Integrating Early Growth Information to Monitor Winter Wheat Powdery Mildew Using Multi-Temporal Landsat-8 Imagery

Huiqin Ma,Jingcheng Zhang,Wenjiang Huang,Yingying Dong,Yue Shi,Yuanshu Jing,Linyi Liu

doi:10.3390/s18103290

Abstract

Powdery mildew is one of the dominant diseases in winter wheat. The accurate monitoring of powdery mildew is important for crop management and production. Satellite-based remote sensing monitoring has been proven as an efficient tool for regional disease detection and monitoring. However, the information provided by single-date satellite scene is hard to achieve acceptable accuracy for powdery mildew disease, and incorporation of early period contextual information of winter wheat can improve this situation. In this study, a multi-temporal satellite data based powdery mildew detecting approach had been developed for regional disease mapping. Firstly, the Lansat-8 scenes that covered six winter wheat growth periods (expressed in chronological order as periods 1 to 6) were collected to calculate typical vegetation indices (VIs), which include disease water stress index (DSWI), optimized soil adjusted vegetation index (OSAVI), shortwave infrared water stress index (SIWSI), and triangular vegetation index (TVI). A multi-temporal VIs-based k-nearest neighbors (KNN) approach was then developed to produce the regional disease distribution. Meanwhile, a backward stepwise elimination method was used to confirm the optimal multi-temporal combination for KNN monitoring model. A classification and regression tree (CART) and back propagation neural networks (BPNN) approaches were used for comparison and validation of initial results. VIs of all periods except 1 and 3 provided the best multi-temporal data set for winter wheat powdery mildew monitoring. Compared with the traditional single-date (period 6) image, the multi-temporal images based KNN approach provided more disease information during the disease development, and had an accuracy of 84.6%. Meanwhile, the accuracy of the proposed approach had 11.5% and 3.8% higher than the multi-temporal images-based CART and BPNN models’, respectively. These results suggest that the use of satellite images for early critical disease infection periods is essential for improving the accuracy of monitoring models. Additionally, satellite imagery also assists in monitoring powdery mildew in late wheat growth periods.

Highlights

Powdery mildew (Blumeria graminis) is one of the most destructive foliar diseases infecting winter wheat and occurs in areas with cool or maritime climates [1]
The objectives of this study were (1) to identify the optimal multi-temporal data set for the monitoring of powdery mildew occurrence in winter wheat from a number of different multi-temporal combinations; (2) to assess the feasibility of using imagery containing information for early critical disease infection periods to monitor the occurrence of powdery mildew in late winter wheat growth period; (3) to evaluate the performance of the multi-temporal indices-based k-nearest neighbors (KNN) disease monitoring approach and its capability for mapping powdery mildew occurrence
We found that classification using multi-temporal vegetation indices (VIs) produced higher accuracies than the traditional single-date VIs when monitoring for powdery mildew in winter wheat

Summary

Introduction

Powdery mildew (Blumeria graminis) is one of the most destructive foliar diseases infecting winter wheat and occurs in areas with cool or maritime climates [1]. The disease interferes with the plant’s normal source-sink relationships. It changes the translocation and distribution of photoassimilate, causing changes in grain starch and protein composition [2]. This in turn results in a reduction in wheat quality and yield [3]. It is vital to develop a more accurate disease monitoring model for winter wheat to prevent the occurrence of powdery mildew

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