Optimal Estimation of Snow and Ice Surface Parameters from Imaging Spectroscopy Measurements
Optimal Estimation of Snow and Ice Surface Parameters from Imaging Spectroscopy Measurements
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通过成像光谱测量对冰雪表面参数进行优化估计
DOI：
10.1002/essoar.10505800.1
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Bohn U
中科院分区：
文献类型：
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作者：
Bohn U
冰雪融化过程是地球能量平衡和水文循环的关键。它们的量化有助于预测融水径流以及淡水的分布和可用性。它们控制着地球冰盖的平衡，对气候变化非常敏感。由于液态水和光吸收颗粒（CO2）的积累，这些过程降低了具有独特光谱图案的表面反射率，这需要成像光谱来映射和测量。在这里，我们提出了一种新的方法来检索雪的颗粒大小，液态水的分数，并从机载和星载成像光谱采集的质量混合比。这种方法是基于同时检索的大气和表面参数使用最佳估计（OE），检索技术，利用先验知识和测量噪声的反演，也产生不确定性估计。我们利用表面反射光谱和冰雪属性之间的统计关系来估计它们的反射率的最可能的数量。为了测试这种新的算法，我们进行了灵敏度分析的基础上模拟的大气层顶部的辐射光谱使用即将到来的EnMAP轨道成像光谱使命，展示了准确的估计性能的雪和冰的表面特性。利用格陵兰冰盖的冰川藻类质量混合比和表面反射率的现场测量进行的验证实验给出的不确定度分别为±16.4μg/g和小于3%。最后，我们评估了所有的雪和冰的属性与AVIRIS-NG收购格陵兰冰盖的检索能力，证明这种方法的潜力和适用性即将到来的轨道成像光谱任务。
Snow and ice melt processes are a key in Earth's energy-balance and hydrological cycle. Their quantification facilitates predictions of meltwater runoff as well as distribution and availability of fresh water. They control the balance of the Earth's ice sheets and are acutely sensitive to climate change. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), that require imaging spectroscopy to map and measure. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and spaceborne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in an inversion that also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. A validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet gave uncertainties of ±16.4μg/giceand less than 3%, respectively. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach's potential and suitability for upcoming orbital imaging spectroscopy missions.