智慧农业（中英文）2022，Vol.4Issue(1)：17-28,12.DOI:10.12133/j.smartag.SA202202009
联合多源多时相卫星影像和支持向量机的小麦白粉病监测方法
Monitoring Wheat Powdery Mildew (Blumeria graminis f. sp. tritici) Using Multisource and Multitemporal Satellite Images and Support Vector Machine Classifier
摘要
Abstract
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 multitempo-ral satellite images were used to monitor the disease and improve the classification accuracy. Specifically, four Land-sat-8 thermal infrared sensor (TIRS) and twenty MODerate-resolution imaging spectroradiometer (MODIS) temper-ature 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 pow-dery 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, multi-temporal 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 ac-curacy 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.关键词
小麦白粉病/高分一号/MODIS/Landsat-8/地表温度/支持向量机Key words
wheat powdery mildew/GF-1/MODIS/Landsat-8/land surface temperature/support vector machine分类
农业科技引用本文复制引用
赵晋陵,杜世州,黄林生..联合多源多时相卫星影像和支持向量机的小麦白粉病监测方法[J].智慧农业（中英文）,2022,4(1):17-28,12.基金项目
The Natural Science Foundation of China(31971789) （31971789）
The Natural Science Foundation of Anhui Province(2008085MF184)Biography:ZHAO Jinling(1981-),male,Ph.D.,associate professor,research interests:remote sensing-based crop disease monitoring.E-mail:zhaojl@ahu.edu.cn. （2008085MF184）
