%0 Generic %1 ibrakhimov2018combining %A Ibrakhimov, Mirzakhayot %A Sultanov, Murodjon %A Conrad, Christopher %A Lamers, John %D 2018 %K conference poster LSFE %T Combining remote sensing and hydrological modeling for assessing the dynamics of soil salinity in the Aral Sea Basin %X Land salinization threatens sustainability of irrigated agriculture. It severely limits land productivity and hence rural livelihoods in the Aral Sea Basin (ASB). Sound monitoring and mapping techniques are required to define proper management countermeasures. However, traditional salinity monitoring methods are resource and time demanding yet limited in spatial and temporal coverage. This study aimed at assessing the predictive quality of soil salinity assessment with remote sensing (RS) tools in the basin’s irrigated areas. Salinity was assessed indirectly, from vegetation indices estimated from Landsat 5TM imagery in 75.5 ha irrigated area during 2008-2009. To assess the precision of salinity prediction, the HYDRUS-1D model was employed based on in-situ sampling for soil EC at depths of 0-30, 30-90 and >100 cm, measurements of groundwater depth and EC, irrigation amounts and evapotranspiration (FAO-56) from climate data. Daily salinity dynamics were modelled in slightly, moderately and highly saline spots, identified in the RS maps. The findings showed that an RS-based salinity assessment alone allows for modest reliable prediction only: the relationship of the salinity maps and empirical data collected with an electromagnetic EM38 device were weak (R2=0.15-0.29) during, but became more reliable (R2=0.35-0.56) beyond irrigation periods. Salinity modelling with HYDRUS-1D at slightly, moderately and highly saline sites at various depths underlined that under present irrigation and drainage infrastructure, salts tend to only move to deeper layers during water applications, but reappear in the profile during dry periods. In contrast, beyond irrigation events, salts gradually increased in the upper soil layers without fluctuations. We argue that coupling RS techniques with numerical modelling provided valuable insight into within-season salinity dynamics than any of these approaches alone. This should be of interest to farmers and policy makers in the ASB since the combination of methods will allow for better planning and management of melioration measures.

@conference{ibrakhimov2018combining, abstract = {Land salinization threatens sustainability of irrigated agriculture. It severely limits land productivity and hence rural livelihoods in the Aral Sea Basin (ASB). Sound monitoring and mapping techniques are required to define proper management countermeasures. However, traditional salinity monitoring methods are resource and time demanding yet limited in spatial and temporal coverage. This study aimed at assessing the predictive quality of soil salinity assessment with remote sensing (RS) tools in the basin’s irrigated areas. Salinity was assessed indirectly, from vegetation indices estimated from Landsat 5TM imagery in 75.5 ha irrigated area during 2008-2009. To assess the precision of salinity prediction, the HYDRUS-1D model was employed based on in-situ sampling for soil EC at depths of 0-30, 30-90 and >100 cm, measurements of groundwater depth and EC, irrigation amounts and evapotranspiration (FAO-56) from climate data. Daily salinity dynamics were modelled in slightly, moderately and highly saline spots, identified in the RS maps. The findings showed that an RS-based salinity assessment alone allows for modest reliable prediction only: the relationship of the salinity maps and empirical data collected with an electromagnetic EM38 device were weak (R2=0.15-0.29) during, but became more reliable (R2=0.35-0.56) beyond irrigation periods. Salinity modelling with HYDRUS-1D at slightly, moderately and highly saline sites at various depths underlined that under present irrigation and drainage infrastructure, salts tend to only move to deeper layers during water applications, but reappear in the profile during dry periods. In contrast, beyond irrigation events, salts gradually increased in the upper soil layers without fluctuations. We argue that coupling RS techniques with numerical modelling provided valuable insight into within-season salinity dynamics than any of these approaches alone. This should be of interest to farmers and policy makers in the ASB since the combination of methods will allow for better planning and management of melioration measures.}, added-at = {2020-09-11T12:04:50.000+0200}, author = {Ibrakhimov, Mirzakhayot and Sultanov, Murodjon and Conrad, Christopher and Lamers, John}, biburl = {https://www.bibsonomy.org/bibtex/2fa1a76c0b23b26697608c4247a760c0e/earthobs_uniwue}, eventdate = {16.-19.04.2018}, eventtitle = {Between Europe and the Orient, Status symposium}, interhash = {2cc9a2c88774b79faec5fc9f14c36781}, intrahash = {fa1a76c0b23b26697608c4247a760c0e}, keywords = {conference poster LSFE}, month = {April}, timestamp = {2020-11-18T22:08:33.000+0100}, title = {Combining remote sensing and hydrological modeling for assessing the dynamics of soil salinity in the Aral Sea Basin }, venue = {Almaty/Kasachstan}, year = 2018 }