A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery
A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery
复制标题
使用 Sentinel-2 多光谱图像有效检测小麦赤霉病的疾病指数
DOI：
10.1109/access.2020.2980310
复制
发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Ma, Huiqin
中科院分区：
文献类型：
--
作者：
Liu, Linyi;Dong, Yingying;Ma, Huiqin
小麦赤霉病的快速、无损检测是小麦病害防治的重要手段。红边(RE)是确定作物状况的重要光谱特征，有可能提高区域FHB监测的准确性。本研究以Sentinel-2多光谱仪器(MSI)数据为基础，探讨稀土在FHb监测中的应用潜力。开发了一种新的红边赤霉病指数(REHBI)，用于在区域尺度上检测赤霉病。综合冠层尺度的高光谱数据，利用传感器的相对光谱响应(RSR)函数模拟哨兵2号的多光谱反射率。然后，选择了许多对FHB严重程度敏感的Sentinel-2区带的差异和比率组合。根据这些基本植被指数(VIS)建立了REHBI模型，由REHBI建立的模型在监测赤潮严重程度方面表现最好(R-2=0.82，RMSE=10.1)。此外，病情指数(DI)在10~50之间的病冠层被归类为轻度病冠层。用普通最小二乘法(OLS)测试了REHBI和两种常规VIS(OSAVI和RDVI)在监测轻微病害冠层中的性能，REHBI优于这两种VIS(R2=0.69，RMSE=3.6)。为了接近真实农业条件，将泊松噪声添加到模拟的Sentinel-2多光谱数据中，并重新评估VIS的综合性能；REHBI仍具有最高的R-2和最低的RMSE值(分别为0.74和12.6)。最后，为了验证REHBI对农业生产中赤霉病的检测能力，将其应用于长丰县和定远县小麦种植区的赤霉病监测。总体而言，REHBI在疾病监测方面的表现优于Oavi和RDVI。总体准确率达78.6%，卡帕系数为0.51。实验结果表明，REHBI可以用于FHB的监测。
Rapid, non-destructive detection of wheat Fusarium head blight (FHB) is an important tool for disease control. Red-edge (RE) is a prominent spectral feature for determining crop conditions with the potential to enhance the accuracy of monitoring FHB regionally. This study explored the potential of RE for FHB monitoring based on Sentinel-2 Multispectral Instrument (MSI) data. The novel red-edge head blight index (REHBI) was developed to detect FHB at a regional scale. Hyperspectral data at the canopy scale was integrated to simulate Sentinel-2 multispectral reflectance using the relative spectral response (RSR) function of the sensor. Then, many differential and ratio combinations of Sentinel-2 bands that were sensitive to FHB severity were selected. REHBI was established based on these basic vegetation indexes (VIs), and the model developed from REHBI performed best in monitoring FHB severity (R-2 = 0.82, RMSE = 10.1). Additionally, the infected canopies with disease index (DI) values between 10 and 50 were classified as slightly diseased canopies. Ordinary least square (OLS) was used to test the performance of REHBI and two conventional VIs, i.e., OSAVI and RDVI, in monitoring slightly diseased canopies; REHBI outperformed these alternatives (R-2 = 0.69, RMSE = 3.6). To approximate real agricultural conditions, Poisson noise was added to the simulated Sentinel-2 multispectral data and generalized performance of VIs was evaluated again; REHBI still had the highest R-2 and lowest RMSE values (0.74 and 12.6, respectively). Finally, to validate REHBI's ability to detect FHB infection in agricultural production, it was applied to monitoring FHB in the wheat planting areas of Changfeng and Dingyuan counties from Sentinel-2 imagery. Generally, REHBI performed better in disease monitoring than OSAVI and RDVI. The overall accuracy was up to 78.6%, and the kappa coefficient was 0.51. Experimental results demonstrate that REHBI can be used to monitor FHB.