title: [Black soil organic matter predicting model based on field hyperspectral reflectance].
abstract: To develop soil organic matter (OM) quick measuring methods, deepen the application of remote sensing in agriculture, improve agricultural production and management way, and promote the development of quantitative remote sensing studies relating to terrestrial ecosystem, field hyperspectral reflectance in the visible/near infrared bands of black soil in Hailun city, northeast China, was collected and analyzed with spectral analysis methods to discover the spectral characteristics of field reflectance and its influencing factors, and the spectral indices were derived, then black soil organic matter predicting model based on the correlation between OM content and spectral indices was built. Root mean squared error (RMSE) was introduced to validate the predictability and precision of the models, and coefficient of the determination (R2) was used to evaluate stability of the models. The results are as follows: the main spectral region of remarkable differences between field black soil reflectance curves is less than 1 250 nm, especially less than 1 000 nm; OM is the main factor determining the curve shape of field black soil reflectance, anc there are single or double spectral wave troughs for different soil samples because of varying OM content at the spectral region less 1 100 nm; correlation between OM and differential coefficient of logarithmic reflectance reciprocal (DCLRR) is much more significant than that between OM and other reflectance or its transforms, and the maximum coefficient of correlation is at 1 260 nm; the predicting model for black soil OM content is built with DCLRR at 1 260 nm as independent varialble and OM as dependent variable, and the coefficients of determination R2 of the model is 0.71, RMSE is 0.42, so the model is quite good in stability and predictability, and can be used in fast testing of organic matter in black soil.
authors.affiliation: Key Laboratory of Resources Remote Sensing & Digital Agriculture, Ministry of Agriculture, Beijing 100081, China. huanjunliu@yeah.net
authors.forename: Huan-Jun
authors.initials: HJ
authors.lastname: Liu
authors.forename: Xin-Le
authors.initials: XL
authors.lastname: Zhang
authors.forename: Shu-Feng
authors.initials: SF
authors.lastname: Zheng
authors.forename: Na
authors.initials: N
authors.lastname: Tang
authors.forename: Yan-Liang
authors.initials: YL
authors.lastname: Hu
mesh_terms: D000383:Agriculture; D002681:China; D008962:Models, Theoretical; D058998:Remote Sensing Technology; D012987:Soil; D013057:Spectrum Analysis
chemical_list: D012987:Soil