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An evaluation of algorithms for the remote sensing of cyanobacterial biomass

机译:蓝藻生物量遥感算法的评估

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Most remote sensing algorithms for phytoplankton in inland waters aim at the retrieval of the pigment chlorophyll a (Chl a), as this pigment is a useful proxy for phytoplankton biomass. More recently, algorithms have been developed to quantify the pigment phycocyanin (PC), which is characteristic of cyanobacteria, a phytoplankton group of relative importance to inland water management due to their negative impact on water quality in response to eutrophication. We evaluated the accuracy of three published algorithms for the remote sensing of PC in inland waters, using an extensive database of field radiometric and pigment data obtained in the Netherlands and Spain in the period 2001-2005. The three algorithms (a baseline, single band ratio, and a nested band ratio approach) all target the PC absorption effect observed in reflectance spectra in the 620 nm region. We evaluated the sensitivity of the algorithms to errors in reflectance measurements and investigated their performance in cyanobacteria-dominated water bodies as well as in the presence of other phytoplankton pigments. All algorithms performed best in moderate to high PC concentrations (50-200 mg m(-3)) and showed the most linear response to increasing PC in cyanobacteria-dominated waters. The highest errors showed at PC<50 mg m(-3). In eutrophic waters, the presence of other pigments explained a tendency to overestimate the PC concentration. In oligotrophic waters, negative PC predictions were observed. At very high concentrations (PC>200 mg m(-3)), PC underestimations by the baseline and single band ratio algorithms were attributed to a non-linear relationship between PC and absorption in the 620 nm region. The nested band ratio gave the overall best fit between predicted and measured PC. For the Spanish dataset, a stable ratio of PC over cyanobacterial Chl a was observed, suggesting that PC is indeed a good proxy for cyanobacterial biomass. The single reflectance ratio was the only algorithm insensitive to changes in the amplitude of reflectance spectra, which were observed as a result of different measurement methodologies. (C) 2008 Elsevier Inc. All rights reserved.
机译:内陆水域浮游植物的大多数遥感算法都旨在回收色素叶绿素a(Chl a),因为这种色素是浮游植物生物量的有用替代物。最近,已经开发出算法来量化色素藻蓝蛋白(PC),这是蓝细菌的特征,蓝藻是一种对内陆水域管理相对重要的浮游植物群,由于它们对富营养化产生负面影响,从而对水质产生负面影响。我们使用范围广泛的2001-2005年在荷兰和西班牙获得的野外辐射和色素数据数据库,评估了三种已公开的内陆水域PC遥感算法的准确性。三种算法(基线,单带比和嵌套带比方法)都针对在620 nm区域的反射光谱中观察到的PC吸收效应。我们评估了算法对反射率测量误差的敏感性,并研究了它们在蓝藻为主的水体以及其他浮游植物色素存在下的性能。所有算法在中等至高PC浓度(50-200 mg m(-3))中表现最佳,并且在蓝藻为主的水中对PC的增加表现出最线性的响应。 PC <50 mg m(-3)时显示最高误差。在富营养化的水中,其他颜料的存在说明了高估PC浓度的趋势。在贫营养水中,PC预测为阴性。在非常高的浓度下(PC> 200 mg m(-3)),基线和单谱带比算法对PC的低估归因于PC和620 nm区域中的吸收之间的非线性关系。嵌套带比率给出了预测PC和测量PC之间的整体最佳拟合。对于西班牙数据集,观察到PC与蓝细菌Chla的比例稳定,这表明PC确实是蓝细菌生物量的良好替代。单一反射率是唯一对反射光谱幅度变化不敏感的算法,这是由于不同的测量方法导致的。 (C)2008 Elsevier Inc.保留所有权利。

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