S-SEBI based Evapotranspiration Estimation Using Landsat 8 Data in Kendrapara District of Odisha, India

Abstract

Background: Accurate estimation of evapotranspiration (ET) is essential for agricultural water management, irrigation planning, and regional water-resource assessment, particularly in areas where ground-based meteorological observations are limited. Aim: This study aimed to assess the spatial and temporal variability of actual evapotranspiration (ETa) and reference evapotranspiration (ETo) over Kendrapara district, Odisha, India, during 2016 and 2021 using the Simplified Surface Energy Balance Index (S-SEBI) model, and to compare satellite-derived ETa with FAO Penman–Monteith and Hargreaves–Samani ETo estimates. Study Design: A remote sensing and GIS-based comparative study was conducted using the S-SEBI surface energy balance model to estimate ETa from Landsat 8 imagery and evaluate its performance against conventional ETo methods. Place and Duration of Study: The study was carried out in Kendrapara district, Odisha, India, using satellite and climate data for the years 2016 and 2021. Two dates were selected primarily based on the availability of suitable Landsat 8 images with adequate quality, spatial coverage, and minimal cloud contamination over the study area. Methodology: Landsat 8 (WRS Path/Row 139/46) Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data were obtained from the USGS Earth Explorer platform to derive surface reflectance and land surface temperature. ETa was estimated using the S-SEBI model, while ETo values based on the FAO Penman–Monteith and Hargreaves–Samani methods were obtained from the Climate Engine platform. Statistical error analysis was performed to assess the agreement between satellite-derived ETa and reference ETo estimates. Results: ETa exhibited significant spatial variability associated with vegetation condition and surface moisture. Mean ETa increased from 3.27 mm day⁻¹ in 2016 to 3.87 mm day⁻¹ in 2021, corresponding to higher net radiation and soil heat flux. S-SEBI-derived ETa closely matched FAO Penman–Monteith ETo, whereas the Hargreaves–Samani method consistently overestimated evapotranspiration. Error analysis indicated lower bias and uncertainty for the S-SEBI estimates. Conclusion: The S-SEBI model reliably estimated spatially explicit ETa with minimal ground data, demonstrating strong potential for drought monitoring, crop water requirement assessment, irrigation planning, and sustainable water resource management in data-scarce regions.