Evaluation of Soil Spectral Indices and Surface Albedo in Desertification Monitoring of Western Khuzestan Province using Remote Sensing Data

Document Type : Research Article

Authors

1 Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 Department of Human Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran.

Abstract

Desertification is a form of land degradation, resulting from both natural processes and human activities. In this regard, the use of remote sensing indicators to prepare desertification maps can be an efficient approach to assess this process. The goal of this study is to evaluate the potential of soil spectral indices and surface albedo in modeling desertification intensity in the western Khuzestan Province. Initially, we extracted indices including BSI, BI, SI, IFe2O3, NSMI, TGSI, CLI, CI, SCI, and Albedo from Landsat 8 satellite images. Then, soil spectral indices were calculated using the band calculation tool available in ENVI software, and raster maps of these indices were generated. To create feature space models and identify correlations between the variables, we also extracted the surface albedo index values from the satellite images.
The maximum and minimum values of surface albedo index were 37518.1 and 8338.75, respectively. Next, we randomly selected spectral values from 753 locations on the soil spectral index map and extracted their corresponding values from the surface albedo index map. To ensure comparability, the index values were standardized through data normalization. After normalization, scatter plots of the spectral pixel value densities were generated in the SAGA 9.5 software environment, and best-fit line equations were determined. Linear regression analysis was then conducted to examine the correlation between the soil spectral indices and Albedo.
In this study, all soil spectral indices were considered independent variables, while the surface albedo index was treated as the dependent variable. Desertification intensity was classified by dividing the feature space perpendicular to the trend of desertification change. Subsequently, the Jenks Natural Breaks method was applied to classify the data values into five desertification classes: areas without desertification, areas with low desertification intensity, areas with moderate desertification intensity, areas with high desertification intensity, and areas with very high desertification intensity.
The results of the correlation analysis of the indices show that the BSI, BI, SI, NSMI, TGSI, CLI, and SCI indices are positively correlated with the Albedo index; so with the increase in the BSI, BI, SI, NSMI, TGSI, CLI, and SCI indices, the Albedo variable also increases. While the IFe2O3 and CI indices were negatively correlated with the Albedo index, such that an increase in IFe2O3 and CI indices resulted in a reduction of the Albedo variable. An analysis of the correlation coefficients between the indices revealed that the BI, SI, and TGSI indices exhibited the strongest correlation with the Albedo index, with correlation coefficients of 0.985, 0.909, and 0.850, respectively. Conversely, the IFe2O3, CLI, and NSMI indices showed the weakest correlation with the Albedo index, with correlation coefficients of -0.026, 0.106, and 0.110, respectively. Therefore, the Albedo-BI, Albedo-SI, and Albedo-TGSI models, due to their highest correlations with the variables, were considered suitable criterion for evaluating and classifying desertification in the region, given its rather arid climate.
Following the classification of desertification intensity, the accuracy of the Albedo-BI, Albedo-SI, and Albedo-TGSI feature space models was assessed using the error matrix, overall accuracy, and the kappa coefficient. The Albedo-BI model achieved an overall accuracy of 98.23% and a kappa coefficient of 0.97; the Albedo-SI model yielded 94.45% accuracy and a kappa value of 0.92; and the Albedo-TGSI model recorded 90.50% accuracy with a kappa coefficient of 0.86. These results indicate that the Albedo-BI model provides the highest classification accuracy among the three models. Furthermore, analysis of the models suggests that the eastern parts of the study area exhibit higher desertification intensity than the western regions.

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Main Subjects

References

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Journal of the Earth and Space Physics
Volume 51, Issue 3
online publication date: 16 December 2025
December 2025
Pages 593-611

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