m m; h b; f k; n e
Abstract
The agricultural sector is known as the largest consumer of water. Due to limited water resources, water productivity needs to be enhanced in this sector. The concept of water productivity has attracted the attention of policy makers in food and water sector at large scale. Remote sensing is used in ...
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The agricultural sector is known as the largest consumer of water. Due to limited water resources, water productivity needs to be enhanced in this sector. The concept of water productivity has attracted the attention of policy makers in food and water sector at large scale. Remote sensing is used in the assessment and management of soil and water resources in recent decades. In the present research, this method was used to estimate water productivity. Evapotranspiration and actual production levels of dry matter were calculated using SEBAL algorithms and five images from the Landsat 5TM satellite in Qazvin Plain. The results of SEBAL algorithm in five images and lysimeter data were compared and evaluated in the region. The coefficient of determination ( 15R2"> ) and their mean absolute difference were 0.9948 and 0.446 mm/day, respectively, which demonstrated the accuracy of remote sensing methods in estimating agricultural water productivity at the basin level. The results showed that water productivity varied from 0.18 to 1.35 in the field. The wheat water productivity values from Landsat 5TM images and lysimeter data were 0.73 and 0.85 kg/m3, respectively, which are relatively close to each other.