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Classification of Rice Heavy Metal Stress Levels Based on Phenological Characteristics Using Remote Sensing Time-Series Images and Data Mining Algorithms

School of Information Engineering, China University of Geosciences, Beijing 100083, China
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Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4425; https://doi.org/10.3390/s18124425
Received: 31 October 2018 / Revised: 30 November 2018 / Accepted: 12 December 2018 / Published: 14 December 2018
(This article belongs to the Section Remote Sensors)
Heavy metal pollution in crops leads to phenological changes, which can be monitored by remote sensing technology. The present study aims to develop a method for accurately evaluating heavy metal stress in rice based on remote sensing phenology. First, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to blend Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat to generate a time series of fusion images at 30 m resolution, and then the vegetation indices (VIs) related to greenness and moisture content of the rice canopy were calculated to create the time-series of VIs. Second, phenological metrics were extracted from the time-series data of VIs, and a feature selection scheme was designed to acquire an optimal phenological metric subset. Finally, an ensemble model with optimal phenological metrics as classification features was built using random forest (RF) and gradient boosting (GB) classifiers, and the classification of stress levels was implemented. The results demonstrated that the overall accuracy of discrimination for different stress levels is greater than 98%. This study suggests that fusion images can be utilized to detect heavy metal stress in rice, and the proposed method may be applicable to classify stress levels. View Full-Text
Keywords: heavy metal stress; time-series; remote sensing phenology; MODIS and Landsat; ensemble model; feature selection heavy metal stress; time-series; remote sensing phenology; MODIS and Landsat; ensemble model; feature selection
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MDPI and ACS Style

Liu, T.; Liu, X.; Liu, M.; Wu, L. Classification of Rice Heavy Metal Stress Levels Based on Phenological Characteristics Using Remote Sensing Time-Series Images and Data Mining Algorithms. Sensors 2018, 18, 4425. https://doi.org/10.3390/s18124425

AMA Style

Liu T, Liu X, Liu M, Wu L. Classification of Rice Heavy Metal Stress Levels Based on Phenological Characteristics Using Remote Sensing Time-Series Images and Data Mining Algorithms. Sensors. 2018; 18(12):4425. https://doi.org/10.3390/s18124425

Chicago/Turabian Style

Liu, Tianjiao, Xiangnan Liu, Meiling Liu, and Ling Wu. 2018. "Classification of Rice Heavy Metal Stress Levels Based on Phenological Characteristics Using Remote Sensing Time-Series Images and Data Mining Algorithms" Sensors 18, no. 12: 4425. https://doi.org/10.3390/s18124425

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