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<p>In this paper, according to the process of remote sensing monitoring of soil salinity and alkalinity process as well as the conditions, the remote sensing images were radiometrically corrected and aligned, and the remote sensing images were enhanced by using digital models to change the gray structure relationship of the image elements and change the gray value of the image elements, and then the changes in the patches of the remote sensing images were analyzed to extract the soil salinity and alkalinity data. In this paper, we also used statistical methods to analyze the acidity and salinity characteristics of soil samples, the soil spectral reflectance characteristics, and the sensitive bands for estimating the soil acidity and salinity characteristics, and we performed the multispectral inversion analysis of soil salinity on the basis of the spectral data. The results show that the remote sensing monitoring and early warning model of the soil salinization process established in this paper has a coefficient of determination <italic>R</italic><sup>2</sup> =0.697, RMSE=0.946, p=1.06*10-7 in the research calculations, and the root-mean-square error between predicted and measured values RMSE=2.33, which indicates that this model has a better performance in monitoring and prediction. The theoretical significance and practical value of this study are crucial for protecting the ecological environment and managing soil salinization.</p>
</abstract>

In this paper, according to the process of remote sensing monitoring of soil salinity and alkalinity process as well as the conditions, the remote sensing images were radiometrically corrected and aligned, and the remote sensing images were enhanced by using digital models to change the gray structure relationship of the image elements and change the gray value of the image elements, and then the changes in the patches of the remote sensing images were analyzed to extract the soil salinity and alkalinity data. In this paper, we also used statistical methods to analyze the acidity and salinity characteristics of soil samples, the soil spectral reflectance characteristics, and the sensitive bands for estimating the soil acidity and salinity characteristics, and we performed the multispectral inversion analysis of soil salinity on the basis of the spectral data. The results show that the remote sensing monitoring and early warning model of the soil salinization process established in this paper has a coefficient of determination R2 =0.697, RMSE=0.946, p=1.06*10-7 in the research calculations, and the root-mean-square error between predicted and measured values RMSE=2.33, which indicates that this model has a better performance in monitoring and prediction. The theoretical significance and practical value of this study are crucial for protecting the ecological environment and managing soil salinization.

Remote sensing monitoring and early warning modeling of soil salinization process

Lanzhou Resources & Environment Voc-Tech University

College of Forestry Gansu Agricultural University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, according to the process of remote sensing monitoring of soil salinity and alkalinity process as well as the conditions, the remote sensing...

Remote sensing monitoring and early warning modeling of soil salinization process

Publié en ligne: 05 août 2024

Pages: -

Reçu: 17 avr. 2024

Accepté: 10 juil. 2024

DOI: https://doi.org/10.2478/amns-2024-2204

Mots clés
Inversion analysis, Statistical analysis, Remote sensing monitoring, Image enhancement, Soil salinization

© 2024 Xiaoxiong Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Remote sensing monitoring and early warning modeling of soil salinization process

Publié en ligne: 05 août 2024

Pages: -

Reçu: 17 avr. 2024

Accepté: 10 juil. 2024

DOI: https://doi.org/10.2478/amns-2024-2204

Mots clésInversion analysis, Statistical analysis, Remote sensing monitoring, Image enhancement, Soil salinization

© 2024 Xiaoxiong Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Inversion analysis, Statistical analysis, Remote sensing monitoring, Image enhancement, Soil salinization