Land cover is a key variable in the context of climate change. In particular, crop type information is essential to understand the spatial distribution of water usage and anticipate the risk of water scarcity and the consequent danger of food insecurity. This applies to arid regions such as the Aral Sea Basin (ASB), Central Asia, where agriculture relies heavily on irrigation. Here, remote sensing is valuable to map crop types, but its quality depends on consistent ground-truth data. Yet, in the ASB, such data are missing. Addressing this issue, we collected thousands of polygons on crop types, 97.7% of which in Uzbekistan and the remaining in Tajikistan. We collected 8,196 samples between 2015 and 2018, 213 in 2011 and 26 in 2008. Our data compile samples for 40 crop types and is dominated by "cotton" (40%) and "wheat", (25%). These data were meticulously validated using expert knowledge and remote sensing data and relied on transferable, open-source workflows that will assure the consistency of future sampling campaigns.
Sustainable and efficient water resources management is important for the irrigation dominated agricultural system and therefore for the rural population and the environment of the arid regions. The objective of this study is to provide an overview of ecohydrology and irrigation water management in the region and to lay down some opportunities for cooperation at the transboundary level with the aim of increasing water productivity and environmental sustainability. Based on extensive literature review and analysis of secondary data from different organizations, we found that water management in the region’s agriculture faces increasing challenges that are accumulated over time. It is hoped that conclusions from this study will help set the stage for productive discussions and to identify research needs in the region.
Managing water sustainably and effi ciently is important for the Fergana Valley's (FV) irrigation-dominated agricultural system and, subsequently, for its rural population and environment. During the past decade, national water legislation and the organisation of integrated water resources management have been reformed in FV and this development continues. Nevertheless, their implementation has been limited by the lack of resources and the weakness of the institutions. Moreover, the future challenges water management faces in the region's agriculture are increasing all the time. These challenges include low water-use effi ciency, fewer incentives for water users to increase land and water productivity, water shortages within the system, salinity and declines in key crop yields. Current irrigation strategies in the region are not adaptable enough to cope with variations in water supply and crop water requirements caused by land use and climate change. The objective of this chapter is to provide an overview of the irrigation water management in the region and to lay down some of the concepts and complexities in maintaining the existing irrigation infrastructure with the aim of increasing water productivity and environmental sustainability. We hope that this will help set the stage for productive discussions and to identify research needs.
We present a hierarchical classification framework for automated detection and mapping of spatial patterns of agricultural performance using satellite-based Earth observation data exemplified for the Aral Sea Basin (ASB) in Central Asia. The core element of the framework is the derivation of a composite agricultural performance index which is composed of different subindicators taking into account cropping intensity, crop diversity, crop rotations, fallow land frequency, land utilization, water use efficiency, and water availability. We derive these subindicators from net primary productivity and evapotranspiration data obtained from the MODIS sensor on board the Terra satellite during the observation period from 2000 to 2016, as well as from cropland maps created through multiannual classification of normalized difference vegetation index (NDVI). We classified pixel-based NDVI time series covering more than 8 × 10 6 ha of irrigated cropland based on a hierarchical approach concatenating unsupervised and supervised classification techniques to automatically generate and refine training labels, which are then used to train a decision fusion classifier, achieving an average overall accuracy of 78%. The results give unprecedented insights into spatial patterns of agricultural performance in the ASB. The proposed method is transferable and applicable for global-scale mapping, and the results of this remote sensing-aided assessment can provide important information for regional agricultural planning purposes. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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