Mapping soil salinity risk using the approach of soil salinity index and land cover: a case study from Tadla plain, Morocco

Chaaou, Abdelwahed; Chikhaoui, Mohamed Taha; Naïmi, Mustapha; Miad, Aissa Kerkour El; Achemrk, Aiman; Seif-Ennasr, Marieme; Harche, Sanae El

doi:10.1007/s12517-022-10009-5

Cited by 9 publications

(5 citation statements)

References 53 publications

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“…Data availability has improved, as well as spatial, temporal, and spectral resolutions. [7]. Despite the fact that large-scale agriculture is a global trend for agricultural development, family farming continues to be the primary management form of agricultural practices in developing countries, namely Morocco [8,9] This type of farming is characterized by small plots, making the distinction and classification of crops a difficult issue.…”

Section: Introductionmentioning

confidence: 99%

Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region

et al. 2022

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Accurate and rapid crop type mapping is critical for agricultural sustainability. The growing trend of cloud-based geospatial platforms provides rapid processing tools and cloud storage for remote sensing data. In particular, a variety of remote sensing applications have made use of publicly accessible data from the Sentinel missions of the European Space Agency (ESA). However, few studies have employed these data to evaluate the effectiveness of Sentinel-1, and Sentinel-2 spectral bands and Machine Learning (ML) techniques in challenging highly heterogeneous and fragmented agricultural landscapes using the Google Earth Engine (GEE) cloud computing platform. This work aims to map, accurately and early, the crop types in a highly heterogeneous and fragmented agricultural region of the Tadla Irrigated Perimeter (TIP) as a case study using the high spatiotemporal resolution of Sentinel-1, Sentinel-2, and a Random Forest (RF) classifier implemented on GEE. More specifically, five experiments were performed to assess the optical band reflectance values, vegetation indices, and SAR backscattering coefficients on the accuracy of crop classification. Besides, two scenarios were used to assess the monthly temporal windows on classification accuracy. The findings of this study show that the fusion of Sentinel-1 and Sentinel-2 data can accurately produce the early crop mapping of the studied area with an Overall Accuracy (OA) reaching 95.02%. The scenarios prove that the monthly time series perform better in terms of classification accuracy than single monthly windows images. Red-edge and shortwave infrared bands can improve the accuracy of crop classification by 1.72% when compared to only using traditional bands (i.e., visible and near-infrared bands). The inclusion of two common vegetation indices (The Normalized Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI)) and Sentinel-1 backscattering coefficients to the crop classification enhanced the overall classification accuracy by 0.02% and 2.94%, respectively, compared to using the Sentinel-2 reflectance bands alone. The monthly windows analysis indicated that the improvement in the accuracy of crop classification is the greatest when the March images are accessible, with an OA higher than 80%.

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Section: Introductionmentioning

confidence: 99%

Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region

et al. 2022

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“…Higher EC (201-725 μS/cm) were found in samples on the immediate western boundary and with some dispersion to the southwest, west and in the northern direction of the sampling area. Silty fraction, revealed minimum EC 37.5 μS/cm in samples at the northern boundary, corresponding to a transition area between dump upper and lower parts (characterized by intense leaching) and can also be associated with the heavy rainfall of the sampling campaign, promoting the dissolution of salts in the soil and consequently a decrease in soil EC (USDA 2011;Chaaou et al 2022). Maximum EC 217 μS/cm was found in the northern direction of the sampling center.…”

Section: Resultsmentioning

confidence: 99%

Soil properties and environmental risk assessment of soils in the surrounding area of Hulene-B waste dump, Maputo (Mozambique)

2022

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Soils in areas surrounding landfills are constantly being enriched by heavy metals contained in the leachates, which can subsequently migrate to groundwater. The present investigation aims to characterize soil properties of 71 soil samples collected in the surroundings of Hulene-B waste dump and to determine the landfill pollution index (Ip). Soils properties studied were texture, pH, electrical conductivity, organic matter, color, and moisture. Results revealed that soils properties in the surroundings of Hulene-B waste dump were significantly altered when compared to local background. Ip index classified these soils with very high pollution, indicating a possible migration of contaminants to subsoil and groundwater, suggesting the need for intervention to mitigate the impact.

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“…The annual Figure 1. Geographic location of the study area (Chaaou et al, 2022). withdrawals of water from the groundwater are estimated at 92 mm cubes and 40 mm cubes per year for Beni Amir and Beni Moussa respectively.…”

Section: Study Areamentioning

confidence: 99%

Assessment of Climate Variability and Agricultural Activities in the Area of Tadla Plain

Baki,

Boutoial,

Medaghri-Alaoui

2023

GEP

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The variations in both precipitation and temperature have far-reaching effects on agricultural activities and the accessibility of water resources. These climatic parameters are pivotal in determining the availability of both groundwater and surface water for agricultural use. The aim of this study was to evaluate the variations in climate parameters, focusing on precipitation and temperature, alongside changes in cultivated land area and crop yields in the Tadla area (Béni Mellal Khénifra region, Morocco); additionally, our research looks at the changes in water inflow into two dams and four aquifers. Trends were assessed over the period of 2010-2020 using the standardized precipitation index (SPI) method, as well as the parametric regression method and nonparametric Mann-Kendall and Sen's slope test. This analysis can be a preliminary step in demonstrating the effects of climate variability on water resource availability and its adverse impacts on agriculture in the region. The results showed a decreasing trend for some yield crops despite the increase in the cultivated area. The results of the groundwater levels and inflow dams showed a significant upward evolution. The analysis of the obtained SPI values and temperatures has revealed a notable and consistent upward trendencies. This upward trajectory indicates that both the SPI values, which reflect precipitation patterns and the temperatures, have been on the rise over the examined period. These results prompt reflection on the effects of climate variability on water resources in the region and economic activities, particularly agriculture.

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Mapping soil salinity risk using the approach of soil salinity index and land cover: a case study from Tadla plain, Morocco

Cited by 9 publications

References 53 publications

Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region

Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region

Soil properties and environmental risk assessment of soils in the surrounding area of Hulene-B waste dump, Maputo (Mozambique)

Assessment of Climate Variability and Agricultural Activities in the Area of Tadla Plain

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