Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions
Abstract
:1. Introduction
2. Materials and Method
2.1. Study Area Description and Field Survey
2.2. Remote Sensing Data Acquisition and Pre-Processing
2.3. Feature Selection Techniques
2.4. Filter Methods
2.5. Wrapper Methods
2.6. Embedded Methods
3. Results
3.1. Modeling Assessment
3.2. Feature Selection Approaches for Digital Soil Salinity Mapping
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Salinity Class | Soil Salinity (dS/m) | Effect on Crop Plants |
---|---|---|
Non-saline | <2 | Negligible salinity effects. |
Slightly saline | 2–4 | Yields of sensitive crops maybe affected. |
Moderately saline | 4–8 | Yields of many crops are affected. |
Strongly saline | 8–16 | Only tolerant crops will survive. |
Very strongly saline | >16 | Only a few tolerant crops will survive. |
Descriptions | Selected Variable |
---|---|
Sentinel-2, Band (b) selection | b2, b3, b4, b5, b6, b7, b8, b8A, b11 and b12. |
Sentinel-2, Indices | WDVI, TNDVI, SAVI, NDWI, NDVI, MSAVI, MSAVI2, MNDWI, MCARI, IPVI, GNDVI, DVI, CI, BI and BI2. |
Sentinal-1, sigma nought (δ0) db | VV and VH. |
V V_ GLCM | Contrast_VV, Dissimilarity_VV, Homogeneity_VV, AngularSecondMoment_VV, Energy_VV, Entropy_VV, MaximumProbability_VV, Correlation_VV, Mean_VV and StandardDviation_VV. |
VH_ GLCM | Contrast_VH, Dissimilarity_VH, Homogeneity_VH, AngularSecondMoment_VH, Energy_VH, Entropy_VH, MaximumProbability_VH, Correlation_VH, Mean_VH and StandardDeviation_VH. |
Learner | Subsetted Variables | No. of Subsetted Variables | RMSE |
---|---|---|---|
LR |
| 29 | 0.48174263 |
RF |
| 24 | 0.52825744 |
SVR |
| 14 | 0.55513133 |
BPNN |
| 18 | 0.302719033 |
Learner | Subsetted Variables | Number of Subsetted Variables | RMSE |
---|---|---|---|
RF | WDVI, TNDVI, SAVI, NDWI, NDVI, MSAVI, MSAVI2, IPVI, GNDVI, DVI, CI. VV, VH Mean_VV. Mean_VH. | 15 | 0.52825744 |
LASSO | band3, band6, band8, band8A, band11, band12, TNDVI, MNDWI, CI, BI2, VV. Contrast_VV, Dissimilarity_VV, Homogeneity_VV, Angular_Second_Moment_VV, Correlation_VV, Mean_VV. Contrast_VH, Dissimilarity_VH, Homogeneity_VH, Angular_Second_Moment_VH, Maximum_Probability_VH, Correlation_VH, Angular_Second_Moment_VH, Maximum_Probability_VH, Correlation_VH. | 23 | 0.5093330 |
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Mohamed, S.A.; Metwaly, M.M.; Metwalli, M.R.; AbdelRahman, M.A.E.; Badreldin, N. Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions. Remote Sens. 2023, 15, 1751. https://doi.org/10.3390/rs15071751
Mohamed SA, Metwaly MM, Metwalli MR, AbdelRahman MAE, Badreldin N. Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions. Remote Sensing. 2023; 15(7):1751. https://doi.org/10.3390/rs15071751
Chicago/Turabian StyleMohamed, Sayed A., Mohamed M. Metwaly, Mohamed R. Metwalli, Mohamed A. E. AbdelRahman, and Nasem Badreldin. 2023. "Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions" Remote Sensing 15, no. 7: 1751. https://doi.org/10.3390/rs15071751
APA StyleMohamed, S. A., Metwaly, M. M., Metwalli, M. R., AbdelRahman, M. A. E., & Badreldin, N. (2023). Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions. Remote Sensing, 15(7), 1751. https://doi.org/10.3390/rs15071751