Estimation of Soil Moisture Content Based on Competitive Adaptive Reweighted Sampling Algorithm Coupled with Machine Learning

Xiangyu Ge; Jianli Ding; Jingzhe Wang; Fei Wang; Lianghong Cai; Huilan Sun

doi:10.3788/AOS201838.1030001

Acta Optica Sinica, Volume. 38, Issue 10, 1030001(2018)

Estimation of Soil Moisture Content Based on Competitive Adaptive Reweighted Sampling Algorithm Coupled with Machine Learning

Xiangyu Ge^1,2,3、*, Jianli Ding^1,2,3、*, Jingzhe Wang^1,2,3, Fei Wang^1,2,3, Lianghong Cai^1,2,3, and Huilan Sun⁴

Author Affiliations

¹ College of Resource and Environment Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China

² Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, Xinjiang 830046, China

³ Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi, Xinjiang 830046, China

⁴ School of Geographical Science and Tourism, Xinjiang Normal University, Urumqi, Xinjiang 830054, China

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Figures & Tables(9)

Fig. 1. Mean-standard deviation distribution of soil-residual prediction for full-sample MCCV

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Fig. 2. Flow chart of calculation process for experience and model

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Fig. 3. Spectral reflectance of soils with different SMCs

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Fig. 4. Variable filtering process using CARS. (a) Variation in wavelength variable number; (b) variation in RMSECV; (c) trend of variable regression coefficient when RMSECV is minimum

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Fig. 5. Mean reflectance of soil samples and optimal spectral bands

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Fig. 6. Predicted and measured SMCs using ELM model

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Table 1. Statistical characteristics of SMC of soil samples

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Table 1. Statistical characteristics of SMC of soil samples

Sample type	Number	Maximum	Minimum	Mean	Standard deviation	Coefficient of variation
Whole set	77	0.252	0.021	0.1421	0.049	0.3458
Calibration set	62	0.252	0.021	0.1406	0.051	0.3659
Validation set	15	0.216	0.067	0.1483	0.039	0.2637

Table 2. Estimated SMC

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Table 2. Estimated SMC

Model	Variable number	Calibration set		Prediction set
Model	Variable number	RMSE	R²	RMSE	R²	RPD	RPIQ
PLSR	20	0.484	0.478	0.622	0.617	0.522	0.18401
BPNN	20	0.027	0.706	0.024	0.799	2.016	1.90200
RFR	20	0.024	0.872	0.021	0.898	1.647	2.18900
ELM	20	0.016	0.879	0.015	0.918	3.123	3.32500

Table 3. Predicted SMC based on different ratios of calibration to prediction

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Table 3. Predicted SMC based on different ratios of calibration to prediction

Model	Ratio of calculation to prediction	Variable number	Calibration set		Prediction set
Model	Ratio of calculation to prediction	Variable number	RMSE	R²	RMSE	R²	RPD	RPIQ
BPNN	62∶15	20	0.027	0.706	0.024	0.799	2.016	1.902
	57∶20	20	0.020	0.842	0.023	0.800	1.826	1.499
	52∶25	20	0.023	0.765	0.024	0.800	1.947	2.010
RFR	62∶15	20	0.024	0.872	0.021	0.898	1.647	2.189
	57∶20	20	0.024	0.863	0.013	0.897	2.217	3.073
	52∶25	20	0.025	0.856	0.014	0.889	2.202	3.041
ELM	62∶15	20	0.016	0.879	0.015	0.918	3.123	3.325
	57∶20	20	0.019	0.869	0.014	0.919	3.102	3.241
	52∶25	20	0.015	0.877	0.016	0.918	2.569	2.958

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Xiangyu Ge, Jianli Ding, Jingzhe Wang, Fei Wang, Lianghong Cai, Huilan Sun. Estimation of Soil Moisture Content Based on Competitive Adaptive Reweighted Sampling Algorithm Coupled with Machine Learning[J]. Acta Optica Sinica, 2018, 38(10): 1030001

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Paper Information

Category: Spectroscopy

Received: Apr. 28, 2018

Accepted: May. 12, 2018

Published Online: May. 9, 2019

The Author Email:

DOI:10.3788/AOS201838.1030001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology