A 20-year dataset (2001-2020) of global cropland water-use efficiency at 1-km grid resolution.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-04-04 DOI:10.1038/s41597-025-04904-1

Min Jiang, Chaolei Zheng, Li Jia, Jiu Chen

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引用次数: 0

Abstract

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.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

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.