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• The physical models and wavelet transform isolated the effects of rice under Cd stress from other abrupt stressors. • Spatio-temporal variation coefficients can capture the spatio-temporal characteristics of rice under Cd stress. • Rice under Cd stress is characterized by spatio-temporal stability over extended time periods. • Long-time-series remote sensing data can be used to detect stable stressors on crops. Stable stressors on crops (e.g., salts, heavy metals), which are characterized by stable spatial patterns over time, are harmful to agricultural production and food security. Satellite data provide temporally and spatially continuous synoptic observations of stable stress on crops. This study presents a method for identifying rice under stable stress (i.e., Cd stress) and exploring its spatio-temporal characteristics indicators. The study area is a major rice growing region located in Hunan Province, China. Moderate-resolution imaging spectroradiometer (MODIS) and Landsat images from 2008–2017 as well as in situ measurements were collected. The coupling of a leaf canopy radiative transfer model with the World Food Study Model (WOFOST) via a wavelet transform isolated the effects of Cd stress from other abrupt stressors. An area wavelet transform stress signal (AWTS), based on a time-series Enhanced Vegetation Index (EVI), was used to detect rice under Cd stress, and its spatio-temporal variation metrics explored. The results indicate that spatial variation coefficients (S V C) of AWTS in the range of 0–1 ha d a coverage area greater than 70% in each experimental region, regardless of the year. Over ten years, the temporal variation coefficients (T V C) of AWTS in the range of 0–1 occurred frequently (more than 60% of the time). In addition, the Pearson correlation coefficient of AWTS over two consecutive years was usually greater than 0.5. We conclude that a combination of multi-year satellite-derived vegetation index data with a physical model simulation is an effective and novel method for detecting crops under environmental stress. A wavelet transform proved promising in differentiating between the effects of stable stress and abrupt stress on rice and may offer a way forward for diagnosing crop stress at continental and global scales. [ABSTRACT FROM AUTHOR]

Highlights • The canopy chlorophyll index NVI(R598, R508) is sensitive to chlorophyll content changes under heavy metal stress. • The microwave index SVI(HV,VH,HH,VV) is sensitive to biomass changes under heavy metal stress. • A two-dimensional feature space model based on optical and radar remote sensing combined for monitoring heavy metal stress was constructed. • This model can be applied to monitoring multiple environmental stresses in crops. Abstract Accurate and extensive monitoring of heavy metal pollution levels in rice fields is crucial to agricultural production and food safety.Most previous studies used optical remote sensing data to monitor heavy metal stress in rice in which way the remote sensing features are not diversified and the monitoring accuracy is relatively low. Compared with that optical remote sensing can monitor features like color and cell internal structure, microwave remote sensing can monitor the morphology and geometrical features. The complementary characteristics of multi-source remote sensing data are reflected in two aspects. On the one hand, the high spectral resolution characteristics of hj-1a HIS and the high spatial resolution characteristics of radarsat-2 SAR satellite are used for feature fusion to obtain the image data of high altitude spectral resolution; on the other hand, the two remote sensing data can depict the vegetation characteristics from different angles. In order to fully extract the characteristics of crop stress, the model was constructed by utilizing the complementary characteristics of multi-source remote sensing data. This paper synergized optical and microwave remote sensing to construct the crop heavy metal stress monitoring model. To this end, certain rice-growing areas polluted by heavy metal in Suzhou city were selected as the experimental areas, where ASD spectrum data, biochemical parameters and heavy metal content data of rice were collected during critical growth periods on one hand, HJ-1 A HIS and Radarsat-2 SAR satellite data were obtained almost simultaneously on the other hand. Based on heavy metal stress-responsive mechanism of rice, NVI (R598, R508) and SVI(HV,VH,HH,VV), spectral indexes sensitive to heavy metal stress, were extracted from the optical and radar data respectively to construct a two-dimensional feature space, based on which an optical-and-radar-remote-sensing-combined model for monitoring heavy metal stress in rice was constructed. In this paper, the study and main conclusions are as follows:(1) It used spectral characteristics analysis combined with statistic methods or random forest algorithm to build the canopy chlorophyll index NVI, which is sensitive to chlorophyll content changes of rice under heavy metal stress, and a heavy metal stress level inversion model was built based on hyperspectral HSI data accordingly. Also, it used statistic method to build the microwave index SVI, which is sensitive to biomass changes of rice under heavy metal stress, and a heavy metal stress level inversion model was built based on microwave SAR images as a result. (2) A combination of biochemical and morphological parameters, both responding to heavy metal stress, was synergized with NVI and SVI, sensitive to chlorophyll and biomass changes respectively, to build a two-dimensional feature space. Heavy metal stress levels were classified in this space. Thus, a synergetic model to retrieve heavy metal stress based on multi-source remote sensing data was developed. In this paper, the innovation lies in a model constructed by synergizing optical and radar remote sensing data to monitor heavy metal stress in rice based on a multi-dimensional spectral feature space and this model can be applied to monitoring multiple environmental stresses in crops. [ABSTRACT FROM AUTHOR]

Heavy metal stress in crops is characterized by stability in space and time, which differs from other stressors that are typically more transient (e.g., drought, pests/diseases, and mismanagement). The objective of this study is to assess regional heavy metal stress in rice by integrating physiological function variability with spatio-temporal stability based on multi-temporal thermal infrared (TIR) remote sensing images. The field in which the experiment was conducted is located in Zhuzhou City, Hunan Province, China. HJ-1B images and in-situ measured data were collected from rice growing in heavy metal contaminated soils. A stress index ( S I ) was devised as an indicator for the degree of heavy metal stress of the rice in different growth stages, and a time-spectrum feature space (TSFS) model was used to determine rice heavy metal stress levels. The results indicate that (i) S I is a good indicator of rice damage caused by heavy metal stress. Minimum values of S I occur in rice subject to high pollution, followed by larger S I with medium pollution and maximum S I for low pollution, for the same growth stage. (ii) S I shows some variation for different growth stages of rice, and the minimum S I occurs at the flowering stage. (iii) The TSFS model is successful at identifying rice heavy metal stress, and stress levels in rice stabilized regardless of the model being applied in the two different years. This study suggests that regional heavy metal stress in crops can be accurately detected using TIR technology, if a sensitive indicator of crop physiological function impairment is used and an effective model is selected. A combination of spectrum and spatio-temporal information appears to be a very promising method for monitoring crops with various stressors. [ABSTRACT FROM AUTHOR]

Heavy metal contamination in crops is a worldwide problem that requires accurate and timely monitoring. This study is aimed at improving the accuracy of monitoring heavy metal stress levels in rice utilizing remote sensing data. An assimilation framework based on remote sensing and improved crop growth model was developed to continuously monitor heavy metal stress levels over the entire period of crop growth based on the growth law of crops and the stress mechanism. Compared with other physiological indices, dry weight of rice roots (WRT) was selected as the best indicator to estimate heavy metal stress levels. The World Food Study (WOFOST) model, widely used for the description of crop growth, was improved by incorporating stress factors with overall consideration for the changes in physiological status under heavy metal stress. Three scenarios were put forward based on the stress factors f DTGA and f CVF , which, respectively, correspond to the daily total gross assimilation of CO 2 and carbohydrate-to-dry matter conversion coefficient, and were analyzed for their efficiency of simulating WRT. A method of assimilating the leaf area index (LAI) retrieved from remotely sensed data into the improved WOFOST model was applied to optimize f DTGA and f CVF . The results suggested that the scenario using both factors can simulate WRT under heavy metal stress more accurately, with a relative percent error (RPE) lower than 14%. Based on the RS-WOFOST assimilation framework, continuous spatial-temporal evaluation of heavy metal stress levels based on WRT can be accomplished. [ABSTRACT FROM AUTHOR]

Abstract: A generalized dynamic fuzzy neural network (GDFNN) was created to estimate heavy metal concentrations in rice by integrating spectral indices and environmental parameters. Hyperspectral data, environmental parameters, and heavy metal content were collected from field experiments with different levels of heavy metal pollution (Cu and Cd). Input variables used in the GDFNN model were derived from 10 variables acquired by gray relational analysis. The assessment models for Cd and Cu concentration employed five and six input variables, respectively. The results showed that the GDFNN for estimating Cu and Cd concentrations in rice performed well at prediction with a compact network structure using the training, validation, and testing sets (for Cu, fuzzy rules=9, R 2 greater than 0.75, and RMSE less than 2.5; for Cd, fuzzy rules=9, R 2 greater than 0.75, and RMSE less than 1.0). The final GDFNN model was then compared with a back-propagation (BP) neural network model, adaptive-network-based fuzzy interference systems (ANFIS), and a regression model. The accuracies of GDFNN model prediction were usually slightly better than those of the other three models. This demonstrates that the GDFNN model is more suitable for predicting heavy metal concentrations in rice. [Copyright &y& Elsevier]

Regional-level information on heavy metal pollution in agro-ecosystems is essential for food security because excessive levels of heavy metals in crops may pose risks to humans. However, collecting this information over large areas is inherently costly. This paper investigates the possibility of applying multi-temporal Sentinel-2 satellite images to detect heavy metal-induced stress (i.e., Cd stress) in rice crops in four study areas in Zhuzhou City, Hunan Province, China. For this purpose, we compared seven Sentinel-2 images acquired in 2016 and 2017 with in situ measured hyper-spectral data, chlorophyll content, rice leaf area index, and heavy metal concentrations in soil collected from 2014 to 2017. Vegetation indices (VIs) related to red edge bands were referred to as the sensitive indicators for screening stressed rice from unstressed rice. The coefficients of spatio-temporal variation (CSTV) derived from the VIs allowed us to discriminate crops exposed to pollution from heavy metals as well as environmental stressors. The results indicate that (i) the red edge chlorophyll index, the red edge position index, and the normalized difference red edge 2 index derived from multi-temporal Sentinel-2 images were good indicators for screening stressed rice from unstressed rice; (ii) Rice under Cd stress remained stable with lower CSTV values of VIs overall growth stages in the experimental region, whereas rice under other stressors (i.e., pests and disease) showed abrupt changes at some growth stages and presented “hot spots” with greater CSTV values; and (iii) the proposed spatio-temporal anomaly detection method was successful at detecting rice under Cd stress; and CSTVs of rice VIs stabilized regardless of whether they were applied to consecutive growth stages or to two different crop years. This study suggests that regional heavy metal stress may be accurately detected using multi-temporal Sentinel-2 images, using VIs sensitive to the spatio-temporal characteristics of crops. [ABSTRACT FROM AUTHOR]