## 1 Introduction  
Along with the rapid development of industrialization and urbanization, soil contamination by heavy metals in China continues to worsen. Heavy metal contamination in the soil can accumulate in rice, which then threatens food security and damages human health [1; 2; 3; 4; 5]. Thus, it is extremely important to control heavy metal pollution in agricultural soil and monitor agricultural eco-environments.  
Remote sensing plays an important role in monitoring heavy metal contamination. Numerous previous studies have investigated the relationship between spectral features (e.g., red edge position, spectral vegetation indices) and heavy metal concentrations or the physical characteristics of plants (e.g., leaf area index, yield, chlorophyll content) [6; 7; 8; 9; 10; 11; 12]. Many researchers have found that roots respond to heavy metal toxicity earlier and more strongly than the parts of the plant that are above ground [13; 14; 15; 16; 17]. Heavy metals can accumulate in roots because they are the entry point for water and nutrimet into plants and can directly absorb heavy metals from contaminated soil. The dry weight of roots (WRT) is a representative indicator for monitoring heavy metal stress; however, the WRT is not able to be directly observed by remote sensing; Therefore, to continuously obtain dynamic WRT values, an optimized method that can assimilate remotely sensed data into improved crop growth models has been used [18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. The World Food Studies (WOFOST) model is a strong model that can describe the fundamental processes of crop growth, such as photosynthesis, respiration, transpiration and biomass partitioning [28]. For different areas, the relevant weather conditions, edatope, and crop parameters can be used to regonalize the model; thus, the model can simulate crop growth locally. The remotely sensed data assimilated in the WOFOST (RS-WOFOST) provide a reference method to dynamically monitor heavy metal contamination in paddy rice [18; 19; 20; 21; 22; 23]. However, in previous studies, the key period used to assimilate remotely sensed data was not considered in the improved crop growth models [24; 25; 26]. It is very helpful to select key periods for the assimilation of remote sensing and the WOFOST model in continuous monitoring heavy metal stress in rice. Selecting key periods not only improves the accuracy and efficiency of the rice assimilation method applied at the regional scale, but also provides a time reference for the field measurements in monitoring stress. Heavy metal contamination has different effects during different plant growth periods [29; 30]; hence, the time of the field measurement and monitoring is directly related to the effect of heavy metal stress and the accuracy of the assimilation model. Therefore, selecting the critical time is extremely important and necessary when monitoring heavy metal stress in rice.  
In this study, the key period of RS-WOFOST assimilation was optimized based on the Harris algorithm to the original leaf area index (LAI) curves. The LAI is a botanical parameter that can be retrieved from remote sensing images [31] and it is also a key index for determining the appropriate period to monitor heavy metal stress in rice tissues. In order to improve the accuracy of LAI simulation, we embedded a specific stress factor (_f_) into the original WOFOST model. Moreover, the time-series WRT values were assimilated into the WOFOST model, and the stress factor was optimized in the process of assimilation. The modified WOFOST model was driven with the optimum (_f_) to realize the dynamic simulation of LAI. [24; 26] The key period of the assimilation algorithm was analyzed using the Harris algorithm [32; 33]. After screening, dominant time points were extracted and applied in the study area. Based on the key period, appropriate remote sensing images could be selected for the RS-WOFOST assimilation framework to realize the spatio-temporal continuous evaluation of heavy metal stress in rice tissues.