**2.2 Data**

The data used in this study consist of remote sensing, climate information and field observations. They collected during April 2017–March 2018 to include the summer and the winter season of the study area. The summer season is considered for Aril–September, while the winter is for October–March.

**Figure 1.** *Location of the study area.*

*Mapping and Assessment of Evapotranspiration over Different Land-Use/Land-Cover Types… DOI: http://dx.doi.org/10.5772/intechopen.96759*

A total number of 22 Landsat–8 images (path/row is 164/042) acquired from the United States Geological Survey (USGS-https://earthexplorer.usgs.gov/) to cover the entire study area, and the characteristics of these data are shown in (**Table 1**). The obtained Landsat-8 images have a cloud cover of less than 10%, and they have been geometrically and radiometrically corrected. All images bands were resampled into a pixel size of 30 m 30 m using the nearest neighbour method. A global digital elevation model (DEM) is generated from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) known as ASTER GDEM obtained from the USGS website. It is a 30 m grid size DEM produced by the National Aeronautics and Space Administration (NASA) and the Ministry of Economy, Trade, and Industry of Japan (METI).

The climate data was collected from two meteorological stations located within the study area. These data include air temperature, relative humidity, wind speed, net radiation, precipitation and vapour pressure, and all data collection on an hourly and daily basis. However, the field observations include the identification of the main land-use system in the study area. Besides field notes, site descriptions, and terrestrial photographs were taken to relate the site location to scene features.

#### **2.3 Analysis of LULC**

A field survey was conducted throughout the study area to identify the LULC classes during the study period. Global Positioning System (GPS) instrument was used to obtain accurate location point data for each LULC class included in the classification process. A total number of 115 ground control points (GCPs) were collected. A supervised maximum likelihood classification (MLC) method was employed to classify images. Based on the study objectives, the supervised classification applied in this study does not compare different classifiers. Therefore, the MLC was adopted to be the only classification method for this study. MLC widely used in remote sensing for image classification [33–35]. The accuracy assessment of the classified images was performed using 30% of the collected GCPs. Also, visual interpretation of the unclassified satellite images supported with the field observations was used to validate the LULC maps. However, to reduce bias, the stratified


#### **Table 1.**

*Characteristics of Landsat-8 data used in this study [32].*

random sampling method was adopted to classify images [36]. Finally, the overall accuracy, user's and producer's accuracies, and the Kappa statistic were derived from the error matrices [37].
