School of Information Engineering, China University of Geosciences, Beijing 100083, China.
Urban and Rural Planning and Design Institute Co., Ltd., Anhui Jianzhu University, Hefei 230022, China.
Int J Environ Res Public Health. 2022 Feb 23;19(5):2567. doi: 10.3390/ijerph19052567.
Heavy metal stress, which is a serious environmental problem, affects both animal and human health through the food chain. However, such subtle stress information is difficult to detect in remote sensing images. Therefore, enhancing the stress signal is key to accurately identifying heavy metal contamination in crops. The aim of this study was to identify heavy metal stress in rice at a regional scale by mining the time-series characteristics of rice growth under heavy metal stress using the gated recurrent unit (GRU) algorithm. The experimental area was located in Zhuzhou City, Hunan Province, China. We collected situ-measured data and Sentinel-2A images corresponding to the 2019-2021 period. First, the spatial distribution of the rice in the study area was extracted using the random forest algorithm based on the Sentinel 2 images. Second, the time-series characteristics were analyzed, sensitive parameters were selected, and a GRU classification model was constructed. Third, the model was used to identify the heavy metals in rice and then assess the accuracy of the classification results using performance metrics such as the accuracy rate, precision, recall rate (recall), and F1-score (F1-score). The results showed that the GRU model based on the time series of the red-edge location feature index has a good classification performance with an overall accuracy of 93.5% and a Kappa coefficient of 85.6%. This study shows that regional heavy metal stress in crops can be accurately detected using the GRU algorithm. A combination of spectrum and temporal information appears to be a promising method for monitoring crops under various types of stress.
重金属胁迫是一个严重的环境问题，通过食物链影响动物和人类健康。然而，这种微妙的胁迫信息很难在遥感图像中检测到。因此，增强胁迫信号是准确识别作物重金属污染的关键。本研究旨在通过挖掘重金属胁迫下水稻生长的时间序列特征，利用门控循环单元（GRU）算法识别水稻的重金属胁迫。实验区位于中国湖南省株洲市。我们收集了 2019-2021 年期间的实测数据和哨兵 2A 图像。首先，基于哨兵 2 图像使用随机森林算法提取研究区域内的水稻空间分布。其次，分析时间序列特征，选择敏感参数，并构建 GRU 分类模型。然后，使用该模型识别水稻中的重金属，并使用准确率、精度、召回率（召回）和 F1 分数（F1 分数）等性能指标评估分类结果的准确性。结果表明，基于红边位置特征指数时间序列的 GRU 模型具有良好的分类性能，总体准确率为 93.5%，Kappa 系数为 85.6%。本研究表明，利用 GRU 算法可以准确检测作物的区域重金属胁迫。光谱和时间信息的结合似乎是监测各种胁迫下作物的一种很有前途的方法。
