results showed that the SPA-SVMR model (R2v0.58, RMSEV 0.364) performed better than the FS-SVMR model (R2v0.49, RMSEV 0.394). We concluded that the SPA could eliminate the uninformative variables and improve the performance of SVMR on estimating TN contents. In further study, using the SPA-SVMR method to estimate other soil properties is necessary to test this finding.