Soil Salinity Detection and Mapping in an Environment under Water Stress between 1984 and 2018 (Case of the Largest Oasis in Africa-Morocco)

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 7; p. 1606
• Main Authors: Rafik, Abdellatif, Ibouh, Hassan, El Alaoui El Fels, Abdelhafid, Eddahby, Lhou, Mezzane, Daoud, Bousfoul, Mohamed, Amazirh, Abdelhakim, Ouhamdouch, Salah, Bahir, Mohammed, Gourfi, Abdelali, Dhiba, Driss, Chehbouni, Abdelghani
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2022; MDPI
• Subjects: Agricultural land; Agricultural production; Climate change; Deserts; Drought; Hydroclimate; Image acquisition; Laboratories; Landsat; Landsat satellites; Mapping; Mathematical analysis; Oases; Polynomials; Precipitation; Remote sensing; Root-mean-square errors; Saline soils; Salinity; Salinity effects; Salinization; Satellite imagery; Sciences of the Universe; Sentinel 2; Soil conditions; Soil degradation; Soil erosion; Soil horizons; Soil mapping; Soil salinity; soil salinization; Tafilalet oasis; Water stress; Morocco; United States > US; water stress; Sentinel 2; Tafilalet Oasis; drought; soil salinization; Landsat
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14071606

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.