The dynamic simulation of rice growth parameters under cadmium stress with the assimilation of multi-period spectral indices and crop model

Abstract Obtaining precise information regarding the levels of heavy metal stress in crops is vital for food security, agricultural production, and ecological protection. In this study, we realized the dynamic simulation of rice growth parameters in three experiment fields that were exposed to varying levels of soil Cd (cadmium) concentration, intending to monitor the stress-induced changes of growth parameters on time scale. To simulate the growth parameters Leaf Area Index (LAI), Weight of Storage Organs (WSO) and Total Above Ground Production (TAGP) more accurately, we imbedded a Cd stress factor f Cd into the initial World Food Study (WOFOST) model. Then, as the spectral sensing technology is a potentially promising method to monitor crop stress conditions, an optimized methodology of assimilating multi-period spectral indices into the coupled WOFOST + PROSPECT + SAIL model was adopted to obtain the optimum value of stress factor; next, the dynamic simulation of growth parameters was adjusted. Particularly, based on the specific sensibility to contamination levels at different growth stages, TCARI (Transformed Chlorophyll Absorption in Reflectance Index), REP (Red Edge Position) and RH (Reflectance Height) were selected as the multi-period spectral indices, serving as the compared targets of the cost function in the process of assimilation. The growth parameters simulated by the modified WOFOST model preferably reflected the variations of rice growth status on a time scale with R 2 over 94% at all of the three levels. This study indicates that the optimized methodology of assimilating multi-period spectral indices into the crop model is applicable for simulating growth parameters under Cd stress, which provides a reference for dynamically monitoring heavy metal contamination in farmland environments.

[1]  C. A. van Diepen,et al.  User's guide for the WOFOST 7.1 crop growth simulation model and WOFOST Control Center 1.5. , 1998 .

[2]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[3]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[4]  James Barber,et al.  Effects of heavy metals on the absorbance and reflectance spectra of plants , 1980 .

[5]  Mui Lay,et al.  Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination. , 2005, Environmental pollution.

[6]  Yanxia Zhao,et al.  Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation , 2013 .

[7]  Wolfram Mauser,et al.  Methods and examples for remote sensing data assimilation in land surface process modeling , 2003, IEEE Trans. Geosci. Remote. Sens..

[8]  J. Lester,et al.  Heavy metal content of vegetables irrigated with mixtures of wastewater and sewage sludge in Zimbabwe: Implications for human health , 2006 .

[9]  C. Mao,et al.  Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.) , 2003 .

[10]  Ping Wang,et al.  The assimilation of spectral sensing and the WOFOST model for the dynamic simulation of cadmium accumulation in rice tissues , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[11]  Jianxi Huang,et al.  Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield , 2013, Math. Comput. Model..

[12]  J. Nyamangara,et al.  Environmental and Potential Health Effects of Growing Leafy Vegetables on Soil Irrigated Using Sewage Sludge and Effluent: A Case of Zn and Cu , 2004, Journal of environmental science and health. Part. B, Pesticides, food contaminants, and agricultural wastes.

[13]  W. Collins,et al.  Remote sensing of crop type and maturity , 1978 .

[14]  Qian Qian,et al.  Characterization and Fine Mapping of Non-panicle Mutant (nop) in Rice , 2009 .

[15]  Yong-guan Zhu,et al.  Health risks of heavy metals in contaminated soils and food crops irrigated with wastewater in Beijing, China. , 2008, Environmental pollution.

[16]  Tan Zheng Spatial-time continuous changes simulation of crop growth parameters with multi-source remote sensing data and crop growth model , 2012 .

[17]  Isaac Asencio,et al.  Capability of Selected Crop Plants for Shoot Mercury Accumulation from Polluted Soils: Phytoremediation Perspectives , 2007, International journal of phytoremediation.

[18]  Wu ZhiHai,et al.  Changes in photosynthetic indexes of rice varieties during forty-seven years of genetic improvement in Jilin Province, China. , 2009 .

[19]  F. Baret,et al.  Coupling canopy functioning and radiative transfer models for remote sensing data assimilation , 2001 .

[20]  L. Buydens,et al.  Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains. , 2004, Environmental pollution.

[21]  F. Baret,et al.  TSAVI: A Vegetation Index Which Minimizes Soil Brightness Effects On LAI And APAR Estimation , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[22]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[23]  W. Ernst Biochemical aspects of cadmium in plants. , 1980 .

[24]  L. Posthuma,et al.  Quantification of Metal Bioavailability for Lettuce (Lactuca sativa L.) in Field Soils , 2000, Archives of environmental contamination and toxicology.

[25]  F. M. Danson,et al.  RED-EDGE RESPONSE TO FOREST LEAF-AREA INDEX (VOL 16, PG 183, 1995) , 1995 .

[26]  L. Friberg,et al.  Cadmium in the environment , 1971 .

[27]  A. Wahid,et al.  Varietal differences in mungbean (Vigna radiata) for growth, yield, toxicity symptoms and cadmium accumulation , 2008 .

[28]  James S. Schepers,et al.  Detection of Phosphorus and Nitrogen Deficiencies in Corn Using Spectral Radiance Measurements , 2002 .

[29]  F. Hall,et al.  Global Crop Forecasting , 1980, Science.

[30]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[31]  E. Hoque,et al.  Spectral blue-shift of red edge minitors damage class of beech trees , 1992 .

[32]  John R. Miller,et al.  Quantitative characterization of the vegetation red edge reflectance 1. An inverted-Gaussian reflectance model , 1990 .

[33]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[34]  Rajesh Kumar Sharma,et al.  Interactive Effects of Cadmium and Zinc on Carrots: Growth and Biomass Accumulation , 2007 .

[35]  Xuezheng Shi,et al.  Hyper-spectral remote sensing to monitor vegetation stress , 2008 .

[36]  C. Black,et al.  Assessing risk to human health from tropical leafy vegetables grown on contaminated urban soils. , 2010, The Science of the total environment.

[37]  C. Kao,et al.  Cadmium toxicity of rice leaves is mediated through lipid peroxidation , 2001, Plant Growth Regulation.

[38]  W. Verhoef Earth observation modelling based on layer scattering matrices , 1984 .

[39]  Sarah C Dunagan,et al.  Effects of mercury on visible/near-infrared reflectance spectra of mustard spinach plants (Brassica rapa P.). , 2007, Environmental pollution.

[40]  D. Podar,et al.  Effect of alkaline pH and associated Zn on the concentration and total uptake of Cd by lettuce: comparison with predictions from the CLEA model. , 2005, The Science of the total environment.

[41]  J. Clevers,et al.  Study of heavy metal contamination in river floodplains using the red-edge position in spectroscopic data , 2004 .