Modelling and Assessment of Irrigation Water Quality Index Using GIS in Semi-arid Region for Sustainable Agriculture

Behairy, Radwa A. El; Baroudy, Ahmed A. El; Mahmoud, I.Y.; Kheir, Ahmed M. S.; Shokr, Mohamed S.

doi:10.1007/s11270-021-05310-0

Cited by 37 publications

(15 citation statements)

References 52 publications

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“…The eastern and western parts of the study area are covered with Holocene clay and Quaternary sediments, respectively [34]. Surface irrigation is the most commonly used system, in which water is pumped from irrigation canals and drained in furrows and basins [18].…”

Section: The Site Descriptionmentioning

confidence: 99%

“…The highest values of SOM (0.9-1.22%) were found in the middle of the northeast and northwest of the study area. The IWQI map (Figure S3) is thought to be a useful tool in future agricultural management plans [18].…”

Section: Fertility and Environmental Soil Capability Indicatorsmentioning

confidence: 99%

“…The soil limiting factors for crop suitability vary in different areas in Egypt, with soil salinity, poor drainage, and compaction as the most common factors in the northern Nile Delta [14][15][16][17]. Agriculture is the greatest user of water in Egypt, especially in the northwestern Nile Delta; thus, determining and controlling surface water quality in such areas is vital for protecting water resources and ensuring long-term sustainable agriculture [18]. Soil property characterization, modeling, and mapping at various spatial and temporal scales are required for the study of diverse environments [19] The Geographic Information System (GIS) technique has accelerated spatial variability studies of different environmental phenomena [20].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Soil Capability and Crop Suitability Using Integrated Multivariate and GIS Approaches toward Agricultural Sustainability

Behairy

Baroudy

Mahmoud

et al. 2022

Land

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Land evaluation has an important role in agriculture. Developing countries such as Egypt face many challenges as far as food security is concerned due to the increasing rates of population growth and the limited agriculture resources. The present study used multivariate analysis (PCA and cluster analysis) to assess soil capability in drylands, Meanwhile the Almagra model of Micro LEIS was used to evaluate land suitability for cultivated crops in the investigated area under the current (CS) and optimal scenario (OS) of soil management with the aim of determining the most appropriate land use based on physiographic units. A total of 15 soil profiles were selected to characterize the physiographic units of the investigated area. The results reveal that the high capability cluster (C1) occupied 31.83% of the total study area, while the moderately high capability (C2), moderate capability (C3), and low capability (C4) clusters accounted for 37.88%, 28.27%, and 2.02%, respectively. The limitation factors in the studied area were the high contents of CaCO3, the shallow soil depth, and the high salinity and high percentage of exchangeable sodium (% ESP) in certain areas. The application of OS enhanced the moderate suitability (S3) and unsuitable clusters (S5) to the suitable (S2) and marginally suitable (S4) categories, respectively, while the high suitability cluster (S1) had increased land area, which significantly affected the suitability of maize crop. The use of multivariate analysis for mapping and modeling soil suitability and capability can potentially help decision-makers to improve agricultural management practices and demonstrates the importance of appropriate management to achieving agricultural sustainability under intensive land use in drylands.

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Section: The Site Descriptionmentioning

confidence: 99%

Section: Fertility and Environmental Soil Capability Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of Soil Capability and Crop Suitability Using Integrated Multivariate and GIS Approaches toward Agricultural Sustainability

Behairy

Baroudy

Mahmoud

et al. 2022

Land

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“…Selected spatial analysis of the parameters is displayed in a diagram chain [57] (Figure 8). Output from each process is considered as the input for the next process [58]. The subsequent stages were applied in this research to obtain the final SFC map of the investigated area: (a) Interpolation of soil properties to raster layer, with the results from this stage (b) reclassified into five classes (Very low, low, moderate, high, and very high), then (c) scoring each SFC parameter based on Table 2, (d) feeding Equation ( 7) utilizing the raster calculator tool and finally (f) the outputs from (d) were weighted and overlayed to obtain and display the final SFC map.…”

Section: Modelling Of Sfc In the Study Areamentioning

confidence: 99%

Integration of Geostatistical and Sentinal-2AMultispectral Satellite Image Analysis for Predicting Soil Fertility Condition in Drylands

Shokr

Mazrou

Abdellatif

et al. 2022

IJGI

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For modelling and predicting soil indicators to be fully operational and facilitate decision-making at any spatial level, there is a requirement for precise spatially referenced soil information to be available as input data. This paper focuses on showing the capacity of Sentinal-2A(S2A) multispectral imaging to predict soil properties and provide geostatistical analysis (ordinary kriging) for mapping dry land soil fertility conditions (SOCs). Conditioned Latin hypercube sampling was used to select the representative sampling sites within the study area. To achieve the objectives of this work, 48 surface soil samples were collected from the western part of Matrouh Governorate, Egypt, and pH, soil organic matter (SOM), available nitrogen (N), phosphorus (P), and potassium (K) levels were analyzed. Multilinear regression (MLR) was used to model the relationship between image reflectance and laboratory analysis (of pH, SOM, N, P, and K in the soil), followed by mapping the predicted outputs using ordinary kriging. Model fitting was achieved by removing variables according to the confidence level (95%).Around 30% of the samples were randomly selected to verify the validity of the results. The randomly selected samples helped express the variety of the soil characteristics from the investigated area. The predicted values of pH, SOM, N, P, and K performed well, with R2 values of 0.6, 0.7, 0.55, 0.6, and 0.92 achieved for pH, SOM, N, P, and K, respectively. The results from the ArcGIS model builder indicated a descending fertility order within the study area of: 70% low fertility, 22% moderate fertility, 3% very low fertility, and 5% reference terms. This work evidence that which can be predicted from S2A images and provides a reference for soil fertility monitoring in drylands. Additionally, this model can be easily applied to environmental conditions similar to those of the studied area.

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“…The continuous development of remote sensing and geographic information science has significantly improved the efficiency of geographical feature analysis [23][24][25]. The increased frequency of image acquisition and advances in data processing capabilities have provided new opportunities for understanding complex inland water systems [26].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Inland Reservoir Water Quality Parameters Using Landsat 8-9 OLI and Sentinel-2 MSI Sensors with Empirical Multivariate Regression

Meng

Zhang

Zhang³

2022

IJERPH

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Improving water quality is one of the top priorities in the global agenda endorsed by the United Nations. To ensure the achievement of this goal, governments have developed plans to continuously monitor the status of inland waters. Remote sensing provides a low-cost, high-frequency, and practical complement to monitoring systems that can cover a large area. However, it is crucial to evaluate the suitability of sensors for retrieving water quality parameters (WQPs), owing to differences in spatial and spectral sampling from different satellites. Taking Shanmei Reservoir in Fuzhou City, Fujian Province as a case study, this study collected and sorted the water quality data measured at the site in 2020 to 2022 and Landsat 8-9 OLI and Sentinel-2 MSI images, simulated the chlorophyll-a (Chl-a) concentration, algae density, and turbidity using empirical multivariate regression, and explored the relationship between different WQPs using correlation analysis and principal component analysis (PCA). The results showed that the fitting effect of Landsat OLI data was better than that of the Sentinel-2 MSI data. The coefficient of determination (R2) values of Chl-a, algal density, and turbidity simulated by Landsat OLI data were 0.70, 0.81, and 0.80, respectively. Furthermore, the parameters of its validation equation were also smaller than those of Sentinel MSI data. The spatial distribution of three key WQPs retrieved from Landsat OLI data shows their values were generally low, with the mean values of the Chl-a concentration, algal density, and turbidity being 4.25 μg/L, 4.11 × 106 cells/L, and 1.86 NTU, respectively. However, from the end of February 2022, the values of the Chl-a concentration and algae density in the reservoir gradually increase, and the risk of water eutrophication also increases. Therefore, it is still necessary to pay continuous attention and formulate corresponding water quality management measures. The correlation analysis shows that the three key WQPs in this study have a high correlation with pH, water temperature (WT), and dissolved oxygen (DO). The results of PCA showed that pH, DO, Chl-a concentration, WT, TN, and CODMn were dominant in PC1, explaining 35.57% of the total variation, and conductivity, algal density, and WT were dominant in PC2, explaining 13.34% of the total variation. Therefore, the water quality of the Shanmei Reservoir can be better evaluated by measuring pH, conductivity, and WT at the monitoring station, or by establishing the regression fitting equations between DO, CODMn, and TN. The regression algorithm used in this study can identify the most important water quality features in the Shanmei Reservoir, which can be used to monitor the nutritional status of the reservoir and provide a reference for other similar inland water bodies.

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Modelling and Assessment of Irrigation Water Quality Index Using GIS in Semi-arid Region for Sustainable Agriculture

Cited by 37 publications

References 52 publications

Assessment of Soil Capability and Crop Suitability Using Integrated Multivariate and GIS Approaches toward Agricultural Sustainability

Assessment of Soil Capability and Crop Suitability Using Integrated Multivariate and GIS Approaches toward Agricultural Sustainability

Integration of Geostatistical and Sentinal-2AMultispectral Satellite Image Analysis for Predicting Soil Fertility Condition in Drylands

Retrieving Inland Reservoir Water Quality Parameters Using Landsat 8-9 OLI and Sentinel-2 MSI Sensors with Empirical Multivariate Regression

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