Identification of Water Layer Presence in Paddy Fields Using UAV-Based Visible and Thermal Infrared Imagery
Identification of Water Layer Presence in Paddy Fields Using UAV-Based Visible and Thermal Infrared Imagery
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DOI：
10.3390/agronomy13071932
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
G. Wei;Huifang Chen;En Lin;Xuhua Hu;Hengwang Xie;Yuanlai Cui;Yufeng Luo
中科院分区：
文献类型：
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作者：
G. Wei;Huifang Chen;En Lin;Xuhua Hu;Hengwang Xie;Yuanlai Cui;Yufeng Luo
水田水层状况的准确识别是水田精确水分管理的前提，对水稻节水灌溉具有重要意义。到目前为止，利用无人机遥感数据监测大田作物水分状况的研究主要集中在旱作作物上，对水田水分状况的研究相对有限。本研究利用无人机遥感平台获取水田关键生长阶段的可见光和热红外图像，通过提取每个水田的颜色特征和温度特征，构建3个模型输入变量，并采用k近邻（KNN）、支持向量机（SVM）、随机森林（RF）和逻辑回归（LR）分析方法，建立水田存在水层的识别模型。结果表明，KNN、SVM和RF对水田存在水层的识别效果较好；通过算法比较和参数偏好，KNN的识别准确率最高，为89.29%。在模型输入变量方面，使用多源遥感数据比单独使用热图像或可见光图像效果更好，热数据比可见光数据更有效地识别稻田水层状态。该研究为稻田水分状况监测提供了一种新的模式，为今后大面积稻田的精准灌溉提供了关键。
The accurate identification of the water layer condition of paddy fields is a prerequisite for precise water management of paddy fields, which is important for the water-saving irrigation of rice. Until now, the study of unmanned aerial vehicle (UAV) remote sensing data to monitor the moisture condition of field crops has mostly focused on dry crops, and research on the water status of paddy fields has been relatively limited. In this study, visible and thermal infrared images of paddy fields at key growth stages were acquired using a UAV remote sensing platform, and three model input variables were constructed by extracting the color features and temperature features of each field, while K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and logistic regression (LR) analysis methods were applied to establish a model for identifying the water layer presence in paddy fields. The results showed that KNN, SVM, and RF performed well in recognizing the presence of water layers in paddy fields; KNN had the best recognition accuracy (89.29%) via algorithm comparison and parameter preference. In terms of model input variables, using multisource remote sensing data led to better results than using thermal or visible images alone, and thermal data was more effective than visible data for identifying the water layer status of rice fields. This study provides a new paradigm for monitoring the water status of rice fields, which will be key to the precision irrigation of paddy fields in large regions in the future.