| 基于无人机多时相遥感影像的冬小麦产量估算 |
| |
| 引用本文： | 申洋洋,陈志超,胡 昊,盛 莉,周洪奎,娄卫东,沈阿林.基于无人机多时相遥感影像的冬小麦产量估算[J].麦类作物学报,2021(10):1298-1306. |
| |
| 作者姓名： | 申洋洋 陈志超 胡 昊 盛 莉 周洪奎 娄卫东 沈阿林 |
| |
| 作者单位： | （1.河南理工大学测绘与国土信息工程学院，河南焦作 454000；2.浙江省农业科学院数字农业研究所，浙江杭州 310021；3.浙江省农业科学院环境资源与土壤肥料研究所，浙江杭州 310021） |
| |
| 基金项目： | 国家重点研发项目（2018YFD0200507） |
| |
| 摘 要： | 为高效准确地预测小麦产量，以浙江省冬小麦为研究对象，利用四旋翼无人机精灵4多光谱相机获取冬小麦5个关键生育时期（拔节期、孕穗期、抽穗期、灌浆期、成熟期）的冠层多光谱数据，选取多光谱相机的五个特征波段计算各生育时期的72个植被指数，分别通过逐步多元线性回归（SMLR）、偏最小二乘回归（PLSR）、BP神经网络（BPNN）、支持向量机（SVM）、随机森林（RF）构建不同生育时期的产量估算模型，最后采用决定系数（R）、均方根误差（RMSE）和相对误差（RE）对估算模型进行评价，筛选出最优估算模型。结果表明，基于随机森林建立的模型估算效果最优，SMLR、PLSR和SVM三种方法建立的模型估算效果接近。利用随机森林算法所建拔节期、孕穗期、抽穗期、灌浆期、成熟期模型的R、RMSE和RE分别为0.92、0.35、11%；0.93、0.33、10%；0.94、0.32、9%；0.92、0.36、9%；0.77、0.67、33%。模型验证时，抽穗期估算效果最好（R、RMSE和RE分别为0.91、0.35和15%），拔节期、孕穗期、灌浆期估算效果接近且有很好的估算能力，成熟期估算精度最差（R、RMSE和RE分别为0.71、0.47和13%）。由此说明，结合机器学习算法和无人机多光谱提取的植被指数可以提高小麦产量估算效果。
|
| 关 键 词： | 无人机多光谱数据 小麦产量 统计分析 机器学习算法 |
Estimation of Winter Wheat Yield Based on UAV Multi-Temporal Remote Sensing Image |
| |
| SHEN Yangyang,CHEN Zhichao,HU Hao,SHENG Li,ZHOU Hongkui,LOU Weidong,SHEN Alin.Estimation of Winter Wheat Yield Based on UAV Multi-Temporal Remote Sensing Image[J].Journal of Triticeae Crops,2021(10):1298-1306. |
| |
| Authors: | SHEN Yangyang CHEN Zhichao HU Hao SHENG Li ZHOU Hongkui LOU Weidong SHEN Alin |
| |
| Abstract: | In order to efficiently and accurately predict wheat yield,winter wheat in Zhejiang province was taken as research object.Canopy multi-spectral data at five key growth stages (jointing stage,booting stage,heading stage,grain filling stage,and maturity stage)of winter wheat was obtained by using multispectral sensor onboard the four-rotor Phantom 4 UAV. Five characteristic bands of multispectral camera were selected to calculate 72 vegetation indices at different growth stages. The yield estimation models at different growth stages were constructed by stepwise multiple linear regression (SMLR),partial least squares regression (PLSR),BP neural network (BPNN),support vector machine (SVM) and random forest (RF). Finally,the coefficient of determination (R),root mean square error (RMSE) and relative error (RE) were used to evaluate the estimation model and select the best estimation model. The results showed that the model based on RF has the best estimation effect,and the models based on SMLR,PLSR and SVM have similar estimation effect. The R,RMSE and RE of the models at jointing stage,booting stage,heading stage,grain filling stage and maturity stage were 0.92,0.35,11%; 0.93,0.33,10%; 0.94,0.32,9%; 0.92,0.36,9%; 0.77,0.67,33%,respectively. In the validation of the model,the estimation effect at heading stage was the best (R,RMSE and RE were 0.91,0.35 and 15%,respectively),and the estimation effect at jointing stage,booting stage and grain filling stage was close and had good estimation ability,and the estimation accuracy at maturity stage was the worst (R,RMSE and RE were 0.71,0.47 and 13%,respectively). Therefore,the combination of machine learning algorithm and UAV multispectral extraction of vegetation index can improve the effect of wheat yield estimation. |
| |
| Keywords: | UAV multi-spectral data Wheat yield Statistical analysis Machine learning algorithm |
|
| 点击此处可从《麦类作物学报》浏览原始摘要信息 |
| 点击此处可从《麦类作物学报》下载免费的PDF全文 |
|
