摘要
随着工业生产规模的扩大、城市环境污染的加剧和农用化学物质种类、数量的增加,土壤重金属污染因其程度加剧、面积扩大而备受关注。重金属污染物在土壤中移动差、滞留时间长、难被微生物降解,并可经水、植物等介质最终影响人体健康,因此对重金属污染的定量监测非常有必要并且意义重大。高光谱遥感技术的发展为宏观、快速获取土壤重金属元素信息提供了新的契机,目前国内外学者基于土壤反射光谱特征,运用多种统计分析方法成功地预测了多种土壤重金属元素的含量。介绍了土壤的光谱特征及光谱特征波段的提取,对利用高光谱遥感技术估算土壤重金属含量的主要方法进行了总结,对影响模型精度的主要因素进行了讨论,介绍了模型在模拟多光谱数据方面的应用,最后对模型反演过程出现的不足及今后的研究方向进行了展望。
With the high speed of urbanization,development of industry,deterioration of urban environment and rapid increasing of agricultural chemicals amount and kinds,and soil heavy metal pollution is serious and it has been concerned by environmental scientists because of its soil environment aggravating and pollution area expanding.Heavy metal pollutant in soil has low level transferability,long time retention features and it is hard to be degraded by microbial.It will finally affect human health by water,plants and other media.So soil heavy metal pollution quantitative monitoring is vitally necessary and is of great significance.The development of hyperspectral remote sensing technology provides a new opportunity for obtaining soil heavy metal information in large scale area,with short period and less cost.Recently,many domestic and overseas researchers had monitored the content of soil heavy metal elements successfully which using various statistical analysis methods based on soil hyperspectral reflectance characteristics.The characteristics of soil reflection spectra and typical spectral bands selection were introduced.Methods for estimating soil heavy metal content by used hyperspectral remote sensing technology were summarized.And main factors which influenced the precision of refutation model were discussed.Also the application of refutation model in simulating multi-spectral data was intro duced,and finally the deficiency of soil heavy metal content refutation methods and future research interesting point were given.
出处
《湖北农业科学》
北大核心
2013年第6期1248-1253,1259,共7页
Hubei Agricultural Sciences
基金
国家自然科学基金项目(40871021)
河北省自然科学基金项目(D2010000867)
关键词
土壤重金属
高光谱遥感
估算方法
统计分析
预测精度
soil heavy metal
hyperspectral remote sensing
estimation method
statistic analysis
prediction accuracy