Li Bo, Qin Lijie, Wang Jianqin, Dang Yongcai, He Hongshi
Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun, 130024, China.
School of Natural Resources, University of Missouri, Columbia, MO, 65211, USA.
Environ Sci Pollut Res Int. 2021 Jul;28(28):38106-38116. doi: 10.1007/s11356-021-13365-z. Epub 2021 Mar 16.
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.
由于水稻这一物种特定的生长需求，其生产过程比其他作物品种消耗更多的水。因此，准确核算水稻生产过程中的耗水量并分析耗水量的时空变化是很有必要的。本文以水足迹（WF）为指标，结合多源数据，在1公里的空间分辨率下探究了吉林省水稻水足迹的区域差异。结果表明，蓝色水足迹始终大于绿色水足迹，在湿润年份总水足迹、绿色水足迹和蓝色水足迹均最低。总水足迹、绿色水足迹和蓝色水足迹高值像素主要分布在吉林省东部地区。与基于行政区数据对水足迹的传统估算相比，遥感技术可以克服行政边界问题，并提供有关特定农业参数的近实时数据，从而为水足迹模型提取更准确的结果。因此，遥感数据与统计、观测和调查数据相结合，可以克服影响遥感的天气条件的限制，减少参数的纳入，并快速准确地估算水足迹。本研究提供了一个利用多源数据评估作物水足迹的框架。
