Developing a New Spectral Index for Detecting Cadmium-Induced Stress in Rice on a Regional Scale

Abstract

In natural farmland ecosystems, cadmium (Cd) pollution in rice has attracted increasing attention because of its high toxicity, relative mobility, and high water solubility. This study aims to develop a spectral index for detecting Cd stress in rice on a regional scale. Three experimental sites are selected in Zhuzhou City, Hunan Province. The hyperspectral data, chlorophyll (Chl) content, leaf area index, average leaf angle, Cd concentration in soil, and Sentinel-2A images from 2017 and 2018 are collected. A new spectral index sensitive to Cd stress in rice is established based on the global sensitivity analysis of the radiative transfer model PROSPECT + SAIL (commonly called PROSAIL) model with the auxiliary of the field-measured data. The heavy metal Cd stress-sensitive spectral index (HCSI) is devised as an indicator of the degree of Cd stress in rice. Results indicate that (1) the HCSI developed based on Chl is a good indicator of rice damage caused by Cd stress, that is, low values of HCSI occur in rice subject to relatively high pollution; (2) compared with common spectral indices, such as red-edge position and red-edge Chl index, HCSI is more sensitive to Chl content with higher Pearson correlation coefficients with respect to Chl content, ranging from 0.85 to 0.95; (3) HCSI is successfully applied in Sentinel-2A images from the two different years of monitoring rice Cd stress on a regional scale. Cd stress levels in rice stabilized, and the largest area percentage of each pollution levels of Cd decreased in the following order: No pollution (i.e., 40%), low pollution (i.e., 35%), and high pollution (i.e., 25%). This study indicates that a combination of simulation data from the PROSAIL model and measured data appears to be a promising method for establishing a sensitivity spectral index to heavy metal stress, which can accurately detect regional Cd stress in crops.

Investigation of Global Trends of Pollutants in Marine Ecosystems around Barrang Caddi Island, Spermonde Archipelago Cluster: An Ecological Approach

“…03725/B/SK/VII/89, the maximum threshold value of heavy metal contamination in fish for pollutants Pb +2 and Cu +2 = 0.008 µg/g [33,55,82,101] The data on the average concentrations of the two heavy metal pollutants (Table 7) show that exposure to Pb +2 and Cu +2 is still below the threshold value. However, using the Australian and New Zealand Environment and Conservation Council (ANZECC) standard, with a smaller tolerance value than the maximum threshold, it was concluded that the two pollutants found in fish around BCI exceeded the standard [10,55,76,102,103]. Based on the data, Table 7 shows that all types of samples (fish, sponges, seawater, and sediments) obtained in the waters around BCI have exceeded the maximum threshold value specified [4,10,48,104].…”

Investigation Pollutants of Global Trends in Marine Ecosystems around Barrang Caddi Island, Spermonde Archipelago Cluster: An Ecological Approach

“…The average concentration of the two types of heavy metal pollutants (Table 7) shows that exposure to Pb +2 and Cu +2 is still below the threshold value. However, using the Australian and New Zealand Environment and Conservation Council (ANZECC) standard with a tolerance value of the smaller the maximum threshold, it was concluded that the two types of pollutants found in fish around BCI had exceeded the standard [101,102] The exposure concentration of Pb +2 in fish obtained from ST 1 (2.4517 g/g) was found to have exceeded the maximum threshold [103]. It is understandable and quite rational because consumption patterns, activities, and dynamics in each type of marine fish vary greatly.…”

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

“…Accurate identification and monitoring of such stress can not only provide reasonable suggestions for the safe production of rice, but also protect human health to some extent. Remote sensing technology represents an efficient and economical means for monitoring and assessing heavy metal pollution in rice on a large scale [ 1 ]. However, in natural farmland ecosystems, changes in the physiological indicators of crop growth appear similar under different types of stresses (heavy metal stress, pest, and disease stress), making it difficult to accurately identify heavy metal stress [ 2 , 3 ].…”