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Smart Agriculture ›› 2022, Vol. 4 ›› Issue (1): 17-28.doi: 10.12133/j.smartag.SA202202009

• Topic--Crop Growth and Its Environmental Monitoring • Previous Articles     Next Articles

Monitoring Wheat Powdery Mildew (Blumeria graminis f. sp. tritici) Using Multisource and Multitemporal Satellite Images and Support Vector Machine Classifier

ZHAO Jinling1(), DU Shizhou2(), HUANG Linsheng1   

  1. 1.National Engineering Research Center for Analysis and Application of Agro-Ecological Big Data, Anhui University, Hefei 230601, China
    2.Institute of Crops, Academy of Agricultural Sciences, Hefei 230031, China
  • Received:2021-08-20 Online:2022-03-30 Published:2022-04-28
  • corresponding author: DU Shizhou E-mail:zhaojl@ahu.edu.cn;dsz315@sina.com
  • About author:ZHAO Jinling (1981-), male, Ph.D., associate professor, research interests: remote sensing-based crop disease monitoring. E-mail: zhaojl@ahu.edu.cn.
  • Supported by:
    The Natural Science Foundation of China(31971789);The Natural Science Foundation of Anhui Province(2008085MF184)


Since powdery mildew (Blumeria graminis f. sp. tritici) mainly infects the foliar of wheat, satellite remote sensing technology can be used to monitor and assess it on a large scale. In this study, multisource and multitemporal satellite images were used to monitor the disease and improve the classification accuracy. Specifically, four Landsat-8 thermal infrared sensor (TIRS) and twenty MODerate-resolution imaging spectroradiometer (MODIS) temperature product (MOD11A1) were used to retrieve the land surface temperature (LST), and four Chinese Gaofen-1 (GF-1) wide field of view (WFV) images was used to identify the wheat-growing areas and calculate the vegetation indices (VIs). ReliefF algorithm was first used to optimally select the vegetation index (VIs) sensitive to wheat powdery mildew, spatial-temporal fusion between Landsat-8 LST and MOD11A1 data was performed using the spatial and temporal adaptive reflectance fusion model (STARFM). The Z-score standardization method was then used to unify the VIs and LST data. Four monitoring models were then constructed through a single Landsat-8 LST, multitemporal Landsat-8 LSTs (SLST), cumulative MODIS LST (MLST) and the combination of cumulative Landsat-8 and MODIS LST (SMLST) using the Support Vector Machine (SVM) classifier, that were LST-SVM, SLST-SVM, MLST-SVM and SMLST-SVM. Four assessment indicators including user accuracy, producer accuracy, overall accuracy and Kappa coefficient were used to compare the four models. The results showed that, the proposed SMLST-SVM obtained the best identification accuracies. The overall accuracy and Kappa coefficient of the SMLST-SVM model had the highest values of 81.2% and 0.67, respectively, while they were respectively 76.8% and 0.59 for the SLST-SVM model. Consequently, multisource and multitemporal LSTs can considerably improve the differentiation accuracies of wheat powdery mildew.

Key words: wheat powdery mildew, GF-1, MODIS, Landsat-8, land surface temperature, support vector machine

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