Abstract Coral reefs are the foundation of productive ecosystems in the global, tropical oceans and are under threat from a variety of local to global scale stressors. Satellite imagery provides a tool to identify and understand the processes that control coral reef degradation, however due to the dynamic nature of seawater constituents, current spaceborne multispectral sensors cannot reliably discriminate between the many coral reef benthic classes necessary to detect change. Hyperspectral imagers may provide sufficient spectral resolution to estimate water column properties and differentiate benthic classes, however, the effects of depth, seawater constituents, and classification algorithm on the accuracy of benthic classifications have not been systematically assessed. Here, we simulate the ability of a spaceborne hyperspectral imager to accurately map fractional cover of coral reef benthic classes under a variety of conditions. Benthic reflectance is simulated by combining pure reflectance spectra of coral, algae, and sand and projecting these mixed spectra through a fully crossed set of water columns. We then use a semi-analytical optimization procedure to estimate the water column properties and multiple endmember spectral mixture analysis to estimate the fractional cover of the benthic classes using many independent endmember spectra. We compare our estimated benthic class fractions to the original, actual fractions used to produce the mixed coral reef spectra to quantify several measures of error. We found that multiple endmember spectral mixture analysis decreases fractional retrieval error, which is also reduced when the first derivative of the mixed and endmember spectra is used prior to unmixing. The estimation of fractional benthic class cover is most accurate for depths ≤3 m for most water conditions. Depths ≥5 m should be classified only if chlorophyll and sediment concentration are

中文翻译：

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水特征对从成像光谱仪数据中辨别珊瑚礁内部和周围底栖覆盖物的影响

摘要 珊瑚礁是全球热带海洋中多产生态系统的基础，并受到来自当地和全球范围内各种压力源的威胁。卫星图像提供了一种工具来识别和了解控制珊瑚礁退化的过程，但是由于海水成分的动态特性，当前的星载多光谱传感器无法可靠地区分检测变化所需的许多珊瑚礁底栖类别。高光谱成像仪可以提供足够的光谱分辨率来估计水柱特性和区分底栖类，但是，尚未系统地评估深度、海水成分和分类算法对底栖分类准确性的影响。这里，我们模拟了星载高光谱成像仪在各种条件下准确绘制珊瑚礁底栖类的部分覆盖的能力。通过组合珊瑚、藻类和沙子的纯反射光谱并将这些混合光谱投影到一组完全交叉的水柱中，模拟底栖反射率。然后，我们使用半解析优化程序来估计水柱特性和多端元光谱混合分析，以使用许多独立端元光谱来估计底栖类的覆盖率。我们将我们估计的底栖类分数与用于产生混合珊瑚礁光谱的原始实际分数进行比较，以量化几种误差度量。我们发现多端元光谱混合分析降低了分数检索误差，当在解混之前使用混合和端元光谱的一阶导数时，这也会减少。对于大多数水体条件，对于 ≤ 3 m 的深度，部分底栖生物种类覆盖率的估计最准确。只有在叶绿素和沉积物浓度为