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Spatio-temporal Index Based on Time Series of Leaf Area Index for Identifying Heavy Metal Stress in Rice under Complex Stressors

Yibo Tang, Meiling Liu, Xiangnan Liu, Ling Wu, Bingyu Zhao and Chuanyu Wu
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Yibo Tang: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Meiling Liu: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Xiangnan Liu: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Ling Wu: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Bingyu Zhao: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Chuanyu Wu: School of Information Engineering, China University of Geosciences, Beijing 100083, China

IJERPH, 2020, vol. 17, issue 7, 1-18

Abstract: Crops under various types of stresses, such as stress caused by heavy metals, drought and pest/disease exhibit similar changes in physiological-biochemical parameters (e.g., leaf area index [LAI] and chlorophyll). Thus, differentiating between heavy metal stress and nonheavy metal stress presents a great challenge. However, different stressors in crops do cause variations in spatiotemporal characteristics. This study aims to develop a spatiotemporal index based on LAI time series to identify heavy metal stress under complex stressors on a regional scale. The experimental area is located in Zhuzhou City, Hunan Province. The situ measured data and Sentinel-2A images from 2017 and 2018 were collected. First, a series of LAI in rice growth stages was simulated based on the WOrld FOod STudies (WOFOST) model incorporated with Sentinel 2 images. Second, the local Moran’s I and dynamic time warping (DTW) of LAI were calculated. Third, a stress index based on spatial and temporal features (SIST) was established to assess heavy metal stress levels according to the spatial autocorrelation and temporal dissimilarity of LAI. Results revealed the following: (1) The DTW of LAI is a good indicator for distinguishing stress levels. Specifically, rice subjected to high stress levels exhibits high DTW values. (2) Rice under heavy metal stress is well correlated with high-high SIST clusters. (3) Rice plants subjected to high pollution are observed in the northwest of the study regions and rice under low heavy metal stress is found in the south. The results suggest that SIST based on a sensitive indicator of rice biochemical impairment can be used to accurately detect regional heavy metal stress in rice. Combining spatial-temporal features and spectral information appears to be a highly promising method for discriminating heavy metal stress from complex stressors.

Keywords: heavy metal stress; rice growth; dynamic time warping; Moran’s I (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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