Estimation of surface runoff using NRCS curve number in some areas in northwest coast, Egypt

Mohamed, Elsayed Said; Abdellatif, Mostafa A.; Abd-Elmabod, Sameh Kotb; Khalil, M.

doi:10.1051/e3sconf/202016702002

Cited by 10 publications

(5 citation statements)

References 35 publications

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“…Finally, the spherical model is suitable for sand content. In addition, the results indicate that RMSSE is close to one and the MSE is close to zero for the selected soil properties; therefore, the selected models fit the data and are suitable for predicting the unsampled soil properties [68,69].…”

Section: Mapping Soil Properties and Soil Quality Index 341 Mappinmentioning

confidence: 97%

Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

et al. 2021

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Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.

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Section: Mapping Soil Properties and Soil Quality Index 341 Mappinmentioning

confidence: 97%

Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

et al. 2021

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“…Land cover/land use (LCLU) changes are related to human activity, while rapid urbanization represents a real threat to the most fertile soils and water quality in the Nile delta [37]. Therefore, creating LCLU maps helps in monitoring the changes of land cover, and correlating the variation in water quality with LCLU changes helps to obtain optimal solutions and to improve irrigation water quality [38][39][40][41][42]. The spatiotemporal data provided by remote sensing analyses play a vital role in observing and investigating land cover changes over time [43].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis for Assessing Irrigation Water Quality: A Case Study of the Bahr Mouise Canal, Eastern Nile Delta

Abdel-Fattah

Abd-Elmabod

Aldosari

et al. 2020

Water

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Water scarcity and suitable irrigation water management in arid regions represent tangible challenges for sustainable agriculture. The current study aimed to apply multivariate analysis and to develop a simplified water quality assessment using principal component analysis (PCA) and the agglomerative hierarchical clustering (AHC) technique to assess the water quality of the Bahr Mouise canal in El-Sharkia Governorate, Egypt. The proposed methods depended on the monitored water chemical composition (e.g., pH, water electrical conductivity (ECiw), Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, and SO42−) during 2019. Based on the supervised classification of satellite images (Landsat 8 Operational Land Imager (OLI)), the distinguished land use/land cover types around the Bahr Mouise canal were agriculture, urban, and water bodies, while the dominating land use was agriculture. The water quality of the Bahr Mouise canal was classified into two classes based on the application of the irrigation water quality index (IWQI), while the water quality was classified into three classes using the PCA and AHC methods. Temporal variations in water quality were investigated, where the water qualities in winter, autumn, and spring (January, February, March, April, November, and December) were classified as class I (no restrictions) based on IWQI application, and the water salinity, sodicity, and/or alkalinity did not represent limiting factors for irrigation water quality. On the other hand, in the summer season (May, June, July, August, and October), the irrigation water was classified as class II (low restrictions); therefore, irrigation processes during summer may lead to an increase in the alkalinity hazard. The PCA classifications were compared with the IWQI results; the PCA classifications had similar assessment results during the year, except in September, while the water quality was assigned to class II using the PCA method and class I by applying the IWQI. Furthermore, the normalized difference vegetation index (NDVI) around the Bahr Mouise canal over eight months and climatic data assisted in explaining the fluctuations in water quality during 2019 as a result of changing the crop season and agriculture management. Assessments of water quality help to conserve soil, reduce degradation risk, and support decision makers in order to obtain sustainable agriculture, especially under water irrigation scarcity and the limited agricultural land in such an arid region.

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“…Moderate values were noticed in those areas which have agricultural activates and deep profiles with a flat or almost flat surface [71,72]. These results demonstrate that the desert areas of Egypt suffer from a poverty of soil fertility in comparison to the Nile Delta [63,65,73,74] and need suitable management to choose the best crops according to soil quality, water availability, and the climate of an area. tral reflectance, which was extracted from bands of S2A satellite imagery, and laboratory analysis, to create maps of soil properties by using OK and finally produce of SOC maps.…”

Section: The Sfc Of Study Areamentioning

confidence: 81%

“…The accuracy values (R 2 calibration) were 0.6, 0.7, 0.55, 0.6, and 0.92 for pH, SOM, N, P, and K, respectively. As a whole, spectral reflections are affected by a variety of soil properties and their concentrations [65,66]. Although the determination of respondent parts of a wavelength to dynamic changes in element concentration is very complex, statistical analysis can resolve this issue [6].…”

Section: Producing Sfc Parameter Using S2a Imagementioning

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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Estimation of surface runoff using NRCS curve number in some areas in northwest coast, Egypt

Cited by 10 publications

References 35 publications

Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

Multivariate Analysis for Assessing Irrigation Water Quality: A Case Study of the Bahr Mouise Canal, Eastern Nile Delta

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

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