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Mohammed, I.B

Amin, M.E

Arafat, S.M

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Authors

AbdelRahman, M.A

Saleh, A.M

El Sharkawy, M.M

Farg, E

Arafat, S.M

Tukur, A

Ajeigbe, H.A

Akinseye, F.M

Mohammed, I.B

Badamasi, M.M

Amin, M.E

Abdelfattah, M.A

Mohamed, E.S

Belal, A.A

Nabil, M

Mahmoud, A.G

Topics

Mapping and Geostatistics

Satellite Imagery

Precision Planting/Harvesting

Type

Oral

Poster

Year

2020

2022

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Authors

Filter results3 paper(s) found.
1. Spatial Interpolation for Mapping Hydraulic Soil Properties in GIS Environment Soil water information is an essential input for environmental, hydrological or land surface models. There is a need for reliable soil water information with current coverage in the area. A number of 60 soil profiles data were evaluated for the performance of estimates inverse distance weighting to map some of the soil quality properties. soil profiles were used for the application of geostatistics. Maps with the investigated coverage were produced with the soil information available about soil... M.A. Abdelrahman, A.M. Saleh, M.M. El sharkawy, E. Farg, S.M. Arafat
2. Predicting in-Season Sorghum yield potential using Remote Sensing Approach: a case study of Kano in Sudan Savannah agro- ecological zone, Nigeria The preliminary estimation of expected yields and the accuracy of this evaluation provide information for decision-making related to the harvest. Estimating crop yield using remote sensing techniques has proven to be successful, having the ability to provide yield estimates prior to harvest. This study was conducted to examine the applicability of Sentinel-2B for estimating sorghum yield during the 2018 rainy season in Bebeji, Dawakin-Kudu and Rano Local Government Areas Kano State, in the Sudan... A. Tukur, H.A. Ajeigbe, F.M. Akinseye, I.B. Mohammed, M.M. Badamasi Download paper
3. Potato Yield Prediction Using Multi-temporal Sentinel-2 Data and Multiple Linear Regression Traditional potato growth models have a number of flaws, i.e., the cost of data collection, quality of input data, and the absence of spatial information in some cases. To address these challenges, we created a multiple linear regression model (MLRM) that uses the multi-temporal Sentinel-2 derived indices to predict potato yield. Along the growing season (from October 2019 to February 2020) eight Sentinel-2 imageries were collected, afterwards, the normalized difference vegetation index (NDVI)... M.E. Amin, M.A. Abdelfattah, E.S. Mohamed, A.A. Belal, M. Nabil, A.G. Mahmoud

Filter results3 paper(s) found.

1. Spatial Interpolation for Mapping Hydraulic Soil Properties in GIS Environment

Soil water information is an essential input for environmental, hydrological or land surface models. There is a need for reliable soil water information with current coverage in the area. A number of 60 soil profiles data were evaluated for the performance of estimates inverse distance weighting to map some of the soil quality properties. soil profiles were used for the application of geostatistics. Maps with the investigated coverage were produced with the soil information available about soil... M.A. Abdelrahman, A.M. Saleh, M.M. El sharkawy, E. Farg, S.M. Arafat

2. Predicting in-Season Sorghum yield potential using Remote Sensing Approach: a case study of Kano in Sudan Savannah agro- ecological zone, Nigeria

The preliminary estimation of expected yields and the accuracy of this evaluation provide information for decision-making related to the harvest. Estimating crop yield using remote sensing techniques has proven to be successful, having the ability to provide yield estimates prior to harvest. This study was conducted to examine the applicability of Sentinel-2B for estimating sorghum yield during the 2018 rainy season in Bebeji, Dawakin-Kudu and Rano Local Government Areas Kano State, in the Sudan... A. Tukur, H.A. Ajeigbe, F.M. Akinseye, I.B. Mohammed, M.M. Badamasi

Download paper

3. Potato Yield Prediction Using Multi-temporal Sentinel-2 Data and Multiple Linear Regression

Traditional potato growth models have a number of flaws, i.e., the cost of data collection, quality of input data, and the absence of spatial information in some cases. To address these challenges, we created a multiple linear regression model (MLRM) that uses the multi-temporal Sentinel-2 derived indices to predict potato yield. Along the growing season (from October 2019 to February 2020) eight Sentinel-2 imageries were collected, afterwards, the normalized difference vegetation index (NDVI)... M.E. Amin, M.A. Abdelfattah, E.S. Mohamed, A.A. Belal, M. Nabil, A.G. Mahmoud