Comparison of multivariate statistical analysis and fuzzy recognition algorithm for quantitative mapping soil organic matter content with hyperspectral data

The soil organic matter is one of the important criterions of soil fertility. Mapping and dating soil organic matter is of great importance in soil use and evaluation. In this paper we compare two measures of multivariate statistical analysis (MSA) and fuzzy recognition algorithm (FRA) for quantitative mapping soil organic matter content using Hyperspectral remote sensing. This study was tested in Henshan County, northern ShanXi Province of China. On the one hand, the ratio of the reflectivity reciprocal-logarithm's first derivative of 623.6nm against the reflectivity reciprocal-logarithm's first derivative of 564.4nm was chosen as the sensitive retrieval parameter and build up the retrieval models. Then, the best quadratic retrieval model was utilized to map the SOM content by calculating each pixel of Hyperion image, the adjusted R square coefficient is 0.8684. On the other hand, by analyzing the correlation between spectrally reflective data and SOM concentrate, the first derivative of logarithmic reflectance at sensitive bands of 393nm, 444nm, 502nm, 1455nm and 1937nm were confirmed as the retrieval indicators due to the notable correlation coefficients. Finally, the most optimized-retrieval model, utilized to the Hyperspectral data for SOM quantitative mapping, was build up by using the fuzzy recognition theory. The correlation coefficient of the retrieval model is 0.981. It is found that result of fuzzy recognition algorithm is better than that of traditionally statistical analysis, with the mean predicted error of 8.43% as compared to 10.42% for quadratic retrieval model. It is concluded that this fuzzy recognition algorithm for Hyperspectrally quantitative mapping SOM is available and the result map is reliable and significantly correlative with known stabilization processes throughout the study area. Moreover, the fuzzy recognition algorithm developed in this paper could be applied to other domain of quantitative remote sensing.

[1]  E. Ben-Dor,et al.  Quantitative mapping of arid alluvial fan surfaces using field spectrometer and hyperspectral remote sensing , 2006 .

[2]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[3]  E. Ben-Dor The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400-2500 nm) during a controlled decomposition process , 1997 .

[4]  J. Hummel,et al.  Reflectance technique for predicting soil organic matter. , 1980 .

[5]  G. Taylor,et al.  Image-derived spectral endmembers as indicators of salinisation , 2003 .

[6]  A. Karnieli,et al.  Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel , 2002 .

[7]  Li Xican,et al.  Fuzzy comprehensive prediction model and its application , 2003 .

[8]  E. Ben-Dor Quantitative remote sensing of soil properties , 2002 .

[9]  Zhao Jie High Spectral Retrieved Deduction of Soil Quality Index Based on Fuzzy Sets Analysis , 2008 .

[10]  G. Taylor,et al.  Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization , 2002 .

[11]  L. S. Galvão,et al.  Variability of Laboratory Measured Soil Lines of Soils from Southeastern Brazil , 1998 .

[12]  Zongjian Lin,et al.  Spectral features of soil organic matter , 2009 .

[13]  David A. Landgrebe,et al.  Spectral band selection for classification of soil organic matter content , 1989 .