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引用本文:朱 磊,张伟业,潘自林,等.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.
ZHU Lei,ZHANG Weiye,PAN Zilin,et al.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.
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基于Sentinel-2的青铜峡灌区春小麦和苜蓿早期识别
朱 磊,张伟业,潘自林,丁一民,雷晓萍,张宗和,孙伯颜,柴明堂
1.宁夏大学 土木与水利工程学院,银川 750021;2.宁夏回族自治区黄河水联网数字治水重点实验室, 银川 750021;3.宁夏回族自治区水利工程建设中心,银川 750001;4.宁夏回族自治区农业勘察设计院, 银川 750002;5.宁夏宁西供水有限公司,银川 750000
摘要:
【目的】基于决策树分类算法对青铜峡灌区春小麦和苜蓿进行早期识别。【方法】在实地调研的基础上,结合Sentinel-2遥感影像,构建能够不断融入遥感数据的决策树分类算法,对青铜峡灌区春小麦和苜蓿进行早期识别。【结果】4月上旬,由于春小麦和苜蓿生长特征较为相似,春小麦和苜蓿的总体分类精度分别为69%和75%。此后,随着观测数据的不断融入,分类精度逐步提升,5月14日二者的分类精度即可达到90%以上。5月31日利用全部5期卫星影像数据时,春小麦的总体精度可达到94%,Kappa系数为0.75,苜蓿的总体精度可达到97%,Kappa为0.86。2023年青铜峡灌区春小麦种植面积为24 000 hm2,苜蓿种植面积为2 000 hm2。其中,春小麦种植结构较为复杂,既存在集中连片的大规模种植,也存在大量面积较小的碎片化种植带。【结论】基于Sentinel-2遥感数据的决策树分类方法,可在4月上旬获取初步分类结果,在5月中旬苜蓿第1次收割之前对青铜峡灌区春小麦和苜蓿进行较为准确的识别。
关键词:  遥感;早期识别;决策树;时间序列;春小麦;苜蓿
DOI:10.13522/j.cnki.ggps.2023539
分类号:
基金项目:
Using Sentinel-2 imagery to differentiate between spring wheat and alfalfa in Qingtongxia Irrigation District
ZHU Lei, ZHANG Weiye, PAN Zilin, DING Yimin, LEI Xiaoping, ZHANG Zonghe, SUN Boyan, CHAI Mingtang
1. School of Civil and Water Resources Engineering, Ningxia University, Yinchuan 750021, China; 2. Key Laboratory of Digital Water Management for Yellow River Water Networking, Ningxia Hui Autonomous Region, Yinchuan 750021, China; 3. Water Conservancy Engineering and Construction Center of Ningxia Hui Autonomous Region, Yinchuan 750001, China; 4. Ningxia Hui Autonomous Region Agricultural Survey and Design Institute, Yinchuan 750002, China; 5. Ningxia Ningxi Water Supply Co., Ltd, Yinchuan 750000, China
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.
Key words:  remote sensing; early identification; decision tree; time series; spring wheat; alfalfa