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Proceedings Paper

Quantitative retrieving of soil organic matter using field spectrometer and hyperspectral remote sensing
Author(s): Luo Zhuo; Yaolin Liu; Jie Chen; Changji Hu; Jian Wu
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Paper Abstract

As the important component of soil, soil organic matter not only provides every nutrient element for crop, but also has determinant effect for forming of soil structure and melioration the soil physical character. Mapping and dating soil organic matter is of great importance in soil use and evaluation. In this study we examine the feasibility of soil organic matter content by using Hyperspectrally reflective remote sensing methodology. This technique was tested in Xiaochang County located in Hubei province. The soil reflectance properties of samples were measured in the laboratory by ASD field spectrometer. The correlation analysis related with organic matter content was processed from three factors: the spectral reflectance parameter ((lgρ)', ρ/ ρ450-750 and (1/lgρ623)'/ (1/lgρ564)'). The results show that the correlation coefficients of r values were: organic matter identification index (ρ/ ρ450-750) > logarithmic first derivative of reflectivity ((lgρ)') > organic matter mix identification index ((1/lgρ623)'/(1/lgρ564)'). Knowing these correlations we were able to use the best prominence correlation of organic matter identification index of 1850nm wavelength as the variable regression to build up statistical regression analysis. We used five model types (Linear Function, Logarithmic Function, Quadratic Function, Power Function and Exponential Function) to forecast the soil organic matter content Hyperion model. The accuracy assessment (R2= 0.8484) by relating forecasted organic matter values with Quadratic Function regression showed that the model is reliable and significantly correlative with known stabilization processes throughout the study area. The quantitative methodology developed in this study for refutations soil organic matter content can be adapted to other regions throughout the world.

Paper Details

Date Published: 29 December 2008
PDF: 10 pages
Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72850A (29 December 2008); doi: 10.1117/12.815986
Show Author Affiliations
Luo Zhuo, Wuhan Univ. (China)
Yaolin Liu, Wuhan Univ. (China)
Jie Chen, Land and Resources Administration Dept. of Xiaogan City (China)
Changji Hu, Land and Resources Administration Dept. of Xiaogan City (China)
Jian Wu, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 7285:
International Conference on Earth Observation Data Processing and Analysis (ICEODPA)

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