Agriculture is the largest global consumer of water. Irrigated areas constitute 40 % of the total area used for agricultural production (FAO, 2014a) Information on their spatial distribution is highly relevant for regional water management and food security. Spatial information on irrigation is highly important for policy and decision makers, who are facing the transition towards more efficient sustainable agriculture. However, the mapping of irrigated areas still represents a challenge for land use classifications, and existing global data sets differ strongly in their results. The following study tests an existing irrigation map based on statistics and extends the irrigated area using ancillary data. The approach processes and analyzes multi-temporal normalized difference vegetation index (NDVI) SPOT-VGT data and agricultural suitability data – both at a spatial resolution of 30 arcsec – incrementally in a multiple decision tree. It covers the period from 1999 to 2012. The results globally show a 18 % larger irrigated area than existing approaches based on statistical data. The largest differences compared to the official national statistics are found in Asia and particularly in China and India. The additional areas are mainly identified within already known irrigated regions where irrigation is more dense than previously estimated. The validation with global and regional products shows the large divergence of existing data sets with respect to size and distribution of irrigated areas caused by spatial resolution, the considered time period and the input data and assumption made.

Miscalculating the volumes of water withdrawn for irrigation, the largest consumer of freshwater in the world, jeopardizes sustainable water management. Hydrological models quantify water withdrawals, but their estimates are unduly precise. Model imperfections need to be appreciated to avoid policy misjudgements.

An assessment of the human impact on the global water cycle requires estimating the volume of water withdrawn for irrigated agriculture. A key parameter in this calculation is the irrigation efficiency, which corrects for the fraction of water lost between irrigation withdrawals and the crop due to management, distribution or conveyance losses. Here we show that the irrigation efficiency used in global irrigation models is flawed for it overlooks key ambiguities in partial efficiencies, irrigation technologies, the definition of ‘large-scale’ irrigated areas or managerial factors. Once accounted for, these uncertainties can make irrigation withdrawal estimates fluctuate by more than one order of magnitude at the country level. Such variability is larger and leads to more extreme values than that caused by the uncertainties related with climate change. Our results highlight the need to embrace deep uncertainties in irrigation efficiency to prevent the design of shortsighted policies at the river basin-water-agricultural interface.

West Africa faces a complex range of challenges arising from climatic, social, economic, and ecological factors, which pose significant risks. The rapidly growing population, coupled with persistently low agricultural yield, further exacerbates these risks. A state-of-the-art monitoring and data derivation of agricultural systems are crucial for improving livelihoods and enhancing food security. Despite smallholder farming systems accounting for 80% of cultivated cropland area and providing about 42% of the total employment in West Africa, there exists a lack of a comprehensive overview of Remote Sensing (RS) products and studies specifically tailored to smallholder farming systems, which this review aims to address. Through a systematic literature review comprising 163 SCI papers sourced from the Web of Science database (Filter I), followed by a full-text review (Filter II), we analyze the RS sensors, spatiotemporal distribution, temporal scales, the crop types examined, and thematic foci employed in existing research. Our findings highlight the predominance of high to very high-resolution, multispectral sensors as the primary data source and we observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. By highlighting existing knowledge, this study identifies the potential of RS and pinpoints the key research gaps. This sets the stage for future investigations aimed at addressing critical challenges in West African smallholder agricultural systems.

The monitoring of irrigated areas still represents a complex and laborious challenge in land use classification. The extent and location of irrigated areas vary in both methodology and scale. One major reason for discrepancies is the choice of spatial resolution. This study evaluates the influence of spatial resolution on the mapped extent and spatial patterns of irrigation using an NDVI threshold approach with Sentinel-2 and operational PROBA-V data. The influence of resolution on irrigation mapping was analyzed in the USA, China and Sudan to cover a broad range of agricultural systems by comparing results from original 10 m Sentinel-2 data with mapped coarser results at 20 m, 40 m, 60 m, 100 m, 300 m, 600 m and 1000 m and with results from PROBA-V. While the mapped irrigated area in China is constant independent of resolution, it decreases in Sudan (−29%) and the USA (−48%). The differences in the mapping result can largely be explained by the spatial arrangement of the irrigated pixels at a fine resolution. The calculation of landscape metrics in the three regions shows that the Landscape Shape Index (LSI) can explain the loss of irrigated area from 10 m to 300 m (r > 0.9).