Soil Salinity Detection and Mapping by Multi-Temporal Landsat Data: Zaghouan Case Study (Tunisia)
Soil salinity is considered one of the biggest constraints to crop production, particularly in arid and semi-arid regions affected by recurrent and long periods of drought, where high salinity levels severely impact plant stress and consequently agricultural production. Climate change accelerates so...
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MDPI AG
2024-12-01
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| author | Karem Saad Amjad Kallel Fabio Castaldi Thouraya Sahli Chahed |
| author_facet | Karem Saad Amjad Kallel Fabio Castaldi Thouraya Sahli Chahed |
| author_sort | Karem Saad |
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| description | Soil salinity is considered one of the biggest constraints to crop production, particularly in arid and semi-arid regions affected by recurrent and long periods of drought, where high salinity levels severely impact plant stress and consequently agricultural production. Climate change accelerates soil salinization, driven by factors such as soil conditions, land use/land cover changes, and water deficits, over extensive spatial and temporal scales. Continuous monitoring of areas at risk of salinization plays a critical role in supporting effective land management and enhancing agricultural production. For these purposes, this work aims to propose a spatiotemporal method for monitoring soil salinization using spectral indices derived from Earth observation data. The proposed approach was tested in the Zaghouan Region in northeastern Tunisia, a region where soils are characterized by alarming levels of salinization. To address this concern, remote sensing techniques were applied for the analysis of satellite imagery generated from Landsat 5, Landsat 8, and Landsat 9 missions. A comprehensive field survey complemented this approach, involving the collection of 229 geo-referenced soil samples. These samples were representative of distinct soil salinity classes, including non-saline, slightly saline, moderately saline, strongly saline, and very strongly saline soils. Soil salinity modeling using Landsat-8 OLI data revealed that the SI-5 index provided the most accurate predictions, with an R<sup>2</sup> of 0.67 and an RMSE of 0.12 dS/m. By 2023, 42.3% of the study area was classified as strongly or very strongly saline, indicating a significant increase in salinity over time. This rise in salinity corresponds to notable land use and land cover (LULC) changes, as 55.9% of the study area experienced LULC shifts between 2000 and 2023. A decline in vegetation cover coincided with increasing salinity, showing an inverse relationship between these factors. Additionally, the results highlight the complex interplay among these variables demonstrating that soil salinity levels are significantly impacted by climate change indicators, with a negative correlation between precipitation and salinity (r = −0.85, <i>p</i> < 0.001). Recognizing the interconnections between soil salinity, LULC changes, and climate variables is essential for developing comprehensive strategies, such as targeted irrigation practices and land suitability assessments. Earth observation and remote sensing play a critical role in enabling more sustainable and effective soil management in response to both human activities and climate-induced changes. |
| format | Article |
| id | doaj-art-4ccb7e13af784655adc5637660a1a2e5 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
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| series | Remote Sensing |
| spelling | doaj-art-4ccb7e13af784655adc5637660a1a2e52024-12-27T14:51:08ZengMDPI AGRemote Sensing2072-42922024-12-011624476110.3390/rs16244761Soil Salinity Detection and Mapping by Multi-Temporal Landsat Data: Zaghouan Case Study (Tunisia)Karem Saad0Amjad Kallel1Fabio Castaldi2Thouraya Sahli Chahed3National Engineering School of Sfax, University of Sfax, Sfax 3038, TunisiaNational Engineering School of Sfax, University of Sfax, Sfax 3038, TunisiaInstitute of BioEconomy, National Research Council of Italy (CNR), Via Giovanni Caproni 8, 50145 Firenze, ItalyNational Center for Mapping and Remote Sensing, Aouina 2045, TunisiaSoil salinity is considered one of the biggest constraints to crop production, particularly in arid and semi-arid regions affected by recurrent and long periods of drought, where high salinity levels severely impact plant stress and consequently agricultural production. Climate change accelerates soil salinization, driven by factors such as soil conditions, land use/land cover changes, and water deficits, over extensive spatial and temporal scales. Continuous monitoring of areas at risk of salinization plays a critical role in supporting effective land management and enhancing agricultural production. For these purposes, this work aims to propose a spatiotemporal method for monitoring soil salinization using spectral indices derived from Earth observation data. The proposed approach was tested in the Zaghouan Region in northeastern Tunisia, a region where soils are characterized by alarming levels of salinization. To address this concern, remote sensing techniques were applied for the analysis of satellite imagery generated from Landsat 5, Landsat 8, and Landsat 9 missions. A comprehensive field survey complemented this approach, involving the collection of 229 geo-referenced soil samples. These samples were representative of distinct soil salinity classes, including non-saline, slightly saline, moderately saline, strongly saline, and very strongly saline soils. Soil salinity modeling using Landsat-8 OLI data revealed that the SI-5 index provided the most accurate predictions, with an R<sup>2</sup> of 0.67 and an RMSE of 0.12 dS/m. By 2023, 42.3% of the study area was classified as strongly or very strongly saline, indicating a significant increase in salinity over time. This rise in salinity corresponds to notable land use and land cover (LULC) changes, as 55.9% of the study area experienced LULC shifts between 2000 and 2023. A decline in vegetation cover coincided with increasing salinity, showing an inverse relationship between these factors. Additionally, the results highlight the complex interplay among these variables demonstrating that soil salinity levels are significantly impacted by climate change indicators, with a negative correlation between precipitation and salinity (r = −0.85, <i>p</i> < 0.001). Recognizing the interconnections between soil salinity, LULC changes, and climate variables is essential for developing comprehensive strategies, such as targeted irrigation practices and land suitability assessments. Earth observation and remote sensing play a critical role in enabling more sustainable and effective soil management in response to both human activities and climate-induced changes.https://www.mdpi.com/2072-4292/16/24/4761remote sensingsoil salinitymachine learningclimate changeGEE |
| spellingShingle | Karem Saad Amjad Kallel Fabio Castaldi Thouraya Sahli Chahed Soil Salinity Detection and Mapping by Multi-Temporal Landsat Data: Zaghouan Case Study (Tunisia) Remote Sensing remote sensing soil salinity machine learning climate change GEE |
| title | Soil Salinity Detection and Mapping by Multi-Temporal Landsat Data: Zaghouan Case Study (Tunisia) |
| title_full | Soil Salinity Detection and Mapping by Multi-Temporal Landsat Data: Zaghouan Case Study (Tunisia) |
| title_fullStr | Soil Salinity Detection and Mapping by Multi-Temporal Landsat Data: Zaghouan Case Study (Tunisia) |
| title_full_unstemmed | Soil Salinity Detection and Mapping by Multi-Temporal Landsat Data: Zaghouan Case Study (Tunisia) |
| title_short | Soil Salinity Detection and Mapping by Multi-Temporal Landsat Data: Zaghouan Case Study (Tunisia) |
| title_sort | soil salinity detection and mapping by multi temporal landsat data zaghouan case study tunisia |
| topic | remote sensing soil salinity machine learning climate change GEE |
| url | https://www.mdpi.com/2072-4292/16/24/4761 |
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