Integrating growth and environmental parameters to discriminate powdery mildew and aphid of winter wheat using bi-temporal Landsat-8 imagery

Authors: MA, HUIQIN - Nanjing University; HUANG, WENJIANG - Chinese Academy Of Sciences; JING, YUANSHU - Nanjing University; Yang, Chenghai; HAN, LIANGXIU - Manchester Metropolitan University; DONG, YINGYING - Chinese Academy Of Sciences; YE, HUICHUN - Chinese Academy Of Sciences; SHI, YUE - Chinese Academy Of Sciences; ZHENG, QIONG - Chinese Academy Of Sciences; LIU, LINYI - Chinese Academy Of Sciences; RUAN, CHAO - Chinese Academy Of Sciences

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/27/2019
Publication Date: 12/29/2019
Citation: Ma, H., Huang, W., Jing, Y., Yang, C., Han, L., Dong, Y., Ye, H., Shi, Y., Zheng, Q., Liu, L., Ruan, C. 2019. Integrating growth and environmental parameters to discriminate powdery mildew and aphid of winter wheat using bi-temporal Landsat-8 imagery. Remote Sensing. 11:846. https://doi.org/10.3390/rs11070846.
DOI: https://doi.org/10.3390/rs11070846

Interpretive Summary: It is of practical importance to monitor and discriminate co-epidemic diseases and pests at regional scales for guiding differential pest treatment options. A model to discriminate crop diseases and pests based on satellite imagery collected during a typical co-epidemic outbreak of winter wheat, powdery mildew, and aphids was developed based on features derived from the imagery to distinguish whether crop damage resulted from the disease or the pest. Results showed that the proposed approach could distinguish wheat powdery mildew and aphid infestations at the regional scale with better accuracy than traditional methods. The methodology derived in this study will improve the discrimination accuracy of co-existing diseases and pests.

Technical Abstract: Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied during a typical co-epidemic outbreak of winter wheat powdery mildew and aphid in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced calibration data set before model construction. Then a back propagation neural network (BPNN) based on a new calibration data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original calibration data set-based BPNN and support vector machine (SVM) methods were used for comparison and validation of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphid at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest.