Uncertainties of SeaWiFS and MODIS remote sensing reflectance: Implications from clear water measurements
Uncertainties of SeaWiFS and MODIS remote sensing reflectance: Implications from clear water measurements
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DOI：
10.1016/j.rse.2013.02.012
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发表时间：
2013-06
影响因子：
13.5
通讯作者：
Chuanmin Hu;Lian Feng;Z. Lee
中科院分区：
文献类型：
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作者：
Chuanmin Hu;Lian Feng;Z. Lee
从卫星海洋颜色测量得出的一个基本参数是光谱遥感反射率 Rrs(λ) (sr−1)，它用作所有反演算法的输入，以导出生物光学特性（例如，叶绿素 a 浓度或以 mgm−3 为单位的叶绿素）和水的固有光学特性 (IOP)。卫星衍生的 Rrs 的准确性和不确定性只能通过与通常在空间和时间上受到限制的现场测量进行比较来评估。这里开发了一种新方法并用于估计清澈水域上 SeaWiFS 和 MODIS/Aqua (MODISA) 测量的不确定性。该研究重点关注北大西洋和南太平洋的两个寡营养海洋环流，并使用最近开发的新 Chl 算法来提供约束，以最小的错误确定最高质量的 Rrs 数据。这些数据被用作“基本事实”或参考（称为 Rrs，true）的替代品，以估计每个数据点的 Rrs 误差，并通过统计分析生成不确定性估计（相对和绝对形式）。这项研究得出了几项发现：第一，SeaWiFS 和 MODISA 都实现了其任务目标，即蓝色波段和蓝色水域的 Rrs 不确定性和绝对精度（假设 Rrs，真实值可以代表事实）在 5% 以内。作为比较，几乎所有先前基于原位的验证工作都报告了原位 Rrs 和卫星 Rrs 在蓝色波段之间的平均（或中值）百分比差异超过 10%。第二，对于绿色波段，Rrs 不确定性明显更高，对于贫营养水域通常在 10-15% 的范围内。第三，SeaWiFS 的不确定性通常高于 MODISA，这可能是由于其信噪比 (SNR) 较低。第四，从蓝色到红色波长，所有 Rrs 误差都以单调的方式在光谱上相关，这表明这些误差主要是由于不完善的大气校正算法而不是传感器噪声或替代校准造成的。这种经验关系被证明有助于减少北大西洋环流的不确定性，并且也可能对大多数海水有用。最后，表格结果提供了生产力更高的 Rrs(λ) 不确定性的下限。研究结果可为未来海洋颜色任务提供参考，对其他海洋颜色数据产品的不确定性估计也具有重要意义。
A fundamental parameter derived from satellite ocean color measurements is the spectral remote sensing reflectance, Rrs(λ) (sr−1), which is used as the input to all inversion algorithms to derive bio-optical properties (e.g., chlorophyll-a concentration or Chl in mgm−3) and water's inherent optical properties (IOPs). The accuracy and uncertainties of the satellite-derived Rrshave only been assessed through comparisons with in situ measurements that were often limited in both space and time. Here, a novel approach was developed and used to estimate Rrsuncertainties from SeaWiFS and MODIS/Aqua (MODISA) measurements over clear waters. The study focused on two oligotrophic ocean gyres in the North Atlantic and South Pacific, and used a recently developed new Chl algorithm to provide a constraint to determine the highest-quality Rrsdata with minimal errors. These data were used as surrogates of “ground truth” or references (termed as Rrs,true) to estimate the Rrserror in each data point, with uncertainty estimates (in both relative and absolute forms) generated from statistical analyses. The study led to several findings: One, both SeaWiFS and MODISA have met their mission goals of achieving Rrsuncertainties and absolute accuracy (assuming that the Rrs,truevalues can represent the truth) to within 5% for blue bands and blue waters. As a comparison, nearly all previous in situ-based validation efforts reported mean (or median) percentage differences exceeding 10% between in situ and satellite Rrsin the blue bands. Two, for the green bands, Rrsuncertainties are significantly higher, often in the range of 10–15% for oligotrophic waters. Three, SeaWiFS Rrsuncertainties are generally higher than those of MODISA, possibly due to its lower signal-to-noise ratio (SNR). Four, all Rrserrors are spectrally related in a monotonous way from the blue to the red wavelengths, suggesting that these errors are resulted primarily from the imperfect atmospheric correction algorithms as opposed to sensor noise or vicarious calibration. Such empirical relationships are shown to be useful in reducing the Rrsuncertainties for the North Atlantic Gyre and may also be useful for most of the ocean waters. Finally, the tabulated results provide lower bounds of Rrs(λ) uncertainties for more productive waters. The findings may serve as references for future ocean color missions, and they have also significant implications for uncertainty estimates of other ocean color data products.