Comparison of two single-source and two-source remote sensing based methods for estimating actual daily evapotranspiration of maize using Landsat images at field scale

Subject Areas : Applications in water resources management

Mosayeb Moqbeli Dameneh ¹ , Seyed Hossein Sanaeinejad ^{2 *} , Mojtaba Sadegh ³

1 - PhD Student of Agricultural Meteorology, Department of Agriculture, Ferdowsi University of Mashhad
2 - Assoc. Prof. College of Water Engineering, Department of Agriculture, Ferdowsi University of Mashhad
3 - Assistant Professor, Department of Civil Engineering, Faculty of Civil Engineering, Boise State University, USA

Received: 2023-02-13 Accepted : 2024-04-16 Published : 2025-07-22

Keywords: Actual Evapotranspiration, Remote Sensing, SEBAL Algorithm, TSEB Algorithm,

Abstract :

Optimizing irrigation in agriculture can be considered one of the most important tasks in research related to the agricultural sector because about 70 percent of the world's water consumption is consumed in this sector. Considering that almost the main factor of water wastage in this sector is equal to the rate of evapotranspiration, therefore, knowing this variable and its accurate estimation helps a lot to the primary goal, irrigation optimization. In this research, the use of two algorithms based on remote sensing data to estimate actual daily evapotranspiration at farm scale was investigated and evaluated. The actual evapotranspiration values were estimated using two remote sensing approaches, Surface Energy Balance Algorithm for Land (SEBAL) and Two Source Energy Balance (TSEB), and data extracted from Landsat 7 and 8 satellite images. To validate remote sensing methods, FAO-56 single and dual crop coefficient methods were used as reference values of actual evapotranspiration. Comparing the output of remote sensing methods with FAO-56 methods showed that both remote sensing methods have reliable output. The average error of the estimations was obtained based on two common statistical indicators, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) on the daily scale, 1.54 and 1.11 mm, respectively, for SEBAL and TSEB algorithms. Also, the bias index showed that the SEBAL algorithm has an underestimation (-0.69) and the TSEB algorithm has an overestimation (+1.24) in the estimation of actual evapotranspiration. The results obtained from the statistical analysis in this research show the higher accuracy of the TSEB algorithm in estimating the actual evapotranspiration of maize at the field scale.