Assessment Of Heavy Metal Stress In Rice Tissues Based On The Temporal-spatial Scale Optimization Of Remote Sensing Assimilation Methods

Heavy metal stress influences the growth parameters of crops, particularly the chlorophyll content, which is an indicator of photosynthesis activity and results in the variations of canopy spectral reflectance. Previous studies put more emphasis on the variations of spectral characteristics, the lack of robustness and portability makes the traditional methods less persuasive from the view point of the crop growth mechanism. With the assimilation of remote sensing and crop model, the continuous simulation of growth parameters could be realized. However, the best coupling mechanism of assimilation model based on the temporal and spatial scale optimization should be taken into account in further study.Heavy metal accumulation in rice tissues has a significant impact on the dry matter production and distribution. We established a field-scale assimilation model in three experiment fields that were exposed to varying levels of soil heavy metal concentration in Changchun City. We imbedded a heavy metal stress factor fM into the initial World Food Study(WOFOST) model, intending to monitor the stress-induced changes of growth parameters on time scale. Meanwhile, based on the specific sensibility to contamination levels at different growth stages, three indices TCARI, REP and RH were selected as the multi-period spectral indices, serving as the compared targets of the cost function in the process of assimilation. The accurate simulation of growth parameters Leaf Area Index(LAI), Weight of Storage Organs(WSO) and Total Above Ground Production(TAGP) were realized, with R2 over 94% at all of the three levels.Considering the spatial heterogeneity of land surface conditions, the key to applying the field-scale RS-WOFOST assimilation model to regional-scale assessment is the regionalization of the input parameters. Moreover, roots are considered to be more directly and severely stressed, so the Dry Weight of Rice Roots(WRT) was used as an indicator for monitoring heavy metal stress levels in rice tissues. Then, an optimized method of assimilating remotely sensed LAI into the modified WOFOST model was used to optimize the simulation process and obtain the optimum value of fM. Thus, the dynamic simulation of WRT under heavy metal stress was adjusted. The variation of WRT values generally reflected the stress mechanism in time scale, indicating that the dynamic simulation of WRT was reliable.The RS-WOFOST assimilation provides a method for achieving the temporal–spatial evaluation of crop growth status, while the optimization of the temporal scale in assimilation framework has rarely been considered. As WRT was demonstrated to be the most stress-sensitive indicator, the measured WRT values were assimilated into the improved WOFOST model to realize the dynamic simulation of LAI in Zhuzhou City. The temporal scale was optimized based on the Daubechies 5 wavelet transform of the LAI curves. Four optimal time points were determined based on the extreme areas in the d4 wavelet coefficient, also providing a reference for the selection of remote sensing images. The verification in the two sample plots indicated that the assimilation with optimized temporal scale could significantly improve the efficiency on the basis of guaranteeing the accuracy, shortening the run time of model operation by more than 30%.