{"title":"1公里网格分辨率下全球农田用水效率20年数据集（2001-2020）","authors":"Min Jiang, Chaolei Zheng, Li Jia, Jiu Chen","doi":"10.1038/s41597-025-04904-1","DOIUrl":null,"url":null,"abstract":"<p><p>Cropland water-use efficiency (WUE) is an essential indicator for the sustainable utilization of agricultural water resources. The lack of long-term global cropland WUE datasets with high spatial resolution limits our understanding of global and regional patterns of cropland WUE. This study developed a long-term global cropland WUE dataset at 1-km spatial resolution from 2001 to 2020. The cropland WUE was obtained as the ratio between net primary productivity (NPP) and evapotranspiration that was retrieved from ETMonitor global evapotranspiration datasets. The global cropland NPP was estimated by subtracting plant respiration from gross primary production (GPP), which was estimated using an improved light-use efficiency model after being optimized for different global climate zones using flux-tower observation data. The generated WUE product showed good accuracy with high correlation efficiency (0.76) and low root mean square error (0.5 g C/kg H<sub>2</sub>O/yr) compared with the ground measurements at flux towers. This dataset can be used as fundamental data to advance the efficient utilization of water use for sustainable development.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"574"},"PeriodicalIF":5.8000,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11971418/pdf/","citationCount":"0","resultStr":"{\"title\":\"A 20-year dataset (2001-2020) of global cropland water-use efficiency at 1-km grid resolution.\",\"authors\":\"Min Jiang, Chaolei Zheng, Li Jia, Jiu Chen\",\"doi\":\"10.1038/s41597-025-04904-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Cropland water-use efficiency (WUE) is an essential indicator for the sustainable utilization of agricultural water resources. The lack of long-term global cropland WUE datasets with high spatial resolution limits our understanding of global and regional patterns of cropland WUE. This study developed a long-term global cropland WUE dataset at 1-km spatial resolution from 2001 to 2020. The cropland WUE was obtained as the ratio between net primary productivity (NPP) and evapotranspiration that was retrieved from ETMonitor global evapotranspiration datasets. The global cropland NPP was estimated by subtracting plant respiration from gross primary production (GPP), which was estimated using an improved light-use efficiency model after being optimized for different global climate zones using flux-tower observation data. The generated WUE product showed good accuracy with high correlation efficiency (0.76) and low root mean square error (0.5 g C/kg H<sub>2</sub>O/yr) compared with the ground measurements at flux towers. This dataset can be used as fundamental data to advance the efficient utilization of water use for sustainable development.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"574\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2025-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11971418/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-025-04904-1\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04904-1","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
农田水分利用效率是衡量农业水资源可持续利用的重要指标。由于缺乏高空间分辨率的长期全球耕地水分利用效率数据集，限制了我们对全球和区域耕地水分利用效率格局的认识。本研究建立了2001 - 2020年1公里空间分辨率的全球耕地水分利用效率长期数据集。耕地水分利用效率为净初级生产力（NPP）与蒸散发的比值，该比值来源于ETMonitor全球蒸散发数据集。利用通量塔观测数据对全球不同气候带进行优化后的改进光利用效率模型，通过从总初级生产（GPP）中减去植物呼吸来估算全球耕地NPP。与通量塔地面测量值相比，生成的WUE产品具有良好的精度，相关效率（0.76）高，均方根误差（0.5 g C/kg H2O/yr）低。该数据集可作为推进水资源高效利用、促进可持续发展的基础数据。
A 20-year dataset (2001-2020) of global cropland water-use efficiency at 1-km grid resolution.
Cropland water-use efficiency (WUE) is an essential indicator for the sustainable utilization of agricultural water resources. The lack of long-term global cropland WUE datasets with high spatial resolution limits our understanding of global and regional patterns of cropland WUE. This study developed a long-term global cropland WUE dataset at 1-km spatial resolution from 2001 to 2020. The cropland WUE was obtained as the ratio between net primary productivity (NPP) and evapotranspiration that was retrieved from ETMonitor global evapotranspiration datasets. The global cropland NPP was estimated by subtracting plant respiration from gross primary production (GPP), which was estimated using an improved light-use efficiency model after being optimized for different global climate zones using flux-tower observation data. The generated WUE product showed good accuracy with high correlation efficiency (0.76) and low root mean square error (0.5 g C/kg H2O/yr) compared with the ground measurements at flux towers. This dataset can be used as fundamental data to advance the efficient utilization of water use for sustainable development.
期刊介绍：
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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