Estimation of agricultural water productivity in Qazvin plain using modis and Avhrr images

Abstract

The world has finite water resources, which are under increasing stress as the human population and water demand per capita both increase. These problems are not new but are now becoming more widespread and their impacts more devastating. This has provided additional impetus for the search for solutions to problems arising from the mismatch between demand and supply in terms of water quantity, quality and timing. Increasing water productivity has been identified as one of the global challenges that require urgent attention.This study examines approaches to assessing water productivity in agriculture. Many studies have been carried out on agricultural water productivity [WP] in the world, where most of these studies, specifically in relation to water and food policies of WP, are considered in regional or watershed scale. There are many methods of calculating evapotranspiration (ETc) on regional scale but remote sensing technique since last decade of this century has become more popular one. SEBAL algorithm was used in this research to calculate ETc. Last versions of SEBAL algorithm can calculate aerial crop biomass and then can calculate crop water productivity. In this research, five cloudless MODIS and AVHRR images from Qazvin plain which is located in the center of Iran were selected, and then water productivity calculated with SEBAL algorithm, which runs with ENVI and ILWIS models on MODIS and AVHRR images. Amount of ETc, which was estimated from SEBAL, compared with lysimeter data with r-square [R2] value of 0.9 for MODIS images and 0.81 for AVHRR images. Estimated ETc was higher than lysimeter data in AVHRR and lower than it in MODIS images. Water productivity was estimated 0.76 and 1.03 kg/m3 in MODIS and AVHRR images respectively, but is 0.9 kg/m3 in lysimeter.