Remote Sensing Based Evapotranspiration Modelling for Irrigation Performance Assessment in Jedeb Watershed, Upper Blue Nile Basin, Ethiopia

Simple item page
dc.contributor.advisorYilkal Gebeyehu
dc.contributor.advisorAbebe Demissie
dc.contributor.authorYilkal Gebeyehu
dc.date.accessioned2025-09-05T21:59:15Z
dc.date.available2025-09-05T21:59:15Z
dc.date.issued2024-10
dc.description.abstractAccurate actual evapotranspiration (ETa) data, alongside irrigated area maps, are important for assessing temporal and spatial irrigation performance indicators, which are critical for improving water resource monitoring, management, and sustainability. This study compared machine learning algorithms on the Google Earth Engine platform (GEE) in irrigated area mapping, customized the surface energy balance for land–improved (SEBALI) model using high-resolution land use/cover (LULC) data and implementation in Python, and evaluated the performance of a small-scale irrigation scheme using remote sensing (RS) and ground truth data in northwestern Ethiopia over two irrigation seasons. The study used Sentinel-1, Sentinel-2, and Landsat 8 satellite data and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) climatic data as the main inputs. The accuracy of irrigated area mapping was evaluated relative to the inputs and agroecology, and the accuracy was improved by incorporating monthly SAR and vegetation index data. Random forest was a consistent classifier in different agroecological zones and inputs. The Python version of the SEBALI model (SEBALIGEEpy) was validated over croplands using publicly available AmeriFlux flux tower eddy covariance data. In addition, the ETa from SEBALIGEEpy was compared with the ETa simulated using the Soil & Water Assessment Tool Plus (SWAT+) model, which was calibrated with observed discharge, and with the FAO’s remote-sensing based Water Productivity Open Access Portal (WaPOR). The results show that SEBALIGEEpy provides more accurate evapotranspiration estimates with fewer missing records and has the potential to be used for agricultural water management. Thus, the performance indicators, including equity, adequacy, overall consumed ratio (OCR), and productivity, were evaluated using SEBALIGEEpy results and ground truth data. The scheme showed good equity, with coefficient of variation (CV) values of 1.90 and 1.63 for the two seasons, alongside satisfactory water distribution among fields and within the field. The overall consumed ratio (OCR) was 0.54 and 0.43 for the two seasons. The mean crop water productivity (CWP) of wheat estimated from SEBALIGEEpy was 2.49 kg/m3. This study revealed the potential of using remote sensing to evaluate irrigation performance and water productivity per field within small-scale irrigation schemes.
dc.identifier.urihttps://etd.aau.edu.et/handle/123456789/7356
dc.language.isoen_US
dc.publisherAddis Ababa University
dc.subjectGoogle Earth Engine
dc.subjectSmallholder Irrigation
dc.subjectMachine Learning
dc.subjectPython
dc.subjectRemote Sensing
dc.subjectSEBALIGEEpy
dc.titleRemote Sensing Based Evapotranspiration Modelling for Irrigation Performance Assessment in Jedeb Watershed, Upper Blue Nile Basin, Ethiopia
dc.typeThesis

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Yilkal Gebeyehu 2024.pdf
Size:
2.32 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download

Collections

Africa Center of Excellence for Water Management