The Optimal Estimation Model for Soil Salinization Based on the FOD-CNN Spectral Index
Globally, diverse regions are experiencing significant salinization, yet research leveraging two-dimensional spectral indices derived from fractional-order differentiated hyperspectral data remains relatively scarce. Given that the Yellow River Delta exemplifies a severely salinized area, this study...
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2025-07-01
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| author | Jicun Yang Bing Guo Rui Zhang |
| author_facet | Jicun Yang Bing Guo Rui Zhang |
| author_sort | Jicun Yang |
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| description | Globally, diverse regions are experiencing significant salinization, yet research leveraging two-dimensional spectral indices derived from fractional-order differentiated hyperspectral data remains relatively scarce. Given that the Yellow River Delta exemplifies a severely salinized area, this study employs it as a case study to advance salinization monitoring by integrating fractional-order differentiation with two-dimensional spectral indices. Compared to fractional-order differentiation (FOD) and deep learning models, integer-order differentiation and traditional detection models suffer from lower accuracy. Therefore, a two-dimensional spectral index was constructed to identify sensitive parameters. Modeling methods such as Convolutional Neural Networks (CNNs), Partial Least Squares Regression (PLSR), and Random Forest (RF) were employed to predict soil salinity. The results show that FOD effectively emphasizes gradual changes in spectral curve transformations, significantly improving the correlation between spectral indices and soil salinity. The 1.6-order NDI spectral index (1244 nm, 2081 nm) showed the highest correlation with soil salinity, with a coefficient of 0.9, followed by the 1.6-order RI spectral index (2242 nm, 1208 nm), with a correlation coefficient of 0.882. The CNN model yielded the highest inversion accuracy. Compared to the PLSR and RF models, the CNN model increased the RPD of the prediction set by 0.710 and 1.721 and improved the R<sup>2</sup> by 0.057 and 0.272, while reducing the RMSE by 0.145 g/kg and 1.470 g/kg. This study provides support for monitoring salinization in the Yellow River Delta. |
| format | Article |
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| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-7bd98f14ded94b61acfdc64efa9507bc2025-08-20T03:56:47ZengMDPI AGRemote Sensing2072-42922025-07-011714235710.3390/rs17142357The Optimal Estimation Model for Soil Salinization Based on the FOD-CNN Spectral IndexJicun Yang0Bing Guo1Rui Zhang2School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaSchool of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, ChinaState Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaGlobally, diverse regions are experiencing significant salinization, yet research leveraging two-dimensional spectral indices derived from fractional-order differentiated hyperspectral data remains relatively scarce. Given that the Yellow River Delta exemplifies a severely salinized area, this study employs it as a case study to advance salinization monitoring by integrating fractional-order differentiation with two-dimensional spectral indices. Compared to fractional-order differentiation (FOD) and deep learning models, integer-order differentiation and traditional detection models suffer from lower accuracy. Therefore, a two-dimensional spectral index was constructed to identify sensitive parameters. Modeling methods such as Convolutional Neural Networks (CNNs), Partial Least Squares Regression (PLSR), and Random Forest (RF) were employed to predict soil salinity. The results show that FOD effectively emphasizes gradual changes in spectral curve transformations, significantly improving the correlation between spectral indices and soil salinity. The 1.6-order NDI spectral index (1244 nm, 2081 nm) showed the highest correlation with soil salinity, with a coefficient of 0.9, followed by the 1.6-order RI spectral index (2242 nm, 1208 nm), with a correlation coefficient of 0.882. The CNN model yielded the highest inversion accuracy. Compared to the PLSR and RF models, the CNN model increased the RPD of the prediction set by 0.710 and 1.721 and improved the R<sup>2</sup> by 0.057 and 0.272, while reducing the RMSE by 0.145 g/kg and 1.470 g/kg. This study provides support for monitoring salinization in the Yellow River Delta.https://www.mdpi.com/2072-4292/17/14/2357salinizationfractional-order differentiationhyperspectralconvolutional neural networksDongying City |
| spellingShingle | Jicun Yang Bing Guo Rui Zhang The Optimal Estimation Model for Soil Salinization Based on the FOD-CNN Spectral Index Remote Sensing salinization fractional-order differentiation hyperspectral convolutional neural networks Dongying City |
| title | The Optimal Estimation Model for Soil Salinization Based on the FOD-CNN Spectral Index |
| title_full | The Optimal Estimation Model for Soil Salinization Based on the FOD-CNN Spectral Index |
| title_fullStr | The Optimal Estimation Model for Soil Salinization Based on the FOD-CNN Spectral Index |
| title_full_unstemmed | The Optimal Estimation Model for Soil Salinization Based on the FOD-CNN Spectral Index |
| title_short | The Optimal Estimation Model for Soil Salinization Based on the FOD-CNN Spectral Index |
| title_sort | optimal estimation model for soil salinization based on the fod cnn spectral index |
| topic | salinization fractional-order differentiation hyperspectral convolutional neural networks Dongying City |
| url | https://www.mdpi.com/2072-4292/17/14/2357 |
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