Multi-source data-based spatial variations of blue and green water footprints for rice production in Jilin Province, China

• Published in: Environmental science and pollution research international Vol. 28; no. 28; pp. 38106 - 38116
• Main Authors: Li, Bo, Qin, Lijie, Wang, Jianqin, Dang, Yongcai, He, Hongshi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-07-2021; Springer Nature B.V
• Subjects: Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Crop production; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Footprint analysis; Nutrient requirements; Parameter estimation; Research Article; Rice; Spatial data; Spatial discrimination; Spatial resolution; Spatial variations; Statistical analysis; Waste Water Technology; Water consumption; Water Management; Water Pollution Control; Weather; China; Jilin China; Spatial distribution; Multi-source data; Rice production; Water footprint
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-021-13365-z

Rice production consumes more water than the production of other crop species due to the specific growth requirements of this species. Accurately accounting for water consumption during rice production and analyzing the spatio-temporal changes in water consumption are thus necessary. Using the water footprint (WF) as an indicator and combining data from multi-sources, this paper explored the regional differences in rice WFs in Jilin Province at a spatial resolution of 1 km. The results showed that the blue WF was always larger than the green WF, and the total, green and blue WFs were lowest during the humid year. The pixels with high values of total, green and blue WFs were mainly distributed in the eastern region of Jilin Province. Compared with the traditional estimation of the WF based on the data of administrative regions, RS techniques can overcome the administrative boundary and provide near real-time data concerning specific agricultural parameters to extract more accurate results for WF models. The combination of RS data and statistical, observational, and survey data can thus overcome the limitations of weather conditions affecting RS, reduce the incorporation of parameters, and estimate WFs quickly and accurately. This study provides a framework to evaluate crop WFs with multi-source data.