基于高光谱特征参数优选的土壤盐分含量建模及其验证

doi:10.6048/j.issn.1001-4330.2021.12.022

新疆农业科学 ›› 2021, Vol. 58 ›› Issue (12): 2342-2352.DOI: 10.6048/j.issn.1001-4330.2021.12.022

• 畜牧兽医·农业信息 • 上一篇

基于高光谱特征参数优选的土壤盐分含量建模及其验证

李志¹(), 苏武峥¹, 李新国²^,³(), 王银方¹, 毛东雷²^,³, 麦麦提吐尔逊·艾则孜²^,³

1.新疆农业科学院农业经济与科技信息研究所,乌鲁木齐 830091
2.新疆师范大学地理科学与旅游学院,乌鲁木齐 830054
3.新疆干旱区湖泊环境与资源实验室,乌鲁木齐 830054

收稿日期:2021-04-01 出版日期:2021-12-20 发布日期:2021-12-31
通信作者: 李新国
作者简介:李志(1992-),男,重庆人,助理研究员,硕士,研究方向为农业遥感与信息技术,(E-mail) lizhi2378@163.com
基金资助:
国家自然科学基金(41661047);国家自然科学基金(41561073);新疆农业科学院科技创新(nkvzzki-019)

Modeling and Verification of Soil Salt Content Based on Hyperspectral Characteristic Parameter Optimization

LI Zhi¹(), SU Wuzheng¹, LI Xinguo²^,³(), WANG Yinfang¹, MAO Donglei²^,³, Mamattursun Eziz²^,³

1. Institute of Agricultural Economics and Scientific Technical Information, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
2. College of Geographic Sciences and Tourism, Xinjiang Normal University, Urumqi 830054, China
3. Xinjiang Key Laboratory of Lake Environment and Resource in Arid Zone, Xinjiang Normal University, Urumqi 830054, China

Received:2021-04-01 Online:2021-12-20 Published:2021-12-31
Correspondence author: LI Xinguo
Supported by:
National Natural Science Foundation of China(41661047);National Natural Science Foundation of China(41561073);Scientific and Technological Innovation Project of Xinjiang Academy of Agricultural Sciences(nkvzzki-019)

摘要/Abstract

摘要： 目的研究不同维度光谱变换下土壤盐分反演模型及其验证。方法以博斯腾湖西岸湖滨绿洲为研究区,面向ASD高光谱数据,利用17种一维数学变换光谱和3种二维变换光谱指数,分别与实测土壤盐分进行相关分析,得到0.01显著性检验水平下初步优选的光谱特征参数,基于VIP准则选入最佳自变量实现PLSR模型构建,进行精度验证。结果研究区干季土壤平均反射率随含盐量的增加而高于湿季土壤平均反射率,尤其体现在590、800、1 810、2 150 nm处;17种一维单波段光谱变换中,对数倒数的一阶微分(1/lgR)变换与土壤盐分含量相关性最好,峰值敏感波段为1 083 nm,相关系数绝对值|r|最高达0.63;3种二维两波段光谱变换中,归一化光谱指数NDSI(R_{1 780},R_{1 742})与土壤盐分含量相关性最好,相关分析决定系数R ²最大值为0.57;基于特征归一化光谱指数结合VIP准则进行自变量筛选的PLSR估算模型效果最佳,土壤盐分建模集和验证集的决定系数 $R V 2$ 达0.77,均方根误差RMSEV为0.64 g/kg,相对分析误差RPD为2.11。结论利用归一化光谱指数NDSI建立PLSR高光谱模型可有效地对研究区土壤盐分进行定量估算。

关键词: 土壤盐分; 光谱变换; 特征参数; 偏最小二乘回归

Abstract:

【Objective】 To explore the feasibility of soil salinity inversion model under different dimensional spectral transformations.【Methods】 The lakeside oasis on the west of Bosten Lake in Xinjiang was taken as the research area, the correlation analysis between 17 one-dimensional mathematical transformation spectra and 3 two-dimensional transformation spectral indexes of ASD hyperspectral data and the measured soil salinity were conducted to obtain the preliminarily optimized spectral characteristic parameters at the significance test level of 0.01. Then, the PLSR model was constructed based on the VIP criteria and selected into the optimal independent variable and the accuracy was verified.【Results】 The average reflectance of dry soil was higher than that of wet season with the increase of salt content, especially at 590, 800, 1,810 nm and 2,150 nm. Among the 17 one-dimensional single-band spectral transformations, the first derivative of logarithmic reciprocal (1/lgR) had the best correlation with soil salinity, the peak sensitive band was 1083nm, and the absolute value of correlation coefficient was up to 0.63. Among the three two-dimensional two-band spectral transforms, the normalized spectral index NDSI(R_{1 780}, R_{1 742}) had the best correlation with soil salt content, and the maximum value of correlation analysis determination coefficient R ² was 0.57. The PLSR estimation model based on characteristic normalized spectral index and VIP criterion for independent variable screening had the best effect. The determination coefficient $R V 2$ of soil salt modeling set and verification set was 0.77, the root mean square error was 0.64 g/kg, and the relative analysis error was 2.11.【Conclusion】 Using the normalized spectral index (NDSI) to establish PLSR hyperspectral model could effectively estimate the soil salinity in the study area.

Key words: soil salinity; spectral transformation; spectral index; characteristic parameters; partial least squares regression

中图分类号:

李志, 苏武峥, 李新国, 王银方, 毛东雷, 麦麦提吐尔逊·艾则孜. 基于高光谱特征参数优选的土壤盐分含量建模及其验证[J]. 新疆农业科学, 2021, 58(12): 2342-2352.

LI Zhi, SU Wuzheng, LI Xinguo, WANG Yinfang, MAO Donglei, Mamattursun Eziz. Modeling and Verification of Soil Salt Content Based on Hyperspectral Characteristic Parameter Optimization[J]. Xinjiang Agricultural Sciences, 2021, 58(12): 2342-2352.

图/表 14

图1 采样区位置及样点布设

Fig.1 Location of the sampling area and its sample layout

图2 建模集和验证集的盐分含量频数分布统计

Fig. 2 Statistics of salt content frequency distribution in model set and validation set

表1 参数定义

Table 1 Definition of parameters

参数Parameter	定义Definition
R_λ	在波段λ处的土壤反射光谱 Soil reflectance spectrum at bandλ
DSI(i,j)	DSI=R_j-R_i
RSI(i,j)	RSI=R_j/R_i
NDSI(i,j)	NDSI=(R_j-R_i)/(R_j+R_i)

表2 模型精度参数

Table 2 Parameters of model accuracy

参数 Parameter	简称 Abbreviation	公式 Formula		描述 Description
建模决定系数 Modeling determination factor	$R C 2$	$\sum^{N}_{i=1}(x_{i}-\bar{x})(y_{i}-\bar{y})/ \sqrt{\sum^{N}_{i=1}(x_{i}-\bar{x})^{2}+\sum^{N}_{i=1}(y_{i}-\bar{y})^{2}}$		当值越接近1,模型越稳定
预测决定系数 Predictive determination coefficient	$R V 2$			当值越接近1,模型越稳定
校正均方根误差 Correct the root mean square error	RMSEC	$\sqrt{\sum^{N}_{i=1}(y_{i}-\bar{x})^{2}/N}$		当值越接近0,模型精度越高
验证均方根误差 Verify the root mean square error	RMSEV	$\sqrt{\sum^{N}_{i=1}(y_{i}-\bar{x})^{2}/N}$		当值越接近0,模型精度越高
相对分析误差 Relative prediction deviation	RPD	$S SD / r RMSEV$	0~1.4,差;1.4~2.0,可用;大于2.0,好

表2 模型精度参数

Table 2 Parameters of model accuracy

参数 Parameter	简称 Abbreviation	公式 Formula		描述 Description
建模决定系数 Modeling determination factor	$R C 2$	$\sum^{N}_{i=1}(x_{i}-\bar{x})(y_{i}-\bar{y})/ \sqrt{\sum^{N}_{i=1}(x_{i}-\bar{x})^{2}+\sum^{N}_{i=1}(y_{i}-\bar{y})^{2}}$		当值越接近1,模型越稳定
预测决定系数 Predictive determination coefficient	$R V 2$			当值越接近1,模型越稳定
校正均方根误差 Correct the root mean square error	RMSEC	$\sqrt{\sum^{N}_{i=1}(y_{i}-\bar{x})^{2}/N}$		当值越接近0,模型精度越高
验证均方根误差 Verify the root mean square error	RMSEV	$\sqrt{\sum^{N}_{i=1}(y_{i}-\bar{x})^{2}/N}$		当值越接近0,模型精度越高
相对分析误差 Relative prediction deviation	RPD	$S SD / r RMSEV$	0~1.4,差;1.4~2.0,可用;大于2.0,好

图3 土壤剖面盐分分布及其变化特征

Fig.3 Distribution and variation of salinity in soil profile

图4 土壤剖面光谱反射曲线特征

Fig.4 Characteristics of spectral reflectance curves of soil profiles

图5 土壤盐分含量与不同变换形式光谱反射率的一维相关性

Fig.5 One-dimensional correlation analysis of soil salinity content and spectral reflectance of different transformed forms

表2 不同光谱变换形式下的土壤盐分特征波段统计

Table 2 Statistical analysis of characteristic bands of soil salinity under different spectral transformation forms

光谱变换 Spectrum transform	极显著相关系数绝对值超出的数量 The number of extremely significant correlation coefficients exceeding the absolute value (\|r\|≥0.45,P<0.01)	相关系数绝对值的最值 (\|r\|_max)	相关系数绝对值((\|r\|)的最值波段位置 The band position of\|r\|_max
R	0	0.27	1 406 nm
R'	46	0.62	1 230 nm
( $R$ )'	35	0.60	1 230 nm
(1/lgR)'	53	0.63	1 083 nm
lg(1/R)'	18	0.58	2 307 nm

表2 不同光谱变换形式下的土壤盐分特征波段统计

Table 2 Statistical analysis of characteristic bands of soil salinity under different spectral transformation forms

光谱变换 Spectrum transform	极显著相关系数绝对值超出的数量 The number of extremely significant correlation coefficients exceeding the absolute value (\|r\|≥0.45,P<0.01)	相关系数绝对值的最值 (\|r\|_max)	相关系数绝对值((\|r\|)的最值波段位置 The band position of\|r\|_max
R	0	0.27	1 406 nm
R'	46	0.62	1 230 nm
( $R$ )'	35	0.60	1 230 nm
(1/lgR)'	53	0.63	1 083 nm
lg(1/R)'	18	0.58	2 307 nm

图6 光谱指数DSI、RSI、NDSI与土壤盐分的R2矩阵

Fig. 6 R2 matrix of spectral index DSI, RSI, NDSI and soil salinity

表3 基于光谱指数的土壤盐分特征波段组合

Table 3 Soil salt characteristic band combination based on spectral index

光谱指数 Spectral indices	R²超出的光谱指数数量(R²≥0.4,P<0.01) The number of spectral indices R² exceeds 0.4	R²最大值 $R max 2$	R²最大值的对应波段组合 The band position of $R max 2$
差值指数(DSI) Differential spectral index	32	0.54	R_{1 780}-R_{1 765}
比值指数(RSI) The ratio of spectral index	25	0.48	R_{1 785}/R_{1 756}
归一化指数(NDSI) Normalized spectral index	38	0.57	(R_{1 780}-R_{1 742})/(R_{1 780}+R_{1 742})

表3 基于光谱指数的土壤盐分特征波段组合

Table 3 Soil salt characteristic band combination based on spectral index

光谱指数 Spectral indices	R²超出的光谱指数数量(R²≥0.4,P<0.01) The number of spectral indices R² exceeds 0.4	R²最大值 $R max 2$	R²最大值的对应波段组合 The band position of $R max 2$
差值指数(DSI) Differential spectral index	32	0.54	R_{1 780}-R_{1 765}
比值指数(RSI) The ratio of spectral index	25	0.48	R_{1 785}/R_{1 756}
归一化指数(NDSI) Normalized spectral index	38	0.57	(R_{1 780}-R_{1 742})/(R_{1 780}+R_{1 742})

图7 基于VIP算法筛选最佳模型光谱波段

Fig.7 Screening the best model spectral band based on the VIP algorithm

图8 基于VIP算法筛选最佳模型光谱指数

Fig. 8 Screening the best model spectral index based on VIP algorithm

表4 土壤盐分含量PLSR模型预测结果

Table 4 Prediction results of soil salt content by PLSR model

光谱特征参数 Spectral characteristic parameter	自变量个数 Number of independent variables	建模集Modeling set
光谱特征参数 Spectral characteristic parameter	自变量个数 Number of independent variables	$R C 2$	RMSEC	$R V 2$	RMSEV	RPD
R'	15	0.78	0.74	0.73	0.79	1.80
( $R$ )'	8	0.74	0.79	0.66	0.82	1.68
(1/lgR)'	16	0.82	0.61	0.77	0.69	2.01
[lg(1/R)]'	7	0.62	0.83	0.57	0.92	1.51
DSI	12	0.81	0.51	0.72	0.73	1.88
RSI	8	0.74	0.75	0.63	0.95	1.62
NDSI	12	0.86	0.46	0.77	0.64	2.11

表4 土壤盐分含量PLSR模型预测结果

Table 4 Prediction results of soil salt content by PLSR model

光谱特征参数 Spectral characteristic parameter	自变量个数 Number of independent variables	建模集Modeling set
光谱特征参数 Spectral characteristic parameter	自变量个数 Number of independent variables	$R C 2$	RMSEC	$R V 2$	RMSEV	RPD
R'	15	0.78	0.74	0.73	0.79	1.80
( $R$ )'	8	0.74	0.79	0.66	0.82	1.68
(1/lgR)'	16	0.82	0.61	0.77	0.69	2.01
[lg(1/R)]'	7	0.62	0.83	0.57	0.92	1.51
DSI	12	0.81	0.51	0.72	0.73	1.88
RSI	8	0.74	0.75	0.63	0.95	1.62
NDSI	12	0.86	0.46	0.77	0.64	2.11

图9 基于一维和二维最佳响应参数光谱估算模型验证

Fig. 9 Verification results based on one-dimensional and two-dimensional optimal response parameterspectral estimation models

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基于高光谱特征参数优选的土壤盐分含量建模及其验证

Modeling and Verification of Soil Salt Content Based on Hyperspectral Characteristic Parameter Optimization

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