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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (1) : 110-116     DOI: 10.6046/gtzyyg.2019.01.15
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Comparison and analysis of estimation models of soil organic matter content established by hyperspectral on ground
Yongmin WANG1, Xican LI2, Linya TIAN1, Bin JIA3, Hui YANG4
1.School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
2.College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
3.CCFED the Third Construction Engineering Co., Zunyi 563000, China
4.School of Transportation, Southeast University, Nanjing 210000, China
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Abstract  

Using the data obtained by hyperspectral techniques to estimate the content of soil organic matter is a hotspot in recent years. For the purpose of determining the effective estimation modeling method, specific data such as reflectance obtained by hyperspectral on ground and organic matter content were used in this paper. Wavelet analysis was used to remove the noise, and continuum removal was used to extract the parameters and compress the data. Combining a variety of different data transformation methods and utilizing BP neural networks, multiple linear regression (MLR) and least squares regression (LSR), many different estimation models of soil were established. It is found that the neural network method is superior to the regression model among various data transformation methods after comparing different estimation models established by the three modeling methods. The optimal estimation model is the model established by the combination of logarithmic square transformation and neural network. The R 2 of the model is 0.933 and the RMSE is 0.069. The authors creatively carried out the data transformation at the modeling factor level and established the good estimation model. It is shown that the learning mechanism of BP + LS model is suitable for hyperspectral estimation of soil organic matter and works well. The methods, models and conclusions of this paper have some reference significance for the hyperspectral estimation of soil organic matter.

Keywords hyperspectra on ground      soil organic matter      data conversion      estimation model      comparative analysis     
:  TP79  
Issue Date: 15 March 2019
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Yongmin WANG
Xican LI
Linya TIAN
Bin JIA
Hui YANG
Cite this article:   
Yongmin WANG,Xican LI,Linya TIAN, et al. Comparison and analysis of estimation models of soil organic matter content established by hyperspectral on ground[J]. Remote Sensing for Land & Resources, 2019, 31(1): 110-116.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.01.15     OR     https://www.gtzyyg.com/EN/Y2019/V31/I1/110
Fig.1  Area of collecting soil samples
样本集 有机质最小值 有机质最大值 有机质平均值 有机质标准差 水含量最小值 水含量最大值 水含量平均值 水含量标准差
总体样本 0.124 1.289 0.600 0.250 0.46 38.65 9.87 7.65
建模样本 0.124 1.289 0.608 0.238 4.83 38.65 9.65 7.62
检验样本 0.176 1.289 0.575 0.241 0.46 35.32 9.97 7.69
Tab.1  Statistical characteristics of soil organic matter content and water content(%)
Fig.2  Comparison of correlation coefficient between spectral reflectance of original and wavelet denoising and organic matter content
Fig.3  Continuum removal results
变换方法 AP DS MP MH MD LM
原始 -0.205 -0.217 -0.196 -0.151 -0.171 0.230
SQ -0.288 -0.380 -0.286 -0.172 -0.365 0.296
RE 0.296 0.357 0.356 0.256 0.386 -0.295
EXP 0.482 -0.534 -0.518 -0.472 -0.484 0.305
LOG -0.390 -0.362 -0.211 -0.209 -0.318 0.295
ES 0.431 -0.437 -0.368 -0.428 -0.499 0.401
LS -0.539 -0.596 -0. 432 -0.631 -0.628 0.521
DE1 -0.282 -0.329 -0.287 -0.273 -0.286 0.224
LGD1 -0.462 -0.309 0.508 0.414 0.511 0.424
EXD1 0.337 -0.321 0.409 0.458 0.406 -0.245
Tab.2  Comparison of correlation coefficient
Fig.4  Comparison of three models’R2and RMSE
参数 方法 SQ RE EXP LOG ES LS DE1 LGD1 EXD1 均值
R2 BP 0.811 0.807 0.885 0.812 0.828 0.933 0.852 0.839 0.841 0.845
MLR 0.778 0.804 0.835 0.731 0.859 0.879 0.792 0.845 0.786 0.812
LSR 0.815 0.797 0.871 0.826 0.833 0.887 0.861 0.847 0.802 0.839
RMSE BP 0.114 0.117 0.081 0.110 0.102 0.069 0.092 0.107 0.105 0.099
MLR 0.122 0.121 0.099 0.131 0.093 0.092 0.118 0.121 0.123 0.113
LSR 0.119 0.125 0.097 0.114 0.115 0.085 0.084 0.092 0.114 0.105
sig BP 0.015 0.019 0.001 0.011 0.006 0.00 0.003 0.013 0.007 0.008
MLR 0.027 0.031 0.033 0.042 0.002 0.001 0.013 0.006 0.035 0.021
LSR 0.019 0.028 0.001 0.021 0.005 0.001 0.002 0.007 0.016 0.011
Tab.3  27 hyperspectral estimation models’ results of test samples
Fig.5  Comparison of predicted and measured values
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