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As water for irrigation purposes becomes increasingly scarce because of climate change and population growth, there is growing interest in regulated deficit irrigation (RDI) as a way to improve efficiency of water usage and farm productivity in arid and semi‐arid areas. Salinity is also becoming an important problem in these same regions. Experiments were performed to investigate the effects of RDI and salt stress on two legumes crops, common bean (Phaseolus vulgaris L.) and mungbean (Vigna radiata (L.) Wilczek); previous work showed contrasting responses to RDI by these two crops under field conditions. The seed and biomass yields of both crops were reduced as a result of increasing water deficit stress; however, mungbean was able to maintain the same proportion of its biomass in reproductive structures and maintain its harvest index under stress, whereas common bean’s decreased. In addition, photosynthesis in mungbean was higher than in common bean and higher at the same levels of transpiration. Finally, salinity stress did not affect the water potential, harvest index or the specific leaf weight of either crop. There were no interactions between salinity and crops or RDI levels, which suggest that the two crops do not differ in their response to salinity stress, and that RDI levels do not modify this response.

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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.