title: [Studies on the estimation of soil organic matter content based on hyper-spectrum].
abstract: Hyperspectral remote sensing technology can be extensively applied in soil nutrient research due to its three special advantages, high spectral resolution, strong waveband continuity as well as a great deal of spectral information. Based on analyzing the soil organic matter content using hyper-spectral remote sensing technology, soil nutrients status and its dynamic changes can be fully understood, thus providing the scientific basis for guidance of the agricultural production and protection of agricultural ecological environment. The present paper studies the relationship between soil spectrum and soil organic fraction based on spectrum curves (ranging from 350 to 2500 nm) of 34 soil samples, which were collected in Yujiang and Taihe County, Jiangxi Province. First, soil reflection spectrum was mathematically manipulated into first derivative reflectance spectra (FDR) and inverse-log spectra (log(1/R)); second, the relationship between soil spectrum and soil organic fraction was investigated by step-wise multiple linear regression (SMLR) and partial least square regression (PLSR) on the ground of characteristic absorption; third, corresponding estimation model was built and examined. The result conveys that spectral data are compressed by carrying out arithmetic average operation by 10 nm for intervals. The first derivative of the reflectivity is an effective spectrum indicator, in the stepwise multiple linear regression analysis of soil organic matter, for the first derivative transformation, the regression models' precision of establishment and verification increased. The model built by PLSR method based on the characteristic absorption bands precedes that of SMLR. In the PLSR model of soil reflection spectrum and the inverse-log spectra, the test samples' average of relative error is 16% and 17% respectively, the correlation coefficient between retrieval value and measured value is 0.84 and 0.91 respectively, for it's faster to estimate the soil organic fraction.
authors: Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China. paopaogouuse@hotmail.com
authors: Lei
authors: L
authors: Liu
authors: Run-ping
authors: RP
authors: Shen
authors: Guo-xiang
authors: GX
authors: Ding