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Publishing Language: Chinese

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

Yan SONG1,2, MingTang CHAI1,2( ), WangCheng LI1, LiYing SUN1, Saiernu Wulianen1, TianZe DU1
School of Civil and Hydraulic Engineering, Ningxia University, Yinchuan 750021
Ningxia Key Laboratory of the Internet of Water and Digital Water Governance of the Yellow River, Yinchuan 750021
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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.

Keywords

image processingfrequency domain reflectometry (FDR)soil apparent color parameters (RGB)texture featuressoil water and salt content

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Scientia Agricultura Sinica
Volume 58 Issue 6,
March 2025
Pages 1159-1172
DOI: 10.3864/j.issn.0578-1752.2025.06.009

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SONG Y, CHAI M, LI W, et al. A Method for Estimating Water-Salinity Information of Soil Surface Using RGB and Texture Features. Scientia Agricultura Sinica, 2025, 58(6): 1159-1172. https://doi.org/10.3864/j.issn.0578-1752.2025.06.009

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Received: 07 May 2024
Accepted: 11 June 2024
Published: 16 March 2025
© 2025 The Journal of Scientia Agricultura Sinica