Combination of Fractional Order Differential and Machine Learning Algorithm for Spectral Estimation of Soil Organic Carbon Content

Qidong Zhao; Xiangyu Ge; Jianli Ding; Jingzhe Wang; Zhenhua Zhang; Meiling Tian

doi:10.3788/LOP57.153001

Laser & Optoelectronics Progress, Volume. 57, Issue 15, 153001(2020)

Combination of Fractional Order Differential and Machine Learning Algorithm for Spectral Estimation of Soil Organic Carbon Content

Qidong Zhao^1,2、**, Xiangyu Ge^1,2, Jianli Ding^1,2、*, Jingzhe Wang^1,2,3, Zhenhua Zhang^1,2, and Meiling Tian^1,2

Author Affiliations

¹Key Laboratory of Oasis Ecology, Ministry of Education, Xinjiang University, Urumqi, Xinjiang 830046, China

²Key Laboratory of Smart City and Environmental Modelling of Higher Education Institute, College of Resource and Environmental Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China

³Guangdong Institute of Eco-Environmental Science and Technology, Guangzhou, Guangdong 510650, China

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Figures & Tables(10)

Fig. 1. SOC content statistics

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Fig. 2. Spectral reflectance curves of soil with different organic carbon content

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Fig. 3. Number of bands whose correlation coefficient passes significance test level of 0.01

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Fig. 4. Thermal map of correlation coefficient between SOC content and different orders at each bands

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Table 1. Simulation results of ELM modeling algorithm

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Table 1. Simulation results of ELM modeling algorithm

Order	R²	RMSE	RPD
0	0.538	2.176	1.347
0.2	0.560	2.161	1.283
0.4	0.641	1.923	1.516
0.6	0.361	2.529	1.061
0.8	0.581	2.229	1.323
1.0	0.274	2.873	0.917
1.2	0.361	2.872	0.997
1.4	0.345	2.667	1.027
1.6	0.247	2.850	0.961
1.8	0.062	3.396	0.801
2.0	0.196	2.976	0.905

Table 2. Simulation results of RF modeling algorithm

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Table 2. Simulation results of RF modeling algorithm

Order	R²	RMSE	PD
0	0.763	1.169	2.389
0.2	0.780	1.132	2.440
0.4	0.799	1.049	2.640
0.6	0.807	1.905	2.313
0.8	0.799	1.997	2.614
1.0	0.803	1.131	2.274
1.2	0.816	1.152	2.458
1.4	0.826	1.130	2.588
1.6	0.828	1.014	2.858
1.8	0.816	1.189	2.259
2.0	0.821	1.142	2.466

Table 3. Simulation results of MARS modeling algorithm

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Table 3. Simulation results of MARS modeling algorithm

Order	R²	RMSE	RPD
0	0.621	2.047	1.470
0.2	0.710	1.724	1.774
0.4	0.795	1.522	1.923
0.6	0.709	1.708	1.670
0.8	0.830	1.464	2.113
1.0	0.829	1.471	2.149
1.2	0.846	1.218	2.604
1.4	0.867	1.071	2.783
1.6	0.845	1.231	2.535
1.8	0.847	1.175	2.656
2.0	0.844	1.172	2.681

Table 4. Simulation results of Elastic Net modeling algorithm

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Table 4. Simulation results of Elastic Net modeling algorithm

Order	R²	RMSE	RPD
0	0.586	2.095	1.325
0.2	0.590	2.081	1.367
0.4	0.626	1.977	1.466
0.6	0.778	1.507	1.989
0.8	0.801	2.706	1.141
1.0	0.837	1.204	2.169
1.2	0.846	1.149	2.207
1.4	0.849	1.130	2.548
1.6	0.869	1.035	2.798
1.8	0.848	1.120	2.663
2.0	0.843	1.122	2.562

Table 5. Simulation results of GBRT modeling algorithm

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Table 5. Simulation results of GBRT modeling algorithm

Order	R²	RMSE	RPD
0	0.788	1.916	2.162
0.2	0.793	1.883	2.275
0.4	0.814	1.712	2.618
0.6	0.830	1.526	2.707
0.8	0.841	1.381	2.848
1.0	0.846	1.276	2.267
1.2	0.847	1.290	2.679
1.4	0.857	1.257	2.882
1.6	0.878	1.125	3.142
1.8	0.851	1.282	2.798
2.0	0.848	1.264	2.813

Table 6. Comparison of effects of different bands on molecular chemical bonds in soil
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Table 6. Comparison of effects of different bands on molecular chemical bonds in soil
Band Visible Near infrared
Wavelength /nm 500 620 890 1400 2200 2300
Chemical bond Fe—O、C—H C—H O—H Al—OH C—H

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Qidong Zhao, Xiangyu Ge, Jianli Ding, Jingzhe Wang, Zhenhua Zhang, Meiling Tian. Combination of Fractional Order Differential and Machine Learning Algorithm for Spectral Estimation of Soil Organic Carbon Content[J]. Laser & Optoelectronics Progress, 2020, 57(15): 153001

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