Monitoring heavy metal stress on rice is of great significance for food security. In this paper, we used NDVI time series during the whole growing period of rice to identifying the rice growing differences under varied heavy metal stress. Here the NDVI time series were with high spatial-temporal resolution and obtained by blending MODIS and Landsat NDVI data. We extracted two kinds of features: Max NDVI value and time-integrated NDVI and use Fisher discrimination to explore the rice phonological differences under mild and severe stress levels. Results indicates that under severe stress the values of the metrics for presenting rice phonological differences in the experimental areas of heavy metal stress were smaller than the ones under mild stress. This means using the phenology differences can help to monitoring the heavy metal contamination.
Effectively assessing cadmium (Cd) contamination in crops is crucial for the sustainable development of an agricultural ecosystem and for environmental security. We developed an integrated stress index (SI) based on two phenological metrics to effectively evaluate Cd stress in rice crops. The selected four experimental areas are located in Zhuzhou City, Hunan Province, China. Six Sentinel-2 images were acquired in 2017, and heavy metal concentrations in soil were measured. The change rate of CIre (CRCIre) and the time-integrated CIre (TICIre) were obtained from daily red-edge chlorophyll index (CIre) time-series using Sentinel-2 data. The CRCIre and TICIre were used to characterize the photosynthetic rate and biomass, respectively. SI was calculated by Fisher discriminant analysis based on CRCIre and TICIre from two experimental areas, and it was verified using another two experimental areas. The results were the following: (i) when SI ≥ 0, rice was under mild stress and when SI < 0, rice was under severe stress. (ii) The SI effectively evaluated Cd stress levels with an overall discriminatory accuracy of 86.02%. This research provides a potential new method to evaluate Cd stress in rice by remote sensing through phenology.
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