摘要
为了评估遥感反演叶绿素a浓度的精度,以2004年8月19日太湖38个水质样本数据和同步Hyperion卫星遥感影像数据为基础,借鉴四波段半分析算法,结合空间数据不确定性原理,构建了基于四波段半分析算法的"带模型"。通过研究与探讨可知,当叶绿素a浓度为10～20μg/L和50～100μg/L时,叶绿素a浓度的反演误差较小,大约为±20%;当叶绿素a浓度在20～50μg/L时,叶绿素a浓度的反演误差较大,大约为±40%,局部区段的误差高达±60%左右。与传统的误差表示方法相比较,"带模型"能更详细且能准确地给出太湖水体叶绿素a浓度反演结果的误差信息。
With the spectral experiment and the simultaneous observation results of Hyperion satellite on 19 August,2004 as the basic dataset,the authors used the uncertainty principle of spatial data to develop a ＂bands model＂ for chlorophyll-a concentration retrieval algorithm of the subsection mapping retrieval model.It is thus found that in the ranges of 10-20 μg/L and 50-100 μg/L,the retrieval error of chlorophyll-a concentration is relatively low,（approximately ±20%）,whereas in the range of 20-50 μg/L,the retrieval error of chlorophyll-a concentration is relatively high,（approximately ±40%）.A comparison with the traditional methods for error describing shows that the ＂bands model＂ could include more detailed and accurate information of data quality for remote sensing products.
出处
《国土资源遥感》
CSCD
2011年第4期83-86,共4页
Remote Sensing for Land & Resources
基金
国土资源部海洋油气资源和环境地质重点实验室基金项目(编号:MRE201109)
中国海陆地质地球物理系列图项目(编号:GZH200900504)共同资助
关键词
遥感
带模型
叶绿素A
太湖
Remote sensing
Bands model
Chlorophll-a
Taihu Lake
