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Prof. Eslam Farg
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Academic Career Length
15Years (2012-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 June 2012 in The Egyptian Journal of Remote Sensing and Space Sciences
Journal Article
EFEslam Farg
Eslam FargSAS.M. Arafat
S.M. Arafat
MEM.S. Abd El-Wahed
M.S. Abd El-WahedAEA.M. El-Gindy
A.M. El-Gindy
Crop water requirements are represented by the actual crop evapotranspiration. Estimation of crop evapotranspiration (ETc) and crop coefficient using remote-sensing data is essential for planning the irrigation water use in arid and semiarid regions. This study focuses on estimating the crop coefficient (Kc) and crop evapotranspiration (ETc) using SPOT-4 satellite data integrated with the meteorological data and FAO-56 approach. Reference evapotranspiration (ETo) were estimated using FAO Penman-Monteith and tabled single crop coefficient values were adjusted to real values. SPOT-4 images geometrically and radio metrically corrected were used to drive the vegetation indices (NDVI and SAVI). Multi linear regression analysis was applied to develop the crop coefficient (Kc) prediction equations for the different growth stages from vegetation indices. The results showed R2 were 0.82, 0.90 and 0.97 as well as adjusted R2 were 0.80, 0.86 and 0.96 for developing, mid-season and late-season growth stage respectively
18 May 2022 in Remote Sensing Applications: Society and Environment
Journal Article
Mohsen NabilEFEslam Farg
Eslam FargSASayed M. Arafat
Sayed M. Arafat
MAMohamed Aboelghar
Mohamed AboelgharNANagwan M. Afify
Nagwan M. Afify
Mohamed M Elsharkawy25 October 2024 in International Journal of Remote Sensing
Journal Article
Mohsen NabilEFEslam Farg
Eslam FargNANagwan M. Afify
Nagwan M. Afify
SASayed M. Arafat
Sayed M. Arafat
Journal Article
ALAshrakat Ahmed Lotfy
Ashrakat Ahmed Lotfy
Mohamed EL-Sayed AbuarabEFEslam Farg
Eslam FargBDBilal Derardja
Bilal DerardjaRKRoula Khadra
Roula KhadraAMali mokhtar
ali mokhtar +1
The aim of this research is to develop and compare single, hybrid, and stacking ensemble machine learning models under spatial and temporal climate variations in the Nile Delta regarding the estimation of the blue and green water footprint (BWFP and GWFP) for wheat. Thus, four single machine learning models (XGB, RF, LASSO, and CatBoost) and eight hybrid machine learning models (XGB-RF, XGB-LASSO, XGB-CatBoost, RF-LASSO, CatBoost-LASSO, CatBoost-RF, XGB-RF-LASSO, and XGB-CatBoost-LASSO) were used, along with stacking ensembles, with five scenarios including climate and crop parameters and remote sensing-based indices. The highest R2 value for predicting wheat BWFP was achieved with XGB-LASSO under scenario 4 at 100%, while the minimum was 0.16 with LASSO under scenario 3 (remote sensing indices). To predict wheat GWFP, the highest R2 value of 100% was achieved with RF-LASSO across scenario 1 (all parameters), scenario 2 (climate parameters), scenario 4 (Peeff, Tmax, Tmin, and SA), and scenario 5 (Peeff and Tmax). The lowest value was recorded with LASSO and scenario 3. The use of individual and hybrid machine learning models showed high efficiency in predicting the blue and green water footprint of wheat, with high ratings according to statistical performance standards. However, the hybrid programs, whether binary or triple, outperformed both the single models and stacking ensemble.
1 August 2020 in The Egyptian Journal of Remote Sensing and Space Sciences
Journal Article
SGSafwat S. Gabr
Safwat S. Gabr
EFEslam Farg
Eslam Farg
Turki Mohammed HabeebullahSASayed M. Arafat
Sayed M. Arafat
1 April 2017 in The Egyptian Journal of Remote Sensing and Space Sciences
Journal Article
EFEslam Farg
Eslam FargSAS. Arafat
S. Arafat
MAMohammed Saifeldeen Abd-Elwahed
Mohammed Saifeldeen Abd-ElwahedAEA. El-Gindy
A. El-Gindy
Traditional methods for center pivot evaluation depend on the water depth distribution along the pivot arm. Estimation and mapping the water depth under pivot irrigation systems using remote sensing data is essential for calculating the coefficient of uniformity (CU) of water distribution. This study focuses on estimating and mapping water depth using Landsat OLI 8 satellite data integrated with Heerman and Hein (1968) modified equation for center pivot evaluation. Landsat OLI 8 image was geometrically and radiometrically corrected to calculate the vegetation and water indices (NDVI and NDWI) in addition to land surface temperature. Results of the statistical analysis showed that the collected water depth in catchment cans is also highly correlated negatively with NDVI. On the other hand water, depth was positively correlated with NDWI and LST. Multi-linear regression analysis using stepwise selection method was applied to estimate and map the water depth distribution. The results showed R2 and adjusted R2 0.93 and 0.88 respectively. Study area or field level verification was applied for estimation equation with correlation 0.93 between the collected water depth and estimated values
About Me
Dr. Eslam Farg is an Associate Professor at the National Authority for Remote Sensing and Space Sciences in Egypt, specializing in the agricultural applications of remote sensing. With a focus on the analysis and monitoring of vegetation cover and its properties, Dr. [Your Last Name] has contributed to advancing the use of satellite data and geospatial technologies to improve agricultural practices, enhance land management, and support sustainable resource use. moreover, my research integrates remote sensing technologies such as satellite imagery and Geographic Information Systems (GIS) to assess crop health, analyze vegetation dynamics, and monitor land use changes. Their work has had a significant impact on understanding and addressing issues related to food security, agricultural productivity, and environmental sustainability, particularly in arid and semi-arid regions. in addition to, I led several national and international projects aimed at improving agricultural practices and environmental management through remote sensing. They have published extensively in peer-reviewed journals and actively contribute to academic conferences and workshops focused on remote sensing, sustainability, and environmental management. Also has a passion for teaching and mentoring, guiding the next generation of scientists in the fields of remote sensing, environmental management, and sustainable agriculture.