SVH-BD : Synthetic Vegetation Hyperspectral Benchmark Dataset for Emulation of Remote Sensing Images

2026-03-30

AI Summary

This study addresses a critical gap in remote sensing research—the lack of large-scale, physically consistent synthetic datasets that combine high spectral resolution with pixel-level ground truth for vegetation traits. To this end, we integrate the PROSAIL radiative transfer model with Sentinel-2 Level-2A inversion results to generate, for the first time, physically realistic hyperspectral image cubes (400–2500 nm, 64×64 pixels) across four ecologically distinct regions, yielding 10,915 samples. Each sample includes paired pixel-level vegetation trait maps, uncertainty bounds, and scene classification layers. The resulting dataset enables rapid radiative transfer simulations, benchmarking of inversion algorithms, and investigations into spectral–biophysical relationships, offering a high-quality, verifiable reference resource for advancing remote sensing modeling and machine learning applications.

Abstract

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.

Problem

• hyperspectral imagery
• vegetation traits
• radiative transfer emulation
• remote sensing
• uncertainty quantification

Innovation

• hyperspectral emulation
• PROSAIL inversion
• vegetation trait retrieval
• uncertainty quantification
• synthetic benchmark dataset