Evaluation Of Heavy Metal Stress Based On Monitoring Index RMR And Optimized Assimilation Scheme

Heavy metal contamination in farmland is hysteretic,concealed and irreversible,which pose a great threaten on human health and farmland environment,thus it is essential to monitor heavy metal contamination timely and accurately.However,the current remote sensing method still need to further improve,such as remote sensing data,monitoring methods and indexes,through which the issues of poor monitoring precision and spatiotemporal discontinuity can be solved.In this paper,the study area,one of the old industrial bases and an important railway transportation hub in China,is located in Zhuzhou city,Hunan Province.The remote sensing images and measured data were collected to optimize the RS-WOFOST framework from the perspective of method and monitoring index.Firstly,a new index for monitoring heavy metal stress based on the assimilation of synthetic aperture radar(SAR)and the crop growth model is firstly performed in this study.The improved World Food Study(WOFOST)model was used,which is embedded with two stress factors to improve the accuracy of assimilation.Biomass(BM)values retrieved by SAR data were assimilated into the improved WOFOST model to simulate dry weight of rice roots(WRT),and the root mass ratio(RMR,WRT/BM)was calculated as an index for monitoring heavy metal stress.Compared with other physiological indices,RMR could weaken the weight change of rice caused by other background factors.In the temporal scale,RMR showed a faster significant decrease when the stress was greater.The spatial distribution of RMR and the stress factors exhibited good consistency.These results suggest that RMR derived from the assimilation method based on SAR data and the improved WOFOST model is effective for dynamically monitoring the rice growth status in cloudy regions under heavy metal stress.Secondly,in order to further improve the simulation accuracy,we developed an assimilation framework that incorporates remotely sensed dry biomass(BM)and leaf area index(LAI)into the improved WOFOST model.The two objectives(LAI and BM)respond two important physiological parameters in the crop growth model.The RS-WOFOST assimilation framework was evaluated against contaminated paddy rice in Zhuzhou,Hunan Province,China,assimilating BM and LAI(retrieved from Sentinel-1 and GF-1)independently and simultaneously.Results suggest that compared with assimilation of BM or LAI independently,bi-objective assimilation scheme has better performance for heavy metal stress level evaluation,with RPE lower than 15%.The RS-WOFOST assimilation framework based on BM and LAI can be successfully applied in spatial-temporal assessment of heavy metal stress levels,with better evaluation accuracy.Therefore,the method of heavy metal stress evaluation based on monitoring index RMR and optimized assimilation scheme could weaken the impact of other environmental factors and improve the simulation accuracy.Besides,the issues of data quantity and poor quality in the assimilation process can be solved using different data sources.