Spatiotemporal Cube Model Based on Stress Features for Identification of Heavy Metal Stress in Rice

The spatiotemporal analysis of crop spectral features has become a mainstream method for identifying crop stresses. However, current spatiotemporal feature extraction methods fragment the relationship between time and space, resulting in low accuracy in identifying different stresses in crops. The spatiotemporal cube (ST-cube) model has the advantage of integrating spatiotemporal features by unifying time and space modeling. This study proposes an ST-cube model to mine spatiotemporal changes under different rice stresses and identify heavy metal stress in rice. First, the stress information in the enhanced vegetation index (EVI) time series of rice pixels was extracted. Second, the ST-cube was segmented to obtain continuous spatiotemporal units of the stressed rice. Finally, an intraannual global spatiotemporal stability index (GSTSintra) and an interannual global spatiotemporal stability index (GSTSinter) were constructed to evaluate the spatiotemporal stability of rice under stresses. A case study was performed on a rice planting area in Zhuzhou, Hunan, China, during 2019–2021 using Sentinel-2A images and field measurement data. The results indicated that rice under the stress exhibits significant spatial clustering. Regardless of the year, the spatial distribution and proportion of GSTSintra values were similar. In the three-year analysis, most GSTSinter values were close to 1, indicating that the sources of the stress experienced during the years were similar. GSTSintra and GSTSinter accurately revealed the stability of continuous spatiotemporal distributions of rice under stress, which can open up new avenues for identifying heavy metal stress in rice under complex stress conditions.