Temporal Characteristics of Stress Signals Using GRU Algorithm for Heavy Metal Detection in Rice Based on Sentinel-2 Images

Yu Zhang, Meiling Liu, Li Kong, Tao Peng, Dong Xie, Li Zhang, Lingwen Tian and Xinyu Zou
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Yu Zhang: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Meiling Liu: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Li Kong: Urban and Rural Planning and Design Institute Co., Ltd., Anhui Jianzhu University, Hefei 230022, China
Tao Peng: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Dong Xie: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Li Zhang: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Lingwen Tian: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Xinyu Zou: School of Information Engineering, China University of Geosciences, Beijing 100083, China

IJERPH, 2022, vol. 19, issue 5, 1-14

Abstract: 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.

Keywords: remote sensing; heavy metal stress; GRU model; red edge; time series (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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