构建了1万+张高光谱遥感图像合成数据集，用于植被反演与不确定性分析。
SVH-BD : Synthetic Vegetation Hyperspectral Benchmark Dataset for Emulation of Remote Sensing Images
- 基于PROSAIL模型生成211波段高光谱图像，每张64×64像素
- 覆盖四大生态区，含5%~95%分位数不确定性图，真实模拟环境变化
- 适合遥感反演、辐射传输模拟器开发及生物物理关系研究
该数据集包含10,915个合成高光谱图像立方体及像素级植被性状地图，支持辐射传输模拟、植被性状反演与不确定性量化研究。每个图像立方体包含211个波段（400–2500 nm，10 nm分辨率），空间布局为64×64像素，提供连续模拟地表反射率光谱，适用于模拟器开发与需高光谱细节的机器学习任务。植被性状通过反演Sentinel-2 Level-2A地表反射率并结合PROSAIL查表法获取，再通过前向PROSAIL模拟生成在物理一致冠层与光照条件下的高光谱反射率。数据覆盖东非、北法、东印和南西四个生态区域，包含5th与95th百分位不确定性图以及Sentinel-2场景分类层。该资源可支持反演方法基准测试、快速辐射传输模拟器开发，以及在受控但真实的环境变异下对光谱-生物物理关系的研究。
原文摘要 · Abstract (English)
This dataset provides a large collection of 10,915 synthetic hyperspectral image cubes paired with pixel-level vegetation trait maps, designed to support research in radiative transfer emulation, vegetation trait retrieval, and uncertainty quantification. Each hyperspectral cube contains 211 bands spanning 400--2500 nm at 10 nm resolution and a fixed spatial layout of 64 \times 64 pixels, offering continuous simulated surface reflectance spectra suitable for emulator development and machine-learning tasks requiring high spectral detail. Vegetation traits were derived by inverting Sentinel-2 Level-2A surface reflectance using a PROSAIL-based lookup-table approach, followed by forward PROSAIL simulations to generate hyperspectral reflectance under physically consistent canopy and illumination conditions. The dataset covers four ecologically diverse regions -- East Africa, Northern France, Eastern India, and Southern Spain -- and includes 5th and 95th percentile uncertainty maps as well as Sentinel-2 scene classification layers. This resource enables benchmarking of inversion methods, development of fast radiative transfer emulators, and studies of spectral--biophysical relationships under controlled yet realistic environmental variability.
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