Estimation of reference evapotranspiration using remote sensing data in Hamedan-Bahar Plain

Reference evapotranspiration (ET0) is a major research area of both hydrology and water resources management, especially in irrigation agriculture. The most important and direct application of ET0 is in the field of irrigation. One of the conventional methods for estimating reference evapotranspiration using meteorological data is the Penman-Monteith-FAO equation. This equation due to satisfactory results has been used in a variety of climates around the world. However, the lack of necessary meteorological data makes it difficult to estimate spatially distributed ET0 using the FAO-PM method in the wider ungauged areas. Penman Monteith method requires air temperature, wind speed, relative humidity, solar radiation and etc.To overcome the existing limits of the FAO-PM model, various attempts aiming to estimate ET0 with limited observed data have been conducted. At present, remote sensing methods are the only way to obtain the various variables at the temporal and spatial scales needed to estimate evapotranspiration. In recent years, several algorithms have been proposed to estimate reference evapotranspiration using remote sensing data. Some of these models, which are based on the relationship of energy balance, are called surface energy balance methods. In addition to remote sensing, data analysis techniques based on machine learning (ML) are more frequently used in agricultural studies in recent years, especially in evapotranspiration. Therefore, analyses performed with ML algorithms, when coupled with remote sensing data, have the potential to predict the biophysical variables, mainly due to the adaptive capacity of the models to find patterns in nonlinear behavior variable, such as ET0. Machine learning methods are well known and have been widely used in other engineering sciences. The purpose of this study is to estimate the reference evapotranspiration using machine learning algorithms and remote sensing data and finally to analyze the algorithms used. In general, the final results of evapotranspiration estimation depend on factors such as the type of data and the method for estimating evapotranspiration.In this study, the standard method of estimating ET0 with meteorological data, Penman-Monteith FAO equation has been used. The NDVI vegetation index indicates the amount of vegetation on the ground and is sensitive to the early stages of phenology. But the enhanced vegetation index (EVI) minimizes atmospheric effects and differences in blue and red reflections. The SAVI index is used to calculate the vegetation of the land surface that has moderated the effect of soil on it. Three machine learning algorithms were introduced to train the ET0 models, including random forest (RF), gradient boosting regressor (GBR) and support vector regression (SVR). Random forest are one of the machine learning methods that performs classification and regression using Bootstrap and Bagging methods. In this research, three machine learning algorithms with different input data (vegetation indices and all bands of Landsat 7 and 8) are used and after comparing the results, the best model was selected. Performance Evaluation Indicators to compare and evaluate the performance of the studied models, the parameters of mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2) and correlation coefficient (CC) are used. Finally, according to the results of the two approaches used in this study, using the values of all Landsat bands, the reference evapotranspiration can be estimated with more accuracy.Accurate estimating of reference evapotranspiration is necessary to estimate irrigation needs and in general, to accurately manage water resources. Conventional methods of measuring evapotranspiration are reference using meteorological data. These measurements are point-based, so they are only suitable for very small scale areas. At present, remote sensing methods are the only non-terrestrial way to obtain the various variables at the temporal and spatial scales needed to estimate reference evapotranspiration. In order to reduce the dependence on climatic data and better resolution, machine learning methods are used to calculate the reference evapotranspiration. In this research, RF, GBR and SVR models have been used. In the present study, there are two approaches Used. In the first approach, the values of all bands of Landsat images are as model input, while in the second approach, vegetation indices are calculated with only a few bands of Landsat images and then used as model inputs. By examining, it can be seen that the information obtained from the Landsat image bands is related to the phenological behavior of the products, and it is also possible to contract very relevant information related to agricultural products that are examined temporarily and spatially. One of the factors influencing the accuracy of estimating reference evapotranspiration is the use of other Landsat bands in addition to the bands related to vegetation indices.