The Dynamic Assessment Model for Monitoring Cadmium Stress Levels in Rice Based on the Assimilation of Remote Sensing and the WOFOST Model

Abstract

Monitoring of heavy metal stress in crops is vital for food security and agricultural production management. Traditional remote sensing methods focus on the stress-induced changes to the aerial organs of plants, whereas roots are considered to be more directly and severely stressed. In this study, the dry weight of rice roots (WRT) was used as an indicator for monitoring cadmium (Cd) stress levels in rice tissues. The World Food Study (WOFOST) model is a widely used analysis tool for describing the fundamental processes of crop growth, and has been tested for similar applications. We used this model to incorporate a Cd stress factor (fCd), allowing us to simulate the WRT values more accurately. Then, an optimized method of assimilating remotely sensed leaf area index (LAI) into the modified WOFOST model was used to optimize the simulation process and obtain the optimum value of f Cd. Thus, the dynamic simulation of WRT under Cd stress was adjusted. Based on the WRT values of two sample plots with different soil Cd concentrations, the ratio between them (WRTStress/WRTSafe) was calculated subsequently. The variation in the ratio curve generally reflected the stress mechanism in time scale, indicating that the dynamic simulation of WRT was reliable. This study suggests that the method of assimilating remote sensing data into the crop growth model is applicable for simulating crop growth under Cd stress on spatial-time scale, providing a reference for dynamically monitoring heavy metal contamination in rice tissues.

Estimating FAPAR of Rice Growth Period Using Radiation Transfer Model Coupled with the WOFOST Model for Analyzing Heavy Metal Stress

“…The model can be implemented under three levels: (1) the potential productivity level, in which the solar radiation and temperature are the main factors limiting the crop growth; (2) the water-limited level; and (3) the nutrients-limited level. In this study, we used the improved WOFOST model under the potential productivity level, which embedded the parameter of "stress factor" to simulate the daily LAI over the course of the season at different levels of heavy metal stress [40]. The improved WOFOST has better performance than the normal version without the 'stress factor' in reliably simulating rice LAI under heavy metal pollution [5,38].…”

“…Based on our previous studies, the stress factor corresponds to the daily total gross assimilation of CO 2 and the range of the stress factor is from 0.7 to 1, which represents serious heavy metal stress and no stress, respectively. However, the stress factor is difficult to acquire and we selected the method of assimilating LAI into the WOFOST model to obtain the optimal stress factor [40]. The assimilation processes for adjusting the stress factor were implemented using a particle swarm optimization algorithm (PSO), which continued until the difference between simulated and retrieved LAI was minimized.…”

“…The improved WOFOST model and assimilation is described in detail with reference to previous studies [3,5,40].…”

“…In this study, the LAI during the rice growing season and daily SKYL were simulated by the WOFOST model and meteorological data. The input parameters of the WOFOST model were set according to previous literature [5,38,40,41], and the primary parameters are shown in Table 2. In the input parameters of the PROSAIL model, the sun zenith angle (SZA), sensor zenith angles (VZA) and azimuth angle between the sun and the sensor (RAA) were consistent with remote sensing data acquired in rice growth period.…”

Evaluation of Three Techniques for Correcting the Spatial Scaling Bias of Leaf Area Index

“…Leaf area index (LAI), defined as half the total foliage area per unit ground horizontal surface area [1], is an essential biophysical property of vegetation for environmental assessment [2,3], crop growth monitoring [4,5] and yield prediction [6][7][8]. The accurate monitoring of LAI on multiple scales has become a key technique in those applications.…”