| 引用本文: | 吴 迪,杨 鹏,周黎勇,等.基于Sentinel-2破碎化地块灌区作物种植结构的提取[J].灌溉排水学报,2023,42(4):74-80. |
| WU Di,YANG Peng,ZHOU Liyong,et al.基于Sentinel-2破碎化地块灌区作物种植结构的提取[J].灌溉排水学报,2023,42(4):74-80. |
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| 摘要: |
| 【目的】探究基于Sentinel-2遥感影像的决策树分类模型提取破碎化地块灌区作物种植结构的适用性。【方法】选取新疆阿拉沟灌区为研究区，以2021年覆盖作物全生育期的Sentinel-2遥感影像为数据源，结合田间调查和Google高清影像目视解译采样，基于主要作物物候信息、NDVI时序特征等分析确定作物识别的关键期阈值，构建决策树模型进行灌区主要作物分类，并对分类结果精度验证。【结果】基于Sentinel-2提取的灌区种植结构分布图地块纹理清晰，能够满足灌区用水管理需要；构建的决策树分类模型可在灌区尺度实现作物分类，方法简便易行，总体精度达到81.56%，Kappa系数为0.716 6。【结论】采用Sentinel-2遥感影像和决策树分类方法识别破碎化地块灌区复杂作物分类是可行的，可为灌区输配水决策和农业用水精细化管理提供基础信息。 |
| 关键词: Sentinel-2；灌区作物分类；NDVI时间序列；决策树；破碎化地块 |
| DOI：10.13522/j.cnki.ggps.2022368 |
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| Using Sentinel-2 Sensing Imagery to Estimate Planting Structure in Fragmented Irrigated Lands |
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WU Di, YANG Peng, ZHOU Liyong, LI Fangsong, LI Lingfeng, ZHANG Xudong
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1. China Irrigation and Drainage Development Center, Beijing 100054, China; 2. College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China; 3. Xinjiang Institute of Water Resources and Hydropower Research, Urumqi 830049, China
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| Abstract: |
| 【Objective】Understanding planting structure and crop growth in a region is important to assess its food supply and security. The objective of this paper is to investigate the feasibility of a decision-tree model derived from the Sentinel-2 remote sensing imagery to map cropping structure in fragmented irrigation regions.【Method】The study site was Alagou irrigation area in Xinjiang. The planting areas of major crops in 2021 were estimated using the Sentinel-2 remote sensing imagery. We then compared these with both field investigation and visual interpretation from the Google HD images. Critical growth stage for identifying each crop was determined based on the phenological information and the NDVI time series, from which we derived a decision tree classification model. Accuracy of the model was verified against ground-truth data.【Result】The planting structure mapped from the Sentinel-2 remote sensing imagery had sharp textures, meeting the requirements for agricultural water management. The decision tree classification model can accurately classify crops at the scale required for irrigation management. The model is simple and feasible. Compared with ground-truth data, its average accuracy is 81.56% and the Kappa coefficient is 0.716 6.【Conclusion】The Sentinel-2 remote sensing imagery and the decision tree classification method derived form it can be used to accurately identify planting structure in fragmented lands. They can provide support information for decision-making in water management, and improve agricultural water usage in irrigation districts. |
| Key words: Sentinel-2; crops classification in irrigated areas; NDVI time series; decision tree; fragmented land parcel |
