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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Algorithms and Schemes for Chlorophyll a Estimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China
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Algorithms and Schemes for Chlorophyll a Estimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China

机译：太湖浑浊的遥感和光学分类估算叶绿素 a 的算法和方案

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摘要

Monitoring chlorophyll (CHLA) by remote sensing is particularly challenging for turbid productive waters. Although several empirical and semianalytical algorithms have been developed for such waters, their accuracy varies significantly due to variability in optical properties. In this paper, we evaluated the performance of six CHLA concentration () estimation algorithms [e.g., two-band ratio algorithm (TBR), normalized difference chlorophyll index (NDCI), synthetic chlorophyll index (SCI), three-band algorithm (TBS), four-band algorithm (FBS), and improved four-band algorithm (IOC3 M)] for a highly turbid lake based on remote sensing reflectance classification. Remote sensing reflectance was classified using the iterative k-mean clustering method. We also developed four estimation schemes (S1–S4) for the six algorithms to assess the effect of the estimation scheme on the accuracy of the algorithms. The estimation schemes were developed based on classification methods (no, soft, or hard classification) and the optimization bands used. The six algorithms performed differently for different remote sensing reflectance classes and different estimation schemes. The optimal algorithms for Classes 1, 2, and 3 were TBS, NDCI, and TBR, respectively. For the four estimation schemes, TBS and NDCI outperformed the other four algorithms. The accuracy of TBS and NDCI was higher than FBS, IOC3 M, TBR, and SCI. The accuracy of all six algorithms was improved by remote sensing reflectance classification, particularly for Classes 2 and 3. Soft classification with recalibration of the bands for each class outperformed hard classification for all the three classes.
机译：对于浑浊的生产水，通过遥感监测叶绿素（CHLA）尤其具有挑战性。尽管已经为这种水开发了几种经验和半分析算法，但是由于光学特性的可变性，它们的精确度也有很大变化。在本文中，我们评估了六种CHLA浓度估算算法的性能[例如，两谱带比率算法（TBR），归一化差异叶绿素指数（NDCI），合成叶绿素指数（SCI），三谱带算法（TBS） ，四频带算法（FBS）和改进的四频带算法（IOC3 M）]基于遥感反射率分类的高度浑浊湖泊。使用迭代k均值聚类方法对遥感反射率进行分类。我们还为六个算法开发了四个估计方案（S1-S4），以评估估计方案对算法准确性的影响。根据分类方法（无分类，软分类或硬分类）和所使用的优化范围，开发了估计方案。对于不同的遥感反射率类别和不同的估计方案，这六个算法的执行结果不同。第1、2和3类的最佳算法分别是TBS，NDCI和TBR。对于这四个估计方案，TBS和NDCI优于其他四个算法。 TBS和NDCI的准确性高于FBS，IOC3M，TBR和SCI。通过遥感反射分类，特别是对于第2类和第3类，通过遥感反射分类对所有6种算法的准确性进行了改进。对于每个类，通过对频带进行重新校准的软分类优于对这三个类的硬分类。

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