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 |
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作者姓名: | 赵晋陵 杜世州 黄林生 |
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作者单位: | 安徽大学农业生态大数据分析与应用技术国家地方联合工程研究中心,安徽 合肥 230601 安徽省农业科学院 作物研究所,安徽 合肥 230031 |
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摘 要: | 白粉病主要侵染小麦叶部,可利用卫星遥感技术进行大范围监测和评估.本研究利用多源多时相卫星遥感影像监测小麦白粉病并提升分类精度.使用四景Landat-8的热红外传感器数据(Thermal Infrared Sensor,TIRS)和20景MODIS影像的MOD11A1温度产品反演地表温度(Land Surface Tem...
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关 键 词: | 小麦白粉病 高分一号 MODIS Landsat-8 地表温度 支持向量机 |
收稿时间: | 2021-08-20 |
Monitoring Wheat Powdery Mildew (Blumeria graminis f. sp. tritici) Using Multisource and Multitemporal Satellite Images and Support Vector Machine Classifier |
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Authors: | ZHAO Jinling DU Shizhou HUANG Linsheng |
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Institution: | National Engineering Research Center for Analysis and Application of Agro-Ecological Big Data, Anhui University, Hefei 230601, China Institute of Crops, Academy of Agricultural Sciences, Hefei 230031, China |
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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 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. |
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Keywords: | wheat powdery mildew GF-1 MODIS Landsat-8 land surface temperature support vector machine |
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