Inversion of Soil Moisture Content Based on Hyperspectral Multi-Scale Decomposition

Cai Lianghong; Ding Jianli

doi:10.3788/LOP55.013001

Laser & Optoelectronics Progress, Volume. 55, Issue 1, 13001(2018)

Inversion of Soil Moisture Content Based on Hyperspectral Multi-Scale Decomposition

Cai Lianghong^1,2 and Ding Jianli^1,2、*

¹Xinjiang Common University Key Laboratory of Smart City and Environmental Stimulation, College of Resource and Environment Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China

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

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Figures & Tables(8)

Fig. 1. Distribution of field samping points

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Fig. 2. Reconstruction spectra of original spectrum at 1-8 wavelet levels

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Fig. 3. Comparison between measured and predicted SMC values

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Table 1. Statistical characteristics of SMC in soil samples
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Table 1. Statistical characteristics of SMC in soil samples
Sampleset Number ofsamples Meanvalue Standarddeviation Maximumvalue Minimumvalue CV
Whole set 39 0.147 0.057 0.339 0.015 0.388
Calibration set 27 0.146 0.050 0.211 0.020 0.342
Validation set 12 0.148 0.084 0.339 0.015 0.568

Table 2. Correlation analysis between SMC and characteristic spectrum in each level

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Table 2. Correlation analysis between SMC and characteristic spectrum in each level

Waveletlevel	Number ofsensitive band	Maximum positive correlation		Maximum negative correlation
Waveletlevel	Number ofsensitive band	Band /nm	Correlationcoefficient	Band /nm	Correlationcoefficient
L1	393	852	0.610	2350	-0.714
L2	402	854	0.561	2351	-0.620
L3	429	860	0.618	2364	-0.690
L4	486	853	0.619	2363	-0.675
L5	505	849	0.585	2341	-0.648
L6	602	858	0.557	2351	-0.573
L7	278	524	0.458	1985	-0.486
L8	254	438	0.382	1827	-0.431

Table 3. Maximum correlation between SMC and different mathematical transformation of characteristic spectrum of each level and position of band

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Table 3. Maximum correlation between SMC and different mathematical transformation of characteristic spectrum of each level and position of band

Waveletlevel	Variable	R	lg R	1/R	lg(1/R)	1/(lg R)	R'	(lg R)'	(1/R)'	[lg(1/R)]'	(1/lg R)'
L0	Band /nm	2229	2244	2190	2243	2165	407	409	407	407	831
L0	r	-0.724	0.729	0.584	-0.729	-0.667	-0.728	-0.757	-0.685	-0.739	0.685
L1	Band /nm	2244	2244	2286	2194	2186	407	1199	407	407	1199
L1	r	-0.723	0.728	0.784	-0.728	-0.667	-0.780	-0.762	0.792	-0.792	-0.662
L2	Band /nm	1924	2242	2186	2242	2171	488	768	2155	1417	2147
L2	r	-0.548	0.728	0.583	-0.728	-0.667	-0.686	-0.735	-0.726	-0.699	-0.582
L3	Band /nm	1962	2147	2182	2134	2161	1951	1760	2174	2160	1860
L3	r	-0.560	0.762	0.621	-0.762	-0.707	-0.669	0.758	-0.760	-0.682	-0.624
L4	Band /nm	2196	2191	2184	2191	2104	2157	1761	2177	1877	1761
L4	r	-0.723	0.728	0.582	-0.728	-0.666	-0.680	0.761	-0.757	0.757	-0.602
L5	Band /nm	2197	2197	2192	2109	2197	1953	1874	2172	2172	1773
L5	r	-0.724	0.729	0.583	-0.729	-0.668	-0.725	0.758	-0.746	0.746	-0.597
L6	Band /nm	2144	2136	2201	2140	2107	1441	1780	2262	2262	1780
L6	r	-0.722	0.728	0.582	-0.728	-0.665	-0.676	0.753	-0.743	0.743	-0.560

Table 4. Gray relational analysis of different mathematical transformation of characteristic spectrum of each level

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Table 4. Gray relational analysis of different mathematical transformation of characteristic spectrum of each level

Waveletlevel	Item	R	lg R	1/R	Lg (1/R)	1/lg R	R'	(lg R)'	(1/R)'	[lg(1/R)]'	(1/lg R)'
L0	GCD	0.700	0.747	0.819	0.750	0.786	0.806	0.851	0.839	0.775	0.815
L0	Order	10	9	3	8	6	5	1	2	7	4
L1	GCD	0.805	0.858	0.898	0.800	0.799	0.870	0.887	0.955	0.890	0.881
L1	Order	9	7	2	8	10	6	4	1	3	5
L2	GCD	0.753	0.796	0.824	0.772	0.808	0.868	0.881	0.870	0.831	0.867
L2	Order	9	8	6	10	7	3	1	2	5	4
L3	GCD	0.759	0.819	0.823	0.755	0.865	0.821	0.916	0.842	0.833	0.887
L3	Order	9	8	6	10	3	7	1	4	5	2
L4	GCD	0.788	0.800	0.862	0.875	0.809	0.884	0.895	0.893	0.892	0.842
L4	Order	10	9	6	5	8	4	1	2	3	7
L5	GCD	0.745	0.831	0.827	0.803	0.806	0.818	0.881	0.835	0.873	0.867
L5	Order	10	5	6	9	8	7	1	4	2	3
L6	GCD	0.770	0.828	0.870	0.779	0.787	0.815	0.903	0.889	0.856	0.816
L6	Order	10	5	3	9	8	6	1	2	4	7

Table 5. Estimation results of SMC

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Table 5. Estimation results of SMC

Variable selectionmethod	Number ofvariable	Calibration set		Validation set
Variable selectionmethod	Number ofvariable	R²_c	e_RMSEC	$\begin{matrix} R_{p}^{2} \end{matrix}$	e_RMSEP	e_RPD
L0	10	0.750	0.024	0.926	0.045	1.867
L1	10	0.769	0.023	0.912	0.042	2.000
L2	10	0.692	0.027	0.890	0.035	2.400
L3	10	0.748	0.024	0.884	0.033	2.545
L4	10	0.670	0.053	0.875	0.034	2.471
L5	10	0.675	0.028	0.872	0.049	1.714
L6	10	0.672	0.028	0.911	0.032	2.625
L-GRA	12	0.710	0.026	0.965	0.030	2.800

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Cai Lianghong, Ding Jianli. Inversion of Soil Moisture Content Based on Hyperspectral Multi-Scale Decomposition[J]. Laser & Optoelectronics Progress, 2018, 55(1): 13001

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Paper Information

Category: Spectroscopy

Received: Jul. 18, 2017

Accepted: --

Published Online: Sep. 10, 2018

The Author Email: Ding Jianli (2187736938@qq.com)

DOI:10.3788/LOP55.013001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Statistical characteristics of SMC in soil samples

Table 1. Statistical characteristics of SMC in soil samples

Table 2. Correlation analysis between SMC and characteristic spectrum in each level

Table 2. Correlation analysis between SMC and characteristic spectrum in each level

Table 3. Maximum correlation between SMC and different mathematical transformation of characteristic spectrum of each level and position of band

Table 3. Maximum correlation between SMC and different mathematical transformation of characteristic spectrum of each level and position of band

Table 4. Gray relational analysis of different mathematical transformation of characteristic spectrum of each level

Table 4. Gray relational analysis of different mathematical transformation of characteristic spectrum of each level

Table 5. Estimation results of SMC

Table 5. Estimation results of SMC