| 用数值方法模拟观测误差对水质浓度反演模型参数的影响
——以叶绿素a浓度为例 |
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| 引用本文： | 陈军,付军,孙记红.用数值方法模拟观测误差对水质浓度反演模型参数的影响
——以叶绿素a浓度为例[J].国土资源遥感,2011,22(1):57-61. |
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| 作者姓名： | 陈军 付军 孙记红 |
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| 作者单位： | 国土资源部海洋油气资源与环境地质重点实验室,青岛,266071;青岛海洋地质研究所,青岛,266071 |
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| 基金项目： | “十一五”国家科技支撑项目(编号： 2008BAC34B03)和中国海陆地质地球物理系列图项目(编号： GZH200900504)共同资助。 |
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| 摘 要： | 在2003年10月27日和28日观测的太湖水质光谱试验数据的支持下，利用数值模拟方法研究了试验数据误差与水质浓度反演模型经验常数之间的关系。研究结果表明，当试验数据误差分别服从正态分布与均匀分布时，反演模型的经验常数随着试验数据误差的增加而呈发散状态。根据试验数据误差与反演精度的关系可知，26.58%的试验误差对应着30%的反演精度，这就意味着在不考虑遥感影像处理所产生误差的前提下，严格控制试验数据误差在26.58%以内，是反演精度打破±30%的技术瓶颈的关键要素之一。
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| 关 键 词： | 水质 模型参数 误差 遥感 |
| 收稿时间： | 2010-04-15 |
| 修稿时间： | 2010-06-10 |
The Application of the Numerical Method to Simulating the Impact of the Observation Errors on the Parameters of the Water Quality Retrieval Model:a Case Study of Chloroplyll-a Concentration |
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| CHEN Jun,FU Jun,SUN Ji-hong.The Application of the Numerical Method to Simulating the Impact of the Observation Errors on the Parameters of the Water Quality Retrieval Model:a Case Study of Chloroplyll-a Concentration[J].Remote Sensing for Land & Resources,2011,22(1):57-61. |
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| Authors: | CHEN Jun FU Jun SUN Ji-hong |
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| Institution: | (1.The Key Laboratory of Marine Hydrocarbon Resources and Environmental Geology, Qingdao 266071, China; 2.Qingdao Institute of Marine Geology, Qingdao 266071, China) |
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| Abstract: | Based on the experimental data collected in the Taihu Lake on October 27 and 28, 2003, the authors utilized the numerical simulation method to study the relationship between the errors of experimental data and the empirical constant of chlorophyll-a concentration retrieval model. The results indicate that, when the errors of experimental data obey normal distribution and uniform distribution respectively, the empirical constant of the retrieval model becomes divergent with the increasing error of the experimental data. In addition, according to the relationship between errors of experimental data and retrieval accuracy, 26.58% experimental errors are corresponding to 30% retrieval accuracy. This means that under the prerequisite that the errors produced during the processing of remote sensing images are not taken into account, the strict control of the errors of experimental data within 26.56% constitutes one of the key elements that break the bottleneck of the retrieval accuracy at ±30%. |
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| Keywords: | Water quality Model&rsquo s parameters Errors Remote sensing |
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