基于高光谱多尺度分解的土壤含水量反演

蔡亮红; 丁建丽

doi:doi:10.3788/LOP55.013001

激光与光电子学进展, 2018, 55 (1): 013001, 网络出版: 2018-09-10

基于高光谱多尺度分解的土壤含水量反演下载： 1258次

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

蔡亮红 ^1,2丁建丽 ^1,2,*

作者单位

¹ 新疆大学资源与环境科学学院智慧城市与环境建模自治区普通高校重点实验室, 新疆乌鲁木齐 830046

² 新疆大学绿洲生态教育部重点实验室, 新疆乌鲁木齐 830046

图 & 表

图 1. 野外样点分布

Fig. 1. Distribution of field samping points

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图 2. 小波变换1~8层重构光谱

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

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图 3. 实测值与估算值的比较

Fig. 3. Comparison between measured and predicted SMC values

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表 1土壤样品SMC统计特征

Table1. 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

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表 2SMC与各层特征光谱的相关分析

Table2. 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

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表 3SMC与各层特征光谱的不同数学变换的最大相关性及其波段所处位置

Table3. 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

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表 4各层特征光谱不同数学变换的灰色关联分析

Table4. 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

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表 5SMC预测结果

Table5. 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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蔡亮红, 丁建丽. 基于高光谱多尺度分解的土壤含水量反演[J]. 激光与光电子学进展, 2018, 55(1): 013001. Cai Lianghong, Ding Jianli. Inversion of Soil Moisture Content Based on Hyperspectral Multi-Scale Decomposition[J]. Laser & Optoelectronics Progress, 2018, 55(1): 013001.

基于高光谱多尺度分解的土壤含水量反演下载： 1258次

图 1. 野外样点分布

Fig. 1. Distribution of field samping points

图 2. 小波变换1~8层重构光谱

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

图 3. 实测值与估算值的比较

Fig. 3. Comparison between measured and predicted SMC values

表 1土壤样品SMC统计特征

Table1. Statistical characteristics of SMC in soil samples

表 2SMC与各层特征光谱的相关分析

Table2. Correlation analysis between SMC and characteristic spectrum in each level

表 3SMC与各层特征光谱的不同数学变换的最大相关性及其波段所处位置

Table3. Maximum correlation between SMC and different mathematical transformation of characteristic spectrum of each level and position of band

表 4各层特征光谱不同数学变换的灰色关联分析

Table4. Gray relational analysis of different mathematical transformation of characteristic spectrum of each level

表 5SMC预测结果

Table5. Estimation results of SMC

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基于高光谱多尺度分解的土壤含水量反演 下载： 1258次

图 1. 野外样点分布

Fig. 1. Distribution of field samping points

图 2. 小波变换1~8层重构光谱

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

图 3. 实测值与估算值的比较

Fig. 3. Comparison between measured and predicted SMC values

表 1土壤样品SMC统计特征

Table1. Statistical characteristics of SMC in soil samples

表 2SMC与各层特征光谱的相关分析

Table2. Correlation analysis between SMC and characteristic spectrum in each level

表 3SMC与各层特征光谱的不同数学变换的最大相关性及其波段所处位置

Table3. Maximum correlation between SMC and different mathematical transformation of characteristic spectrum of each level and position of band

表 4各层特征光谱不同数学变换的灰色关联分析

Table4. Gray relational analysis of different mathematical transformation of characteristic spectrum of each level

表 5SMC预测结果

Table5. Estimation results of SMC

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基于高光谱多尺度分解的土壤含水量反演下载： 1258次