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Rice is susceptible to various types of stresses during its growth cycle, including heavy metals, drought, nutritional stress, pests, and diseases, resulting in inaccurately distinguishing heavy metal stress from other stresses. First, this study calculated the spectral response characteristic parameters using the red edge difference to extract the stressed rice regions in Zhuzhou City, Hunan Province, China using the Support Vector Machine (SVM) algorithm. Then, the Fast Dynamic Time Warping (FastDTW) algorithm was introduced to extract the distance of the Normalized Difference Red-Edge (NDRE) index time series curve of adjacent years for stressed rice as the temporal characteristics. And the local Geary's C index was used to measure the spatial pattern of spatial characteristics. A heavy metal stress monitoring model for rice was constructed based on the temporal and spatial characteristics of rice stress. Finally, a heavy metal stress index was constructed to measure the heavy metal stress levels. The extraction accuracy of the heavy metal stress model was 83.2% based on the fusion of temporal and spatial characteristics, indicating that heavy metal stress in rice has a relatively high identification performance in the study area. The correlation between the measured concentration of Cd in the soil and the normalized Annual Mean Composite NDRE ( A M C _ N D R E n ) was 0.816, indicating that it can better represent the level of heavy metal stress. Thus, the combination of temporal and spatial characteristics is a promising method for distinguishing heavy metal stress from other stresses in rice form sophisticated stress sources. [ABSTRACT FROM AUTHOR]

• 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]

• FAPAR can fully explain crops' appearance under stress due to physiological function alterations. • CEEMDAN Analysis isolated the effects of rice under heavy metal stress from other signals. • Local spatiotemporal Moran analysis was adopted to assess heavy metal stress levels in rice. Crops are often affected by heavy metal stress and other types of stresses present in farming. Distinguishing heavy metal stress from other types of stresses can be addressed through agricultural remote sensing. Unlike color and morphology, monitoring physiological functions can lead to detect heavy metal contamination even in early stages or at low stress levels by considering characteristics that are not visible to the naked eye. We aimed to distinguish heavy metal stress in rice using physiological function variability that shows spatiotemporal stability based on the multitemporal fraction of absorbed photosynthetically active radiation (FAPAR). Zhuzhou City in the Hunan Province of China was selected as the study area, where Sentinel-2 remote sensing images and FAPAR data were collected. Overall, 121 Sentinel-2 remote sensing images during the April–September season from 2018 to 2020 were collected along with FAPAR data in the canopy from 25 rice paddy fields in the same region. First, the FAPAR was calculated using the biophysical processor of the Sentinel Application Platform software, and the time-series FAPAR data were determined using cubic spline interpolation. Second, different intrinsic mode function components of the FAPAR time series were derived by applying the complete ensemble empirical mode decomposition with adaptive noise at multiple scales. Third, the local spatiotemporal Moran index of the interannual and residual components was calculated. The results show that the FAPAR estimated from Sentinel-2 data suitably correlates with the measured FAPAR and can be used for detecting heavy metal stress in rice. In addition, the adopted decomposition method can successfully extract heavy metal stress signals by eliminating intra-annual and interannual components. Moreover, the local spatiotemporal Moran index of interannual and residual components correctly indicates heavy metal stress, suitably characterizing the temporal stability of spatial aggregation of heavy metal stress in rice. Therefore, combining spatiotemporal features with signal decomposition for describing a physiological function is a promising approach for identifying heavy metal stress from complex stress sources. [ABSTRACT FROM AUTHOR]

Gradual change is prevalent across the forest landscape and generates long-lasting effects for the landscape surface; thus, tracking long-term gradual change can effectively characterize forest change processes. The objective of this study was to establish a vegetation index for change monitoring so as to determine long-term gradual change processes of forest ecosystems in typical red soil areas. The study area was located in Hengyang City, Hunan Province, China. Landsat images, field survey, and auxiliary data were collected to devise a disturbance sensitive vegetation index (DSVI) as an indicator of forest change. Long-term (1985–2019) forest changes were detected using the LandTrendr algorithm on the Google Earth Engine (GEE), while three evaluation aspects of velocity, frequency, and variance were used to analyze the processes of forest gradual change in red soil regions. Results indicate that the DSVI is a suitable index for forest change detection due to its stronger sensitivity compared to other indexes. Further, it shows excellent change detection ability for different types of gradual changes, such as those caused by drought and significant soil erosion. Furthermore, 97.26% of the forests showed gradual change, and approximately 2/3 of monitored forests showed an increasing growth trend while 1/3 showed a decreasing trend. The dominant (28.33%) forest disturbance frequency indicated instability in red soil regions. Dispersion degree of forest variance was mainly low (48.46%) or medium (28.84%). This research establishes the DSVI as a promising method to track forest gradual change and contributes to a better understanding of gradual change processes of forest landscape over time. • The disturbance sensitive vegetation index (DSVI) is a good indicator for subtle disturbance changes of forest. • Tracking gradual change of forest ecosystems in the hilly red soil region. • The evaluation aspects (velocity, disturbance frequency, and variance) selected can effectively analysis of the long-term processes of forest gradual change and evaluate the quality and stability of forest ecosystems. [ABSTRACT FROM AUTHOR]