Remote sensing-based assessments of land use, soil and vegetation status, crop production and water use in irrigation systems of the Aral Sea Basin. A review

Abstract Irrigated agriculture In the Aral Sea Basin (ASB) is commonly known for its high water consumption, inefficient water management, and dysfunctional irrigation and drainage infrastructure. Since 1991, six states have been engaged in intensive irrigated agriculture in the Aral Sea Basin (ASB), Afghanistan, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. In this region, irrigated agriculture is commonly known for its high water consumption, inefficient water management, and dysfunctional irrigation and drainage infrastructure. Extensive land degradation (e.g., soil salinization) is considered as the main result of mismanagement in the irrigation sector and sustainable solutions are urgently required. This study analysed international peer-reviewed scientific studies based on satellite remote sensing (RS) products and methods addressing potential improvements of irrigation water and land management in the ASB. Ways to transfer RS-based knowledge into practice were discussed using the example of the online tool WUEMoCA that was developed from 2015 to 2019 within the German Water Initiative in Central Asia (CAWa). For the period 2008–2019, a total of 49 studies contributed knowledge about land use, soils and vegetation, crop production and use of irrigation water in the ASB. The use of RS revealed increased diversification of agricultural production, spatial-temporal patterns of land degradation, and effects of varying water availability on cropping intensity. Modelling of crop yields and evapotranspiration at varying scales (i.e., farm to provincial scale) underlined the comparably moderate water productivity in the ASB. One relevant future research task is to intensively collect in-situ data for validation and secondary data and hence to mitigate the situation. In particular, improved socio-ecological and economic information could help to better understand the spatially differing drivers of soil and land degradation. Eventually, this study provides relevant information and data sources for decision-making and requirements for better integration of RS-based information into practice using online-tools like WUEMoCA.

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