Study On Assessment Model About Rice Under Heavy Metal Pollution Stress Level By Integrating Remote Sensing With Multiple Environmental Factors

It is important for agricultural production, food security and human survival environment using remote sensing technology to identify and monitor crops heavy metal contamination. However, the pollution level in the'real world'agro-ecosystems is relatively low, which means there are subtle and unstable characteristic in leaf reflectance spectra. And therefore it is incredible in assessing stress levels of crop under heavy metal pollution using leaf reflectance spectra directly. So, how to enhance subtle spectral characteristic information associated with heavy metal pollution, how to establish effective model for assessing stress levels of crop with heavy metal pollution, they are key issues for applying remote sensing technology to achieve fast and accurate identification of crop heavy metal contamination. At the same time, they are also scientific problems to be solved for remote sensing technology in quantitative and fine application.Several experiment paddies located in Changchun, Jilin Province, and Suzhou, Jiangsu Province, China with different pollution levels were selected. We collected various data from experimental farms in rice during the typical growth stages, including ASD data, Hyperion hyperspectral data, biochemical data, heavy metal content data (i.e.soil, rice), soil properties and meteorological factors, and other basic data. Based on ground measured data, soil heavy metal effect on stress mechanisms in rice were analyzed, spectral parameters sensitive to rice under heavy metal pollution stress were calculated, and the theory and technical methods about enhancing and deriving subtle spectral characteristic information of rice under heavy metal pollution were proposed, and the models for assessing stress levels of rice under heavy metal pollution were constructed. The most important findings and conclusions drawn from this study include:(1) Wavelet transform was adopted to enhance and derive subtle spectral characteristic information of rice under heavy metal pollution. Three categories of sensitive spectral parameters were extracted,â‘ the fractal dimension of reflectance with wavelet transform(FDWT) as a quantitative and comprehensive indicator by capturing'global variation'of spectrum curve,â‘¡wavelet coefficients (WC)by capturing spectral reflectance singularity information,â‘¢vegetation indices based on singularity points.(2)According to different characteristic in various types'parameters, Firstly, the stepwise regression and gray correlation analysis were used to choose the sensitive spectral parameters and environmental factors which are closely correlated to heavy metal diffusion in rice, respectively. Secondly, based on dynamic fuzzy neural-network model, spectral analysis model for assessing heavy metal stress levels of rice in'real world'agro-ecosystems were constructed by integrating spectral parameters with environmental factors. In addition, the constructed models were verified using training and validation sets, they got satisfactory results with a high level of accuracy (R2: 0.69-0.98), a compact structure (fuzzy rules: 5-13). As compared with other model algorithm (such as BP, ANFIS) for constructing local spectral model to assess rice with heavy metal stress, it had more accurate, stable results and higher reliability. In addition, it can obtain rules with a physical meaning. The models were characterized by strong stability and better effects in evaluation of stress levels of rice under heavy metal pollution.(3) Hyperion data was used, and spatial interpolation technology was adopted. The established spectral analysis models were applied to the polluted area in a large scale, and satellite remote sensing model for assessing rice with heavy metal stress level were constructed successfully. The established spectral analysis model for assessing rice with heavy metal stress level succeeded in completing scale transformation.In this paper, it is believed that the wavelet technique will play an important role in detecting other environmental aspects of crop stress in the future. The method, the spectral analysis models were established by integrating spectral parameters with environmental factors on the basis of dynamic fuzzy neural-network algorithm, can provide important reference and theory basis for modeling in the various geosciences application.