Remote estimation of chlorophyll concentration in hyper-eutrophic aquatic systems: Model tuning and accuracy optimization
Remote estimation of chlorophyll concentration in hyper-eutrophic aquatic systems: Model tuning and accuracy optimization
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DOI：
10.1016/j.aquaculture.2006.02.038
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发表时间：
2006-06
期刊：
影响因子：
4.5
通讯作者：
P. Zimba;A. Gitelson
中科院分区：
文献类型：
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作者：
P. Zimba;A. Gitelson
在浑浊的富营养化水域中，通过遥感准确评估浮游植物叶绿素 a (chla) 浓度具有挑战性。本文评估了解决该问题的方法。使用手持式光谱辐射计测量可见光和近红外光谱范围内的地下光谱反射率 (R)。同时收集水样，其中叶绿素 a 浓度（叶绿素 a 从 107 到超过 3000 mg/m3）和浊度（从 11 到 423 NTU）水平不同。概念三波段模型 [R−1(λ1)−R−1(λ2)]×R(λ3) 及其特殊情况，两波段模型 R(λ3)/R(λ1)，根据介质的光学特性进行光谱调谐，以优化光谱波段（λ1、λ2 和 λ3），以实现准确的叶绿素 a 估计。分析测量的叶绿体 a 与三波段 [R−1(650)−R−1(710)]×R(740) 和反射率比模型 R(714)/R(650) 之间建立了强线性关系。三带模型比比率模型解释的叶绿素 a 浓度变化多 7%（78% 对 71%）。藻类分布的空间和时间不均匀性阻碍了对密集藻华模型准确性的评估——在这些水域中，非随机藻类分布占叶绿素 a 浓度超过 20% 的空间变化和高达 8% 的时间变化。研究结果强调了概念模型背后的基本原理，并证明了在非常浑浊、超富营养化的水域中叶绿素检索算法的稳健性。
Accurate assessment of phytoplankton chlorophyll a (chl a) concentration by remote sensing is challenging in turbid hyper-eutrophic waters. This paper assessed methods to resolve this problem. A hand-held spectroradiometer was used to measure subsurface spectral reflectance (R) in the visible and near infrared range of the spectrum. Water samples were collected concurrently and contained variable chlorophyll a concentration (chl a from 107 to more than 3000 mg/m3) and turbidity (from 11 to 423 NTU) levels. The conceptual three-band model [R−1(λ1)−R−1(λ2)]×R(λ3) and its special case, the two-band model R(λ3)/R(λ1), were spectrally tuned in accord with optical properties of the media to optimize spectral bands (λ1, λ2and λ3) for accurate chlorophyll a estimation. Strong linear relationships were established between analytically measured chl a and both the three-band [R−1(650)−R−1(710)]×R(740) and the reflectance ratio model R(714)/R(650). The three-band model accounted for 7% more variation of chl a concentration than the ratio model (78 vs. 71%). Assessment of the model accuracy in dense algal blooms is hampered by the spatial and temporal inhomogeneity of algal distributions—in these waters, non-random algal distributions accounted for more than 20% spatial and up to 8% temporal variation in chlorophyll a concentration. The findings underlined the rationale behind the conceptual model and demonstrated the robustness of the algorithm for chl a retrieval in very turbid, hyper-eutrophic waters.