A quarter century experience in soil salinity mapping and its contribution to sustainable soil management and food security in Morocco

Silatsa, Francis B.T.; Kebede, Fassil

doi:10.1016/j.geodrs.2023.e00695

Cited by 5 publications

(2 citation statements)

References 69 publications

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“…Failing to adequately implement improved measures, such as salt discharge, will lead to imbalanced irrigation and drainage. Consequently, underground salt is carried to the surface with the water, leading to the salinization of cultivated land [72]. The soil salinity of the wetland vegetation area was similar to that of the farmland, indicating that some wetland plant species, namely Setaria viridis, S. salsa, Phragmites australis, and tamarix chinensis, can absorb salt from the soil [73].…”

Section: Characterization Of Soil Salinity Spatial Distributionmentioning

confidence: 96%

Mapping Multi-Depth Soil Salinity Using Remote Sensing-Enabled Machine Learning in the Yellow River Delta, China

Zhang,

Fu,

Zhang

et al. 2023

Remote Sensing

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Soil salinization is a crucial type in the degradation of coastal land, but its spatial distribution and drivers have not been sufficiently explored especially at the depth scale owing to its multidimensional nature. In this study, we proposed a multi-depth soil salinity prediction model (0–10 cm, 10–20 cm, 20–40 cm, and 40–60 cm) fully using the advantages of satellite image data and field sampling to rapidly estimate the multi-depth soil salinity in the Yellow River Delta, China. Firstly, a multi-depth soil salinity predictive factor system was developed through correlation analysis of soil sample electrical conductivity with a series of remote-sensing parameters containing heat, moisture, salinity, vegetation indices, spectral value, and spatial location. Then, three machine learning methods including back propagation neural network (BPNN), support vector machine (SVM), and random forest (RF) were adopted to construct a coastal soil salinity inversion model. By using the best inversion model, we obtain the spatial distribution of soil salinity in the Yellow River Delta. The results show the following: (1) Environmental variables in this study are all effective variables for soil salinity prediction. The most sensitive indicators to multi-depth soil salinity are GDVI, ENDVI, SI-T, NDWI, and LST. (2) The RF model was chosen as the optimal approach for predicting and mapping soil salinity based on performance at four soil depths. (3) The soil salinity profiles exhibited intricate coexistence of two distinct types: surface aggregated and homogeneous. The former was dominant in the east, where salinity was higher. The central and southwestern parts were mostly homogeneous, with lower soil salinity. (4) The soil salinity throughout the four depths examined was found to be most elevated in saltern and bare land and lowest in wetland vegetation and farmland, according to land-cover type. This study proposed a remote sensing prediction method for salinization in multiple soil layers in the coastal plain, which could provide decision support for spatial monitoring of land salinization and achieving land degradation neutrality targets.

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Section: Characterization Of Soil Salinity Spatial Distributionmentioning

confidence: 96%

Mapping Multi-Depth Soil Salinity Using Remote Sensing-Enabled Machine Learning in the Yellow River Delta, China

Zhang,

Fu,

Zhang

et al. 2023

Remote Sensing

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“…In Morocco, agriculture significantly contributes to the economy, representing 14-20% of the GDP and employing 40% of the total workforce [4]. As a cornerstone of economic development policy, the government has implemented various measures, including the "Green Generation" initiative, aimed at fostering agricultural growth and making it a driving force for economic prosperity [5]. However, agriculture faces the challenge of simultaneously meeting the increasing demand for food and maintaining sustainable practices to preserve soil-mediated ecosystem services, including carbon sequestration, nutrient supply, and water cycle regulation [6].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Soil Spatial Variability in Agricultural Ecosystems Using Multivariate Analysis, Soil Quality Index (SQI), and Geostatistical Approach: A Case Study of the Mnasra Region, Gharb Plain, Morocco

Sanad,

Moussadek,

Mouhir

et al. 2024

Agronomy

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Accurate assessment of soil quality is crucial for sustainable agriculture and soil conservation. Thus, this study aimed to assess soil quality in the agricultural ecosystem of the Mnasra region within the Gharb Plain of Morocco, employing a comprehensive approach integrating multivariate analysis and geostatistical techniques. Thirty soil samples were collected from the surface layers across thirty selected sites. The results showed significant variations in soil properties across the study area, influenced by factors such as soil texture, parent material, and agricultural practices. Pearson correlation and principal component analysis (PCA) were employed to analyze the relationships among soil properties and compute the Soil Quality Index (SQI). The SQI revealed values ranging from 0.48 to 0.74, with 46.66% of sampled soils classified as “Good” and 53.33% as “Fair”. Geostatistical analysis, particularly ordinary kriging (OK) interpolation and semivariogram modeling, highlighted the spatial variability of soil properties, aiding in mapping soil quality across the landscape. The integrated approach demonstrates the importance of combining field assessments, statistical analyses, and geospatial techniques for comprehensive soil quality evaluation and informed land management decisions. These findings offer valuable insights for decision-makers in monitoring and managing agricultural land to promote sustainable development in the Gharb region of Morocco.

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Potential of land degradation index for soil salinity mapping in irrigated agricultural land in a semi-arid region using Landsat-OLI and Sentinel-MSI data

Chaaou,

Chikhaoui,

Naimi

et al. 2024

Environ Monit Assess

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A quarter century experience in soil salinity mapping and its contribution to sustainable soil management and food security in Morocco

Cited by 5 publications

References 69 publications

Mapping Multi-Depth Soil Salinity Using Remote Sensing-Enabled Machine Learning in the Yellow River Delta, China

Mapping Multi-Depth Soil Salinity Using Remote Sensing-Enabled Machine Learning in the Yellow River Delta, China

Assessment of Soil Spatial Variability in Agricultural Ecosystems Using Multivariate Analysis, Soil Quality Index (SQI), and Geostatistical Approach: A Case Study of the Mnasra Region, Gharb Plain, Morocco

Potential of land degradation index for soil salinity mapping in irrigated agricultural land in a semi-arid region using Landsat-OLI and Sentinel-MSI data

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