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Global rain-fed,irrigated, and paddy croplands: A new high resolution map derived from remote sensing,crop inventories and climate data
Institution:1. Department of Land Management, School of Public Affairs, Zhejiang University, China;2. Center for Global Change and Earth Observations, Michigan State University, United States;3. Institute of Applied Remote Sensing & Information Technology, College of Environmental and Resource Sciences, Zhejiang University, China;4. School of Environmental & Resource Sciences, Zhejiang Agriculture and Forestry University, China;5. Department of Geography, Environment, and Spatial Sciences, Michigan State University, United States;6. Department of Civil and Environmental Engineering, Michigan State University, United States;7. Department of Electrical and Computer Engineering, Michigan State University, United States;8. Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, China
Abstract:Irrigation accounts for 70% of global water use by humans and 33–40% of global food production comes from irrigated croplands. Accurate and timely information related to global irrigation is therefore needed to manage increasingly scarce water resources and to improve food security in the face of yield gaps, climate change and extreme events such as droughts, floods, and heat waves. Unfortunately, this information is not available for many regions of the world. This study aims to improve characterization of global rain-fed, irrigated and paddy croplands by integrating information from national and sub-national surveys, remote sensing, and gridded climate data sets. To achieve this goal, we used supervised classification of remote sensing, climate, and agricultural inventory data to generate a global map of irrigated, rain-fed, and paddy croplands. We estimate that 314 million hectares (Mha) worldwide were irrigated circa 2005. This includes 66 Mha of irrigated paddy cropland and 249 Mha of irrigated non-paddy cropland. Additionally, we estimate that 1047 Mha of cropland are managed under rain-fed conditions, including 63 Mha of rain-fed paddy cropland and 985 Mha of rain-fed non-paddy cropland. More generally, our results show that global mapping of irrigated, rain-fed, and paddy croplands is possible by combining information from multiple data sources. However, regions with rapidly changing irrigation or complex mixtures of irrigated and non-irrigated crops present significant challenges and require more and better data to support high quality mapping of irrigation.
Keywords:Irrigation  MODIS  Remote sensing  Paddy  Cropland  Water management
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