article · Remote Sensing
This research developed a method for detecting and mapping soil salinity in the Tafilalet plain, Morocco, a region experiencing water stress. Using GIS tools and remote sensing data from various Landsat and Sentinel satellites between 1984 and 2018, 16 soil salinity indices were tested. Five indices were initially selected, and further analysis identified a second-degree polynomial model of the SI-KHAN index as the most efficient for predicting saline soil, demonstrating high accuracy. The study found that drought cyclicity intensifies soil salinisation, with hydroclimate conditions being a primary controlling factor. Salinised areas fluctuated over the study period, showing increases between 1984-1996 and 2000-2005, and decreases in other periods.
Soil salinisation is a major global problem, particularly in water-stressed agricultural regions, leading to reduced crop yields and land degradation. This research provides an accurate, remote sensing-based method to monitor soil salinity, which is crucial for effective land management and agricultural planning in affected areas.
This research offers an applied method for monitoring and mapping soil salinity, which could be used by agricultural organisations, land management agencies, and environmental monitoring bodies. The validated model provides a practical tool for assessing land degradation and informing mitigation strategies in water-stressed regions. This is an applied research finding, ready for implementation in similar contexts.
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Water stress is one of the factors controlling agricultural land salinization and is also a major problem worldwide. According to FAO and the most recent estimates, it already affects more than 400 million hectares. The Tafilalet plain in Southeastern Morocco suffers from soil salinization. In this regard, the GIS tools and remote sensing were used in the processing of 19 satellite images acquired from Landsat 4–5, (Landsat 7), (Landsat 8), and (Sentinel 2) sensors. The most used indices in the literature were (16 indices) tested and correlated with the results obtained from 25 samples taken from the first soil horizon at a constant depth of 0.20 m from the 2018 campaign. The linear model, at first, allows the selection of five better indices of the soil salinity discrimination (SI-Khan, VSSI, BI, S3, and SI-Dehni). These last indices were the subject of the application of a logarithmic model and polynomial models of degree two and four to increase the prediction of saline soil.. After studies and analysis, we concluded that the second-degree polynomial model of the salinity index (SI-KHAN) is the most efficient one for detecting and mapping soil salinity in the Tafilalet oasis, with a coefficient of determination (R2) and the Nash–Sutcliffe efficiency (NSE) equal to 0.93 and 0.86, respectively. Percent bias (PBIAS) calculated for this model equal was 1.868% < 10%, and the low value of the root mean square error (RMSE) confirms its very good performance. The drought cyclicity led to the intensification of the soil salinization process and accelerated soil degradation. The standardized precipitation anomaly index (SPAI) is strongly correlated to soil salinity. The hydroclimate condition is the factor that further controls this phenomenon. An increase in salinized surfaces is observed during the periods of 1984–1996 and 2000–2005, which cover a surface of 11.50 and 24.20 km2, respectively, while a decrease of about 50% is observed during the periods of 1996–2000 and 2005–2018.
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DOI: 10.3390/rs14071606
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