Remote chlorophyll-a estimates for inland waters based on a cluster-based classification.
Remote chlorophyll-a estimates for inland waters based on a cluster-based classification.
复制标题
DOI：
10.1016/j.scitotenv.2012.11.058
复制
发表时间：
2013-02
期刊：
影响因子：
--
通讯作者：
Kun Shi;Yunmei Li;Lin Li;Heng Lu;K. Song;Zhong-hua Liu;Yifan Xu;Zuchuan Li
中科院分区：
文献类型：
--
作者：
Kun Shi;Yunmei Li;Lin Li;Heng Lu;K. Song;Zhong-hua Liu;Yifan Xu;Zuchuan Li
内陆沃茨的叶绿素a浓度（Chl-a）遥感数据由于其光学特性的复杂性而难以精确估算。本文基于水体光学分类和两种半经验算法相结合的方法，建立了一个适用于光学复杂内陆沃茨的叶绿素a估算框架。利用遥感反射率（Rrs）聚类方法，从太湖、巢湖、滇池和三峡水库的231个样本中识别出3种光谱特征明显的水体类型（I ~ III型）。每种光学水类型的分类标准，随后定义MERIS图像的基础上的光谱特性的三种水类型。该标准通过比较MERIS图像的第7波段（中心波段：665 nm）、第8波段（中心波段：681.25nm）和第9波段（中心波段：708.75nm）的值，将每个Rrs光谱聚类为三种水类型之一。在水分类的基础上，针对同一数据集，针对每种水类型分别开发了类型特异性三波段算法（TBA）和类型特异性高级三波段算法（ATBA）。通过预分类，这两种算法的误差都有所减少，对于校准数据集，TBA的平均绝对百分比误差（MAPE）从36.5%下降到23%，ATBA从40%下降到28%。验证数据的两种算法的准确性表明，光学分类消除了需要调整的三个波段的最佳位置或重新参数化，以估计其他沃茨的叶绿素a。分类标准和特定类型的ATBA另外验证两个MERIS图像。首先根据反射率特征对光学水类型进行分类，然后针对不同的水类型开发特定类型的算法，这一框架是减少光学复杂内陆沃茨叶绿素a估计误差的有效方案。
Accurate estimates of chlorophyll-a concentration (Chl-a) from remotely sensed data for inland waters are challenging due to their optical complexity. In this study, a framework of Chl-a estimation is established for optically complex inland waters based on combination of water optical classification and two semi-empirical algorithms. Three spectrally distinct water types (Type I to Type III) are first identified using a clustering method performed on remote sensing reflectance (Rrs) from datasets containing 231 samples from Lake Taihu, Lake Chaohu, Lake Dianchi, and Three Gorges Reservoir. The classification criteria for each optical water type are subsequently defined for MERIS images based on the spectral characteristics of the three water types. The criteria cluster every Rrsspectrum into one of the three water types by comparing the values from band 7 (central band: 665nm), band 8 (central band: 681.25nm), and band 9 (central band: 708.75nm) of MERIS images. Based on the water classification, the type-specific three-band algorithms (TBA) and type-specific advanced three-band algorithm (ATBA) are developed for each water type using the same datasets. By pre-classifying, errors are decreased for the two algorithms, with the mean absolute percent error (MAPE) of TBA decreasing from 36.5% to 23% for the calibration datasets, and from 40% to 28% for ATBA. The accuracy of the two algorithms for validation data indicates that optical classification eliminates the need to adjust the optimal locations of the three bands or to re-parameterize to estimate Chl-a for other waters. The classification criteria and the type-specific ATBA are additionally validated by two MERIS images. The framework of first classifying optical water types based on reflectance characteristics and subsequently developing type-specific algorithms for different water types is a valid scheme for reducing errors in Chl-a estimation for optically complex inland waters.