发表机构
Jet Propulsion Laboratory, California Institute of Technology; University of Utah（加州理工学院喷气推进实验室; 犹他大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于AVIRIS-3数据的VSWIR成像光谱学野火温度反演框架，采用全物理方法与非线性最小二乘优化，验证其适用于星载EMIT等设备，亚像素温度反演误差较小。
AI 中文摘要
本研究提出一种针对VSWIR成像光谱学数据的野火温度反演框架，该框架应用于NASA机载可见光红外成像光谱仪（AVIRIS-3）的数据。该反演框架采用全物理方法，其中使用正向模型求解由温度分布产生的太阳辐射和发射辐射，并在残差拟合中利用全光谱范围。为优化正向模型反演，我们使用最先进的非线性最小二乘方法，该方法针对机载GPU上的快速收敛实现，可在飞行周期内估算有效火灾温度。我们通过注入热信号的模拟光谱验证正向模型假设，发现其与均方根误差（RMSE）为41.8开尔文（K）的结果吻合良好。我们将该反演应用于2025年FireSense AVIRIS-3全任务，总计168次飞越的可能活跃火灾光谱数据，在短波红外（SWIR）波段实现了≤10%的残差辐射拟合。最后，我们通过在粗空间分辨率下进行反演，验证了所获取的后验火灾温度参数对星载成像光谱仪（如EMIT）的适用性，发现后验分布能良好覆盖潜在的亚像素温度范围，分位数间绝对误差为30 K，不同空间分辨率间的平均绝对误差为27.16 K。
英文摘要
In this work, we present a wildfire temperature retrieval framework for VSWIR imaging spectroscopy data, employed on data from NASA's Airborne Visible Infrared Imaging Spectrometer (AVIRIS-3). The retrieval framework utilizes a full-physics approach in which a forward model is employed to resolve both solar and emitted radiance derived from a temperature distribution and utilizes the full spectral range in the residual fit. To optimize the forward model retrieval, we use state-of-the-art nonlinear least squares methods implemented for fast convergence on the on-board GPU, allowing for estimation of effective fire temperature within flight cadence. We verify the forward model assumptions on simulated spectra with an injected thermal signature and find good agreement with an RMSE of $41.8$ Kelvin (K). We apply the retrieval over the full 2025 FireSense AVIRIS-3 campaign, totaling 168 overflights with probable active fire spectra, and demonstrate a residual radiance fit of $\leq 10\%$ across bands in the short-wave infrared (SWIR). Lastly, we verify the applicability of the retrieved posterior fire temperature parameters to generalize to space-borne imaging spectrometers such as EMIT, by retrieving at coarsened spatial resolution. We find that the posterior distribution exhibits good coverage of the underlying sub-pixel temperature range with an absolute error of $30$ K across quantiles and a mean absolute error of $27.16$ K between spatial resolutions.
CommentsIn Review in Remote Sensing of Environment
