Dynamic Monitoring Of Heavy Metal Stress In Rice Based On The Assimilation Of Remote Sensing Data And WOFOST Model

Heavy metal pollution of farmland is one of the major environmental pollution in the world, heavy metal contaminants get into the farmland through various channels (garbage, sewage irrigation, use of fertilizers and atmospheric deposition, etc.), resulting in soil contamination, affecting crop growth and even endanger human health. Remote sensing monitoring of heavy metal stresses in farmland have been proved to be feasible, but the monitoring time is not continuous, monitoring is not very accurate.Monitoring of heavy metal stress by remote sensing assimilated WOFOST model can solve the problem that remote monitoring is not time continuous, at the same time can combine the remote sensing observations and WOFOST model simulations value well. Enable the remote sensing monitoring time domain wider and more accurate. Heavy metal contamination of soil will cause the inhibition of crop growth by influencing the eco-physiological parameters of crop, especially the root which grow in the ground. In this research, three cultivation areas in Zhuzhou, Hunan Province were selected as the experimental sites (A、B and C) with different heavy metal stress levels. This study dynamic Monitoring the WRT (Weight of Root) of rice through the assimilation of remote sensing data and the WOFOST model with three methods. LAI is one of output parameters of the WOFOST model and it can be used as the connection between the WOFOST model and remote sensing data. Commonly, it is hardly to get the WRT of rice through remote sensing method directly. However the WRT can be well simulated through the assimilation of remote sensing data and the WOFOST (World Food Studies) model. The assimilation process was conducted through the LAI by PSO (Particle Swarm Optimization). Grey correlation analysis was performed to select the crop parameter that is sensitive to the WRT. The results show that CVR (Efficiency of turning dry matter into root weight) is highly correlated to WRT. The assimilation of remote sensing data and the WOFOST model mean using PSO to optimize pollution stress factors F、CVR and CVR&F respectively. Analysis of the heavy metal stress of rice was conducted afterwards using the WRT simulated by the optimized WOFOST model. The conclusions have three sides:i. Sensitivity analysis shows that underground crop parameter WRT has the most relevant to the heavy metal stress of rice compared to LAI and chlorophyll. WRT is more accurate and directly than chlorophyll and LAI to characterize heavy metal stress situation. CVR is highly correlated to WRT. Using WRT simulated by the remote sensing assimilate WOFOST to achieve the monitoring of heavy metal stress in rice is feasible.ii. The trends of heavy metals contamination influence on WRT is tillering stage <jointing-booting stage> heading-flowering stage>ripening stage, that means WRT can be used to monitor heavy metal stress in rice in the early growth period jointing-booting stage and tillering stage rapidly.iii. In the assimilation of crop growth model WOFOST and remote sensing, the contrast of two optimized parameter, the WOFOST model internal parameter CVR and additional parameter F, show that, optimizing the CVR&F is more accurate than any one of them only to be optimized.