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This paper emphasizes the importance of remote sensing and continued research about ways to use its assets in global agricultural cropland mapping and water use evaluation. Current cropland map products are derived from coarse resolution remotely sensed data and traditional classification methods that require substantial human involvement. We have discussed the advances and developmental needs of semi-automated and automated classification algorithms in routine, rapid, and accurate mapping of global croplands and their characteristics. Advances in global cropland mapping will require data fusion and\or combination techniques from multiple satellite sensors, secondary data sources, and a large and systematic collection of in-situ information, including temporal phenologies and hyperspectral signatures. As Beddington (2010) stresses, the fundamental issues for policy makers and scientists are whether by the year 2050 over nine billion people can be fed equitably, healthily, and sustainably and how sound management can make water use more sustainable as a growing population moves up from poverty. In this context, the role of remote sensing is clear. There is an unequivocal need to provide a more systematic and integrated approach to global cropland mapping to support a range of initiatives, including assessments of crop productivity, helping to identify food security "hotspots" of vulnerability and resiliency, assessing the agricultural risks due to climate change and quantifying agricultural water demand.