顾及时空差异性的太湖水体中叶绿素a浓度的遥感估算实验研究
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摘要
太湖是中国长三角区域的最大淡水湖,日益严重的水质污染和富营养化问题影响了当地经济的发展和居民的健康,利用遥感手段进行太湖水体水质参数的估算和监测具有重要意义。叶绿素a是水体重要的水质参数之一,是表征水体富营养化程度的主要指标。
本研究针对二类水体中叶绿素a浓度(Chla)估算模型的不足,以改进模型的应用精度为目标,提出了考虑数据时空差异性的Chla估算模型构建方法。基于2004-2012年共19期太湖野外调查数据,对已有的典型Chla估算模型进行了验证,分析了模型残差的时空差异性特征,提出了基于月份和湖区的水质类型划分规则,并构建了顾及时空差异性的太湖水体Chla估算模型,提高了Chla估算模型的应用精度。主要研究内容和成果如下：
(1)典型二类水体Chla估算模型的太湖验证与残差分析
基于不同数据集,验证了18个Chla高光谱估算模型在太湖水体的适用性,分析了模型残差的时空差异特征及其影响因素。结果表明：R-NIR算法在太湖中的应用精度优于荧光算法,重新率定模型参数或优化波段位置后所建模型的应用精度显著高于模型的直接应用,模型参数的率定验证的均方根误差(RMSE)可降低到20mg/m3以下。不同数据集的验证精度不同,模型残差随着不同样点时间和空间位置的变化而变化,表明水体的时空差异性影响着Chla估算模型的表现。
(2)考虑季节性差异的Chla估算与模型改进
使用相邻月的观测数据,研究了改进模型应用精度的数据变换方法,使用夏秋季数据,构建了新的水体Chla指数,考虑数据的季节性差异,构建了Chla估算模型。结果表明：使用Chla的对数变换与光谱核回归平滑处理可以改进反演模型残差的方差齐性和模型的应用精度,使用2004年7月平滑后数据建立的三波段模型,8月数据验证的RMSE从平滑前的33.56mg/m3降低到了平滑后的25.60mg/m3；基于夏季和秋季的调查数据,构建了新的叶绿素a指数(NCI=(R690/R550-R675/R700)/(R690/R550+R675/R700))所建立的模型在多期数据集上的验证精度优于已有的三波段和四波段算法。数据季节划分后部分改进了Chla估算模型的精度和残差分布。
(3)水质的时空差异性分析和划分
结合太湖水质的时空差异性及水质参数的时空分布规律,以样点的月份和所在湖区编号作为输入,使用CRT和C5.0决策树方法构建了顾及时空差异性的Chla分组规则。调查水域按Chla级别的不同分成三种类型,其划分规则为：
①如果月份∈(3,5)|(月份∈(4,8,9,11)&区域∈(湖心区)),则为类型Ⅰ；
②如果月份∈(6,7,10)|(月份∈(4,11)&区域∈(梅梁湾,竺山湾)),则为类型Ⅱ；
③如果月份∈(8,9)&区域∈(梅梁湾,竺山湾),则为类型Ⅲ。
数据划分后三种水体类型的遥感反射率和光学特性具有较明显的差异,分别代表了悬浮物主导、悬浮物和浮游藻类共同主导以及浮游藻类主导的水体。基于上述规则对数据进行划分,不同类型水体光学特性的相互区分度及其对Chla的指示作用优于基于Chla分级和光谱分类的结果。
(4)顾及时空差异性的太湖Chla遥感估算模型的构建
基于所建立的规则,将建模集和验证集的样点进行了划分,使用建模集构建了波段比值、三波段、四波段和NCI模型,通过比较模型精度和残差分布,确定了新模型中各类型所使用的模型变量和参数。新模型为Chla=exp(ax2+bx+c),x在类型Ⅰ、Ⅱ、Ⅲ中分别为R701/R677、(1/R686-1/R695)×R710、NCI,模型在验证集的应用精度(R2=0.75,RSE=11.54mg/m3)优于基于季节划分的模型(R2=0.56,RMSE=17.63mg/m3)及比值、三波段、四波段和NCI组合参数率定的验证结果(R2<0.61;RSE>16.79mg/m3)。基于HJ1/HSI数据的模型应用结果表明,新模型计算出的水体叶绿素a浓度时空分布与已有的调查研究具有可比性。本文构建的顾及时空差异性的Chla估算模型,改进了模型的稳健性和应用精度,在太湖水体的叶绿素a浓度的遥感估算中具有较好的适用性。
Taihu Lake is the largest freshwater lake in the Yangtze River Delta, China, its increasing water pollution and eutrophication problem seriously hindered the economic development and the health of local residents. Estimating the content of water components and monitoring water quality in Taihu lake using the remote sensing technology is of great convenience and significance. Chlorophyll-a is the main indicator of water eutrophication, also one of the most important parameters for water quality monitoring.
In order to improve the performance and validation accuracy of existing estimation models, this study take the temporal and spatial variation of water quality into account and promote a new method to estimate chlorophyll a concentration (Chla) in Taihu lake. Based on19field experiments from2004-2012in Taihu lake,18typical Chla estimation models were validated and model residuals'variation with time and position was analyzed. This study proposed a rule for water quality division using the input of month and lake region, and constructed a new Chla estimation model that considers temporal and spatial variation, which can improve the accuracy of Chla estimation when validated by other datasets. The main contents and conclusions are as follows:
(1) Validation of typical Chla estimation models using data in Taihu lake and analysis of the model residuals.
Based on several different datasets,18existing Chla estimation models was validated and model residuals' variation with month/season and lake district, as well as its influencing factors were analyzed. The results show that:the applicability of R-NIR algorithms in Taihu lake is better than the fluorescence algorithms, and the accuracy of model validation can be greatly improved after reparameterization of model parameters (RMSE is less than20mg/m3) or tuning of band position. Model accuracy validated by different datasets varies a lot, and the model residuals change with the sampling time and space, indicating that the accuracy of inversion model is correlated with the spatial and temporal variation of water body.
(2) Estimation of Chla considering seasonal variation and model improvement.
This paper studied the data transformation method for improving the accuracy of model application based on adjacent monthly data, and constructed a new Chla index in water based on data in summer and autumn. Then the Chla estimation model considering the seasonal variation was constructed. The results show that:logarithmic transformation and spectral kernel regression smoothing can be used to improve the homogeneity of model residual and the accuracy of model validation, for example, when a model built by data in July2004was validation by data in August2004, the RMSE reduced from33.56mg/m3before smoothing to25.60mg/m3after smoothing. A new chlorophyll index (NCI=(R690/R550-R675/R700)/(R690/R550+R675/R700)) was built based on data in summer and autumn, which presented better accuracy when validated by multiple datasets than the existing three band and four-band model. The accuracy and residual distribution can be partially improved by constructing Chla estimation model using data after seasonal devision.
(3) Analysis of spatial and temporal difference of water quality and its division rule.
Based on the spatial and temporal difference of water quality of Taihu lake and the spatial and temporal distribution of water quality parameters, this study promoted a division rule of water quality using the input of month and lake district and the method of CRT and C5.0decision tree. The investigated water was divided into three types according to the Chla level, and the division rule is as follows:
(Dlf the month∈(3,5)|(month∈(4,8,9,11)®ion∈(centeral lake)), then it was divided into type Ⅰ;
②If the month(6,7,10)|(month∈(4,11)®ion∈(Meiliang Bay, Zhushan Bay)), then it was divided into type Ⅱ;
③If the month∈(8,9)®ion∈(Meiliang Bay, Zhushan Bay), then it was divided into type Ⅲ.
The remote sensing reflectance and optical properties of these three types of water bodies are able to distinguish from each other, representing the water that suspended sediment dominated, both suspended sediment and phytoplankton dominated, and phytoplankton dominated, respectively. The distinguishability of optical characteristics among the three types divided by the new rule, as well as its correlation with Chla is better than those divided by Chla grade or spectral classification.
(4) Construction of Chla estimation model in Taihu lake considering the spatial and temporal difference.
The modeling and validation datasets were divided based on the constructed rule. Construct the band ratio, three-band, four-band and NCI model using the modeling dataset, and determine the best model expression and parameters for each type in the new model by comparing model precision and residual distribution. The expression of the new model is Chla=exp(ax2+bx+c), and x is R701/R677、(1/R686-1/R695)×R710、 NCI for type Ⅰ、Ⅱ、Ⅲ. The validation accuracy of the new model (R2=0.75, RMSE=11.54mg/m3) is better than the model after seasonal division (R2=0.56, RMSE=17.63mg/m3) and the reparameterized bands ratio, three-band, four-band and NCI (R2<0.61; RMSE>16.79mg/m3). Model application based on HJ1/HSI data show that the temporal and spatial distribution of Chla calculated by the proposed model is comparable to existing investigation. The Chla estimation model that considering the spatial and temporal variation built in this study can improve the accuracy of model application, thus can be applied to estimate chlorophyll a concentration in Taihu Lake.
本研究针对二类水体中叶绿素a浓度(Chla)估算模型的不足,以改进模型的应用精度为目标,提出了考虑数据时空差异性的Chla估算模型构建方法。基于2004-2012年共19期太湖野外调查数据,对已有的典型Chla估算模型进行了验证,分析了模型残差的时空差异性特征,提出了基于月份和湖区的水质类型划分规则,并构建了顾及时空差异性的太湖水体Chla估算模型,提高了Chla估算模型的应用精度。主要研究内容和成果如下：
(1)典型二类水体Chla估算模型的太湖验证与残差分析
基于不同数据集,验证了18个Chla高光谱估算模型在太湖水体的适用性,分析了模型残差的时空差异特征及其影响因素。结果表明：R-NIR算法在太湖中的应用精度优于荧光算法,重新率定模型参数或优化波段位置后所建模型的应用精度显著高于模型的直接应用,模型参数的率定验证的均方根误差(RMSE)可降低到20mg/m3以下。不同数据集的验证精度不同,模型残差随着不同样点时间和空间位置的变化而变化,表明水体的时空差异性影响着Chla估算模型的表现。
(2)考虑季节性差异的Chla估算与模型改进
使用相邻月的观测数据,研究了改进模型应用精度的数据变换方法,使用夏秋季数据,构建了新的水体Chla指数,考虑数据的季节性差异,构建了Chla估算模型。结果表明：使用Chla的对数变换与光谱核回归平滑处理可以改进反演模型残差的方差齐性和模型的应用精度,使用2004年7月平滑后数据建立的三波段模型,8月数据验证的RMSE从平滑前的33.56mg/m3降低到了平滑后的25.60mg/m3；基于夏季和秋季的调查数据,构建了新的叶绿素a指数(NCI=(R690/R550-R675/R700)/(R690/R550+R675/R700))所建立的模型在多期数据集上的验证精度优于已有的三波段和四波段算法。数据季节划分后部分改进了Chla估算模型的精度和残差分布。
(3)水质的时空差异性分析和划分
结合太湖水质的时空差异性及水质参数的时空分布规律,以样点的月份和所在湖区编号作为输入,使用CRT和C5.0决策树方法构建了顾及时空差异性的Chla分组规则。调查水域按Chla级别的不同分成三种类型,其划分规则为：
①如果月份∈(3,5)|(月份∈(4,8,9,11)&区域∈(湖心区)),则为类型Ⅰ；
②如果月份∈(6,7,10)|(月份∈(4,11)&区域∈(梅梁湾,竺山湾)),则为类型Ⅱ；
③如果月份∈(8,9)&区域∈(梅梁湾,竺山湾),则为类型Ⅲ。
数据划分后三种水体类型的遥感反射率和光学特性具有较明显的差异,分别代表了悬浮物主导、悬浮物和浮游藻类共同主导以及浮游藻类主导的水体。基于上述规则对数据进行划分,不同类型水体光学特性的相互区分度及其对Chla的指示作用优于基于Chla分级和光谱分类的结果。
(4)顾及时空差异性的太湖Chla遥感估算模型的构建
基于所建立的规则,将建模集和验证集的样点进行了划分,使用建模集构建了波段比值、三波段、四波段和NCI模型,通过比较模型精度和残差分布,确定了新模型中各类型所使用的模型变量和参数。新模型为Chla=exp(ax2+bx+c),x在类型Ⅰ、Ⅱ、Ⅲ中分别为R701/R677、(1/R686-1/R695)×R710、NCI,模型在验证集的应用精度(R2=0.75,RSE=11.54mg/m3)优于基于季节划分的模型(R2=0.56,RMSE=17.63mg/m3)及比值、三波段、四波段和NCI组合参数率定的验证结果(R2<0.61;RSE>16.79mg/m3)。基于HJ1/HSI数据的模型应用结果表明,新模型计算出的水体叶绿素a浓度时空分布与已有的调查研究具有可比性。本文构建的顾及时空差异性的Chla估算模型,改进了模型的稳健性和应用精度,在太湖水体的叶绿素a浓度的遥感估算中具有较好的适用性。
Taihu Lake is the largest freshwater lake in the Yangtze River Delta, China, its increasing water pollution and eutrophication problem seriously hindered the economic development and the health of local residents. Estimating the content of water components and monitoring water quality in Taihu lake using the remote sensing technology is of great convenience and significance. Chlorophyll-a is the main indicator of water eutrophication, also one of the most important parameters for water quality monitoring.
In order to improve the performance and validation accuracy of existing estimation models, this study take the temporal and spatial variation of water quality into account and promote a new method to estimate chlorophyll a concentration (Chla) in Taihu lake. Based on19field experiments from2004-2012in Taihu lake,18typical Chla estimation models were validated and model residuals'variation with time and position was analyzed. This study proposed a rule for water quality division using the input of month and lake region, and constructed a new Chla estimation model that considers temporal and spatial variation, which can improve the accuracy of Chla estimation when validated by other datasets. The main contents and conclusions are as follows:
(1) Validation of typical Chla estimation models using data in Taihu lake and analysis of the model residuals.
Based on several different datasets,18existing Chla estimation models was validated and model residuals' variation with month/season and lake district, as well as its influencing factors were analyzed. The results show that:the applicability of R-NIR algorithms in Taihu lake is better than the fluorescence algorithms, and the accuracy of model validation can be greatly improved after reparameterization of model parameters (RMSE is less than20mg/m3) or tuning of band position. Model accuracy validated by different datasets varies a lot, and the model residuals change with the sampling time and space, indicating that the accuracy of inversion model is correlated with the spatial and temporal variation of water body.
(2) Estimation of Chla considering seasonal variation and model improvement.
This paper studied the data transformation method for improving the accuracy of model application based on adjacent monthly data, and constructed a new Chla index in water based on data in summer and autumn. Then the Chla estimation model considering the seasonal variation was constructed. The results show that:logarithmic transformation and spectral kernel regression smoothing can be used to improve the homogeneity of model residual and the accuracy of model validation, for example, when a model built by data in July2004was validation by data in August2004, the RMSE reduced from33.56mg/m3before smoothing to25.60mg/m3after smoothing. A new chlorophyll index (NCI=(R690/R550-R675/R700)/(R690/R550+R675/R700)) was built based on data in summer and autumn, which presented better accuracy when validated by multiple datasets than the existing three band and four-band model. The accuracy and residual distribution can be partially improved by constructing Chla estimation model using data after seasonal devision.
(3) Analysis of spatial and temporal difference of water quality and its division rule.
Based on the spatial and temporal difference of water quality of Taihu lake and the spatial and temporal distribution of water quality parameters, this study promoted a division rule of water quality using the input of month and lake district and the method of CRT and C5.0decision tree. The investigated water was divided into three types according to the Chla level, and the division rule is as follows:
(Dlf the month∈(3,5)|(month∈(4,8,9,11)®ion∈(centeral lake)), then it was divided into type Ⅰ;
②If the month(6,7,10)|(month∈(4,11)®ion∈(Meiliang Bay, Zhushan Bay)), then it was divided into type Ⅱ;
③If the month∈(8,9)®ion∈(Meiliang Bay, Zhushan Bay), then it was divided into type Ⅲ.
The remote sensing reflectance and optical properties of these three types of water bodies are able to distinguish from each other, representing the water that suspended sediment dominated, both suspended sediment and phytoplankton dominated, and phytoplankton dominated, respectively. The distinguishability of optical characteristics among the three types divided by the new rule, as well as its correlation with Chla is better than those divided by Chla grade or spectral classification.
(4) Construction of Chla estimation model in Taihu lake considering the spatial and temporal difference.
The modeling and validation datasets were divided based on the constructed rule. Construct the band ratio, three-band, four-band and NCI model using the modeling dataset, and determine the best model expression and parameters for each type in the new model by comparing model precision and residual distribution. The expression of the new model is Chla=exp(ax2+bx+c), and x is R701/R677、(1/R686-1/R695)×R710、 NCI for type Ⅰ、Ⅱ、Ⅲ. The validation accuracy of the new model (R2=0.75, RMSE=11.54mg/m3) is better than the model after seasonal division (R2=0.56, RMSE=17.63mg/m3) and the reparameterized bands ratio, three-band, four-band and NCI (R2<0.61; RMSE>16.79mg/m3). Model application based on HJ1/HSI data show that the temporal and spatial distribution of Chla calculated by the proposed model is comparable to existing investigation. The Chla estimation model that considering the spatial and temporal variation built in this study can improve the accuracy of model application, thus can be applied to estimate chlorophyll a concentration in Taihu Lake.
引文
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