Distinguishing Heavy-Metal Stress Levels in Rice Using Synthetic Spectral Index Responses to Physiological Function Variations

Accurately assessing the heavy-metal contamination in crops is crucial to food security. This study provides a method to distinguish heavy-metal stress levels in rice using the variations of two physiological functions as discrimination indices, which are obtained by assimilation of remotely sensed data with a crop growth model. Two stress indices, which correspond to daily total CO2 assimilation and dry-matter conversion coefficient were incorporated into the World Food Study (WOFOST) crop growth model and calculated by assimilating the model with leaf area index (LAI), which was derived from time-series HJ1-CCD data. The stress levels are not constant with rice growth; thus, to improve the reliability, the two stress indices were obtained at both the first and the latter half periods of rice growth. To compare the stress indices of different stress levels, a synthetic stress index was established by combining the two indices; then, three types of stress index discriminant spaces based on the synthetic index of different growth periods were constructed, in which the two-dimensional discriminant space based on two growth periods showed the highest accuracy, with a misjudgment rate of 4.5%. When the discrimination rules were applied at a regional scale, the average correct discrimination rate was 95.0%.

[1]  Michael E. Schaepman,et al.  A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[2]  Carlos Cervantes,et al.  Chromium toxicity in plants. , 2005, Environment international.

[3]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

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

[5]  Mohd Irfan,et al.  Soil cadmium enrichment: Allocation and plant physiological manifestations. , 2013, Saudi journal of biological sciences.

[6]  W. J. Shuttleworth,et al.  Incorporating NDVI-Derived LAI into the Climate Version of RAMS and Its Impact on Regional Climate , 2002 .

[7]  L. Chouba,et al.  Photosynthesis and growth responses of pea Pisum sativum L. under heavy metals stress. , 2009, Journal of environmental sciences.

[8]  M. Droppa,et al.  The Role of Copper in Photosynthesis , 1990 .

[9]  Miroslav Trnka,et al.  Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions , 2004 .

[10]  Shuiping Cheng,et al.  Effects of Heavy metals on plants and resistance mechanisms , 2003 .

[11]  Cheng Wen,et al.  China's food security soiled by contamination. , 2013, Science.

[12]  Qiu Dongsheng.  Canopy Hyperspectral Characteristics of Paddy Plants Contaminated by Lead , 2008 .

[13]  H. Hasegawa,et al.  Effect of arsenic on photosynthesis, growth and yield of five widely cultivated rice (Oryza sativa L.) varieties in Bangladesh. , 2007, Chemosphere.

[14]  Sujay V. Kumar,et al.  A GIS framework for surface-layer soil moisture estimation combining satellite radar measurements and land surface modeling with soil physical property estimation , 2007, Environ. Model. Softw..

[15]  Wade T. Crow,et al.  Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment , 2012 .

[16]  Zbigniew Krupa,et al.  Cadmium-affected level of inorganic phosphate in rye leaves influences Rubisco subunits , 1999, Acta Physiologiae Plantarum.

[17]  Jing Wang,et al.  Estimating near future regional corn yields by integrating multi-source observations into a crop growth model , 2013 .

[18]  Pierre Defourny,et al.  Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment , 2011 .

[19]  L. Dente,et al.  Assimilation of leaf area index derived from ASAR and MERIS data into CERES - wheat model to map wheat yield , 2008 .

[20]  Conceição Santos,et al.  Cadmium toxicity affects photosynthesis and plant growth at different levels , 2012, Acta Physiologiae Plantarum.

[21]  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 .

[22]  Liu Lin,et al.  Soil Heavy Metal Pollution of Cultivated Land in China , 2013 .

[23]  S. D. Cunningham,et al.  Lead phytoextraction: species variation in lead uptake and translocation , 1996 .

[24]  Zhang Jian-guo.  Effects of Cadmium Stress on Leaf Photosynthesis and Dry Matter Allocation of Populus×euramericana(Dode)Guineir cv. 'Neva' , 2012 .

[25]  Patrick Matgen,et al.  Enhanced biomass prediction by assimilating satellite data into a crop growth model , 2014, Environ. Model. Softw..

[26]  Jasmeet Judge,et al.  Assimilation of SMOS Soil Moisture for Quantifying Drought Impacts on Crop Yield in Agricultural Regions , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Gregory Duveiller,et al.  Estimating regional winter wheat yield with WOFOST through the assimilation of green area index retrieved from MODIS observations , 2012 .

[28]  Vittorio E. Brando,et al.  Satellite data assimilation and estimation of a 3D coastal sediment transport model using error-subspace emulators , 2013, Environ. Model. Softw..

[29]  James Hansen,et al.  Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction , 2013 .

[30]  Patrick E. Van Laake,et al.  Mapping PAR using MODIS atmosphere products , 2005 .

[31]  Meiling Liu,et al.  Wavelet-based detection of crop zinc stress assessment using hyperspectral reflectance , 2011, Comput. Geosci..

[32]  Meiling Liu,et al.  An improved assimilation method with stress factors incorporated in the WOFOST model for the efficient assessment of heavy metal stress levels in rice , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[33]  Jeffrey W. White,et al.  Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models , 2012 .

[34]  Guofeng Wu,et al.  Visible and near-infrared reflectance spectroscopy-an alternative for monitoring soil contamination by heavy metals. , 2014, Journal of hazardous materials.

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

[36]  R. Kastori,et al.  Effect of excess lead on sunflower growth and photosynthesis , 1998 .

[37]  Huang Wenjiang,et al.  Analysis of consistency between HJ-CCD images and TM images in monitoring rice LAI , 2010 .

[38]  Xinde Cao,et al.  Accumulation of Pb, Cu, and Zn in native plants growing on a contaminated Florida site. , 2006, The Science of the total environment.

[39]  Kazimierz Strzałka,et al.  Influence of Cd(II), Cr(VI) and Fe(III) on early steps of deetiolation process in wheat: fluorescence spectral changes of protochlorophyllide and newly formed chlorophyllide , 2005 .

[40]  Shi Qiu,et al.  Estimating the Aboveground Dry Biomass of Grass by Assimilation of Retrieved LAI Into a Crop Growth Model , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[41]  P. C. Nagajyoti,et al.  Heavy metals, occurrence and toxicity for plants: a review , 2010 .

[42]  WU Deng-wei,et al.  Review on Remote Sensing Monitoring on Contaminated Plant , 2012 .

[43]  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 .

[44]  Ari Posner,et al.  MODIS Inundation Estimate Assimilation into Soil Moisture Accounting Hydrologic Model: A Case Study in Southeast Asia , 2014, Remote. Sens..

[45]  Yu Zhang,et al.  Assimilation of Passive Microwave Streamflow Signals for Improving Flood Forecasting: A First Study in Cubango River Basin, Africa , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[46]  Xie Wen-xia,et al.  Simulation and Validation of Rice Potential Growth Process in Zhejiang by Utilizing WOFOST Model , 2006 .

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

[48]  Raksha Singh,et al.  Understanding the responses of rice to environmental stress using proteomics. , 2013, Journal of proteome research.

[49]  Nataliia Kussul,et al.  Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[50]  Robert K. Vincent,et al.  Remote sensing of soybean stress as an indicator of chemical concentration of biosolid amended surface soils , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[51]  Chen Huamain,et al.  HEAVY METAL POLLUTION IN SOILS IN CHINA : STATUS AND COUNTERMEASURES , 1999 .

[52]  Dehai Zhu,et al.  Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model , 2015 .