Abstract Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science. To make these measurements, investigators calibrate instrument data to absolute units, invert physical models to estimate atmospheric effects, and then determine surface properties from the spectral reflectance. This study quantifies the uncertainty in this process. Global missions demand predictive uncertainty models that can estimate future errors for varied environments and observing conditions. Here we validate uncertainty predictions with remote surface composition retrievals and in situ measurements in a field analogue of Earth and planetary exploration. We consider rover transects at Cuprite, Nevada, and remote observations by NASA's Next-Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). We show that accounting for input uncertainties can benefit mineral detection methods such as constrained spectrum fitting. This suggests that operational uncertainty estimates could improve future NASA missions like the Earth Mineral dust source InvesTigation (EMIT) and the Lunar Trailblazer mission, as well as NASA's Decadal Surface Biology and Geology (SBG) Investigation.

中文翻译：

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量化表面成分远程光谱的不确定性

摘要 成像光谱仪的远程表面测量在行星和地球科学中发挥着重要作用。为了进行这些测量，研究人员将仪器数据校准为绝对单位，反转物理模型以估计大气影响，然后根据光谱反射率确定表面特性。这项研究量化了这个过程中的不确定性。全球任务需要预测不确定性模型，该模型可以估计不同环境和观测条件下的未来误差。在这里，我们通过远程地表成分反演和地球和行星探索的现场模拟中的原位测量来验证不确定性预测。我们考虑了内华达州 Cuprite 的漫游车横断面，以及 NASA 下一代机载可见红外成像光谱仪 (AVIRIS-NG) 的远程观测。我们表明，考虑输入不确定性可以有益于矿物检测方法，例如受限光谱拟合。这表明操作不确定性估计可以改进未来的 NASA 任务，如地球矿物尘埃源调查 (EMIT) 和月球开拓者任务，以及 NASA 的十年表面生物学和地质 (SBG) 调查。