Remote estimation of chlorophyll a in optically complex waters based on optical classification
Remote estimation of chlorophyll a in optically complex waters based on optical classification
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基于光学分类的光学复杂水域中叶绿素a的远程估算
DOI：
10.1016/j.rse.2010.10.014
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发表时间：
2011-02-15
影响因子：
13.5
通讯作者：
Zhang, Hong
中科院分区：
文献类型：
--
作者：
Le, Chengfeng;Li, Yunmei;Zhang, Hong
由于案例 2 水域的光学复杂性和显着变异性，尤其是在具有多种光学类型的内陆水域，通过遥感准确评估浑浊水域中浮游植物叶绿素 a (Chla) 浓度具有挑战性。在这项研究中，开发了一种水光学分类算法，并使用从太湖、巢湖和三峡水库收集的四个独立数据集对两种用于估计 Chla 的半解析算法（三波段和四波段算法）进行了校准和验证。光学分类算法是利用2006年至2009年在太湖采集的数据集开发的。该数据集还用于校准三波段和四波段Chla估计算法。光学分类技术使用三个波段的遥感反射率：Rrs(G)、Rrs(650)和Rrs(NIR)，其中G表示绿色区域（560 nm左右）反射率峰值的位置，NIR表示近红外区域（700 nm左右）反射率峰值的位置。通过模型调整和精度优化来确定三波段和四波段算法的最佳参考波长。使用其他三个独立数据集进一步评估三频段和四频段算法的准确性。通过比较两种算法对非分类和分类水域的性能，揭示了 Chla 估计中光学分类的改进。利用三个反射带的斜率，将校准数据集中的 138 个反射光谱样本分为三类，每类都具有特定的光谱形状特征。三波段和四波段算法在估计 Chla 时对于非分类和分类水域都表现良好。对于非分类水体，测量的叶绿素和预测的叶绿素之间存在很强的关系，但两种算法在低叶绿素条件下的性能并不令人满意，特别是对于叶绿素低于30 mg m(-3)的样品。对于分类水域，特定类别的算法比非分类水域的性能更好。在 Chla 预测中，特定类别的算法大大减少了非分类水域算法的平均相对误差。光学分类使得无需调整最佳位置即可使用特定类别的算法来估计其他水域的 Chla。本研究的结果表明，光学分类可以极大地提高光学复杂水域中 Chla 估计的准确性。 Crown 版权所有 (C) 2010 由 Elsevier Inc. 出版。保留所有权利。
Accurate assessment of phytoplankton chlorophyll a (Chla) concentration in turbid waters by means of remote sensing is challenging due to optically complexity and significant variability of case 2 waters, especially in inland waters with multiple optical types. In this study, a water optical classification algorithm is developed, and two semi-analytical algorithms (three- and four-band algorithm) for estimating Chla are calibrated and validated using four independent datasets collected from Taihu Lake, Chaohu Lake, and Three Gorges Reservoir. The optical classification algorithm is developed using the dataset collected in Taihu Lake from 2006 to 2009. This dataset is also used to calibrate the three- and four-band Chla estimation algorithms. The optical classification technique uses remote sensing reflectance at three bands: Rrs(G), Rrs(650), and Rrs (NIR), where G indicates the location of reflectance peak in the green region (around 560 nm), and NIR is the location of reflectance peak in the near-infrared region (around 700 nm). Optimal reference wavelengths of the three- and four-band algorithm are located through model tuning and accuracy optimization. The three- and four-band algorithm accuracy is further evaluated using other three independent datasets. The improvement of optical classification in Chla estimation is revealed by comparing the performance of the two algorithms for non-classified and classified waters.Using the slopes of the three reflectance bands, the 138 reflectance spectra samples in the calibration dataset are classified into three classes, each with a specific spectral shape character. The three- and four-band algorithm performs well for both non-classified and classified waters in estimating Chla. For non-classified waters, strong relationships are yielded between measured and predicted Chla, but the performance of the two algorithms is not satisfactory in low Chla conditions, especially for samples with Chla below 30 mg m(-3). For classified waters, the class-specific algorithms perform better than for non-classified waters. Class-specific algorithms reduce considerable mean relative error from algorithms for non-classified waters in Chla predicting. Optical classification makes that there is no need to adjust the optimal position to estimate Chla for other waters using the class-specific algorithms. The findings in this study demonstrate that optical classification can greatly improve the accuracy of Chla estimation in optically complex waters. Crown Copyright (C) 2010 Published by Elsevier Inc. All rights reserved.