Hyperspectral estimation of black soil organic matter content based on wavelet transform and successive projections algorithm
XIAO Yan1(), XIN Hongbo1, WANG Bin2, CUI Li1, JIANG Qigang3
1. College of Exploration and Surveying Engineering, Changchun Insititute of Technology, Changchun 130012, China 2. Changchun Institute of Surveying and Mapping, Changchun 130021, China 3. College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Black soil is a valuable land resource, and the content of organic matter is an important index reflecting soil fertility, state and degradation degree. In order to estimate black soil organic matter content more accurately, this paper proposes a hyperspectral estimation method based on wavelet transform and successive projections algorithm. In this paper,the soil samples collected in the typical black soil region were used as the research object,and the Vis-NIR spectral data of the soil obtained from analytical spectral deviees (ASD) spectrometer and the organic matter content through chemical analysis were used as the data sources.Firstly, wavelet transform was used to extract the wavelet coefficients of 1 to 7 levels, and then successive projections algorithm was used to select the variables from soil original spectrum and the wavelet coefficients of 1 to 7 levels respectively. Finally, based on the soil original spectrum, the wavelet coefficients of 1 to 7 levels and the selected variables based on successive projections algorithm respectively, partial least squares and support vector machine were used to build the estimation models. The results show that, by using wavelet transform and successive projections algorithm, not only the number of variables is reduced greatly, but also the accuracies of the models are improved. When using partial least squares method,R2 increases from 0.79 of the soil original spectrum to 0.93 of the wavelet coefficient of the sixth level, and RMSE decreases from 6.06 g·kg-1 to 3.48 g·kg-1. When support vector machine method is used, R2 increases from 0.75 of the soil original spectrum to 0.91 of the wavelet coefficient of the third level, and RMSE decreases from 7.46 g·kg-1 to 4.12 g·kg-1. The results indicate that the proposed method can be effectively used for the hyperspectral estimation of black soil organic matter content.
Yan XIAO,Hongbo XIN,Bin WANG, et al. Hyperspectral estimation of black soil organic matter content based on wavelet transform and successive projections algorithm[J]. Remote Sensing for Land & Resources,
2021, 33(2): 33-39.
Tab.1 Descriptive statistics of soil organic matter content
Fig.2 Distribution of the variables selected by successive projections algorithm
级别
PLS
SVM
变量数 量/个
R2
RMSE/ (g·kg-1)
R2
RMSE/ (g·kg-1)
土壤全谱
0.79
6.06
0.75
7.46
2 051
第1层
0.77
6.37
0.73
7.37
1 028
第2层
0.73
7.01
0.73
7.20
516
第3层
0.73
6.96
0.74
7.12
260
第4层
0.86
5.08
0.84
5.50
132
第5层
0.88
4.56
0.87
4.96
68
第6层
0.86
5.02
0.79
6.37
36
第7层
0.72
7.12
0.72
7.18
20
Tab.2 Evaluation results of the estimation models of soil original spectrum and the wavelet coefficients of 1 to 7 levels
级别
PLS
SVM
变量数 量/个
R2
RMSE/ (g·kg-1)
R2
RMSE/ (g·kg-1)
土壤全谱
0.77
6.38
0.85
5.80
18
第1层
0.33
10.93
0.77
6.51
26
第2层
0.86
4.94
0.79
6.46
33
第3层
0.77
6.41
0.91
4.12
18
第4层
0.91
4.00
0.87
4.96
14
第5层
0.92
3.84
0.85
5.39
11
第6层
0.93
3.48
0.80
6.18
11
第7层
0.82
5.70
0.72
7.15
9
Tab.3 Evaluation results of the estimation models of the soil original spectrum and the wavelet coefficients of 1 to 7 levels screened by successive projections algorithm
Fig.3-1 Scatter diagram of measured and predicted values of black soil organic matter content
Fig.3-2 Scatter diagram of measured and predicted values of black soil organic matter content
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url: http://www.mdpi.com/2072-4292/6/4/2699
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2021,
Vol. 33
), XIN Hongbo1, WANG Bin2, CUI Li1, JIANG Qigang3
