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Abdellatif, B

Asante, B

Ajeigbe, H.A

Amin, M.E

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Authors

Tukur, A

Ajeigbe, H.A

Akinseye, F.M

Mohammed, I.B

Badamasi, M.M

Abdellatif, B

Metwalli, M.R

Metwaly, M.M

AbdelRahman, M.A

Amin, M.E

Abdelfattah, M.A

Mohamed, E.S

Belal, A.A

Nabil, M

Mahmoud, A.G

Asante, B

Topics

Satellite Imagery

Climate Smart Agriculture

Precision Planting/Harvesting

Precision Water Management

Type

Oral

Poster

Year

2020

2022

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Authors

Filter results4 paper(s) found.
1. 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
2. A Time Series Investigation to Assess Climate Change and Anthropogenic Impacts on the Degradation of Nile Delta, Egypt. Land degradation risk, status and rate in Nile Delta, Egypt were assessed for a time series using remote sensing data over the past five decades using TM, ETM and OLI. Quantitative deterioration of the area was produced based on the comparative study approach in the integrated weighted sum, weighted overlay and fuzzy model. Where degradation factors were compiled in a raster and each data set scored on a 1 to 5 (very low, low, moderate, high and very high scale). The data sets were then weighted... B. Abdellatif, M.R. Metwalli, M.M. Metwaly, M.A. Abdelrahman
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
4. Spatial-Temporal Assessment of Drought in the Northern Region, Ghana ... B. Asante

Filter results4 paper(s) found.

1. 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

2. A Time Series Investigation to Assess Climate Change and Anthropogenic Impacts on the Degradation of Nile Delta, Egypt.

Land degradation risk, status and rate in Nile Delta, Egypt were assessed for a time series using remote sensing data over the past five decades using TM, ETM and OLI. Quantitative deterioration of the area was produced based on the comparative study approach in the integrated weighted sum, weighted overlay and fuzzy model. Where degradation factors were compiled in a raster and each data set scored on a 1 to 5 (very low, low, moderate, high and very high scale). The data sets were then weighted... B. Abdellatif, M.R. Metwalli, M.M. Metwaly, M.A. Abdelrahman

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

4. Spatial-Temporal Assessment of Drought in the Northern Region, Ghana

... B. Asante