基于RGB和纹理特征的土壤表层水盐信息反演方法

doi:10.3864/j.issn.0578-1752.2025.06.009

Abstract

Abstract:

【Objective】 Saline soil poses a global ecological challenge. The rapid and precise monitoring of surface soil water and salt information was crucial for effective control and remediation of soil salinization. 【Method】 The present study proposed a quantitative estimation method, which combined high-precision optical remote sensing and image digital processing technology at a small scale, to predict soil water content and salt content based on soil apparent color parameters (RGB) and texture feature values. Firstly, the calibration of the soil multi-parameter sensor was based on the relationship between dielectric constant and water content, electrical conductivity, and salt content. Secondly, the image digital processing technology was employed to extract the RGB and texture features of the soil. The most relevant variables were determined through correlation analysis, and an optimal fitting model incorporating RGB, texture features, water content, and salt content was constructed. Finally, the accuracy of the inversion method was verified using the sensor approach.【Result】 The trivariate regression model, which fitted the water content and RGB, exhibited the most optimal fitting effect with an R² value of 0.97. For the fitting of salt content to RGB and texture features, a one-variable polynomial model incorporating salt content and soil apparent white ratio demonstrated superior fitting performance when the salt content was greater than or equal to 6%, yielding an R² value of 0.97. Conversely, for salt content below 6%, the autocorrelation (AUT) fitting between salt content and texture feature values was proved to be the most effective approach with an R² value of 0.93. Upon comparing and calculating the water content and salt content obtained through both multi-parameter sensor calibration method and the inversion method proposed in this paper, it was observed that relative error ranges for water content measurement using these two methods fell within 0.27%-9.48%, while relative error ranges for salt content ranged from 0.07% to 8.64%. In both cases, the absolute errors remained below 1%. 【Conclusion】 The present study presented a methodology for the inversion of soil apparent water and salt information, thereby establishing a theoretical foundation and offering the technical support for the rapid and precise determination of soil surface water and salt.

Key words: image processing, frequency domain reflectometry (FDR), soil apparent color parameters (RGB), texture features, soil water and salt content

SONG Yan, CHAI MingTang, LI WangCheng, SUN LiYing, Wulianen Saiernu, DU TianZe. A Method for Estimating Water-Salinity Information of Soil Surface Using RGB and Texture Features[J].Scientia Agricultura Sinica, 2025, 58(6): 1159-1172.

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Figures/Tables 18

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Table 3

The texture features in this paper"

纹理特征 Texture feature	名称 Name	描述 Description	公式 Formula
MEA	均值 Mean	纹理的平均情况 Average situation of texture	$M E A=\sum_{i=0}^{N-1} \sum_{j=0}^{N-1} i \times p(i, j)$
STD	标准差 Standard deviation	图像中像素值的变化程度 The degree of change of pixel value in the image	$S T D=\sqrt{\sum_{i} \sum_{j} p(i, j) \times(i-M E A)^{2}}$
VAR	方差 Variance	纹理变化的大小 The size of texture change	$V A R=\sum_{i} \sum_{j}(i-M E A)^{2} p(i, j)$
CON	对比度 Contrast	纹理的清晰度 The clarity of texture	$C O N=\sum_{n=0}^{N g-1} n^{2}\left\{\begin{array}{c} \sum_{i=1}^{N g} \sum_{j=1}^{N g} p(i, j) \\ \|i-j\|=n \end{array}\right\}$
DIS	非相似性 Dissimilarity	图像中灰度级对之间的差异性 The difference between gray level pairs in the image	$D I S=\sum_{i} \sum_{j} p(i, j) \times\|i-j\|$
HOM	均一性 Homogeneity	局部纹理同质性 Local texture homogeneity	$H O M=\sum_{i} \sum_{j} \frac{1}{1+\|i-j\|} p(i, j)$
ASM	角二阶矩 Angle two matrix	图像中纹理的均匀性和清晰度 The uniformity and clarity of the texture in the image	$A S M=\sum_{i} \sum_{j}\{p(i, j)\}^{2}$
ENR	能量 Energy	图像灰度分布均匀程度和纹理粗细度 Image gray distribution uniformity and texture thickness	$E N R=\sum_{i} \sum_{j}\{p(i, j)\}^{2}$
MAX	最大值 Maximum value	图像中最明显或最突出的纹理元素的强度或尺度 The intensity or scale of the most obvious or prominent texture element in the image	$M A X=\max \{p(i, j)\}$
ENT	熵 Entropy	图像中纹理的非均匀程度或复杂程度 The non-uniformity or complexity of the texture in the image	$E N T=-\sum_{i} \sum_{j} p(i, j) \log (p(i, j))$
COR	相关性 Correlation	纹理的一致性 Texture consistency	$C O R=\frac{\sum_{i} \sum_{j}(i, j) p(i, j)-\mu_{x} \mu_{y}}{\sigma_{x} \sigma_{y}}$
IDM	逆差矩 Inverse difference moment	图像纹理的同质性，度量图像纹理局部变化的多少 The homogeneity of image texture measures the local change of image texture	$I D M=\sum_{i} \sum_{j} \frac{1}{1+(i-j)^{2}} p(i, j)$
AUT	自相关 Autocorrelation	纹理的粗细度及方向性等特征参数 Feature parameters such as texture thickness and directionality	$A U T=\sum_{i} \sum_{j} i \times j \times p(i, j)$

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参数 Parameter	测量范围 Measurement range	分辨率 Resolution ratio	测量精度 Measurement accuracy
介电常数 Dielectric constant	0.88—81.88	0.1	1
电导率 Electrical conductivity	0—20 dS·m^-1	0.01 dS·m^-1	±5%
温度 Temperature	-40.00—80.00 ℃	0.1 ℃	±0.5 ℃

测定项目 Item	质量含水量Moisture content (%)	含盐量Salt content (%)
土壤含水量 T1	0、2、4、6、8、10、12、14、16、18	0、2
土壤含盐量 T2	4、6	0、2、4、6、8、10、12、14、16、18

编号 Numbering	含水量 Water content (%)	含盐量 Salt content (%)
1	2	4、6、8
2	4
3	6
4	8

探头序号 Probe serial number	参数a Parameter a	决定系数 Coefficient of determination (R²)
1	4.3305	0.90
2	0.6691
3	4.0943
4	0.8464
5	-6.5299
6	-14.9267

样本数 Number of samples	参数 Parameter	R²	Sig.
10	R	-0.93	<0.01
	G	-0.94
	B	-0.91

A Method for Estimating Water-Salinity Information of Soil Surface Using RGB and Texture Features

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名称Name	意义Meaning	结果Result
KMO检验 KMO test	测量变量之间的共同度，即共享的统计变异量 The commonality between the measured variables, that is, the shared statistical variation	KMO=0.82，适合进行因子分析 KMO=0.82, which is suitable for factor analysis
巴特利特检验 Bartlett test	评估变量之间的相关性是否足够支持进行主成分分析 To assess whether the correlation between variables is sufficient to support principal component analysis	Sig.值（显著性概率）小于0.001 The Sig. value (probability of significance) is less than 0.001
总方差解释 Total variance explanation	衡量各主成分对原始数据信息的解释的重要指标 An important indicator to measure the interpretation of the original data information by each principal component	R、G、B三者的总方差解释为99.71% The total variance of R, G and B is 99.71%
Durbin-Waston检验 Durbin-Waston test	检验回归分析中残差的一阶自相关 Test the first-order autocorrelation of residuals in regression analysis	Durbin-Waston=2.13, 不存在自相关性 Durbin-Waston=2.13，no autocorrelation exists

自变量 Independent variable	线性回归方程 Equation of linear regression	模型检验 Model calibration
自变量 Independent variable	线性回归方程 Equation of linear regression	R²	RMSE
AUT	y=6817.6×AUT²-204398×AUT+5×10⁶	0.89	3.1801×10¹¹
R	y=-0.2599×R+48.1690	0.96	1.17
G	y=-0.2620×G+39.3632	0.95	1.21
B	y=-0.2690×B+32.2558	0.96	1.16
R、G、B	y=-0.097×R-0.052×G-0.118×B+39.874	0.97	0.85

含盐量 Salt content	自变量 Independent variable x	模型方程 Model equation	R²	RMSE
≥6%	含盐量白色占比 Salt content of white proportion	y=2.8720x+2.7696	0.91	1.62
		y=3.2606lnx+8.6280	0.88	5.96
		y=-0.3396x²+4.7752x+1.3118	0.97	0.85
<6%	纹理特征 Texture features	y=1.919×10^-17AUT³-2.522×10^-10AUT²+0.001AUT-1602.839	0.93	9.18
		y=-42720.899MAX³+28956.138MAX²-623.716MAX+5.737	0.71	39.13
		y=6.35×10¹³COR³-1.27×10¹⁴COR²+6.351×10¹³COR+81.921	0.77	38.26

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