灌溉排水学报2024，Vol.43Issue(5)：86-94,9.DOI:10.13522/j.cnki.ggps.2023539
基于Sentinel-2的青铜峡灌区春小麦和苜蓿早期识别
Using Sentinel-2 imagery to differentiate between spring wheat and alfalfa in Qingtongxia Irrigation District
摘要
Abstract
[Objective]Spring wheat and alfalfa and two crops widely grown by farmers in Northwestern China.A knowledge of their planting areas is important for agricultural management but challenging at regional scale.This paper investigates the feasibility of using air-born technologies to identify their areas at different growing stages.[Method]The studies were based on Sentinel-2 imagery acquired from the Qingtongxia Irrigation District,with which we developed a decision tree classification algorithm to identify spring wheat and alfalfa.Accuracy of the method was tested against ground-truth data.[Result]The accuracy of the model for identifying spring wheat and alfalfa in early April was 69%and 75%,respectively,due to the similarities of the canopies of the two plants.With the growth of the plants and increase in available data,the accuracy of the model improved gradually,with its accuracy for identifying the spring wheat and alfalfa exceeding 90%on 14th May.Using all five satellite imageries available by 13th May,the accuracy of the model for identifying spring wheat and alfalfa reached 94%and 97%,with their associated Kappa coefficient being 0.75 and 0.86,respectively.The estimated planting areas of the spring wheat and alfalfa in Qingtongxia Irrigation District was 24,000 hm2 and 2,000 hm2,respectively.The spatial distribution of spring wheat was complex,characterized by a large number of fragmented planting zones.[Conclusion]The decision tree classification method combined with the Sentinel-2 images can preliminarily identify spring wheat and alfalfa in early April.Its accuracy improves steadily as more data become available,with the accuracy exceeding 90%after the middle of May.关键词
遥感/早期识别/决策树/时间序列/春小麦/苜蓿Key words
remote sensing/early identification/decision tree/time series/spring wheat/alfalfa分类
信息技术与安全科学引用本文复制引用
朱磊,张伟业,潘自林,丁一民,雷晓萍,张宗和,孙伯颜,柴明堂..基于Sentinel-2的青铜峡灌区春小麦和苜蓿早期识别[J].灌溉排水学报,2024,43(5):86-94,9.基金项目
国家自然科学基金项目(52209059) （52209059）
宁夏自然科学基金优秀青年项目(2023AAC05013) （2023AAC05013）
清华大学水沙科学水利水电工程国家重点实验室及宁夏银川水联网数字治水联合研究院联合开放研究基金资助课题(sklhse-2022-Iow09) （sklhse-2022-Iow09）
