School of Information Engineering, China University of Geosciences, Beijing 100083, China.
Sensors (Basel). 2018 Dec 14;18(12):4425. doi: 10.3390/s18124425.
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.
重金属污染对农作物的物候变化有影响，可以通过遥感技术进行监测。本研究旨在开发一种基于遥感物候学的水稻重金属胁迫准确评估方法。首先，应用增强型时空自适应反射率融合模型（ESTARFM）融合 MODIS 和 Landsat 数据，生成 30 m 分辨率的融合时间序列图像，然后计算与水稻冠层绿色度和含水量相关的植被指数（VIs），以创建 VIs 的时间序列。其次，从 VIs 的时间序列数据中提取物候指标，并设计特征选择方案，以获取最佳物候指标子集。最后，使用随机森林（RF）和梯度提升（GB）分类器构建具有最佳物候指标作为分类特征的集成模型，并对胁迫水平进行分类。结果表明，不同胁迫水平的判别总体准确率大于 98%。本研究表明，融合图像可用于检测水稻中的重金属胁迫，所提出的方法可用于分类胁迫水平。
