| 基于高光谱遥感的长江口叶绿素a浓度反演 |
| Estimation of Chlorophyll-a Concentrations in the Yangtze River Estuary Obtained from Hyperspectral Remote Sensing Images |
| 投稿时间：2019-05-29 修订日期：2021-05-19 |
| DOI：10.15928/j.1674-3075.201905290136 |
| 中文关键词:高光谱遥感 叶绿素a 长江口 水体叶绿素a提取指数 |
| 英文关键词:hyperspectral remote sensing chlorophyll-a Yangtze River stuary water chlorophyll-a index |
| 基金项目:国家自然科学基金(61991454) |
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| 中文摘要: |
| 为了解长江口的水质状况，现场测量叶绿素a浓度，结合高光谱遥感影像，运用波段比值模型、一阶微分模型和水体叶绿素a提取指数(Water Chlorophyll-a Index，WCI)对整个研究区域叶绿素a浓度进行反演推算，并进行空间分布评价；利用实测数据和遥感影像的关系建立反演模型，并结合相关系数、均方根误差和平均相对误差，分析和评价反演效果。结果显示，波段比值模型和叶绿素a浓度的相关性达到0.9099，均方根误差为1.7922，平均相对误差为9.09%；一阶微分模型的相关性为0.9483，均方根误差为2.2073，平均相对误差为15.31%；WCI模型的相关性高达0.9778，均方根误差为1.4405，平均相对误差为6.20%。利用WCI模型对整个研究区域的叶绿素a浓度进行模拟，可见研究区域的中间部分叶绿素a含量较低，从中间到两边逐渐增大，南部出现最大值，造成此差异的原因可能是因为北靠近居民生活区，南邻上海青草沙水库，并且附近存在植被。研究表明，WCI模型的反演效果优于波段比值模型和一阶微分模型，是一种计算简单、精度较高的方法，可以有效地提取水体叶绿素a的浓度，未来可广泛应用于水体环境质量监测。 |
| 英文摘要: |
| The use of remote sensing technology to estimate water quality parameters is characterized by high efficiency and low cost, and can be used for water quality monitoring on a large scale. In this study, chlorophyll-a data was collected to assess water quality in the Yangtze River estuary and develop a model for estimating chlorophyll-a concentration from hyperspectral images collected remotely. Field data on chlorophyll-a concentration were collected at 14 sampling sites on March 26 and 28 of 2016 and hyperspectral remote sensing imagery were obtained for March of 2016. Chlorophyll-a concentration was estimated from remotely sensed hyperspectral imagery using three models：a band ratio model, a first-order differential model, and the water chlorophyll-a index (WCI). All three models were based on relationships between the measured chlorophyll-a concentrations and hyperspectral imagery. The accuracy of the models were evaluated by comparing the measured values of chlorophyll-a with the model values using the correlation coefficient, root mean square error (RMSE) and mean relative error (MRE). The respective values were 0.9099, 1.7922, 9.09% for the band ratio model, 0.9483, 2.2073, 15.31% for the first-order differential model and 0.9778, 1.4405, 6.20% for the WCI model. All three statistical parameters indicate that the WCI model gives the most reliable estimates of chlorophyll-a concentration. The chlorophyll-a concentration of the entire study area was then simulated using the WCI model and the results were used to analyze the spatial distribution of chlorophyll-a in the estuary. The chlorophyll-a content in the central study area was low and gradually increased from the middle to the north and south, with the maximum value occurring in the southern portion of the study area. The northern part of the study area is near a residential area and the southern part is near Shanghai Qingcaosha reservoir with abundant vegetation. The spatial distribution of chlorophyll-a concentration in the Yangtze River estuary was attributed to differences in land use. In summary, the WCI model gives more reliable estimates of chlorophyll-a concentration than the band ratio model or first-order differential model and provides an economical, accurate method for estimating chlorophyll-a concentrations in water bodies from hyperspectral imagery. We expect that remote sensing will be widely used for water quality monitoring in the future. |
| 沈 蔚,纪 茜,邱耀炜,吴忠强.2021.基于高光谱遥感的长江口叶绿素a浓度反演[J].水生态学杂志,42(3):1-6. |
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